Inside the Optimization Compiler: Agentic Tuning, Memory Planning, and Hardware-Aware Codegen

TL;DR: Inceptron’s compiler turns generic model graphs into device-tuned binaries. We combine graph-level rewrites, hardware-aware codegen, agentic auto-tuning, and memory layout planning. The result: lower p95 latency, higher tokens/sec, and predictable costs—without hand-written kernels.

Why generic runtimes leave performance on the table

Most inference stacks do three things well: load weights, launch kernels, and scale horizontally. The gap is what they launch and how they schedule memory. Vendor libraries cover common ops, but real workloads mix shapes, custom layers, and attention variants that don’t map cleanly. A compiler reshapes the graph, fuses ops, and chooses device-specific implementations.

The multi-level pipeline

1. Graph IR (SSA) — normalize shapes/dtypes; run constant folding, CSE, DCE.

2. Fusion & scheduling — pattern-match producer→consumer chains (e.g., matmul→bias→activation), tile for locality, and choose cooperative thread blocks.

3. Hardware-aware codegen — lower to device intrinsics (tensor cores, async copy) and pick algorithms per GPU family.

4. Agentic auto-tuning — Bayesian + rule-based search over tile sizes, warps, unroll factors; cache winners by {op, shape, dtype, device, driver}.

5. Memory planning — pack weights, promote to shared memory, plan static reuse, keep access coalesced.

6. Validation & fallbacks — numerical parity checks, canary deploys, instant rollback on regression.

What agentic tuning actually does

• Seeds candidates from architecture heuristics.

• Early-stops weak configs; focuses search on promising regions.

• Persists results so future compiles reuse known winners.
  Outcome: 10–30% latency cuts on hot paths without app changes, and faster compiles over time.

Memory planning: the quiet unlock

AOT compilation enables layout-level wins: tiling/padding for vectorization, promotion to shared memory, static buffer reuse, and coalesced access. These stabilize p95 during bursty load.

Hardware awareness without lock-in

Keep high-level intent in IR; isolate vendor specifics in backends; gate advanced features behind capability flags; ship portable fallbacks.

Reproducible benchmarking

Warm the runtime; fix seeds/prompts; measure p50/p95 and tokens/sec across single-shot/bursty/steady profiles; enforce numerical parity thresholds; report deltas with confidence intervals.