{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.0.0'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "import torchvision\n", "torch.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.5 多GPU并行训练\n", "\n", "在我们进行神经网络训练的时候，因为计算量巨大所以单个GPU运算会使得计算时间很长，使得我们不能够及时的得到结果，例如我们如果使用但GPU使用ImageNet的数据训练一个分类器，可能会花费一周甚至一个月的时间。所以在Pytorch中引入了多GPU计算的机制，这样使得训练速度可以指数级的增长。\n", "\n", "stanford大学的[DAWNBench](https://dawn.cs.stanford.edu/benchmark/) 就记录了目前为止的一些使用多GPU计算的记录和实现代码，有兴趣的可以看看。\n", "\n", "这章里面我们要介绍的三个方式来使用多GPU加速" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.5.1 torch.nn.DataParalle\n", "一般情况下我们都会使用一台主机带多个显卡，这样是一个最节省预算的方案，在Pytorch中为我们提供了一个非常简单的方法来支持但主机多GPU，那就`torch.nn.DataParalle` 我们只要将我们自己的模型作为参数，直接传入即可，剩下的事情Pytorch都为我们做了" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#使用内置的一个模型，我们这里以resnet50为例\n", "model = torchvision.models.resnet50()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DataParallel(\n", " (module): ResNet(\n", " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", " (layer1): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (downsample): Sequential(\n", " (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " )\n", " (layer2): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (downsample): Sequential(\n", " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (3): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " )\n", " (layer3): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (downsample): Sequential(\n", " (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (3): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (4): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (5): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " )\n", " (layer4): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (downsample): Sequential(\n", " (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " )\n", " (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)\n", " (fc): Linear(in_features=2048, out_features=1000, bias=True)\n", " )\n", ")" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#模型使用多GPU\n", "mdp = torch.nn.DataParallel(model)\n", "mdp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "只要这样一个简单的包裹，Pytorch已经为我们做了很多复杂的工作。我们只需要增大我们训练的batch_size(一般计算为N倍，N为显卡数量)，其他代码不需要任何改动。\n", "虽然代码不需要做更改，但是batch size太大了训练收敛会很慢，所以还要把学习率调大一点。大学率也会使得模型的训练在早期的阶段变得十分不稳定，所以这里需要一个学习率的热身（warm up） 来稳定梯度的下降，然后在逐步的提高学习率。\n", "\n", "这种热身只有在超级大的批次下才需要进行，一般我们这种一机4卡或者说在batch size 小于 5000（个人测试）基本上是不需要的。例如最近富士通使用2048个GPU,74秒训练完成resnet50的实验中使用的batch size 为 81920 [arivx](http://www.arxiv.org/abs/1903.12650)这种超大的size才需要。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataParallel的并行处理机制是，首先将模型加载到主 GPU 上(默认的第一个GPU，GPU0为主GPU)，然后再将模型复制到各个指定的从 GPU 中，然后将输入数据按 batch 维度进行划分，具体来说就是每个 GPU 分配到的数据 batch 数量是总输入数据的 batch 除以指定 GPU 个数。每个 GPU 将针对各自的输入数据独立进行 forward 计算，最后将各个 GPU 的 loss 进行求和，再用反向传播更新单个 GPU 上的模型参数，再将更新后的模型参数复制到剩余指定的 GPU 中，这样就完成了一次迭代计算。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataParallel其实也是一个nn.Model所以我们可以保存权重的方法和一般的nn.Model没有区别，只不过如果你想使用单卡或者cpu作为推理的时候需要从里面读出原始的model " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ResNet(\n", " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", " (layer1): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, 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momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (downsample): Sequential(\n", " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (3): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " )\n", " (layer3): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (downsample): Sequential(\n", " (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (3): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (4): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (5): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " )\n", " (layer4): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " (downsample): Sequential(\n", " (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace)\n", " )\n", " )\n", " (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)\n", " (fc): Linear(in_features=2048, out_features=1000, bias=True)\n", ")" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#获取到原始的model\n", "m=mdp.module\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataParallel会将定义的网络模型参数默认放在GPU 0上，所以dataparallel实质是可以看做把训练参数从GPU拷贝到其他的GPU同时训练，这样会导致内存和GPU使用率出现很严重的负载不均衡现象，即GPU 0的使用内存和使用率会大大超出其他显卡的使用内存，因为在这里GPU0作为master来进行梯度的汇总和模型的更新，再将计算任务下发给其他GPU，所以他的内存和使用率会比其他的高。\n", "\n", "所以我们使用新的torch.distributed来构建更为同步的分布式运算。使用torch.distributed不仅可以支持单机还可以支持多个主机，多个GPU进行计算。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.5.2 torch.distributed\n", "`torch.distributed`相对于`torch.nn.DataParalle` 是一个底层的API，所以我们要修改我们的代码，使其能够独立的在机器（节点）中运行。我们想要完全实现分布式，并且在每个结点的每个GPU上独立运行进程，这一共需要N个进程。N是我们的GPU总数，这里我们以4来计算。\n", "\n", "首先 初始化分布式后端，封装模型以及准备数据，这些数据用于在独立的数据子集中训练进程。修改后的代码如下" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 以下脚本在jupyter notebook执行肯定会不成功，请保存成py文件后测试\n", "import torch\n", "import argparse\n", "from torch.utils.data.distributed import DistributedSampler\n", "from torch.utils.data import DataLoader\n", "\n", "# 这里的node_rank是本地GPU的标识\n", "parser = argparse.ArgumentParser()\n", "parser.add_argument(\"--node_rank\", type=int)\n", "args = parser.parse_args()\n", "\n", "# 使用Nvdea的nccl来初始化节点 \n", "torch.distributed.init_process_group(backend='nccl')\n", "\n", "# 封装分配给当前进程的GPU上的模型\n", "device = torch.device('cuda', arg.local_rank)\n", "model = model.to(device)\n", "distrib_model = torch.nn.parallel.DistributedDataParallel(model,\n", " device_ids=[args.node_rank],\n", " output_device=args.node_rank)\n", "\n", "# 将数据加载限制为数据集的子集（不包括当前进程）\n", "sampler = DistributedSampler(dataset)\n", "\n", "dataloader = DataLoader(dataset, sampler=sampler)\n", "for inputs, labels in dataloader:\n", " predictions = distrib_model(inputs.to(device)) # 正向传播\n", " loss = loss_function(predictions, labels.to(device)) # 计算损失\n", " loss.backward() # 反向传播\n", " optimizer.step() # 优化\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在运行时我们也不能简单的使用`python 文件名`来执行了，我们这里需要使用PyTorch中为我们准备好的torch.distributed.launch运行脚本。它能自动进行环境变量的设置，并使用正确的node_rank参数调用脚本。\n", "\n", "这里我们要准备一台机器作为master，所有的机器都要求能对它进行访问。因此，它需要拥有一个可以访问的IP地址（示例中为：196.168.100.100）以及一个开放的端口（示例中为：6666）。我们将使用torch.distributed.launch在第一台机器上运行脚本：\n", "```bash\n", "python -m torch.distributed.launch --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=\"192.168.100.100\" --master_port=6666 文件名 (--arg1 --arg2 等其他参数)\n", "```\n", "第二台主机上只需要更改 `--node_rank=0`即可" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "很有可能你在运行的时候报错，那是因为我们没有设置NCCL socket网络接口\n", "我们以网卡名为ens3为例，输入\n", "```bash\n", "export NCCL_SOCKET_IFNAME=ens3\n", "```\n", "ens3这个名称 可以使用ifconfig命令查看确认 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "参数说明：\n", "\n", "--nproc_per_node ： 主机中包含的GPU总数\n", "\n", "--nnodes ： 总计的主机数\n", "\n", "--node_rank ：主机中的GPU标识\n", "\n", "其他一些参数可以查看[官方的文档](https://github.com/pytorch/pytorch/blob/master/torch/distributed/launch.py)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "torch.distributed 不仅支持nccl还支持其他的两个后端 gloo和mpi，具体的对比这里就不细说了，请查看[官方的文档](https://pytorch.org/docs/stable/distributed.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.5.3 torch.utils.checkpoint\n", "在我们训练时，可能会遇到（目前我还没遇到）训练集的单个样本比内存还要大根本载入不了，那我我们如何来训练呢？\n", "\n", "pytorch为我们提供了梯度检查点（gradient-checkpointing）节省计算资源，梯度检查点会将我们连续计算的元正向和元反向传播切分成片段。但由于需要增加额外的计算以减少内存需求，该方法效率会有一些下降，但是它在某些示例中有较为明显的优势，比如在长序列上训练RNN模型，这个由于复现难度较大 就不介绍了，官方文档在[这里](https://pytorch.org/docs/stable/checkpoint.html) 遇到这种情况的朋友可以查看下官方的解决方案。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Machine Learning", "language": "python", "name": "ml" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }