Pytorch mixed precision 概述（混合精度）

矫情吗；* 2022-11-18 08:06 209阅读 0赞

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作者：知乎 superjie13

https://www.zhihu.com/people/superjie13

本文对pytorch中的mixed precision进行测试。主要包括两部分，第一部分为mixed precision使用概述，第二部分为实际测试。参考torch官网 Automatic Mixed Precision

**01**

**Mixed precision使用概述**

通常，automatic mixed precision training 需要使用 torch.cuda.amp.autocast 和 torch.cuda.amp.GradScaler 。

**1. 1** 首先实例化 torch.cuda.amp.autocast(enable=True) 作为上下文管理器或者装饰器，从而使脚本使用混合精度运行。注意：autocast 一般情况下只封装前向传播过程（包括loss的计算），并不包括反向传播（反向传播的数据类型与相应前向传播中的数据类型相同）。

**1. 2** 使用Gradient scaling 防止在反向传播过程由于中梯度太小（float16无法表示小幅值的变化）从而下溢为0的情况。torch.cuda.amp.GradScaler() 可以自动进行gradient scaling。注意：由于GradScaler()对gradient进行了scale，因此每个参数的gradient应该在optimizer更新参数前unscaled，从而使学习率不受影响。

import torchvisionimport torchimport torch.cuda.ampimport gcimport time
    # Timing utilitiesstart_time = None
    def start_timer():    global start_time    gc.collect()    torch.cuda.empty_cache()    torch.cuda.reset_max_memory_allocated()    torch.cuda.synchronize()  # 同步后得出的时间才是实际运行的时间    start_time = time.time()
    def end_timer_and_print(local_msg):    torch.cuda.synchronize()    end_time = time.time()    print("\n" + local_msg)    print("Total execution time = {:.3f} sec".format(end_time - start_time))    print("Max memory used by tensors = {} bytes".format(torch.cuda.max_memory_allocated()))
    num_batches = 50batch_size = 70epochs = 3
    # 随机创建训练数据data = [torch.randn(batch_size, 3, 224, 224, device="cuda") for _ in range(num_batches)]targets = [torch.randint(0, 1000, size=(batch_size, ), device='cuda') for _ in range(num_batches)]# 创建一个模型net = torchvision.models.resnext50_32x4d().cuda()# 定义损失函数loss_fn = torch.nn.CrossEntropyLoss().cuda()# 定义优化器opt = torch.optim.SGD(net.parameters(), lr=0.001)
    # 是否使用混合精度训练use_amp = True
    # Constructs scaler once, at the beginning of the convergence run, using default args.# If your network fails to converge with default GradScaler args, please file an issue.# The same GradScaler instance should be used for the entire convergence run.# If you perform multiple convergence runs in the same script, each run should use# a dedicated fresh GradScaler instance.  GradScaler instances are lightweight.scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
    start_timer()for epoch in range(epochs):    for input, target in zip(data, targets):        with torch.cuda.amp.autocast(enabled=use_amp):            output = net(input)            loss = loss_fn(output, target)        # 放大loss  Calls backward() on scaled loss to create scaled gradients.        scaler.scale(loss).backward()
            # scaler.step() first unscales the gradients of the optimizer's assigned params.        # If these gradients do not contain infs or NaNs, optimizer.step() is then called,        # otherwise, optimizer.step() is skipped.        scaler.step(opt)
            # Updates the scale for next iteration.        scaler.update()        opt.zero_grad(set_to_none=True) # set_to_none=True here can modestly improve performanceend_timer_and_print("Mixed precision:")
    
    
    02
    
    
    混合精度测试

测试环境：ubuntu18.04, pytorch 1.7.1, python3.7, RTX2080-8G

**2.1 use\_amp = False**

batch size = 40

![4d5f9872f0665d84971e86761aae4148.png][]

![0f236f0980a77b2eb1bd569e96bd0b19.png][]

**2.2 use\_amp = True**

batch size = 40

![8556e02035b946f853a640c8bb50c43a.png][]

![81ca90c20bd28c4bafe7b56e467b1497.png][]

从实验2.1和2.2中，可以发现在batch size=40的情况下，不使用混合精度时，GPU内存占用为7011MB，运行时间为47.55 s。而使用混合精度时，GPU内存占用为4997MB，运行时间为27.006 s。在当前运行配置中，内存占用节省了约28.73%，运行时间节省了约43.21%。这也就意味着我们可以使用更大的batch size来提升运行效率。

**2.3 use\_amp = True**

batch size = 70

![0a508a3882efeaf73fa29e3d7790c96b.png][]

![83234090088c3697a1ac21850185329a.png][]

![f0a7df001769c5a2f4d914d8b3f8c8b4.png][]

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