GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other tasks. To address the need for efficient and task-independent model parallelism, we introduce GPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, GPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, GPipe utilizes a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators. We demonstrate the advantages of GPipe by training large-scale neural networks on two different tasks with distinct network architectures: (i) Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (ii) Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models.

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Results from the Paper


Ranked #4 on Fine-Grained Image Classification on Birdsnap (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Fine-Grained Image Classification Birdsnap GPIPE Accuracy 83.6% # 4
Image Classification CIFAR-10 GPIPE + transfer learning Percentage correct 99 # 12
Image Classification CIFAR-100 GPIPE Percentage correct 91.3 # 18
Image Classification ImageNet GPIPE Top 1 Accuracy 84.4% # 158
Top 5 Accuracy 97% # 38
Fine-Grained Image Classification Stanford Cars GPipe Accuracy 94.6% # 25

Methods