AI Engineering/Phase 19/Lesson 76

Collective Ops From Scratch

The four collective operations that hold distributed training together are allreduce, broadcast, allgather, and reduce_scatter. Every other primitive a training framework offers is a wrapper around these. Build them once over a `multiprocessing.Queue` mesh, verify them against a reference implementation, and the rest of the track becomes plumbing.

Build 1.5 hr Python

Prerequisites: Phase 19 Track C lessons 42-49

#Learning Objectives

Implement ring allreduce in two passes (reduce-scatter then allgather) and prove the per-rank communication volume is 2(N-1)/N bytes per element.
Build broadcast, allgather, and reduce_scatter on top of point-to-point sends over multiprocessing.Queue.
Verify every primitive against a torch.distributed gloo reference for the same input.
Defend the choice of ring versus tree on cluster shape, latency floor, and bandwidth ceiling.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.