safetensors 是一种用于安全存储张量（与 pickle 相反）的新型简单格式，并且仍然很快（零拷贝）。
safetensors 真的很快。
一、安装
1.1 pip 安装
pip install safetensors
1.2 conda 安装
conda install -c huggingface safetensors
二、加载张量
from safetensors import safe_open
tensors = {}
with safe_open("model.safetensors", framework="pt", device=0) as f:
for k in f.keys():
tensors[k] = f.get_tensor(k)
仅加载部分张量（在多个GPU上运行时很有趣）：
from safetensors import safe_open
tensors = {}
with safe_open("model.safetensors", framework="pt", device=0) as f:
tensor_slice = f.get_slice("embedding")
vocab_size, hidden_dim = tensor_slice.get_shape()
tensor = tensor_slice[:, :hidden_dim]
三、保存张量
import torch
from safetensors.torch import save_file
tensors = {
"embedding": torch.zeros((2, 2)),
"attention": torch.zeros((2, 3))
}
save_file(tensors, "model.safetensors")
四、速度比较
4.1 下载 gpt2 的文件
safetensors 真的很快。让我们通过加载 gpt2 权重将其进行比较。要运行 GPU 基准测试，请确保您的机器具有 GPU，或者您已选择是否使用的是 Google Colab。
在开始之前，请确保已安装所有必要的库：
pip install safetensors huggingface_hub torch
让我们从导入所有将使用的包开始：
import os
import datetime
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import torch
Download safetensors & torch weights for gpt2：
sf_filename = hf_hub_download("gpt2", filename="model.safetensors")
pt_filename = hf_hub_download("gpt2", filename="pytorch_model.bin")
4.2 CPU 基准测试
start_st = datetime.datetime.now()
weights = load_file(sf_filename, device="cpu")
load_time_st = datetime.datetime.now() - start_st
print(f"Loaded safetensors {load_time_st}")
输出结果为：
Loaded safetensors 0:00:00.026842
start_pt = datetime.datetime.now()
weights = torch.load(pt_filename, map_location="cpu")
load_time_pt = datetime.datetime.now() - start_pt
print(f"Loaded pytorch {load_time_pt}")
输出结果为：
Loaded pytorch 0:00:00.182266
print(f"on CPU, safetensors is faster than pytorch by: {load_time_pt/load_time_st:.1f} X")
输出结果为：
on CPU, safetensors is faster than pytorch by: 6.8 X
这种加速是由于该库通过直接映射文件来避免不必要的副本。实际上可以在 torch 上完成。 当前显示的加速比已打开：
- 操作系统： Windows
- 处理器： 英特尔® 至强® CPU @ 2.00GHz
4.3 GPU 基准测试
os.environ["SAFETENSORS_FAST_GPU"] = "1"
torch.zeros((2, 2)).cuda()
start_st = datetime.datetime.now()
weights = load_file(sf_filename, device="cuda:0")
load_time_st = datetime.datetime.now() - start_st
print(f"Loaded safetensors {load_time_st}")
start_pt = datetime.datetime.now()
weights = torch.load(pt_filename, map_location="cuda:0")
load_time_pt = datetime.datetime.now() - start_pt
print(f"Loaded pytorch {load_time_pt}")
print(f"on GPU, safetensors is faster than pytorch by: {load_time_pt/load_time_st:.1f} X")
输出结果为：
Loaded safetensors 0:00:00.497415
Loaded pytorch 0:00:00.250602
on GPU, safetensors is faster than pytorch by: 0.5 X
加速有效是因为此库能够跳过不必要的 CPU 分配。不幸的是，据我们所知，它无法在纯 pytorch 中复制。该库的工作原理是内存映射文件，使用 pytorch 创建空张量，并直接调用以直接在 GPU 上移动张量。
显卡：GTX 3060
