Zusammenfassung
Deep learning software demands reliability and performance. However, many of
the existing deep learning frameworks are software libraries that act as an
unsafe DSL in Python and a computation graph interpreter. We present DLVM, a
design and implementation of a compiler infrastructure with a linear algebra
intermediate representation, algorithmic differentiation by adjoint code
generation, domain-specific optimizations and a code generator targeting GPU
via LLVM. Designed as a modern compiler infrastructure inspired by LLVM, DLVM
is more modular and more generic than existing deep learning compiler
frameworks, and supports tensor DSLs with high expressivity. With our
prototypical staged DSL embedded in Swift, we argue that the DLVM system
enables a form of modular, safe and performant frameworks for deep learning.
Nutzer