Sparsity plays a crucial part to achieve enhanced efficiency in deep learning. But, to understand its true potential and utilize sparsity in real life, there must be a perfect mix of hardware, software, and algorithms research.

And to make this happen, a versatile and flexible library is required. Google AI took a step in this direction with JaxPruner – Google AI’s concise library for machine learning research.

What is JaxPruner?

JaxPruner is an open-source JAX-based pruning and sparse training library. The main focus is on parameter sparsity. Its goal is to strengthen the research work on sparse networks by offering brief solutions to well-known pruning and sparse training methods.

The popular optimization library Optax and the algorithms used in JaxPruner share the same API, making it simple to integrate JaxPruner with other JAX-based libraries.

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According to the paper, the research is a combination of the methods used in obtaining parameter sparsity – pruning and sparse training. Pruning attempts to generate sparse networks from dense networks for enhanced inference. Sparse training is basically restricted to developing sparse networks from scratch while also bringing down the training costs.

The scientific and research community has heavily relied on JAX in the past few years. Its unique states and functions make it stand out among other renowned frameworks like TensorFlow and PyTorch. Its independence from data makes it an ideal contender in hardware acceleration. This reduces the time needed to implement difficult ideas by making function transformations like taking gradients, hessian computations, or vectorization very simple (Babuschkin et al., 2020). At the same time, it is simple to alter a function when its complete state is contained in a single location.

Despite specific techniques like global magnitude pruning (Kao, 2022) and sparse training with N:M sparsity and quantization (Lew et al., 2022) there isn’t a comprehensive library for sparsity research in JAx. This led to the introduction of JaxPruner.

Fast Integration, Minimal Overhead And Research First

With JaxPruner, scientists wish to address crucial questions like “Which sparsity pattern achieves a desired trade-off between accuracy and performance?”, “Is it possible to train sparse networks without training a large dense model first?”, and others. In order to accomplish these objectives, three principles served as our guidance when developing the library:

Fast-paced research in Machine Learning and the humongous variety of ML applications often lead to infinite, ever-changing codebases. With Jaxpruner, the researchers wanted to bring down friction for those who were integrating JaxPruner into existing codebases. To do this, JaxPruner employs the well-known Optax optimization library, which requires little modification when integrated with other libraries.

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In most projects, a combination of multiple algorithms and baselines is required. JaxPruner commits to a generic API that is shared among various algorithms, and these further enable easy switches between various algorithms.

Deep learning and sparsity optimization has advanced significantly with the release of JaxPruner, a machine-learning research library. The fulfillment of sparsity’s promise in real-world applications is facilitated by this discovery, which opens the door for improved cooperation between academics working on hardware, software, and algorithms. JaxPruner enables enterprises to make use of the advantages of parameter sparsity in neural networks by speeding function conversions, facilitating speedy prototyping, and offering seamless connections with existing codebases.