您正在查看 主分支 版本，它需要从源代码安装。如果您想要使用常规的 pip 安装，请查看最新的稳定版本（v0.3.2）。
速度比较
Safetensors 速度非常快。让我们通过加载 gpt2 权重来将其与 PyTorch 进行比较。要运行 GPU 基准测试，请确保您的机器具有 GPU 或如果您正在使用 Google Colab，则已选择 GPU 运行时。
在开始之前，请确保已安装所有必要的库
pip install safetensors huggingface_hub torch
让我们首先导入将要使用的所有包
>>> import os
>>> import datetime
>>> from huggingface_hub import hf_hub_download
>>> from safetensors.torch import load_file
>>> import torch下载 gpt2 的 safetensors 和 torch 权重
>>> sf_filename = hf_hub_download("gpt2", filename="model.safetensors")
>>> pt_filename = hf_hub_download("gpt2", filename="pytorch_model.bin")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}")
>>> 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}")
>>> print(f"on CPU, safetensors is faster than pytorch by: {load_time_pt/load_time_st:.1f} X")
Loaded safetensors 0:00:00.004015
Loaded pytorch 0:00:00.307460
on CPU, safetensors is faster than pytorch by: 76.6 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复制。实际上可以在 纯 pytorch 上实现。当前显示的加速是在以下环境中获得的
- 操作系统：Ubuntu 18.04.6 LTS
- CPU：Intel(R) Xeon(R) CPU @ 2.00GHz
GPU 基准测试
>>> # This is required because this feature hasn't been fully verified yet, but
>>> # it's been tested on many different environments
>>> os.environ["SAFETENSORS_FAST_GPU"] = "1"
>>> # CUDA startup out of the measurement
>>> 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")
Loaded safetensors 0:00:00.165206
Loaded pytorch 0:00:00.353889
on GPU, safetensors is faster than pytorch by: 2.1 X加速之所以有效是因为该库能够跳过不必要的 CPU 分配。据我们所知，它在纯 pytorch 中不幸的是无法复制。该库通过内存映射文件，使用 pytorch 创建空的张量并直接调用 cudaMemcpy 来将张量直接移动到 GPU 上。当前显示的加速是在以下环境中获得的
- 操作系统：Ubuntu 18.04.6 LTS。
- GPU：Tesla T4
- 驱动程序版本：460.32.03
- CUDA 版本：11.2