GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding (2006.16668v1)

Abstract: Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computation cost, ease of programming, and efficient implementation on parallel devices. GShard is a module composed of a set of lightweight annotation APIs and an extension to the XLA compiler. It provides an elegant way to express a wide range of parallel computation patterns with minimal changes to the existing model code. GShard enabled us to scale up multilingual neural machine translation Transformer model with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters using automatic sharding. We demonstrate that such a giant model can efficiently be trained on 2048 TPU v3 accelerators in 4 days to achieve far superior quality for translation from 100 languages to English compared to the prior art.

• The paper introduces automatic sharding and conditional computation to scale Transformer models, achieving efficient training of a 600B parameter model in 4 days on 2048 TPU cores.
• It employs a lightweight annotation API and extends the XLA compiler for SPMD partitioning, ensuring seamless tensor partitioning and reduced compilation time.
• Experimental results reveal sublinear increases in computation cost, demonstrating state-of-the-art performance improvements in multilingual neural machine translation.

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Overview

The paper "GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding" presents GShard, a module for efficiently scaling neural networks through automatic parallelization and sharding. GShard is integrated with TensorFlow and involves a set of lightweight annotation APIs and an extension to the XLA compiler to facilitate the training of massive neural networks on clusters of accelerators such as TPUs. The authors highlight the efficacy of GShard through extensive experiments on multilingual neural machine translation (NMT) models, scaling to beyond 600 billion parameters.

Key Contributions

1. Lightweight Annotation API: GShard introduces an API that allows users to annotate critical tensors in their models. The annotations specify how tensors should be partitioned across multiple devices, enabling seamless interaction between model developers and the underlying partitioning mechanics.
2. SPMD Partitioning in XLA: The paper details a specialized compiler extension within XLA to implement Single Program Multiple Data (SPMD) partitioning. This approach generalizes the computation to all devices by transforming a single device program to a partitioned program, thus achieving scalability with constant compilation time.
3. Conditional Computation with MoE: The authors demonstrate the effectiveness of Sparsely-Gated Mixture-of-Experts (MoE) layers for scaling the Transformer model. This conditional computation technique activates sub-networks on a per-input basis, resulting in sublinear computational and memory growth relative to the number of experts and devices.

Numerical Results and Findings

The experiments presented in the paper focus on a 600B parameter Transformer model for translating 100 languages to English. The principal outcomes include:

• Training Efficiency: The 600B parameter model can be trained in four days using 2048 TPU v3 cores, translating into a training cost of 22 TPU v3 core-years. This model achieved state-of-the-art translation quality demonstrated by significant BLEU score improvements over baselines.
• Sublinear Computation Cost: The paper demonstrates that while model size increased 16x from 37.5B to 600B parameters, the computation cost only increased by 3.6x, showcasing the scalability and efficiency of their approach.

Practical and Theoretical Implications

Practical Implications

The practical implications of this research predominantly focus on the ability to scale deep learning models efficiently, thus democratizing access to large-scale model training. Real-world applications in NMT, such as Google's translation services, stand to benefit significantly from the ability to train models that handle multiple languages with improved performance and reduced operational costs.

Theoretical Implications

From a theoretical perspective, the research reiterates the importance of sparse activation in neural networks for achieving scalability. By incorporating conditional computation, the paper contributes to the body of work suggesting that not all parts of a neural network need to be active for every input, thus paving the way for more efficient and scalable model architectures.

Future Directions

1. Further Scalability Improvements: There is the potential to explore scaling models beyond the demonstrated 600B parameters, and further optimization can be conducted on the SPMD partitioning algorithm to handle even larger datasets and models.
2. Generalization to Other Domains: While the paper focuses on NMT, the principles and techniques could be adapted to other domains such as computer vision and speech recognition.
3. Advancements in Conditional Computation: Future work could involve refining the gating mechanisms to further improve load balancing and efficiency in MoE layers.

In summary, "GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding" presents significant advancements in the field of neural network training by introducing efficient sharding mechanisms and leveraging conditional computation. The results demonstrate not only improvements in training efficiency but also scalability, marking an important step towards practical deployment of large-scale deep learning models.