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Project status — 2026 update

Archived snapshot (browser/perf era). The live status board is the root PROJECT_STATUS.md.

A review-oriented snapshot of where posttrainllm stands. The detailed docs are linked at the bottom; this page is the map.

posttrainllm is finished as a teaching project and continuing as a performance project. The original ten milestones (PyTorch ref, training, LoRA, WASM backend, browser app, WebGPU matmul, checkpointing, metrics dashboard, write-up, public repo) are all complete and on main. The work past that point is the perf round-trip — kernels, parity tests, the speedup curve, and the lessons each failed lever taught.

What’s measured and shipped beyond the original milestones

Area What State
Perf WASM SIMD (-msimd128) — measured 1.6× shipped
Perf Multi-threaded WASM (pthreads + SAB) — measured ~2× shipped
Perf WebGPU full stack (blocked4 + vec4 + subgroups + FA2 fwd+bwd) shipped
Perf End-to-end curve vs multi-thread WASM SIMD: Small 2.6×, Medium 6.8×, Large 9.3×, XL 12.1× measured
Capacity Memory64 module (posttrainllm64.{js,wasm}) — 473M params in Node, browser blocked at d_model ≥ 256 (task #66) partial
Data Default corpus switched from inline 863-byte paragraph to TinyShakespeare (1.1 MB, /shakespeare.txt) shipped
Data Hugging Face dataset loading via public datasets-server API shipped
Config Default LR fixed: was 3e-3 (10× the Python ref), now 3e-4 — see lessons.md shipped
UX Banner reworked to make “load pretrained / train from scratch (~15 min)” explicit shipped
UX Pretrained Shakespeare demo model (Huge preset, 5000 steps) replaces browser/public/demo.tinygpt shipped
Site Astro migration — 5 static routes built into dist/ shipped

What’s verified, and how

Suite Covers Result
tests/test_phase1.py model, training, sampling 8/8
tests/test_lora.py LoRA 6/6
wasm/build_native.sh C++ kernels (finite-diff) + C++ model overfit pass
tests/smoke_wasm_node.mjs compiled WASM module trains pass
browser/npm run webgpu-test 24 WebGPU kernel parity checks + GPU overfit pass
tests/test_webgpu_train.mjs 50-step WASM vs WebGPU end-to-end parity pass (drift 1.1–2.5%)
tests/test_fa2_parity.mjs + test_fa2_backward_parity.mjs FA2 fwd + bwd vs naive ref pass (≤ 1 ULP)
tests/test_wasm64_xl_node.mjs reproduces the Memory64 ABI bug reproduces (task #66)
browser/npm run e2e full app in headless browser pass

Everything that can be checked by a machine, is — that was the method throughout.

Open — worth your attention

  • The Memory64 ABI bug. Same .wasm runs cleanly in Node when called directly, but the browser path hits a JS↔WASM bridge bug at d_model ≥ 256. _malloc returns Number; cwrap pointer args expect BigInt; the conversion throws. Tracked as task #66; the playground falls back to the 32-bit module for XL/Massive/Mega/Behemoth presets. Reproducer: tests/test_wasm64_xl_node.mjs. Full write-up: lessons.md.
  • The LR-default bug, fixed. Browser default LR was 3e-3 for months (10× the Python reference’s 3e-4). Loss plateaued at ~2.45 on real corpora and read as a modelling ceiling. Fixed in browser/src/types.ts:35 and browser/src/pages/index.astro:2621.
  • Speedup is a curve, not a single number. “9.7× end-to-end” is the Medium-preset point on the curve; Small is 2.6×, XL is 12.1×. Don’t cite a flat ratio — see lessons.md for the framing.
  • PR history past #17 is in GitHub. Too long to mirror here, but every shipped item above has a merged PR on main.

Where the docs are

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