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<p>SINGA支持跨多个GPU的数据并行训练（在单个节点上或跨不同节点）。下图说明了数据并行训练的情况：</p>
<p><img src="/docs/assets/MPI.png" alt="MPI.png"></p>
<p>在分布式训练中，每个进程(称为worker)在单个GPU上运行一个训练脚本，每个进程都有一个单独的通信等级，训练数据被分发给各个worker，模型在每个worker上被复制。在每次迭代中，worker从其分区中读取数据的一个mini-batch（例如，256张图像），并运行BackPropagation算法来计算权重的梯度，通过all-reduce（由<a href="https://developer.nvidia.com/nccl">NCCL</a>提供）进行平均，按照随机梯度下降算法（SGD）进行权重更新。</p>
<p>NCCL的all-reduce操作可以用来减少和同步不同GPU的梯度。假设我们使用4个GPU进行训练，如下图所示。一旦计算出4个GPU的梯度，all-reduce将返回GPU的梯度之和，并使其在每个GPU上可用，然后就可以轻松计算出平均梯度。</p>
<p><img src="/docs/assets/AllReduce.png" alt="AllReduce.png"></p>
<h2><a class="anchor" aria-hidden="true" id="使用"></a><a href="#使用" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>使用</h2>
<p>SINGA提供了一个名为<code>DistOpt</code>（<code>Opt</code>的一个子类）的模块，用于分布式训练。它封装了一个普通的SGD优化器，并调用<code>Communicator</code>进行梯度同步。下面的例子说明了如何使用<code>DistOpt</code>在MNIST数据集上训练一个CNN模型。源代码在<a href="https://github.com/apache/singa/blob/master/examples/cnn/">这里</a>，或者可以使用<a href="">Colab notebook</a>。</p>
<h3><a class="anchor" aria-hidden="true" id="代码示例"></a><a href="#代码示例" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>代码示例</h3>
<ol>
<li>定义神经网络模型：</li>
</ol>
<pre><code class="hljs css language-python"><span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">CNN</span><span class="hljs-params">(model.Model)</span>:</span>

    <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">__init__</span><span class="hljs-params">(self, num_classes=<span class="hljs-number">10</span>, num_channels=<span class="hljs-number">1</span>)</span>:</span>
        super(CNN, self).__init__()
        self.conv1 = layer.Conv2d(num_channels, <span class="hljs-number">20</span>, <span class="hljs-number">5</span>, padding=<span class="hljs-number">0</span>, activation=<span class="hljs-string">"RELU"</span>)
        self.conv2 = layer.Conv2d(<span class="hljs-number">20</span>, <span class="hljs-number">50</span>, <span class="hljs-number">5</span>, padding=<span class="hljs-number">0</span>, activation=<span class="hljs-string">"RELU"</span>)
        self.linear1 = layer.Linear(<span class="hljs-number">500</span>)
        self.linear2 = layer.Linear(num_classes)
        self.pooling1 = layer.MaxPool2d(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, padding=<span class="hljs-number">0</span>)
        self.pooling2 = layer.MaxPool2d(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, padding=<span class="hljs-number">0</span>)
        self.relu = layer.ReLU()
        self.flatten = layer.Flatten()
        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()

    <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">forward</span><span class="hljs-params">(self, x)</span>:</span>
        y = self.conv1(x)
        y = self.pooling1(y)
        y = self.conv2(y)
        y = self.pooling2(y)
        y = self.flatten(y)
        y = self.linear1(y)
        y = self.relu(y)
        y = self.linear2(y)
        <span class="hljs-keyword">return</span> y

    <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">train_one_batch</span><span class="hljs-params">(self, x, y, dist_option=<span class="hljs-string">'fp32'</span>, spars=<span class="hljs-number">0</span>)</span>:</span>
        out = self.forward(x)
        loss = self.softmax_cross_entropy(out, y)

        <span class="hljs-comment"># Allow different options for distributed training</span>
        <span class="hljs-comment"># See the section "Optimizations for Distributed Training"</span>
        <span class="hljs-keyword">if</span> dist_option == <span class="hljs-string">'fp32'</span>:
            self.optimizer(loss)
        <span class="hljs-keyword">elif</span> dist_option == <span class="hljs-string">'fp16'</span>:
            self.optimizer.backward_and_update_half(loss)
        <span class="hljs-keyword">elif</span> dist_option == <span class="hljs-string">'partialUpdate'</span>:
            self.optimizer.backward_and_partial_update(loss)
        <span class="hljs-keyword">elif</span> dist_option == <span class="hljs-string">'sparseTopK'</span>:
            self.optimizer.backward_and_sparse_update(loss,
                                                      topK=<span class="hljs-literal">True</span>,
                                                      spars=spars)
        <span class="hljs-keyword">elif</span> dist_option == <span class="hljs-string">'sparseThreshold'</span>:
            self.optimizer.backward_and_sparse_update(loss,
                                                      topK=<span class="hljs-literal">False</span>,
                                                      spars=spars)
        <span class="hljs-keyword">return</span> out, loss

<span class="hljs-comment"># create model</span>
model = CNN()
</code></pre>
<ol start="2">
<li>创建<code>DistOpt</code>实例并将其应用到创建的模型上：</li>
</ol>
<pre><code class="hljs css language-python">sgd = opt.SGD(lr=<span class="hljs-number">0.005</span>, momentum=<span class="hljs-number">0.9</span>, weight_decay=<span class="hljs-number">1e-5</span>)
sgd = opt.DistOpt(sgd)
model.set_optimizer(sgd)
dev = device.create_cuda_gpu_on(sgd.local_rank)
</code></pre>
<p>下面是关于代码中一些变量的解释：</p>
<p>(i) <code>dev</code></p>
<p>dev代表<code>Device</code>实例，在设备中加载数据并运行CNN模型。</p>
<p>(ii)<code>local_rank</code></p>
<p>Local rank表示当前进程在同一节点中使用的GPU数量。例如，如果你使用的节点有2个GPU，<code>local_rank=0</code>表示这个进程使用的是第一个GPU，而<code>local_rank=1</code>表示使用的是第二个GPU。使用MPI或多进程，你能够运行相同的训练脚本，唯一的区别<code>local_rank</code>的值不同。</p>
<p>(iii)<code>global_rank</code></p>
<p>global中的rank代表了你使用的所有节点中所有进程的全局排名。让我们考虑这样的情况：你有3个节点，每个节点有两个GPU， <code>global_rank=0</code>表示使用第1个节点的第1个GPU的进程， <code>global_rank=2</code>表示使用第2个节点的第1个GPU的进程， <code>global_rank=4</code>表示使用第3个节点的第1个GPU的进程。</p>
<ol start="3">
<li>加载和分割训练/验证数据：</li>
</ol>
<pre><code class="hljs css language-python"><span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">data_partition</span><span class="hljs-params">(dataset_x, dataset_y, global_rank, world_size)</span>:</span>
    data_per_rank = dataset_x.shape[<span class="hljs-number">0</span>] // world_size
    idx_start = global_rank * data_per_rank
    idx_end = (global_rank + <span class="hljs-number">1</span>) * data_per_rank
    <span class="hljs-keyword">return</span> dataset_x[idx_start:idx_end], dataset_y[idx_start:idx_end]

train_x, train_y, test_x, test_y = load_dataset()
train_x, train_y = data_partition(train_x, train_y,
                                  sgd.global_rank, sgd.world_size)
test_x, test_y = data_partition(test_x, test_y,
                                sgd.global_rank, sgd.world_size)
</code></pre>
<p>这个<code>dev</code>的数据集的一个分区被返回。</p>
<p>这里，<code>world_size</code>代表你用于分布式训练的所有节点中的进程总数。</p>
<ol start="4">
<li>初始化并同步所有worker的模型参数:</li>
</ol>
<pre><code class="hljs css language-python"><span class="hljs-comment">#Synchronize the initial parameter</span>
tx = tensor.Tensor((batch_size, <span class="hljs-number">1</span>, IMG_SIZE, IMG_SIZE), dev, tensor.float32)
ty = tensor.Tensor((batch_size, num_classes), dev, tensor.int32)
model.compile([tx], is_train=<span class="hljs-literal">True</span>, use_graph=graph, sequential=<span class="hljs-literal">True</span>)
...
<span class="hljs-comment">#Use the same random seed for different ranks</span>
seed = <span class="hljs-number">0</span>
dev.SetRandSeed(seed)
np.random.seed(seed)
</code></pre>
<ol start="5">
<li>运行BackPropagation和分布式SGD</li>
</ol>
<pre><code class="hljs css language-python"><span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> range(max_epoch):
    <span class="hljs-keyword">for</span> b <span class="hljs-keyword">in</span> range(num_train_batch):
        x = train_x[idx[b * batch_size: (b + <span class="hljs-number">1</span>) * batch_size]]
        y = train_y[idx[b * batch_size: (b + <span class="hljs-number">1</span>) * batch_size]]
        tx.copy_from_numpy(x)
        ty.copy_from_numpy(y)
        <span class="hljs-comment"># Train the model</span>
        out, loss = model(tx, ty)
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="执行命令"></a><a href="#执行命令" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>执行命令</h3>
<p>有两种方式可以启动训练，MPI或Python multiprocessing。</p>
<h4><a class="anchor" aria-hidden="true" id="python-multiprocessing"></a><a href="#python-multiprocessing" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Python multiprocessing</h4>
<p>它可以在一个节点上使用多个GPU，其中，每个GPU是一个worker。</p>
<ol>
<li>将上述训练用的代码打包进一个函数：</li>
</ol>
<pre><code class="hljs css language-python"><span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">train_mnist_cnn</span><span class="hljs-params">(nccl_id=None, local_rank=None, world_size=None)</span>:</span>
    ...
</code></pre>
<ol start="2">
<li>创建<code>mnist_multiprocess.py</code>。</li>
</ol>
<pre><code class="hljs css language-python"><span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">'__main__'</span>:
    <span class="hljs-comment"># Generate a NCCL ID to be used for collective communication</span>
    nccl_id = singa.NcclIdHolder()

    <span class="hljs-comment"># Define the number of GPUs to be used in the training process</span>
    world_size = int(sys.argv[<span class="hljs-number">1</span>])

    <span class="hljs-comment"># Define and launch the multi-processing</span>
    <span class="hljs-keyword">import</span> multiprocessing
    process = []
    <span class="hljs-keyword">for</span> local_rank <span class="hljs-keyword">in</span> range(<span class="hljs-number">0</span>, world_size):
        process.append(multiprocessing.Process(target=train_mnist_cnn,
                       args=(nccl_id, local_rank, world_size)))

    <span class="hljs-keyword">for</span> p <span class="hljs-keyword">in</span> process:
        p.start()
</code></pre>
<p>下面是关于上面所创建的变量的一些说明：</p>
<p>(i) <code>nccl_id</code></p>
<p>需要注意的是，我们在这里需要生成一个NCCL ID，用于集体通信，然后将其传递给所有进程。NCCL ID就像一个门票，只有拥有这个ID的进程才能加入到all-reduce操作中。(如果我们接下来使用MPI，NCCL ID的传递就没有必要了，因为在我们的代码中，这个ID会由MPI自动广播。)</p>
<p>(ii) <code>world_size</code></p>
<p>world_size是您想用于训练的GPU数量。</p>
<p>(iii) <code>local_rank</code></p>
<p>local_rank决定分布式训练的本地顺序，以及在训练过程中使用哪个gpu。在上面的代码中，我们使用for循环来运行训练函数，其中参数local_rank从0迭代到world_size。在这种情况下，不同的进程可以使用不同的GPU进行训练。</p>
<p>创建<code>DistOpt</code>实例的参数应按照如下方式更新：</p>
<pre><code class="hljs css language-python">sgd = opt.DistOpt(sgd, nccl_id=nccl_id, local_rank=local_rank, world_size=world_size)
</code></pre>
<ol start="3">
<li>运行<code>mnist_multiprocess.py</code>：</li>
</ol>
<pre><code class="hljs css language-sh">python mnist_multiprocess.py 2
</code></pre>
<p>与单GPU训练相比，它最主要的意义是速度提升：</p>
<pre><code class="hljs">Starting Epoch <span class="hljs-number">0</span>:
Training loss = <span class="hljs-number">408.909790</span>, training accuracy = <span class="hljs-number">0.880475</span>
Evaluation accuracy = <span class="hljs-number">0.956430</span>
Starting Epoch <span class="hljs-number">1</span>:
Training loss = <span class="hljs-number">102.396790</span>, training accuracy = <span class="hljs-number">0.967415</span>
Evaluation accuracy = <span class="hljs-number">0.977564</span>
Starting Epoch <span class="hljs-number">2</span>:
Training loss = <span class="hljs-number">69.217010</span>, training accuracy = <span class="hljs-number">0.977915</span>
Evaluation accuracy = <span class="hljs-number">0.981370</span>
Starting Epoch <span class="hljs-number">3</span>:
Training loss = <span class="hljs-number">54.248390</span>, training accuracy = <span class="hljs-number">0.982823</span>
Evaluation accuracy = <span class="hljs-number">0.984075</span>
Starting Epoch <span class="hljs-number">4</span>:
Training loss = <span class="hljs-number">45.213406</span>, training accuracy = <span class="hljs-number">0.985560</span>
Evaluation accuracy = <span class="hljs-number">0.985276</span>
Starting Epoch <span class="hljs-number">5</span>:
Training loss = <span class="hljs-number">38.868435</span>, training accuracy = <span class="hljs-number">0.987764</span>
Evaluation accuracy = <span class="hljs-number">0.986278</span>
Starting Epoch <span class="hljs-number">6</span>:
Training loss = <span class="hljs-number">34.078186</span>, training accuracy = <span class="hljs-number">0.989149</span>
Evaluation accuracy = <span class="hljs-number">0.987881</span>
Starting Epoch <span class="hljs-number">7</span>:
Training loss = <span class="hljs-number">30.138697</span>, training accuracy = <span class="hljs-number">0.990451</span>
Evaluation accuracy = <span class="hljs-number">0.988181</span>
Starting Epoch <span class="hljs-number">8</span>:
Training loss = <span class="hljs-number">26.854443</span>, training accuracy = <span class="hljs-number">0.991520</span>
Evaluation accuracy = <span class="hljs-number">0.988682</span>
Starting Epoch <span class="hljs-number">9</span>:
Training loss = <span class="hljs-number">24.039650</span>, training accuracy = <span class="hljs-number">0.992405</span>
Evaluation accuracy = <span class="hljs-number">0.989083</span>
</code></pre>
<h4><a class="anchor" aria-hidden="true" id="mpi"></a><a href="#mpi" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>MPI</h4>
<p>只要有多个GPU，MPI既适用于单节点，也适用于多节点。</p>
<ol>
<li>创建<code>mnist_dist.py</code>。</li>
</ol>
<pre><code class="hljs css language-python"><span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">'__main__'</span>:
    train_mnist_cnn()
</code></pre>
<ol start="2">
<li>为MPI生成一个hostfile，例如下面的hostfile在一个节点上使用了2个进程（即2个GPU）：</li>
</ol>
<pre><code class="hljs css language-txt">localhost:<span class="hljs-number">2</span>
</code></pre>
<ol start="3">
<li>通过<code>mpiexec</code>启动训练：</li>
</ol>
<pre><code class="hljs css language-sh">mpiexec --hostfile host_file python mnist_dist.py
</code></pre>
<p>与单GPU训练相比，它同样可以带来速度的提升：</p>
<pre><code class="hljs">Starting Epoch <span class="hljs-number">0</span>:
Training loss = <span class="hljs-number">383.969543</span>, training accuracy = <span class="hljs-number">0.886402</span>
Evaluation accuracy = <span class="hljs-number">0.954327</span>
Starting Epoch <span class="hljs-number">1</span>:
Training loss = <span class="hljs-number">97.531479</span>, training accuracy = <span class="hljs-number">0.969451</span>
Evaluation accuracy = <span class="hljs-number">0.977163</span>
Starting Epoch <span class="hljs-number">2</span>:
Training loss = <span class="hljs-number">67.166870</span>, training accuracy = <span class="hljs-number">0.978516</span>
Evaluation accuracy = <span class="hljs-number">0.980769</span>
Starting Epoch <span class="hljs-number">3</span>:
Training loss = <span class="hljs-number">53.369656</span>, training accuracy = <span class="hljs-number">0.983040</span>
Evaluation accuracy = <span class="hljs-number">0.983974</span>
Starting Epoch <span class="hljs-number">4</span>:
Training loss = <span class="hljs-number">45.100403</span>, training accuracy = <span class="hljs-number">0.985777</span>
Evaluation accuracy = <span class="hljs-number">0.986078</span>
Starting Epoch <span class="hljs-number">5</span>:
Training loss = <span class="hljs-number">39.330826</span>, training accuracy = <span class="hljs-number">0.987447</span>
Evaluation accuracy = <span class="hljs-number">0.987179</span>
Starting Epoch <span class="hljs-number">6</span>:
Training loss = <span class="hljs-number">34.655270</span>, training accuracy = <span class="hljs-number">0.988799</span>
Evaluation accuracy = <span class="hljs-number">0.987780</span>
Starting Epoch <span class="hljs-number">7</span>:
Training loss = <span class="hljs-number">30.749735</span>, training accuracy = <span class="hljs-number">0.989984</span>
Evaluation accuracy = <span class="hljs-number">0.988281</span>
Starting Epoch <span class="hljs-number">8</span>:
Training loss = <span class="hljs-number">27.422146</span>, training accuracy = <span class="hljs-number">0.991319</span>
Evaluation accuracy = <span class="hljs-number">0.988582</span>
Starting Epoch <span class="hljs-number">9</span>:
Training loss = <span class="hljs-number">24.548153</span>, training accuracy = <span class="hljs-number">0.992171</span>
Evaluation accuracy = <span class="hljs-number">0.988682</span>
</code></pre>
<h2><a class="anchor" aria-hidden="true" id="针对分布式训练的优化"></a><a href="#针对分布式训练的优化" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>针对分布式训练的优化</h2>
<p>SINGA为分布式训练提供了多种优化策略，以降低模块间的通信成本。参考<code>DistOpt</code>的API，了解每种策略的配置。</p>
<p>当我们使用<code>model.Model</code>建立模型时，我们需要在<code>training_one_batch</code>方法中启用分布式训练的选项，请参考本页顶部的示例代码。我们也可以直接复制这些选项的代码，然后在其他模型中使用。</p>
<p>有了定义的选项，我们可以在使用<code>model(tx, ty, dist_option, spars)</code>开始训练时，设置对应的参数<code>dist_option</code>和<code>spars</code>。</p>
<h3><a class="anchor" aria-hidden="true" id="不采取优化手段"></a><a href="#不采取优化手段" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>不采取优化手段</h3>
<pre><code class="hljs css language-python">out, loss = model(tx, ty)
</code></pre>
<p><code>loss</code>是损失函数的输出张量，例如分类任务中的交叉熵。</p>
<h3><a class="anchor" aria-hidden="true" id="半精度梯度（half-precision-gradients）"></a><a href="#半精度梯度（half-precision-gradients）" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>半精度梯度（Half-precision Gradients）</h3>
<pre><code class="hljs css language-python">out, loss = model(tx, ty, dist_option = <span class="hljs-string">'fp16'</span>)
</code></pre>
<p>在调用all-reduce之前，它将每个梯度值转换为16-bit表示（即半精度）。</p>
<h3><a class="anchor" aria-hidden="true" id="部分同步（partial-synchronization）"></a><a href="#部分同步（partial-synchronization）" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>部分同步（Partial Synchronization）</h3>
<pre><code class="hljs css language-python">out, loss = model(tx, ty, dist_option = <span class="hljs-string">'partialUpdate'</span>)
</code></pre>
<p>在每一次迭代中，每个rank都做本地SGD更新。然后，只对一个部分的参数进行平均同步，从而节省了通信成本。分块大小是在创建<code>DistOpt</code>实例时配置的。</p>
<h3><a class="anchor" aria-hidden="true" id="梯度稀疏化（gradient-sparsification）"></a><a href="#梯度稀疏化（gradient-sparsification）" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>梯度稀疏化（Gradient Sparsification）</h3>
<p>该策略应用稀疏化方案来选择梯度的子集进行all-reduce，有两种方式：</p>
<ul>
<li>选择前k大的元素，spars是被选择的元素的一部分（比例在0 - 1之间）。</li>
</ul>
<pre><code class="hljs css language-python">out, loss = model(tx, ty, dist_option = <span class="hljs-string">'sparseTopK'</span>, spars = spars)
</code></pre>
<ul>
<li>所有绝对值大于预定义阈值的梯度都会被选中。</li>
</ul>
<pre><code class="hljs css language-python">out, loss = model(tx, ty, dist_option = <span class="hljs-string">'sparseThreshold'</span>, spars = spars)
</code></pre>
<p>超参数在创建<code>DistOpt</code>实例时配置。</p>
<h2><a class="anchor" aria-hidden="true" id="实现"></a><a href="#实现" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>实现</h2>
<p>本节主要是让开发者了解分布训练模块的代码是如何实现的。</p>
<h3><a class="anchor" aria-hidden="true" id="nccl-communicator的c接口"></a><a href="#nccl-communicator的c接口" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>NCCL communicator的C接口</h3>
<p>首先，通信层是用C语言<a href="https://github.com/apache/singa/blob/master/src/io/communicator.cc">communicator.cc</a>编写的，它调用用NCCL库进行集体通信。</p>
<p>communicator有两个构造器，一个是MPI的，另一个是Multiprocess的。</p>
<p>(i) MPI构造器</p>
<p>构造器首先先获取全局rank和world_size，计算出本地rank，然后由rank 0生成NCCL ID并广播给每个rank。之后，它调用setup函数来初始化NCCL communicator、cuda流和缓冲区。</p>
<p>(ii) Python multiprocess构造器</p>
<p>构造器首先从输入参数中获取rank、world_size和NCCL ID。之后，调用setup函数来初始化NCCL communicator、cuda流和缓冲区。</p>
<p>在初始化之后，它提供了all-reduce功能来同步模型参数或梯度。例如，synch接收一个输入张量，通过NCCL例程进行all-reduce，在我们调用synch之后，需要调用wait函数来等待all-reduce操作的完成。</p>
<h3><a class="anchor" aria-hidden="true" id="distopt的python接口"></a><a href="#distopt的python接口" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>DistOpt的Python接口</h3>
<p>然后，python接口提供了一个<a href="https://github.com/apache/singa/blob/master/python/singa/opt.py">DistOpt</a>类来封装一个<a href="https://github.com/apache/singa/blob/master/python/singa/opt.py">optimizer</a>对象，以执行基于MPI或Multiprocess的分布式训练。在初始化过程中，它创建了一个NCCL communicator对象（来自于上面小节提到的C接口），然后，<code>DistOpt</code>中的每一次all-reduce操作都会用到这个communicator对象。</p>
<p>在MPI或Multiprocess中，每个进程都有一个独立的rank，它给出了各个进程使用的GPU的信息。训练数据是被拆分的，因此每个进程可以根据一部分训练数据来评估子梯度。一旦每个进程的子梯度被计算出来，就可以将所有进程计算出的子梯度做all-reduce，得到总体随机梯度。</p>
</span></div></article></div><div class="docLastUpdate"><em>Last updated on 09/11/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/3.1.0_Chinese/onnx"><span class="arrow-prev">← </span><span>ONNX</span></a><a class="docs-next button" href="/docs/3.1.0_Chinese/time-profiling"><span>Time Profiling</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#使用">使用</a><ul class="toc-headings"><li><a href="#代码示例">代码示例</a></li><li><a href="#执行命令">执行命令</a></li></ul></li><li><a href="#针对分布式训练的优化">针对分布式训练的优化</a><ul class="toc-headings"><li><a href="#不采取优化手段">不采取优化手段</a></li><li><a href="#半精度梯度（half-precision-gradients）">半精度梯度（Half-precision Gradients）</a></li><li><a href="#部分同步（partial-synchronization）">部分同步（Partial Synchronization）</a></li><li><a href="#梯度稀疏化（gradient-sparsification）">梯度稀疏化（Gradient Sparsification）</a></li></ul></li><li><a href="#实现">实现</a><ul class="toc-headings"><li><a href="#nccl-communicator的c接口">NCCL communicator的C接口</a></li><li><a href="#distopt的python接口">DistOpt的Python接口</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><a href="/" class="nav-home"><img src="/img/singa-logo-square.png" alt="Apache SINGA" width="66" height="58"/></a><div><h5>Docs</h5><a href="/docs/installation">Getting Started</a><a href="/docs/device">Guides</a><a href="/en/https://apache-singa.readthedocs.io/en/latest/">API Reference</a><a href="/docs/examples">Examples</a><a href="/docs/download-singa">Development</a></div><div><h5>Community</h5><a href="/en/users.html">User Showcase</a><a href="/docs/history-singa">SINGA History</a><a href="/docs/team-list">SINGA Team</a><a href="/blog">SINGA News</a><a href="https://github.com/apache/singa">GitHub</a><div class="social"><a class="github-button" href="https://github.com/apache/singa" data-count-href="/apache/singa/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star this project on GitHub">apache/singa-doc</a></div><div class="social"><a href="https://twitter.com/ApacheSINGA" class="twitter-follow-button">Follow @ApacheSINGA</a></div></div><div><h5>Apache Software Foundation</h5><a href="https://apache.org/" target="_blank" rel="noreferrer noopener">Foundation</a><a href="http://www.apache.org/licenses/" target="_blank" rel="noreferrer noopener">License</a><a href="http://www.apache.org/foundation/sponsorship.html" target="_blank" rel="noreferrer noopener">Sponsorship</a><a href="http://www.apache.org/foundation/thanks.html" target="_blank" rel="noreferrer noopener">Thanks</a><a href="http://www.apache.org/events/current-event" target="_blank" rel="noreferrer noopener">Events</a><a href="http://www.apache.org/security/" target="_blank" rel="noreferrer noopener">Security</a></div></section><div style="width:100%;text-align:center"><a href="https://apache.org/" target="_blank" rel="noreferrer noopener" class="ApacheOpenSource"><img src="/img/asf_logo_wide.svg" alt="Apache Open Source"/></a><section class="copyright" style="max-width:60%;margin:0 auto">Copyright © 2021
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