From Jax to VLIW: Tracing a Computation Through the TPU Compiler Stack

4 months ago

The article traces the compilation path of JAX code on TPUs, from high-level Python to low-level VLIW bundles.
A simple JAX function (matmul + RMS norm + softmax + matmul) is used to demonstrate the compilation process.
The TPU compiler pipeline involves multiple stages: JAX → HLO → LLO → VLIW bundles, with optimizations at each level.
Key optimizations include algebraic simplification, layout assignment, fusion of operations, and memory space assignment.
The TPU compiler automatically fuses operations, schedules hardware units, and orchestrates memory movements, achieving high performance without manual intervention.
The article provides detailed IR dumps and explanations for each compilation stage, including HLO and LLO representations.
The TPU's hardware units (MXU, VPU, XLU, DMA engines) are utilized efficiently through compiler optimizations.
The final VLIW bundles demonstrate how independent operations are packed and executed in parallel.
The article highlights the TPU compiler's ability to generalize to novel workloads and optimize complex computation patterns automatically.
Practical takeaways include debugging tips, performance considerations, and the trade-offs between TPUs and GPUs.

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