How to automatically design better machine learning programs is an open
problem within AutoML. While evolution has been a popular tool to search for
better ML programs, using learning itself to guide the search has been less
successful and less understood on harder problems but has the promise to
dramatically increase the speed and final performance of the optimization
process. We propose guiding evolution with a binary discriminator, trained
online to distinguish which program is better given a pair of programs. The
discriminator selects better programs without having to perform a costly
evaluation and thus speed up the convergence of evolution. Our method can
encode a wide variety of ML components including symbolic optimizers, neural
architectures, RL loss functions, and symbolic regression equations with the
same directed acyclic graph representation. By combining this representation
with modern GNNs and an adaptive mutation strategy, we demonstrate our method
can speed up evolution across a set of diverse problems including a 3.7x
speedup on the symbolic search for ML optimizers and a 4x speedup for RL loss
functions.

使用二元辨别器进行进化引导可以加速演化过程并解决自动设计机器学习程序的问题。

借助二元判别器引导的机器学习程序搜索技术

Guided Evolution with Binary Discriminators for ML Program Search

Resource scheduling and allocation is a critical component of many high
impact systems ranging from congestion control to cloud computing. Finding more
optimal solutions to these problems often has significant impact on resource
and time savings, reducing device wear-and-tear, and even potentially improving
carbon emissions. In this paper, we focus on a specific instance of a
scheduling problem, namely the memory mapping problem that occurs during
compilation of machine learning programs: That is, mapping tensors to different
memory layers to optimize execution time.
We introduce an approach for solving the memory mapping problem using
Reinforcement Learning. RL is a solution paradigm well-suited for sequential
decision making problems that are amenable to planning, and combinatorial
search spaces with high-dimensional data inputs. We formulate the problem as a
single-player game, which we call the mallocGame, such that high-reward
trajectories of the game correspond to efficient memory mappings on the target
hardware. We also introduce a Reinforcement Learning agent, mallocMuZero, and
show that it is capable of playing this game to discover new and improved
memory mapping solutions that lead to faster execution times on real ML
workloads on ML accelerators. We compare the performance of mallocMuZero to the
default solver used by the Accelerated Linear Algebra (XLA) compiler on a
benchmark of realistic ML workloads. In addition, we show that mallocMuZero is
capable of improving the execution time of the recently published AlphaTensor
matrix multiplication model.

通过采用强化学习技术，本文提出了一个解决编译机器学习程序中内存映射问题的新方法，称之为 mallocMuZero 算法，并将其应用在 ML 加速器上以缩短执行时间，表现优于目前的默认求解器。