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cli_demo_sat.py
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# -*- encoding: utf-8 -*-
import os, sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
import argparse
from sat.model.mixins import CachedAutoregressiveMixin
from sat.quantization.kernels import quantize
from sat.model import AutoModel
from utils.utils import chat, llama2_tokenizer, llama2_text_processor_inference, get_image_processor
from utils.models import CogAgentModel, CogVLMModel
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default=2048, help='max length of the total sequence')
parser.add_argument("--top_p", type=float, default=0.4, help='top p for nucleus sampling')
parser.add_argument("--top_k", type=int, default=1, help='top k for top k sampling')
parser.add_argument("--temperature", type=float, default=.8, help='temperature for sampling')
parser.add_argument("--chinese", action='store_true', help='Chinese interface')
parser.add_argument("--version", type=str, default="chat", choices=['chat', 'vqa', 'chat_old', 'base'], help='version of language process. if there is \"text_processor_version\" in model_config.json, this option will be overwritten')
parser.add_argument("--quant", choices=[8, 4], type=int, default=None, help='quantization bits')
parser.add_argument("--from_pretrained", type=str, default="cogagent-chat", help='pretrained ckpt')
parser.add_argument("--local_tokenizer", type=str, default="lmsys/vicuna-7b-v1.5", help='tokenizer path')
parser.add_argument("--fp16", action="store_true")
parser.add_argument("--bf16", action="store_true")
parser.add_argument("--stream_chat", action="store_true")
args = parser.parse_args()
rank = int(os.environ.get('RANK', 0))
world_size = int(os.environ.get('WORLD_SIZE', 1))
args = parser.parse_args()
# load model
model, model_args = AutoModel.from_pretrained(
args.from_pretrained,
args=argparse.Namespace(
deepspeed=None,
local_rank=rank,
rank=rank,
world_size=world_size,
model_parallel_size=world_size,
mode='inference',
skip_init=True,
use_gpu_initialization=True if (torch.cuda.is_available() and args.quant is None) else False,
device='cpu' if args.quant else 'cuda',
**vars(args)
), overwrite_args={'model_parallel_size': world_size} if world_size != 1 else {})
model = model.eval()
from sat.mpu import get_model_parallel_world_size
assert world_size == get_model_parallel_world_size(), "world size must equal to model parallel size for cli_demo!"
language_processor_version = model_args.text_processor_version if 'text_processor_version' in model_args else args.version
print("[Language processor version]:", language_processor_version)
tokenizer = llama2_tokenizer(args.local_tokenizer, signal_type=language_processor_version)
image_processor = get_image_processor(model_args.eva_args["image_size"][0])
cross_image_processor = get_image_processor(model_args.cross_image_pix) if "cross_image_pix" in model_args else None
if args.quant:
quantize(model, args.quant)
if torch.cuda.is_available():
model = model.cuda()
model.add_mixin('auto-regressive', CachedAutoregressiveMixin())
text_processor_infer = llama2_text_processor_inference(tokenizer, args.max_length, model.image_length)
if args.chinese:
if rank == 0:
print('欢迎使用 CogAgent-CLI ，输入图像URL或本地路径读图，继续输入内容对话，clear 重新开始，stop 终止程序')
else:
if rank == 0:
print('Welcome to CogAgent-CLI. Enter an image URL or local file path to load an image. Continue inputting text to engage in a conversation. Type "clear" to start over, or "stop" to end the program.')
with torch.no_grad():
while True:
history = None
cache_image = None
if args.chinese:
if rank == 0:
image_path = [input("请输入图像路径或URL： ")]
else:
image_path = [None]
else:
if rank == 0:
image_path = [input("Please enter the image path or URL: ")]
else:
image_path = [None]
if world_size > 1:
torch.distributed.broadcast_object_list(image_path, 0)
image_path = image_path[0]
assert image_path is not None
if image_path == 'stop':
break
if args.chinese:
if rank == 0:
query = [input("用户：")]
else:
query = [None]
else:
if rank == 0:
query = [input("User: ")]
else:
query = [None]
if world_size > 1:
torch.distributed.broadcast_object_list(query, 0)
query = query[0]
assert query is not None
while True:
if query == "clear":
break
if query == "stop":
sys.exit(0)
try:
response, history, cache_image = chat(
image_path,
model,
text_processor_infer,
image_processor,
query,
history=history,
cross_img_processor=cross_image_processor,
image=cache_image,
max_length=args.max_length,
top_p=args.top_p,
temperature=args.temperature,
top_k=args.top_k,
invalid_slices=text_processor_infer.invalid_slices,
args=args
)
except Exception as e:
print(e)
break
if rank == 0 and not args.stream_chat:
if args.chinese:
print("模型："+response)
else:
print("Model: "+response)
image_path = None
if args.chinese:
if rank == 0:
query = [input("用户：")]
else:
query = [None]
else:
if rank == 0:
query = [input("User: ")]
else:
query = [None]
if world_size > 1:
torch.distributed.broadcast_object_list(query, 0)
query = query[0]
assert query is not None
if __name__ == "__main__":
main()