Lush: my favorite small programming language
A blog post all about Lush, a small language that the authors
present as a “Lisp Universal SHell” whose main features are its
ability to integrate simply with underlying libraries with the
interactivity of Lisp. And it’s a full Lisp, with a compiler, a
macro system, an object system, and (importantly for what comes
next) high-performance arrays and tensors.
Lush’s main claim to interest is that its authors include Yann
LeCun, Yoshua Bengio, and the other researchers who went on to
have a massive influence on the resurgences of interest in deep
learning neural networks. Indeed, the first version of the Torch
package, which later became PyTorch, was written in Lush. Lush’s
arrays make use of a sub-language modelled on APL that leverage
Lush’s ease with foreign functions to call into underlying C
kernels for tensor operations.
This is a great example of how Lisp’s claim to fame as a language
for doing AI has continued into the “new” AI era of deep learning
models. It also shows that, if you believe that Lisp’s dynamism
and flexibility are an advantage to you, there are no barriers to
continuing to benefit from that even if you need to access a wider
ecosystem of tools. It’d be interesting to understand the
performance trade-offs in this: what is the performance of a Lush
algorithm that makes extensive use of its array language, and
how can this performance be maximised using higher-level language
constructs of the sort Lisp encourages?
