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Relay: A New IR for Machine Learning Frameworks
Machine learning powers diverse services in industry including search,
translation, recommendation systems, and security. The scale and importance of
these models require that they be efficient, expressive, and portable across an
array of heterogeneous hardware devices. These constraints are often at odds;
in order to better accommodate them we propose a new high-level intermediate
representation (IR) called Relay. Relay is being designed as a
purely-functional, statically-typed language with the goal of balancing
efficient compilation, expressiveness, and portability. We discuss the goals of
Relay and highlight its important design constraints. Our prototype is part of
the open source NNVM compiler framework, which powers Amazon's deep learning
framework MxNet
Specific "scientific" data structures, and their processing
Programming physicists use, as all programmers, arrays, lists, tuples,
records, etc., and this requires some change in their thought patterns while
converting their formulae into some code, since the "data structures" operated
upon, while elaborating some theory and its consequences, are rather: power
series and Pad\'e approximants, differential forms and other instances of
differential algebras, functionals (for the variational calculus), trajectories
(solutions of differential equations), Young diagrams and Feynman graphs, etc.
Such data is often used in a [semi-]numerical setting, not necessarily
"symbolic", appropriate for the computer algebra packages. Modules adapted to
such data may be "just libraries", but often they become specific, embedded
sub-languages, typically mapped into object-oriented frameworks, with
overloaded mathematical operations. Here we present a functional approach to
this philosophy. We show how the usage of Haskell datatypes and - fundamental
for our tutorial - the application of lazy evaluation makes it possible to
operate upon such data (in particular: the "infinite" sequences) in a natural
and comfortable manner.Comment: In Proceedings DSL 2011, arXiv:1109.032
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