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| <h1 class="post-title">Model Parallel</h1> |
| <h3></h3></header> |
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| |
| <h1 id="training-with-multiple-gpus-using-model-parallelism">Training with Multiple GPUs Using Model Parallelism</h1> |
| |
| <p>Training deep learning models can be resource intensive. |
| Even with a powerful GPU, some models can take days or weeks to train. |
| Large long short-term memory (LSTM) recurrent neural networks |
| can be especially slow to train, |
| with each layer, at each time step, requiring eight matrix multiplications. |
| Fortunately, given cloud services like AWS, |
| machine learning practitioners often have access |
| to multiple machines and multiple GPUs. |
| One key strength of <em>MXNet</em> is its ability to leverage |
| powerful heterogeneous hardware environments to achieve significant speedups.</p> |
| |
| <p>There are two primary ways that we can spread a workload across multiple devices. |
| In a previous document, <a href="/api/faq/distributed_training">we addressed data parallelism</a>, |
| an approach in which samples within a batch are divided among the available devices. |
| With data parallelism, each device stores a complete copy of the model. |
| Here, we explore <em>model parallelism</em>, a different approach. |
| Instead of splitting the batch among the devices, we partition the model itself. |
| Most commonly, we achieve model parallelism by assigning the parameters (and computation) |
| of different layers of the network to different devices.</p> |
| |
| <p>In particular, we will focus on LSTM recurrent networks. |
| LSTMS are powerful sequence models, that have proven especially useful |
| for <a href="https://arxiv.org/pdf/1409.0473.pdf">natural language translation</a>, <a href="https://arxiv.org/abs/1512.02595">speech recognition</a>, |
| and working with <a href="https://arxiv.org/abs/1511.03677">time series data</a>. |
| For a general high-level introduction to LSTMs, |
| see the excellent <a href="https://colah.github.io/posts/2015-08-Understanding-LSTMs/">tutorial</a> by Christopher Olah.</p> |
| |
| <h2 id="model-parallelism-using-multiple-gpus-as-a-pipeline">Model Parallelism: Using Multiple GPUs As a Pipeline</h2> |
| |
| <p>Model parallelism in deep learning was first proposed |
| for the <em>extraordinarily large</em> convolutional layer in GoogleNet. |
| From this implementation, we take the idea of placing each layer on a separate GPU. |
| Using model parallelism in such a layer-wise fashion |
| provides the benefit that no GPU has to maintain all of the model parameters in memory.</p> |
| |
| <p><img width="517" alt="screen shot 2016-05-06 at 10 13 16 pm" src="https://cloud.githubusercontent.com/assets/5545640/15089697/d6f4fca0-13d7-11e6-9331-7f94fcc7b4c6.png"></p> |
| |
| <p>In the preceding figure, each LSTM layer is assigned to a different GPU. |
| After GPU 1 finishes computing layer 1 for the first sentence, it passes its output to GPU 2. |
| At the same time, GPU 1 fetches the next sentence and starts training. |
| This differs significantly from data parallelism. |
| Here, there is no contention to update the shared model at the end of each iteration, |
| and most of the communication happens when passing intermediate results between GPUs.</p> |
| |
| <h2 id="workload-partitioning">Workload Partitioning</h2> |
| |
| <p>Implementing model parallelism requires knowledge of the training task. |
| Here are some general heuristics that we find useful:</p> |
| |
| <ul> |
| <li>To minimize communication time, place neighboring layers on the same GPUs.</li> |
| <li>Be careful to balance the workload between GPUs.</li> |
| <li>Remember that different kinds of layers have different computation-memory properties.</li> |
| </ul> |
| |
| <p><img width="449" alt="screen shot 2016-05-07 at 1 51 02 am" src="https://cloud.githubusercontent.com/assets/5545640/15090455/37a30ab0-13f6-11e6-863b-efe2b10ec2e6.png"></p> |
| |
| <p>Let's take a quick look at the two pipelines in the preceding diagram. |
| They both have eight layers with a decoder and an encoder layer. |
| Based on our first principle, it's unwise to place all neighboring layers on separate GPUs. |
| We also want to balance the workload across GPUs. |
| Although the LSTM layers consume less memory than the decoder/encoder layers, they consume more computation time because of the dependency of the unrolled LSTM. |
| Thus, the partition on the left will be faster than the one on the right |
| because the workload is more evenly distributed.</p> |
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