Training modern large neural networks (NNs) requires a combination of parallelization strategies, including data, model, or optimizer sharding. To address the growing complexity of these strategies, we introduce PartIR, a hardware-and-runtime agnostic NN partitioning system. PartIR is: 1) Expressive: It allows for the composition of multiple sharding strategies, whether user-defined or automatically derived; 2) Decoupled: the strategies are separate from the ML implementation; and 3) Predictable: It follows a set of well-defined general rules to partition the NN. PartIR utilizes a schedule-like API that incrementally rewrites the ML program intermediate representation (IR) after each strategy, allowing simulators and users to verify the strategy's performance. PartIR has been successfully used both for training large models and across diverse model architectures, demonstrating its predictability, expressiveness, and performance.

This article consists only of a collection of slides from the author's conference presentation.

The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today’s massively parallel computers, and therefore hard to deploy in a real-time production setting. This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20 times faster than real-time, a 1000x speed up relative to the original WaveNet, and capable of serving multiple English and Japanese voices in a production setting.

PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup – O(log N) sampling instead of O(N) – enabling the practical generation of 512x512 images. We evaluate the model on class-conditional image generation, text-to-image synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling.