| commit | 964595835a0f09681138e02d4f4cd28d6def3232 | [log] [tgz] |
|---|---|---|
| author | Chris Jones <cjfj@google.com> | Wed Oct 13 00:51:50 2021 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Wed Oct 13 00:56:38 2021 -0700 |
| tree | 0ffc71ea295bb5069aa3859fde5bc6a43e23b2a6 | |
| parent | 30d704726d19792cf8837f6a7ab9222d197d8ab7 [diff] |
[XLA:GPU] Add pass to decompose all-reduce operations using the BlueConnect algorithm. Paper: "BLUECONNECT: DECOMPOSING ALL-REDUCE FOR DEEP LEARNING ON HETEROGENEOUS NETWORK HIERARCHY", https://mlsys.org/Conferences/2019/doc/2019/130.pdf An AllReduce op may be decomposed into a ReduceScatter-AllReduce-AllGather sequence, and this may be applied recursively. The decomposition is performed here at the HLO level. The BlueConnect algorithm use this decomposition to minimize the number of levels of network hierarchy traversed for as much data transfer as possible. This algorithm was independently (re-)discovered in an exploration of all-reduce algorithms by DeepMind intern, Ningning Xie (work described in upcoming paper). In a search over the space of valid all-reduce algorithms, this algorithm is the best found for the majority of hardware arrangements (according to simulation). We have observed overall speed-ups of ~10% by applying the decomposition, on several different models. PiperOrigin-RevId: 402757031 Change-Id: Ia486c9127da724e94ade9d2ced72188dfcd9adcc
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