<h2 dir="auto"><g-emoji class="g-emoji" alias="rocket" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f680.png">🚀</g-emoji> Feature</h2>
<p dir="auto">If a model consists of a DDP submodule, the module cannot be torchscripted since DDP itself cannot be torchscripted.</p>
<h2 dir="auto">Motivation</h2>
<p dir="auto">Certain complex models (especially with model parallelism) might consist of submodules where only some of the submodules need to be replicated via DDP. Torchscripting such a complex module would fail since DDP is not torchscriptable. Supporting torchscript for DDP would allow users much more flexibility in how they build and torchscript complex models.</p>
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<h2 dir="auto"><g-emoji class="g-emoji" alias="rocket" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f680.png">🚀</g-emoji> Feature</h2>
<p dir="auto">The <a href="https://pytorch.org/docs/stable/distributed.html" rel="nofollow"><code class="notranslate">torch.distributed</code></a> package provides PyTorch support and communication primitives for multiprocess parallelism across several computation nodes running on one or more machines. It is the new basis for the <code class="notranslate">DistributedDataParallel</code> wrapper class, which provides a higher-level toolkit for performing synchronous distributed training.</p>
<p dir="auto">An internal request is that we extend TorchScript to support the <code class="notranslate">torch.distributed</code> package, so that people can use the distributed communication primitives within a TorchScript program.</p>
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