Files
DB-GPT/pilot/model/loader.py
2023-05-08 00:34:36 +08:00

68 lines
1.9 KiB
Python

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import torch
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
AutoModel
)
from fastchat.serve.compression import compress_module
class ModelLoader:
"""Model loader is a class for model load
Args: model_path
"""
kwargs = {}
def __init__(self,
model_path) -> None:
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model_path = model_path
self.kwargs = {
"torch_dtype": torch.float16,
"device_map": "auto",
}
def loader(self, num_gpus, load_8bit=False, debug=False):
if self.device == "cpu":
kwargs = {}
elif self.device == "cuda":
kwargs = {"torch_dtype": torch.float16}
if num_gpus == "auto":
kwargs["device_map"] = "auto"
else:
num_gpus = int(num_gpus)
if num_gpus != 1:
kwargs.update({
"device_map": "auto",
"max_memory": {i: "13GiB" for i in range(num_gpus)},
})
else:
raise ValueError(f"Invalid device: {self.device}")
if "chatglm" in self.model_path:
tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True).half().cuda()
else:
tokenizer = AutoTokenizer.from_pretrained(self.model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(self.model_path,
low_cpu_mem_usage=True, **kwargs)
if load_8bit:
compress_module(model, self.device)
if (self.device == "cuda" and num_gpus == 1):
model.to(self.device)
if debug:
print(model)
return model, tokenizer