This document contains frequently asked questions.
See Installation
There are usually two levels of abstractions of IR in the deep learning systems. NNVM, TensorFlow‘s XLA and Intel’s ngraph uses computation graph representation. This representation is high level, and can be helpful to perform generic optimizations such as memory reuse, layout transformation and automatic differentiation.
TVM adopts a low level representation, that explicitly express the choice of memory layout, parallelization pattern, locality and hardware primitives etc. This level of IR is closer to directly target hardwares. The low level IR adopt ideas from existing image processing languages like Halide, darkroom and loop transformation tools like loopy and polyhedra based analysis. We specifically focus of expressing deep learning workloads(e.g. recurrence), optimization for different hardware backends and embedding with frameworks to provide end-to-end compilation stack.
TVM can incorporate these library as external calls. One goal of TVM is to be able to generate high performing kernels. We will evolve TVM an incremental manner as we learn from the technics of manual kernel crafting and add these as primitives in DSL. See also TVM Operator Inventory for recipes of operators in TVM.