diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..ce50ea1492e3a39cc9b69827226bee228222f113 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,10 @@ +cff-version: 1.2.0 +message: "If you use LoopBench, please cite it using this metadata." +title: "LoopBench: Length Generalization in Looped Transformers" +type: software +authors: + - family-names: Smirnov + given-names: Vadim + email: ukint-vs@proton.me +version: 0.1.0 +license: Apache-2.0 diff --git a/DYCK_REPLICATION_PLAN.md b/DYCK_REPLICATION_PLAN.md new file mode 100644 index 0000000000000000000000000000000000000000..cb9dbaee993b760266840542e97df2e8c1769212 --- /dev/null +++ b/DYCK_REPLICATION_PLAN.md @@ -0,0 +1,51 @@ +# Dyck-1 independent replication protocol + +Locked before training at **2026-07-21T14:32:51+04:00**. + +## Motivation + +The discovery cohort (seeds 0–14) found a large far-OOD exact-match gain for +`p=.5` over `p=0`, but the primary paired test was marginal (`t=2.247`, +two-sided threshold `2.145`) and its balanced-accuracy sensitivity check did not +clear the same threshold (`t=2.127`). Because the decision to collect more seeds +followed that result, a pooled 30-seed test would inherit optional-stopping bias. +Seeds 15–29 are therefore a separate confirmatory replication cohort. + +## Frozen experiment + +- Task: Dyck-1. +- Arms: `p=0` and `p=.5`. +- Training seeds: 15–29, paired across arms; no overlap with discovery seeds. +- Six isolated five-seed jobs: seed starts 15, 20, and 25 for each arm. +- Configuration: exactly `scripts/dyck1_seedscale.py::job_config` — two physical + LoopDeepNorm blocks, `R=8`, recall-add, fixed schedule, `n_ref=max`, batch 128, + AdamW LR `3e-4`, Muon LR `.01`, 2,500 maximum steps, curriculum enabled, + truncated-BPTT window `k=8`, and MPS/bfloat16. +- Checkpoint selection: best ID validation accuracy using seed 4242, unchanged + from Stage 1a. +- Held-out evaluation: 2,048 shared examples per bucket, evaluation seed + 20260721 (bucket-specific offset as implemented), inference `R=8`. +- All 30 runs will be trained and reported regardless of interim outcomes. + +## Confirmatory endpoint + +The sole confirmatory endpoint is per-seed far-OOD exact-match accuracy on +lengths 62–80. For each seed, compute `p=.5 - p=0`, then apply a paired, +two-sided t-test across replication seeds 15–29 (`n=15`, `df=14`, `alpha=.05`, +critical `|t|=2.1447867`). Replication succeeds only if the mean difference is +positive and `t > 2.1447867`. + +The replication means, mean paired difference, paired t-statistic, and decision +will be reported regardless of direction. No task, bucket, metric, seed, or +checkpoint will be excluded after training except a documented technical failure +that prevents a checkpoint from being produced. + +## Secondary and descriptive analyses + +- Far-OOD balanced exact match on the same replication cohort. +- Near-OOD (22–40), mid-OOD (42–60), and ID (2–20) exact match. +- Discovery (seeds 0–14) and replication (seeds 15–29) results side by side. +- Pooled 30-seed mean differences and 95% confidence intervals as descriptive + estimates only; no pooled confirmatory p-value. + +Any analysis not listed above is exploratory and will be labeled as such. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d12f7e662e84cbc044b36c6d9decb56ec06ff20a --- /dev/null +++ b/LICENSE @@ -0,0 +1,200 @@ +Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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We also recommend that a +file or class name and description of purpose be included on the +same "printed page" as the copyright notice for easier +identification within third-party archives. + +Copyright 2026 Vadim Smirnov + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c74cd4c62be36242d288312eb5c313bedbfe2d72 --- /dev/null +++ b/README.md @@ -0,0 +1,149 @@ +# LoopBench + +A length-generalization study of **looped transformers** on algorithmic tasks, +run entirely on a single Apple Silicon machine (M4 Max, PyTorch/MPS). Tiny +decoder-only models (~0.8–3M params) are trained on seeded algorithmic +generators and evaluated for out-of-distribution (OOD) length extrapolation. + +The confirmed R=8 result: **the depth-scaling exponent `p` improves held-out +Dyck-1 length extrapolation without materially changing ID accuracy**. An +independent, predeclared 15-pair replication improved far-OOD exact match from +.403 to .614 (+.211, paired t=2.734, 95% CI [.046, .377]); its class-balanced +sensitivity check also replicated. Addition remains at 0% OOD and parity +returns to chance despite better ID training. The completed five-task R=8 sweep +also finds that p=.5 strongly improves prefix-sum ID trainability (.649→.953; +exploratory paired t=2.816) and removes a copy seed failure, but both tasks stay +at effectively 0% OOD: better optimization is not algorithmic extrapolation. +An exploratory inference-depth probe finds that the Dyck advantage is already +present at R=1 and peaks at R=4, so it does not support a simple mechanism where +`p` merely prevents degradation in later loop iterations. + +## Method in one screen + +- **Model** (`loopbench/model.py`): decoder-only, RMSNorm, RoPE (base 10000), + SwiGLU. Two block styles: + - `preln` — standard pre-norm residual block (the control). + - `loopdeepnorm` — [DeepLoop's](https://arxiv.org/abs/2607.13491) hybrid + sandwich, `α = (2N)^p` on the residual, `β = (8N)^(−p)` applied once at + init to the output projections, `N = K · R_ref`. `p = 0` ⇒ `α = β = 1` + (unscaled). +- **Loop**: `K` physical blocks applied `R` times; optional Bansal-style input + injection (`recall = add`). Truncated backprop keeps grad for the last + `backprop_window = 8` iterations. This gives full gradient coverage at R=8; + fixed k=8 is **not** comparable at larger R. +- **Schedules** (`loopbench/schedules.py`): `fixed`, `uniform`, + `poisson_lognormal`; `n_ref ∈ {max, mean, per_batch}` sets the `N` behind + `α/β` (research question RQ2). +- **Tasks** (`loopbench/tasks/`): binary addition, parity, copy, Dyck-1, + prefix-sums (mod 7). Pure-Python, seeded, emitting `[runs, batch, seq]` + tensors directly. **Full-answer prediction (FAP)**: the model reads + ` EOQ` and predicts the whole ` EOS`; loss is masked to the + answer span; the metric is exact match over that span. + +## Throughput levers (built in from day one) + +Tiny models on MPS are overhead-bound, so the grid's wall-clock is attacked by: + +1. **Run-stacking** (`loopbench/stacking.py`) — all seeds of a cell train as one + `vmap`-ed model (`stack_module_state` + `functional_call` + `vmap`). One + process, N runs, ~one run's wall time. bf16 is applied by explicit casting, + not `torch.autocast` (which does not compose under `vmap` on MPS). + Memory-heavy copy/prefix cells use deterministic seed shards `(0,1)`, `(2,3)`, + `(4)` and merge their logs; this changes parallelism, not experiment factors. +2. **No hidden syncs** — metrics accumulate on-device; host transfer every + `log_every` steps; divergence is a device-side check. +3. **Truncated backprop** through the loop (in the model). +4. **Early stopping** with a token ledger. +5. **On-device pregenerated data** (`loopbench/data.py`) — one pool tensor, + moved to the device once, indexed per step. No DataLoader. + +Per-run divergence is masked out so one diverging seed cannot poison its +stack-mates. + +## Quickstart + +```bash +uv sync --frozen +uv run pytest # unit tests (tasks, model, stacking, schedules) + +# Stage 0 bring-up + divergence probe (the gate before the grid) +uv run python scripts/stage0.py + +# A single stacked job +uv run python -m loopbench.train --task addition --seeds 0,1,2,3,4 \ + --k-layers 2 --R 8 --recall add --device mps + +# Rebuild the committed Stage-1a summary and hero figure +uv run python scripts/analyze_stage1a.py + +# Rebuild the exploratory Dyck inference-depth probe +uv run python scripts/analyze_iteration_dynamics.py +``` + +Outputs: per-job JSON in `runs/.json` (config, per-seed training curves, +ID/OOD exact-match by eval length × inference-R, per-iteration activation norms, +per-block grad norms, divergence flags, token ledger, tokens/sec) and per-seed +checkpoints in `checkpoints//seed_.pt` for full reevaluation. +The committed CSV/JSON/SVG evidence can be regenerated without the raw training +runs. Full reevaluation requires the separately distributed checkpoint bundle. + +## Comparability pins (read this before comparing numbers) + +Results here are **internally comparable at R=8 and only qualitatively comparable +to prior papers**. The pins for the confirmed R=8 study are: + +- **Tokenizer**: task-specific symbol vocabularies (< 50 symbols), shared + special tokens (`PAD=0`, `EOS=1`, `EOQ=2`). +- **Supervision**: full-answer prediction (FAP), matching Fan et al. Next-token + variants are out of scope. +- **Positions**: RoPE, base 10000 (NoPE available as a flag but PE ablations are + out of scope). +- **Optimizer**: Muon on attention/MLP matrices (muon_lr 0.01) + AdamW + β=(0.9, 0.95), lr 3e-4 on embeddings/head/norms; cosine schedule, warmup, and + grad clip 1.0. These LoopDeepNorm values were frozen for Stage 1a. Embeddings + use init std 0.02 to remove the cold-start logit spike. +- **Precision**: bf16 on MPS (fp32 fallback off-device). Seeds control + data + init; run-level numeric noise on MPS is reported as part of the + seed variance. +- **Checkpoint/evaluation**: weights are selected only by ID validation seed + 4242, then reloaded and evaluated on shared held-out seed 20260721. +- **Causal scope**: `p` changes residual scaling and output-projection + initialization together. Without a non-recurrent model trained under the same + α/β values, the Dyck result is not evidence that the benefit is specific to + weight tying or recurrence. +- **Gradient coverage**: k=8 covers all R=8 iterations. The aborted R=32 gate had + only 25% coverage, so it is documented as a blocked method extension rather + than evidence about high-recurrence capacity. + +## What this contributes + +- A small Apple-Silicon research harness for stacked independent runs, + per-seed early stopping, best-checkpoint capture, and memory-safe evaluation. +- A reproducible separation between improved ID optimization and genuine length + extrapolation across five tasks, with checkpoint hashes and generated evidence + artifacts. Prefix-sum is the sharp negative control: large ID gain, zero OOD. +- A methodological warning for recurrent-depth studies: fixed truncated-BPTT + windows change credit coverage as R changes. +- A bounded practical result: a separate one-seed HDFS short-to-long anomaly + gate did not validate a loop-model advantage; the matched Transformer won on + far-OOD AUPRC (.699 versus .509), so the transfer study stopped at its gate. + +Stage 1a is consolidated in [STAGE1A_RESULTS.md](STAGE1A_RESULTS.md). The R=32 +extension is deferred until k/R gradient comparability is solved. + +## Documentation + +- [STAGE1A_RESULTS.md](STAGE1A_RESULTS.md) — final metrics, statistical tests, + limitations, and evidence inventory. +- [DYCK_REPLICATION_PLAN.md](DYCK_REPLICATION_PLAN.md) — frozen independent + replication protocol, committed before seeds 15–29 were trained. +- [RESEARCH_NOTES.md](RESEARCH_NOTES.md) — RASP-L framing and practical-transfer + rationale. +- [STAGE1_PLAN.md](STAGE1_PLAN.md) — frozen experiment contract. + +## Citation / license / contact + +Citation metadata is in [CITATION.cff](CITATION.cff). +LoopBench is available under the [Apache License 2.0](LICENSE). + +Vadim Smirnov · `ukint-vs@proton.me` diff --git a/STAGE1A_RESULTS.md b/STAGE1A_RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..b5220f98135f15b67b8b8cbdc240a5fd5cecaac5 --- /dev/null +++ b/STAGE1A_RESULTS.md @@ -0,0 +1,178 @@ +# Stage 1a results — depth scaling and length extrapolation + +## Setup and final evaluation + +Training swept `p ∈ {0, 0.25, 0.5}` over addition, Dyck-1, parity, copy, and +prefix-sum with five seeds, R=8, K=2, fixed schedule, additive recall, and the +retuned LoopDeepNorm optimizer settings. Dyck-1 p∈{0,0.5} uses a 15-pair +discovery cohort (seeds 0–14) and a separately predeclared 15-pair replication +cohort (seeds 15–29). Copy and prefix-sum were the completed R=8 remaining-task +expansion; their memory-heavy cells were split into seed shards, which changes +only execution parallelism, then merged into the canonical five-seed logs. + +The final numbers below do **not** select a logged evaluation step. Each saved +best-ID checkpoint (selected on deterministic validation seed 4242) was loaded +and evaluated on a new shared held-out set: seed 20260721, 2,048 examples per +ID/OOD bucket, inference R=8. See `scripts/analyze_stage1a.py` and +`artifacts/stage1a_summary.json`. + +| task | p | seeds | held-out ID | OOD by increasing length bucket | +|---|---:|---:|---:|---| +| addition | 0 | 5 | .827 | .000 / .000 / .000 | +| addition | .25 | 5 | .945 | .000 / .000 / .000 | +| addition | .5 | 5 | .998 | .000 / .000 / .000 | +| Dyck-1 | 0 | 30 | .923 | .706 / .497 / .412 | +| Dyck-1 | .25 | 5 | .929 | .790 / .683 / .557 | +| Dyck-1 | .5 | 30 | .942 | .816 / .708 / .613 | +| parity | 0 | 5 | .524 | .502 / .486 / .395 / .378 | +| parity | .25 | 5 | .567 | .500 / .499 / .485 / .478 | +| parity | .5 | 5 | .883 | .507 / .503 / .493 / .498 | +| copy | 0 | 5 | .847 | .000 / .000 / .000 | +| copy | .25 | 5 | .996 | .0002 / .000 / .000 | +| copy | .5 | 5 | .996 | .0009 / .000 / .000 | +| prefix-sum | 0 | 5 | .649 | .000 / .000 / .000 | +| prefix-sum | .25 | 5 | .769 | .000 / .000 / .000 | +| prefix-sum | .5 | 5 | .953 | .000 / .000 / .000 | + +## Findings + +1. **The mean ID direction is consistent across all five tasks, but the + strength of evidence varies.** Nominal paired tests clear the two-sided .05 + threshold only for parity (t=7.56) and prefix-sum (t=2.82); addition + (t=1.72) and copy (t=1.05) have large mean gains driven by seed failures, + while Dyck-1 ID is nearly unchanged (.923→.942). These task-wise ID tests + are exploratory and not multiplicity-corrected. +2. **ID improvement does not imply length extrapolation.** Addition remains at + zero OOD exact match, parity approaches chance, and both multi-token copy and + prefix-sum remain effectively zero across every OOD bucket even at p=.5. +3. **Dyck-1 is the positive result.** With ID held effectively constant, p=.5 + improves OOD exact match, and the gain is larger away from the training + range. + +## Exploratory trainability expansion + +These comparisons were added after the three-task core and are exploratory, +with no multiple-comparison correction. On the fresh held-out ID set, +prefix-sum p=.5 improves exact match from .649±.187 to .953±.103 (+.304; five +paired seeds, t=2.816, df=4; nominal two-sided threshold 2.776). Copy improves +from .847±.318 to .996±.006 because p>0 removes a p=0 seed failure, but the +paired statistic is not significant (t=1.050). The substantive conclusion is +trainability/stability, not length generalization: prefix-sum OOD is exactly +zero, and copy has only near-bucket traces below .001 before returning to zero. + +## Dyck-1 discovery and pre-specified replication + +Both cohorts use the paired p=0 versus p=.5 comparison on the far OOD bucket +(62–80), with training seed as the pairing unit. The first five pairs suggested +an underpowered effect, after which seeds 5–14 were added; seeds 0–14 are +therefore an adaptive discovery cohort, not a clean confirmatory test. After +observing that cohort's marginal result, the replication protocol was committed +before training seeds 15–29; see +`DYCK_REPLICATION_PLAN.md`. Each cohort uses a two-sided α=.05 critical value +of |t|>2.145 at df=14. + +### Discovery cohort (seeds 0–14) + +| bucket | p=0 | p=.5 | paired diff | paired t | role | +|---|---:|---:|---:|---:|---| +| near 22–40 | .735 | .832 | +.096 | 1.852 | secondary | +| mid 42–60 | .536 | .725 | +.189 | 2.626 | secondary | +| **far 62–80** | **.422** | **.613** | **+.191** | **2.247** | **discovery; nominal** | + +The discovery far-OOD difference has a 95% CI of [.009, .373]. Its balanced +far-OOD sensitivity result is .437→.618 (+.182, t=2.127), just below the same +critical threshold. The earlier `t=2.56` was a legacy estimate reconstructed +from logged evaluations and is not a headline statistic. + +### Independent replication cohort (seeds 15–29) + +| bucket | p=0 | p=.5 | paired diff | paired t | 95% CI for diff | +|---|---:|---:|---:|---:|---:| +| ID 2–20 | .920 | .951 | +.031 | 1.561 | [-.012, .074] | +| near 22–40 | .676 | .800 | +.124 | 2.472 | [.016, .231] | +| mid 42–60 | .458 | .691 | +.233 | 3.439 | [.088, .378] | +| **far 62–80** | **.403** | **.614** | **+.211** | **2.734** | **[.046, .377]** | + +The sole confirmatory endpoint replicates: the far-OOD mean difference is +positive and its paired statistic exceeds the frozen threshold. The +predeclared balanced sensitivity also replicates (.407→.622, +.215, t=2.853, +95% CI [.053, .377]). Thus the result no longer depends on ordinary rather than +class-balanced exact match. + +Pooling the cohorts only to estimate magnitude gives far-OOD .412→.613, a ++.201 paired difference with 95% CI [.086, .316]. No pooled confirmatory +p-value is reported because adding seeds was decided after seeing the discovery +result. + +All six replication jobs exited successfully and recorded no divergent runs, +but emitted late, non-fatal MPS command-buffer memory warnings. This technical +anomaly was resolved at the evidence gate rather than ignored: all 30 selected +replication checkpoints reloaded in a fresh process, all recomputed metrics are +finite, and every archived checkpoint hash matches the evidence CSV. The +reported results use only those fresh checkpoint evaluations. + +## Interpretation and limits + +- The predeclared 15-pair replication, consistent with the adaptive discovery + cohort, supports a narrow claim: in this R=8 setup, depth scaling improves + Dyck-1 length extrapolation without materially changing ID accuracy. +- The study does not establish that the benefit is specific to weight tying or + recurrence. The p=.5 α/β scheme is also an optimization/initialization change, + and there is no non-recurrent model trained with the same fixed scaling. The + R=1 probe still uses checkpoints trained at R=8, so it cannot fill that role. +- On copy and prefix-sum, p improves optimization robustness but does not rescue + multi-token length extrapolation. It also does not rescue arithmetic or + parity, nor show that the effect generalizes to arbitrary recurrent depth. +- R=32 under fixed k=8 is not comparable to R=8 because gradient coverage falls + from 100% to 25%. Truncated credit assignment is the leading explanation for + the failed R=32 gate, but k=16 did not establish causality. + +## Exploratory iteration-dynamics probe + +We tested one cheap mechanism hypothesis using the original 30 saved Dyck-1 +discovery checkpoints: +if `p=.5` mainly stabilizes repeated computation, its advantage should grow +as inference recurrence increases. The same 512 held-out far-OOD examples were +evaluated at `R ∈ {1,2,4,8}` for all 15 paired seeds. The predeclared exploratory +threshold was at least 0.10 growth in the paired p-effect from R=1 to R=8. + +| inference R | p=0 | p=.5 | paired difference | paired t | +|---:|---:|---:|---:|---:| +| 1 | .165 | .337 | +.172 | 1.744 | +| 2 | .074 | .366 | +.292 | 3.208 | +| 4 | .168 | .521 | +.352 | 3.942 | +| 8 | .417 | .610 | +.192 | 2.296 | + +The hypothesis fails: effect growth from R=1 to R=8 is only +.021, and the +largest separation occurs at R=4. Thus the current evidence does not support a +simple "p prevents degradation in later iterations" mechanism. The advantage +already present at R=1 instead points to changed training dynamics or a changed +learned representation. This is post-hoc mechanism evidence, not a new +confirmatory endpoint. + +There is a narrower post-hoc pattern: the p=.5 cohort mean rises monotonically +with inference R, while the p=0 mean does not. That is not evidence of greater +per-seed stability: only 8/15 p=.5 seeds are individually monotonic versus 6/15 +at p=0, and p=.5 has higher across-seed SD at every tested R. It is a useful +hypothesis for a dedicated control, not a rescued mechanism claim. + +The originally logged diagnostics cannot resolve that distinction: activation +RMS was measured after post-normalization and is mechanically about 1, while +block-gradient norms were measured after global clipping and therefore censor +absolute scale. A future causal run would need pre-normalization residual ratios +and pre-clip gradient norms, but no such run is justified by the current gate. + +## Evidence artifacts + +- `artifacts/stage1a_metrics.csv` — per-checkpoint metrics and SHA-256 hashes; + all 125 checkpoint hashes were independently verified (125/125 matches). +- `artifacts/stage1a_summary.json` — evaluation contract and aggregate tests. +- `artifacts/stage1a_hero.svg` — OOD versus p, mean±SD by task and bucket. +- `artifacts/dyck1_iteration_dynamics.{csv,json}` — exploratory inference-R + probe over the 30 saved Dyck-1 checkpoints. + +Regenerate the summary and figure with `uv run python scripts/analyze_stage1a.py`. +With the separately distributed checkpoint bundle present, rebuild all metrics +with `uv run python scripts/analyze_stage1a.py --reevaluate`. +Regenerate the iteration probe with +`uv run python scripts/analyze_iteration_dynamics.py`. diff --git a/artifacts/dyck1_iteration_dynamics.csv 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