Applied LLMs
Operator Lowering and IRs
Operator lowering is the process of progressively translating high-level tensor operations through a sequence of intermediate representations until hardware-executable instructions are produced.
intermediate · 8 min read · Premium
A PyTorch matmul call carries no information about cache lines, warp occupancy, or register pressure. The CUDA kernel that eventually runs on the GPU does. Something has to bridge that gap, and that something is a lowering pipeline: a chain of intermediate representations (IRs) where each step either optimises at one abstraction level or discards abstraction to expose the level below. Getting this chain right is why torch.compile can give 2-3x throughput gains on the same hardware without changing a single line of user code.
What an IR actually is
An IR is a data structure that represents a computation in a form that is simultaneously easy to analyse and easy to transform. Unlike source code, an IR is designed for machines to read. Unlike binary instructions, it retains enough structure to permit rewrites that would be impossible or illegal at a lower level.
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