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diff --git a/README.md b/README.md new file mode 100644 index 0000000..c9f5821 --- /dev/null +++ b/README.md @@ -0,0 +1,23 @@ +# zbp-scaling + +Scaling study for **ZBP (zeroth-order backpropagation)** vs BP on decoder-only language models +(part 2 of the ZBP paper). Every nonlinear block is treated as a physical black box trained from +forward queries only (score-space attention core + per-token SwiGLU FFN); linear maps and the score +product are digital. See the main zobp repo for the method, theory (NSR ≈ c·d/n) and part-1/3 results. + +## Layout +- `src/zbp_scaling/zbp/` — vendored ZBP package (probes, estimators, ZBPBlock autograd) +- `src/zbp_scaling/model.py` — OLMo2-style transformer (RMSNorm, SwiGLU, untied embeddings; learned + positions in v1) with the ZBP physical/digital partition +- `src/zbp_scaling/data.py` — FineWeb-Edu → GPT-2-BPE uint16 shards (WikiText-103 fallback for smoke) +- `src/zbp_scaling/trainer.py` — DDP (torchrun, 4 or 8 GPUs), bf16 autocast with fp32 measurement + accumulation, gradient accumulation to a fixed global batch, cosine + warmup, resume, JSONL logs +- `src/zbp_scaling/diagnostics.py` — per-block branch-gain rho_k profile and the NSR constant c(scale) +- `configs/` — model sizes (60m/124m/350m/1b) x training arms (bp / zbp_n16 / zbp_n4) + +## Run +``` +python scripts/prepare_data.py --dataset fineweb-edu --tokens 3e9 --out data/fineweb +torchrun --nproc_per_node=8 scripts/train.py --model configs/model/m124.yaml --train configs/train/zbp_n16.yaml +``` +Global batch is fixed in the train config; per-rank micro-batch and accumulation adapt to world size. |