diff --git a/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/README.md b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/README.md new file mode 100644 index 0000000000..a3354d719b --- /dev/null +++ b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/README.md @@ -0,0 +1,227 @@ +# Record: SP8192 + Parallel Residuals + 3-Layer Recurrence + Token-Only N-gram Tilt — val_bpb 1.08091 (5-seed mean, causal-corrected) + +**val_bpb: 1.08091** (5-seed mean, std 0.00043) | **2.79210 nats per token** | **~16.00 MB** | 8×H100 SXM, 600 s | Legal Score-First TTT + Causal Token-Only N-gram Tilt + +Beats [PR #1394](https://github.com/openai/parameter-golf/pull/1394) (1.08563) by **+0.01219 nats per token** — comfortably clearing the 0.005-nat record threshold (2.4× the bar). Also beats merged SOTA [PR #1019](https://github.com/openai/parameter-golf/pull/1019) (1.11473) by **+0.08736 nats per token**. + +> **2026-04-07 PM correction note** — see [Legality Fix](#legality-fix-2026-04-07-pm) section. The originally posted 5-seed mean (1.07807) was produced with a non-causal n-gram kernel inherited from [PR #1420](https://github.com/openai/parameter-golf/pull/1420). @abaybektursun [has acknowledged the bug and proposed the same fix I applied here](https://github.com/openai/parameter-golf/pull/1420#issuecomment-4199452189). The current 5-seed mean (1.08091) is ~+0.00284 BPB above the originally posted (illegal) 1.07807, but it still passes the 0.005-nat record bar against PR #1394 by 2.4×, so this remains a valid record submission. Pre-fix per-seed values are preserved in `submission.json` under `seed_results_pre_fix` for the public record. + +## Bar comparisons (5-seed mean 1.08091, val_loss 2.79210 nats/token) + +| Comparator | val_bpb | Δ (nats per token) | 0.005-nat bar | +|---|---:|---:|---| +| Merged SOTA [PR #1019](https://github.com/openai/parameter-golf/pull/1019) (abaybektursun) | 1.11473 | **+0.08736** | ✅ comfortably | +| [PR #1394](https://github.com/openai/parameter-golf/pull/1394) (clarkkev) | 1.08563 | **+0.01219** | ✅ clears (2.4× the bar) | +| Our [PR #1413](https://github.com/openai/parameter-golf/pull/1413) | 1.08279 | +0.00486 | ❌ misses by 0.00014 (essentially tied) | +| [PR #1420](https://github.com/openai/parameter-golf/pull/1420) (same kernel family; direct pre-fix comparison is not apples-to-apples) | 1.08014 | -0.00199 | ⚠️ see note below | + +The unit is nats per token (per the README's record threshold). The bpb-to-nats conversion factor is the mean bytes-per-token in the sp8192 val set: 1 bpb ≈ 2.5831 nats per token (verified against this submission's own `val_bpb / val_loss` ratio). + +## Results (8×H100 80GB SXM, PyTorch 2.9.1+cu128, causal token-only n-gram tilt) + +### Core (TTT) table — 5-seed verification, all seeds re-run via shipped mini wrapper with the patched kernel + +| Seed | Steps | Pre-quant BPB | Sliding BPB | **Post-TTT (causal token-only) BPB** | val_loss (nats) | Artifact (bytes) | +|---:|---:|---:|---:|---:|---:|---:| +| 0 | 4911 | 1.08730 | 1.08219 | **1.08035** | 2.79067 | **15,994,644** ✅ | +| 42 | 4906 | 1.08792 | 1.08272 | **1.08097** | 2.79225 | **15,995,572** ✅ | +| 1234 | 4915 | 1.08823 | 1.08336 | **1.08127** | 2.79303 | **15,993,531** ✅ | +| 1337 | 4905 | 1.08759 | 1.08235 | **1.08060** | 2.79131 | **15,988,802** ✅ | +| 2025 | 4911 | 1.08833 | 1.08302 | **1.08135** | 2.79324 | **15,993,360** ✅ | +| **5-seed mean** | | **1.08787** | **1.08273** | **1.08091** | **2.79210** | all < 16,000,000 | + +**Verification status:** +- All 5 seeds independently re-run via the shipped `train_gpt.py` (~18.9 KB code) with the **patched** `fused_expert_kernel.cpp` and `NGRAM_WITHIN_BETA=0 NGRAM_WORD_BETA=0`. Each artifact is the actual `Total submission size quantized+brotli` from the mini-wrapper run. +- All 5 artifacts fit under 16,000,000 bytes (corrected runs use the same model weights as the original submission; only the eval-time kernel changed). +- 5-seed standard deviation: **0.00043 BPB**. +- Pre-fix (illegal) per-seed values are preserved in `submission.json` under `seed_results_pre_fix`. + +## Legality Fix (2026-04-07 PM) + +The original kernel from [PR #1420](https://github.com/openai/parameter-golf/pull/1420) (which this submission ported with `nanobind` removed) had a causality bug in `get_hints_batch`: + +- Lines 384-386 read `tok = tokens_[p]` (the **target** token at the position being scored) and derived `is_bnd = is_bnd_[tok]` and `is_ws = has_ls_[tok]`. +- Lines 399-400 then passed those flags to `within_hint(is_bnd, is_ws, ...)` and `word_hint(is_ws, ...)`, gating hint emission on whether the **current target** is mid-word vs word-start vs boundary. + +This means the predictive distribution at position `p` depended on metadata derived from `x_p` itself, leaking 1-2 bits per scored position about the answer. Under the [Issue #1017](https://github.com/openai/parameter-golf/issues/1017) framing, this is a violation of the prefix-only causality requirement. The original 1.07807 5-seed mean reported in PR #1437's first version is therefore tainted. + +**The fix** (matches @abaybektursun's [proposed patch](https://github.com/openai/parameter-golf/pull/1420#issuecomment-4199452189)): + +1. **Kernel patch**: derive `prev_is_bnd`/`prev_is_ws` from `tokens_[p-1]` (last prefix token) for hint gating only. The current-token reads at lines 384-386 are kept only for the *update* calls at lines 437-439 (causal because they run after hint emission for that position). +2. **Disable within/word experts**: set `NGRAM_WITHIN_BETA=0 NGRAM_WORD_BETA=0`. Empirically, the within/word experts under prefix-only gating fire for the wrong positions (within fires for word-starts, word fires for mid-word) and contribute *negative* BPB. Only `token_hint` (which has always been causal — `compute_hashes` only reads `tokens[pos - k - 1]` for `k ≥ 0`) is left active. + +**Measured leak magnitude (this submission, 5-seed mean):** TTT `1.07807 BPB` → `1.08091 BPB`, delta **+0.00284 BPB ≈ +0.00734 nats per token** (using 1 bpb ≈ 2.5831 nats per token, the mean bytes-per-token in the sp8192 val set). Sliding (no tilt) and pre-quant numbers are unchanged because the kernel only affects the TTT eval pass. + +**PR #1420 cross-reference**: PR #1420 originally shipped the same kernel-family bug. @abaybektursun has [acknowledged it in their thread](https://github.com/openai/parameter-golf/pull/1420#issuecomment-4199452189) and proposed the same fix. Because the original `1.08014` number was reported before that correction, direct pre-fix comparison is not apples-to-apples. + +## Key Innovations + +A 3-lever stack on top of [@clarkkev's PR #1394](https://github.com/openai/parameter-golf/pull/1394) sp8192 baseline: + +### 1. Parallel Residuals on layers 7–10 (from [PR #1412](https://github.com/openai/parameter-golf/pull/1412) by @Robby955) + +GPT-J-style parallel attention + MLP for the last 4 layers. Both attention and MLP read the same pre-residual input and their outputs are summed in parallel. Reduces interference between attention and MLP during GPTQ calibration → tighter quantization gap. + +```python +# Parallel (layers 7-10): +x_out = x + attn_scale * Attn(norm(x)) + mlp_scale * MLP(norm(x)) + +# Sequential (layers 0-6, unchanged): +h = x + attn_scale * Attn(norm(x)) +x_out = h + mlp_scale * MLP(norm(h)) +``` + +Verified standalone contribution: **−0.00048 BPB** on 3-seed mean (par7 alone vs control). + +### 2. 3-Layer Depth Recurrence (extending PR #1394's 2-layer recurrence) + +Loop layers **3–5 twice** instead of 4–5 twice. Encoder pattern `[0,1,2,3,4,5,3,4]` and decoder `[5,3,4,5,6,7,8,9,10]`. Costs ~200 training steps but the additional virtual depth (17 vs 15 layers) more than compensates. + +Verified standalone contribution on top of par7: **−0.00128 BPB** on s42. + +### 3. Eval-Time Causal N-gram Tilt (from [PR #1420](https://github.com/openai/parameter-golf/pull/1420) by @abaybektursun, lineage [PR #1145](https://github.com/openai/parameter-golf/pull/1145) @AnirudhRahul) + +A causal open-addressing n-gram cache (token orders 8/10/12/14/16, within-word orders 1–3, word-start order 4) proposes a single hint token from strict prefix state. The model's full softmax distribution is then **rescaled with a one-token exponential tilt**: + +``` +p_tilt(t) = p_model(t) · exp(β · 𝟙[t==hint]) / Z +Z = 1 + p_model(hint) · (exp(β) − 1) +``` + +This is a **renormalized full-vocab distribution**, not a `p(correct_token)`-only blend. The hint at position `p` is computed from `tokens[0..p−1]` only; the cache is updated with `tokens[p]` AFTER position `p`'s score is locked. + +Per-position NLL becomes: +```python +mixed_nll = scored_nll + has_hint * (Z.log() - β * is_hit) +``` + +C++ kernel ported from PR #1420 with the nanobind dependency removed (replaced with a `extern "C"` shim and ctypes loader). Build is a single `g++ -O3 -march=native -std=c++17 -fPIC -shared` invocation against `fused_expert_kernel.cpp`. The kernel processes ~3M tokens/sec; the precompute over the full ~40.5M val tokens runs in ~32 s on rank 0 then broadcasts hints/betas to other ranks. + +Verified standalone contribution on top of par7: **−0.00297 BPB** on s42 (PR #1420 reports −0.0029 — port is byte-correct). + +## Stacking decomposition (s42) + +| Stack | TTT BPB | Δ vs control | +|---|---|---| +| Control (PR #1413) | 1.08315 | — | +| + Parallel residuals layers 7+ | 1.08239 | −0.00076 | +| + 3-layer recurrence | 1.08111 | −0.00204 | +| + N-gram tilt | **1.07808** | **−0.00507** | + +The three levers stack approximately linearly with slight positive synergy (predicted −0.00473, actual −0.00507). + +## Changes from baseline (PR #1394 → this PR) + +| Component | PR #1394 | This PR | +|---|---|---| +| Tokenizer | SentencePiece BPE 8192 | (same) | +| Architecture core | 11L / 512d / 8H / 4KV, MLP 4× | (same) | +| Depth recurrence | Loop layers 4–5 twice | **Loop layers 3–5 twice** | +| Block forward pattern | Sequential attn → MLP all 11 layers | **Parallel attn+MLP for layers 7–10**, sequential layers 0–6 | +| Optimizer | MuonEq-R, WD=0.085 | (same) | +| Quantization | GPTQ int6 + int8 embed + SDClip | (same) | +| Eval | sliding window | sliding window **+ score-first TTT + causal n-gram tilt** | +| QK_GAIN_INIT | 4.0 | **5.0** | +| TTT | none | **score-first, LR=0.005, epochs=3, freeze=0** | +| val_bpb (3-seed mean) | 1.08563 | **1.07800** | +| Δ vs PR #1394 (per-token nats) | — | **−0.01971** | + +## Architecture + +11L × 512d × 8H / 4KV, MLP 4×, LeakyReLU(0.5)² activation, Partial RoPE (16/64 dims), tied token embeddings. Depth recurrence: encoder `[0,1,2,3,4,5,3,4]`, decoder `[5,3,4,5,6,7,8,9,10]` (loops layers 3–5 twice, activated at frac=0.5 of training, ~step 2924). Layers 7–10 use the GPT-J parallel attention+MLP pattern; layers 0–6 stay sequential. + +Quantization: full-Hessian GPTQ on all attention/MLP matrices at int6 with SD-based clip (`row_std × 12.85 / 31`); token embedding at int8 with clip `20 × row_std`; small control tensors and scalars kept float16/float32 via passthrough. Compression: byte-shuffle + Brotli-11. Self-extracting LZMA mini runner (~18,905 bytes code). + +N-gram tilt subsystem: 5 token-order open-addressing hash tables (orders 8, 10, 12, 14, 16) at `open_table_bits=26` ≈ 67M slots × 16 B/entry = 1 GB each (5 GB token-cache) + 3 within-word tables and 1 word-start table at `bits=20` (≈ 16 MB total) + 1 `WordStartState` Python dict. **Host RAM only** — not counted toward the 16 MB artifact. Built fresh from val tokens on rank 0 in ~32 s, hints/betas broadcast to other ranks before TTT eval starts. + +## Rule Compliance + +Per [repo README](https://github.com/openai/parameter-golf) and [Issue #1017](https://github.com/openai/parameter-golf/issues/1017) four conditions: + +- **Condition 1 (Causality)**: The n-gram cache state at position `p` is built solely from `tokens[0..p−1]`; the C++ kernel's `compute_hashes` reads only `tokens[pos − k − 1]` for `k ≥ 0`. The hint at position `p` is written to the output buffer BEFORE the kernel mutates any table with `tokens[p]`. The model forward pass is the standard causal transformer; sliding-window eval never references future tokens. See `fused_expert_kernel.cpp` `get_hints_batch` lines around the explicit `hints[i] = best_hint; betas[i] = best_beta; ... token_update(...);` ordering. +- **Condition 2 (Normalized full distribution)**: Standard softmax over the full sp8192 vocab. The n-gram tilt rescales each per-position distribution as `p_tilt(t) = p_model(t) · exp(β · 𝟙[t==hint]) / Z` with `Z = 1 + p_model(hint) · (exp(β) − 1)`. This is a proper probability distribution over the entire alphabet — not a `p_t(correct_token)`-only blend. The hint token is chosen from prefix-only state BEFORE the realized target is consulted; the only target dependence is the indicator `𝟙[tgt==hint]`, which is the legitimate "did the realized token land on the boosted token" term. +- **Condition 3 (Score before update)**: Every TTT chunk is scored under `torch.no_grad()` before any parameter update. Every n-gram tilt position is scored before its target token is mixed into the cache state. No same-symbol adaptation, no self-exclusion. +- **Condition 4 (Single pass)**: Each token is scored exactly once. Sliding-window eval is forward-only (`stride < seq_len`). The C++ kernel's `get_hints_batch` walks positions in monotonic order. No rescoring, no oracle selection. + +Additional: +- **No SLOT** (standard or causal). No eval-time delta optimization in hidden space. +- **No pre-quant TTT on val data**. The model is quantized once after training, then the quantized model is evaluated under score-first TTT + n-gram tilt. +- **No ETLB**. +- **No tokenizer change** — uses PR #1394's SentencePiece BPE 8192 unchanged. +- **GPTQ calibration uses `fineweb_train_*` exclusively**, inside the 588 s training cap (12 s GPTQ reserve). +- **N-gram cache state lives in host RAM only**, not in the 16 MB artifact. +- **C++ kernel and Python wrapper live alongside `train_gpt.py`** in the records folder; only `train_gpt.py` (the LZMA self-extracting mini wrapper, ~18.9 KB) counts toward the 16 MB artifact, matching the precedent set by [PR #1145](https://github.com/openai/parameter-golf/pull/1145). +- **3 distinct seeds** (0, 42, 1234) — independent runs on the same hardware. + +## Requirements + +``` +torch==2.9.1+cu128 +flash-attn==2.8.3 +flash-attn-3 (interface wheel; Hopper build) +sentencepiece +numpy +brotli +gcc (any version supporting C99/C++17) +``` + +GCP 8×H100 80GB SXM pod with `NCCL_NET=Socket` (GCP-specific; NCCL 2.27.5 + gIB device issue). + +## Run Command + +```bash +export NCCL_NET=Socket +export QK_GAIN_INIT=5.0 +export PARALLEL_RESIDUAL_START=7 +export LOOP_START=3 +export LOOP_END=5 +export TTT_ENABLED=1 +export TTT_LR=0.005 +export TTT_EPOCHS=3 +export NGRAM_TILT_ENABLED=1 +export NGRAM_BASE_BETA=2.0 +export NGRAM_AGREE_BONUS=0.1 +export NGRAM_WITHIN_THRESHOLD=0.25 +# CAUSAL CORRECTION: disable within/word experts +export NGRAM_WITHIN_BETA=0.0 +export NGRAM_WORD_BETA=0.0 + +for SEED in 0 42 1234 1337 2025; do + SEED=$SEED uv run torchrun --standalone --nproc_per_node=8 train_gpt.py +done +``` + +The first run will compile `fused_expert_kernel.cpp` to `libfused_ngram.so` via gcc; subsequent runs reuse the cached `.so`. + +## Lineage + +- **[PR #1394](https://github.com/openai/parameter-golf/pull/1394)** (@clarkkev) — sp8192 + GPTQ embeddings + SDClip + MuonEq-R + 2-layer depth recurrence — base stack +- **[PR #1413](https://github.com/openai/parameter-golf/pull/1413)** (@dexhunter, ours) — sp8192 + QK-Gain 5 + legal score-first TTT — direct predecessor +- **[PR #1412](https://github.com/openai/parameter-golf/pull/1412)** (@Robby955) — Parallel Residuals + Hessian-Aware SDClip — parallel residuals lever +- **[PR #1420](https://github.com/openai/parameter-golf/pull/1420)** (@abaybektursun) — Triple Loop + Fused Kernels + N-gram Tilt — n-gram tilt kernel and tilt math +- **[PR #1145](https://github.com/openai/parameter-golf/pull/1145)** (@AnirudhRahul) — Online Best-Agree N-gram — first legal normalized n-gram cache, organizer-discussed precedent in [issue #677](https://github.com/openai/parameter-golf/issues/677) +- **[PR #1019](https://github.com/openai/parameter-golf/pull/1019)** (@abaybektursun, merged) — AR Self-Gen GPTQ + XSA + BigramHash 3072 — current merged SOTA, GPTQ pipeline ancestor +- **[PR #549](https://github.com/openai/parameter-golf/pull/549)** (@abaybektursun, merged) — LeakyReLU² + score-first TTT — legal-TTT precedent + +## Credits + +- **@clarkkev** for the sp8192 base stack (PR #1394) this submission builds on +- **@Robby955** for parallel residuals on layers 7–10 (PR #1412) +- **@abaybektursun** for the n-gram tilt mechanism, the C++ kernel, and the merged-precedent legal-TTT (PRs #1420, #1019, #549) +- **@AnirudhRahul** for the original normalized causal n-gram cache pattern (PR #1145) +- **@msisovic** for depth recurrence (PR #1204) +- **@bigbag** for MuonEq-R (PR #1217) +- **@unnir** for XSA (PR #265) +- **@simon-marcus** for the corrected Scylla byte-accounting reference (PR #1314) — used for legality discussions, not in this submission +- **@NoesisGenesis** for the four-conditions framework (issue #1017) + +## Included Files + +- `README.md` (this file) +- `submission.json` +- `train_gpt.py` — self-extracting LZMA mini wrapper, ~18.9 KB. The only file counted toward the 16 MB artifact. +- `ngram_tilt.py` — Python ctypes wrapper for the C++ n-gram kernel. Imported at runtime by `train_gpt.py`. Not counted toward artifact (parallel pattern to PR #1145's separate `online_best_agree_eval.py`). +- `fused_expert_kernel.cpp` — C++ source for the n-gram cache. Built to `libfused_ngram.so` at runtime via `gcc -O3 -march=native -std=c++17 -fPIC -shared`. Not counted toward artifact. +- `train_seed0.log` +- `train_seed42.log` +- `train_seed1234.log` diff --git a/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/fused_expert_kernel.cpp b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/fused_expert_kernel.cpp new file mode 100644 index 0000000000..990c9faac0 --- /dev/null +++ b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/fused_expert_kernel.cpp @@ -0,0 +1,495 @@ +#include +#include +#include + +#ifdef __linux__ +#include +#endif + +static constexpr uint64_t PRIMES[] = { + 36313ULL, 27191ULL, 51647ULL, 81929ULL, 131071ULL, 196613ULL, + 262147ULL, 393241ULL, 524309ULL, 655373ULL, 786433ULL, 917521ULL, + 1048583ULL, 1179653ULL, 1310729ULL, 1441801ULL, 1572869ULL, 1703941ULL, + 1835017ULL, 1966087ULL, 2097169ULL, 2228243ULL, 2359319ULL, 2490389ULL, + 2621471ULL, 2752549ULL, 2883617ULL, 3014687ULL, 3145757ULL, 3276833ULL, + 3407903ULL, 3538973ULL, +}; +static constexpr int N_PRIMES = 32; +static constexpr uint64_t PAIR_MIX = 1000003ULL; +static constexpr uint64_t PREFIX_BASE = 1099511628211ULL; +static constexpr uint64_t LEN_MIX = 0x9E3779B185EBCA87ULL; +static constexpr uint64_t TABLE_MIX = 0x9e3779b97f4a7c15ULL; +static constexpr uint64_t EMPTY_KEY = 0xFFFFFFFFFFFFFFFFULL; + +struct CtxEntry { + uint64_t key; + uint32_t count; + uint16_t best_tok; + uint16_t best_count; +}; + +struct PairEntry { + uint64_t key; + uint32_t count; + uint32_t _pad; +}; + +struct OpenTable { + uint32_t mask; + static constexpr int MAX_PROBES = 16; + + CtxEntry* ctx = nullptr; + PairEntry* pair = nullptr; + size_t cap = 0; + + ~OpenTable() { free_tables(); } + + void free_tables() { +#ifdef __linux__ + if (ctx) { munmap(ctx, cap * sizeof(CtxEntry)); ctx = nullptr; } + if (pair) { munmap(pair, cap * sizeof(PairEntry)); pair = nullptr; } +#else + delete[] ctx; ctx = nullptr; + delete[] pair; pair = nullptr; +#endif + } + + void init(int bits) { + free_tables(); + cap = size_t(1) << bits; + mask = uint32_t(cap - 1); +#ifdef __linux__ + ctx = (CtxEntry*)mmap(nullptr, cap * sizeof(CtxEntry), + PROT_READ | PROT_WRITE, + MAP_PRIVATE | MAP_ANONYMOUS | MAP_POPULATE, -1, 0); + pair = (PairEntry*)mmap(nullptr, cap * sizeof(PairEntry), + PROT_READ | PROT_WRITE, + MAP_PRIVATE | MAP_ANONYMOUS | MAP_POPULATE, -1, 0); +#else + ctx = new CtxEntry[cap]; + pair = new PairEntry[cap]; +#endif + clear(); + } + + void clear() { + for (size_t i = 0; i < cap; i++) ctx[i] = {EMPTY_KEY, 0, 0, 0}; + for (size_t i = 0; i < cap; i++) pair[i] = {EMPTY_KEY, 0, 0}; + } + + void reset() { clear(); } + + void prefetch_ctx(uint64_t key) const { + uint32_t slot = uint32_t((key * TABLE_MIX) & mask); + __builtin_prefetch(&ctx[slot], 0, 0); + } + void prefetch_update(uint64_t ctx_key, uint64_t pair_key) const { + __builtin_prefetch(&ctx[uint32_t((ctx_key * TABLE_MIX) & mask)], 1, 0); + __builtin_prefetch(&pair[uint32_t((pair_key * TABLE_MIX) & mask)], 1, 0); + } + + void ctx_lookup(uint64_t key, int& out_tok, double& out_conf, + uint32_t& out_count) const { + uint32_t slot = uint32_t((key * TABLE_MIX) & mask); + for (int p = 0; p < MAX_PROBES; p++) { + uint32_t s = (slot + p) & mask; + if (ctx[s].key == key) { + out_count = ctx[s].count; + out_tok = ctx[s].best_tok; + out_conf = double(ctx[s].best_count) / double(out_count); + return; + } + if (ctx[s].key == EMPTY_KEY) break; + } + out_tok = -1; out_conf = 0.0; out_count = 0; + } + + void update(uint64_t ctx_key, uint64_t pair_key, uint16_t token) { + uint32_t pair_count = 0; + { + uint32_t slot = uint32_t((pair_key * TABLE_MIX) & mask); + for (int p = 0; p < MAX_PROBES; p++) { + uint32_t s = (slot + p) & mask; + if (pair[s].key == pair_key) { + pair[s].count++; pair_count = pair[s].count; break; + } + if (pair[s].key == EMPTY_KEY) { + pair[s].key = pair_key; pair[s].count = 1; + pair_count = 1; break; + } + } + } + { + uint32_t slot = uint32_t((ctx_key * TABLE_MIX) & mask); + for (int p = 0; p < MAX_PROBES; p++) { + uint32_t s = (slot + p) & mask; + if (ctx[s].key == ctx_key) { + ctx[s].count++; + if (token == ctx[s].best_tok) ctx[s].best_count++; + else if (pair_count > ctx[s].best_count) { + ctx[s].best_tok = token; + ctx[s].best_count = uint16_t(std::min(pair_count, 65535u)); + } + return; + } + if (ctx[s].key == EMPTY_KEY) { + ctx[s] = {ctx_key, 1, token, 1}; return; + } + } + } + } +}; + +class ContextMixer { + static constexpr int OPEN_MIN = 8; + static constexpr int OPEN_MAX = 16; + static constexpr int N_OPEN = OPEN_MAX - OPEN_MIN + 1; + + OpenTable open_[N_OPEN]; + + struct OrderConfig { double threshold; uint32_t min_count; }; + OrderConfig cfg_[N_OPEN]; + + bool order_active_[N_OPEN]; + int order_stride_; + + static constexpr int WITHIN_ORDERS = 3; + OpenTable within_[WITHIN_ORDERS]; + uint64_t within_hash_; + uint32_t within_len_; + double within_threshold_, within_beta_; + + static constexpr int WORD_ORDER = 4; + OpenTable word_table_; + uint64_t word_ring_[4]; + int word_ring_head_, word_ring_fill_; + uint64_t current_word_hash_; + int current_word_len_; + double word_threshold_, word_beta_; + + double base_beta_, agree_bonus_; + + const int64_t* tokens_ = nullptr; + int64_t n_tokens_ = 0; + const int16_t* base_bytes_ = nullptr; + const uint8_t* has_ls_ = nullptr; + const uint8_t* is_bnd_ = nullptr; + + static void compute_hashes(const int64_t* tokens, int64_t pos, int max_ord, + uint64_t* hashes) { + uint64_t h = 0; + int lim = std::min(max_ord, int(pos)); + for (int k = 0; k < lim; k++) { + h ^= uint64_t(tokens[pos - k - 1]) * PRIMES[k % N_PRIMES]; + hashes[k] = h; + } + for (int k = lim; k < max_ord; k++) hashes[k] = 0; + } + + static uint64_t pair_key(uint64_t ctx, uint16_t tok, int order) { + return (ctx * PAIR_MIX) ^ (uint64_t(tok) * PRIMES[order % N_PRIMES]); + } + + static uint64_t extend_prefix(uint64_t h, uint16_t tok, uint32_t pos) { + return (h * PREFIX_BASE) ^ ((uint64_t(tok) + 1) * PRIMES[pos % N_PRIMES]); + } + + void token_hint(const uint64_t* hashes, int max_avail, + int& out_tok, double& out_beta) { + for (int order = std::min(OPEN_MAX, max_avail); order >= OPEN_MIN; order--) { + int oi = order - OPEN_MIN; + if (!order_active_[oi]) continue; + uint64_t ch = hashes[order - 1]; + int hint; double conf; uint32_t count; + open_[oi].ctx_lookup(ch, hint, conf, count); + if (hint >= 0 && conf >= cfg_[oi].threshold + && count >= cfg_[oi].min_count) { + out_tok = hint; + out_beta = base_beta_ * conf; + return; + } + } + out_tok = -1; out_beta = 0.0; + } + + void token_update(const uint64_t* hashes, int max_avail, uint16_t token) { + for (int order = OPEN_MIN; order <= std::min(OPEN_MAX, max_avail); order++) { + int oi = order - OPEN_MIN; + if (!order_active_[oi]) continue; + uint64_t ch = hashes[order - 1]; + uint64_t pk = pair_key(ch, token, order); + open_[oi].update(ch, pk, token); + } + } + + void within_hint(bool is_bnd, bool is_ws, int& out_tok, double& out_beta) { + if (is_bnd || is_ws || within_len_ == 0) { + out_tok = -1; out_beta = 0.0; return; + } + uint64_t ctx = within_hash_ ^ (uint64_t(within_len_) * LEN_MIX); + int oi = std::min(int(within_len_) - 1, WITHIN_ORDERS - 1); + int hint; double conf; uint32_t count; + within_[oi].ctx_lookup(ctx, hint, conf, count); + if (hint >= 0 && conf >= within_threshold_ && count >= 1) { + out_tok = hint; out_beta = within_beta_; + } else { + out_tok = -1; out_beta = 0.0; + } + } + + void within_update(uint16_t token, bool is_bnd, bool is_ws) { + if (is_bnd) { within_hash_ = 0; within_len_ = 0; return; } + if (is_ws || within_len_ == 0) { + within_hash_ = extend_prefix(0, token, 0); + within_len_ = 1; return; + } + uint64_t ctx = within_hash_ ^ (uint64_t(within_len_) * LEN_MIX); + uint64_t pk = (ctx * PAIR_MIX) ^ (uint64_t(token) * PRIMES[0]); + int oi = std::min(int(within_len_) - 1, WITHIN_ORDERS - 1); + within_[oi].update(ctx, pk, token); + within_hash_ = extend_prefix(within_hash_, token, within_len_); + within_len_++; + } + + uint64_t word_ctx_hash() const { + uint64_t h = 0; + int n = std::min(word_ring_fill_, WORD_ORDER); + for (int j = 0; j < n; j++) { + int idx = (word_ring_head_ - n + j + WORD_ORDER) % WORD_ORDER; + h ^= word_ring_[idx] * PRIMES[j % N_PRIMES]; + } + return h; + } + + void word_hint(bool is_ws, int& out_tok, double& out_beta) { + if (!is_ws || word_ring_fill_ < WORD_ORDER) { + out_tok = -1; out_beta = 0.0; return; + } + uint64_t ctx = word_ctx_hash(); + int hint; double conf; uint32_t count; + word_table_.ctx_lookup(ctx, hint, conf, count); + if (hint >= 0 && conf >= word_threshold_ && count >= 3) { + out_tok = hint; out_beta = word_beta_; + } else { + out_tok = -1; out_beta = 0.0; + } + } + + void flush_word() { + if (current_word_len_ == 0) return; + word_ring_[word_ring_head_] = current_word_hash_; + word_ring_head_ = (word_ring_head_ + 1) % WORD_ORDER; + if (word_ring_fill_ < WORD_ORDER) word_ring_fill_++; + current_word_hash_ = 0; current_word_len_ = 0; + } + + void word_update(uint16_t token, bool is_bnd, bool is_ws) { + if (is_bnd) { flush_word(); return; } + if (is_ws) { + flush_word(); + if (word_ring_fill_ >= WORD_ORDER) { + uint64_t ctx = word_ctx_hash(); + uint64_t pk = pair_key(ctx, token, WORD_ORDER); + word_table_.update(ctx, pk, token); + } + } + current_word_hash_ = current_word_hash_ * 31 + token; + current_word_len_++; + } + + void prefetch_open_lookups(const uint64_t* hashes, int max_avail) const { + for (int order = std::min(OPEN_MAX, max_avail); order >= OPEN_MIN; order--) { + int oi = order - OPEN_MIN; + if (!order_active_[oi]) continue; + open_[oi].prefetch_ctx(hashes[order - 1]); + } + } + + void prefetch_open_updates(const uint64_t* hashes, int max_avail, uint16_t token) const { + for (int order = OPEN_MIN; order <= std::min(OPEN_MAX, max_avail); order++) { + int oi = order - OPEN_MIN; + if (!order_active_[oi]) continue; + uint64_t ch = hashes[order - 1]; + uint64_t pk = pair_key(ch, token, order); + open_[oi].prefetch_update(ch, pk); + } + } + +public: + ContextMixer(double base_beta = 1.0, double agree_bonus = 0.5, + double within_threshold = 0.80, double within_beta = 0.75, + double word_threshold = 0.80, double word_beta = 0.50, + int open_table_bits = 22, double token_threshold_scale = 1.0, + int order_stride = 1) + : within_hash_(0), within_len_(0), + within_threshold_(within_threshold), within_beta_(within_beta), + word_ring_head_(0), word_ring_fill_(0), + current_word_hash_(0), current_word_len_(0), + word_threshold_(word_threshold), word_beta_(word_beta), + base_beta_(base_beta), agree_bonus_(agree_bonus), + order_stride_(order_stride) { + + std::memset(word_ring_, 0, sizeof(word_ring_)); + + for (int i = 0; i < N_OPEN; i++) { + int order = OPEN_MIN + i; + order_active_[i] = ((order - OPEN_MIN) % order_stride == 0); + if (order_active_[i]) + open_[i].init(open_table_bits); + } + + double s = token_threshold_scale; + for (int o = 8; o <= 10; o++) cfg_[o - OPEN_MIN] = {0.70 * s, 3}; + for (int o = 11; o <= 13; o++) cfg_[o - OPEN_MIN] = {0.60 * s, 2}; + for (int o = 14; o <= 16; o++) cfg_[o - OPEN_MIN] = {0.50 * s, 2}; + + for (int i = 0; i < WITHIN_ORDERS; i++) + within_[i].init(20); + + word_table_.init(20); + } + + void set_tokens(const int64_t* t, int64_t n) { + tokens_ = t; n_tokens_ = n; + } + + void set_luts(const int16_t* bb, const uint8_t* ls, const uint8_t* bd) { + base_bytes_ = bb; has_ls_ = ls; is_bnd_ = bd; + } + + void reset() { + for (auto& o : open_) if (o.ctx) o.reset(); + for (auto& w : within_) w.reset(); + word_table_.reset(); + within_hash_ = 0; within_len_ = 0; + word_ring_head_ = 0; word_ring_fill_ = 0; + current_word_hash_ = 0; current_word_len_ = 0; + } + + void get_hints_batch(const int64_t* pos, int n, + int32_t* hints, double* betas) { + + uint64_t hashes[OPEN_MAX]; + uint64_t next_hashes[OPEN_MAX]; + + if (n > 0) { + int64_t p0 = pos[0]; + compute_hashes(tokens_, p0, OPEN_MAX, hashes); + int ma0 = std::min(OPEN_MAX, int(p0)); + prefetch_open_lookups(hashes, ma0); + } + + // CAUSAL FIX (matches @abaybektursun's fix in PR #1420 — see + // https://github.com/openai/parameter-golf/pull/1420#issuecomment-4199452189): + // 1. Hint gating: is_bnd / is_ws derived from tokens_[p-1] (last prefix + // token), not tokens_[p]. This makes the predictive distribution at + // position p depend only on the strict prefix, satisfying Issue #1017 + // condition 2. + // 2. Update functions: tok_is_bnd / tok_is_ws derived from the actual + // target tok so within_update / word_update still segment words + // correctly. This is causal because updates happen AFTER the hint + // for position p has been written to the output buffer. + // + // (Variable naming and structure copied verbatim from PR #1420's fix. + // In addition, this submission is run with NGRAM_WITHIN_BETA=0 + // NGRAM_WORD_BETA=0 to disable the within/word experts entirely, + // because empirically they contribute negative BPB once the leak is + // removed — see Legality Fix section in the README.) + for (int i = 0; i < n; i++) { + int64_t p = pos[i]; + auto tok = uint16_t(tokens_[p]); + auto prev_tok = (p > 0) ? uint16_t(tokens_[p - 1]) : uint16_t(0); + bool is_bnd = is_bnd_ && is_bnd_[prev_tok]; + bool is_ws = has_ls_ && has_ls_[prev_tok]; + int max_avail = std::min(OPEN_MAX, int(p)); + + if (i + 1 < n) { + int64_t np = pos[i + 1]; + compute_hashes(tokens_, np, OPEN_MAX, next_hashes); + int nma = std::min(OPEN_MAX, int(np)); + prefetch_open_lookups(next_hashes, nma); + } + + int tok_hint, within_tok, word_tok; + double tok_beta, within_b, word_b; + token_hint(hashes, max_avail, tok_hint, tok_beta); + within_hint(is_bnd, is_ws, within_tok, within_b); + word_hint(is_ws, word_tok, word_b); + + struct Cand { int hint; double beta; }; + Cand cands[3]; int nc = 0; + if (tok_hint >= 0) cands[nc++] = {tok_hint, tok_beta}; + if (within_tok >= 0) cands[nc++] = {within_tok, within_b}; + if (word_tok >= 0) cands[nc++] = {word_tok, word_b}; + + int best_hint = -1; double best_beta = 0.0; + if (nc > 0) { + for (int a = 0; a < nc; a++) + for (int b = 0; b < nc; b++) + if (b != a && cands[b].hint == cands[a].hint) + { cands[a].beta += agree_bonus_; break; } + int bi = 0; + for (int a = 1; a < nc; a++) + if (cands[a].beta > cands[bi].beta) bi = a; + best_hint = cands[bi].hint; + best_beta = cands[bi].beta; + } + + hints[i] = best_hint; + betas[i] = best_beta; + + prefetch_open_updates(hashes, max_avail, tok); + + bool tok_is_bnd = is_bnd_ && is_bnd_[tok]; + bool tok_is_ws = has_ls_ && has_ls_[tok]; + token_update(hashes, max_avail, tok); + within_update(tok, tok_is_bnd, tok_is_ws); + word_update(tok, tok_is_bnd, tok_is_ws); + + std::memcpy(hashes, next_hashes, sizeof(hashes)); + } + } + +}; + + + +extern "C" { + +void* ctxmixer_new(double base_beta, double agree_bonus, + double within_threshold, double within_beta, + double word_threshold, double word_beta, + int open_table_bits, double token_threshold_scale, + int order_stride) { + return new ContextMixer(base_beta, agree_bonus, + within_threshold, within_beta, + word_threshold, word_beta, + open_table_bits, token_threshold_scale, + order_stride); +} + +void ctxmixer_delete(void* self) { + delete static_cast(self); +} + +void ctxmixer_set_tokens(void* self, const int64_t* tokens, int64_t n) { + static_cast(self)->set_tokens(tokens, n); +} + +void ctxmixer_set_luts(void* self, + const int16_t* bb, + const uint8_t* ls, + const uint8_t* bd) { + static_cast(self)->set_luts(bb, ls, bd); +} + +void ctxmixer_reset(void* self) { + static_cast(self)->reset(); +} + +void ctxmixer_get_hints_batch(void* self, const int64_t* pos, int n, + int32_t* out_hints, double* out_betas) { + static_cast(self)->get_hints_batch(pos, n, out_hints, out_betas); +} + +} // extern "C" diff --git a/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/ngram_tilt.py b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/ngram_tilt.py new file mode 100644 index 0000000000..7d0691f06a --- /dev/null +++ b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/ngram_tilt.py @@ -0,0 +1,218 @@ +"""N-gram tilt eval-time helper. + +Wraps the C++ ContextMixer kernel from PR #1420 (legality argument in +issue #1017) via ctypes. Builds the open-addressing hash tables on rank 0, +broadcasts hints/betas to other ranks, and provides a torch helper that +applies the one-token exponential tilt to per-position NLL. + +Math: + p_tilt(t) = p_model(t) * exp(beta * 1[t==hint]) / Z + Z = 1 + p_hint * (exp(beta) - 1) + -log p_tilt(target) = nll + has_hint * (log(Z) - beta * 1[tgt==hint]) + +Score-before-update is enforced inside the C kernel: hint for position p +is read from the prefix-only hash tables BEFORE the kernel updates them +with token at position p. +""" +from __future__ import annotations + +import ctypes +import os +import subprocess +import time +from pathlib import Path + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn.functional as F + + +_HERE = Path(__file__).resolve().parent +# Look in ./ngram/ subdir first (dev layout), then current dir (submission layout) +if (_HERE / "ngram" / "fused_expert_kernel.cpp").exists(): + _NGRAM_DIR = _HERE / "ngram" +else: + _NGRAM_DIR = _HERE +_LIB_PATH = _NGRAM_DIR / "libfused_ngram.so" +_SRC_PATH = _NGRAM_DIR / "fused_expert_kernel.cpp" + +_lib = None + + +def _ensure_lib(): + global _lib + if _lib is not None: + return _lib + if (not _LIB_PATH.exists()) or ( + _SRC_PATH.exists() and _SRC_PATH.stat().st_mtime_ns > _LIB_PATH.stat().st_mtime_ns + ): + subprocess.run( + [ + "g++", "-O3", "-march=native", "-std=c++17", + "-fPIC", "-shared", + str(_SRC_PATH), + "-o", str(_LIB_PATH), + ], + check=True, + ) + lib = ctypes.CDLL(str(_LIB_PATH)) + lib.ctxmixer_new.restype = ctypes.c_void_p + lib.ctxmixer_new.argtypes = [ + ctypes.c_double, ctypes.c_double, + ctypes.c_double, ctypes.c_double, + ctypes.c_double, ctypes.c_double, + ctypes.c_int, ctypes.c_double, ctypes.c_int, + ] + lib.ctxmixer_delete.restype = None + lib.ctxmixer_delete.argtypes = [ctypes.c_void_p] + lib.ctxmixer_set_tokens.restype = None + lib.ctxmixer_set_tokens.argtypes = [ + ctypes.c_void_p, ctypes.POINTER(ctypes.c_int64), ctypes.c_int64, + ] + lib.ctxmixer_set_luts.restype = None + lib.ctxmixer_set_luts.argtypes = [ + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_int16), + ctypes.POINTER(ctypes.c_uint8), + ctypes.POINTER(ctypes.c_uint8), + ] + lib.ctxmixer_reset.restype = None + lib.ctxmixer_reset.argtypes = [ctypes.c_void_p] + lib.ctxmixer_get_hints_batch.restype = None + lib.ctxmixer_get_hints_batch.argtypes = [ + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_int64), ctypes.c_int, + ctypes.POINTER(ctypes.c_int32), ctypes.POINTER(ctypes.c_double), + ] + _lib = lib + return _lib + + +class NgramTiltState: + """Owns the precomputed hints/betas for the entire validation stream. + + Construction is collective: all ranks call build_hints() but only + rank 0 actually runs the C++ kernel; other ranks receive the hints + via broadcast. + """ + + def __init__( + self, + val_tokens: torch.Tensor, + has_leading_space_lut: torch.Tensor, + is_boundary_token_lut: torch.Tensor, + rank: int, + world_size: int, + device: torch.device, + base_beta: float = 2.0, + agree_bonus: float = 0.1, + within_threshold: float = 0.25, + within_beta: float = 0.92, + word_threshold: float = 0.80, + word_beta: float = 0.50, + open_table_bits: int = 26, + token_threshold_scale: float = 1.0, + order_stride: int = 2, + log=print, + ): + self.rank = rank + self.world_size = world_size + self.device = device + + n_tok = val_tokens.numel() + # Hints[i] = hint for position i (the token at val_tokens[i] given + # the prefix val_tokens[:i]). Position 0 has no prefix => hint -1. + # We compute for positions 1..n_tok-1. + self.hints_cpu = torch.full((n_tok,), -1, dtype=torch.int32) + self.betas_cpu = torch.zeros((n_tok,), dtype=torch.float64) + + if rank == 0: + t0 = time.perf_counter() + lib = _ensure_lib() + tokens_np = val_tokens.cpu().numpy().astype(np.int64, copy=False) + tokens_np = np.ascontiguousarray(tokens_np) + # base_bytes is unused by the kernel hints (only LUTs that + # determine word boundaries matter), but the API expects it. + base_bytes_np = np.zeros(has_leading_space_lut.numel(), dtype=np.int16) + ls_np = has_leading_space_lut.cpu().numpy().astype(np.uint8, copy=False) + bd_np = is_boundary_token_lut.cpu().numpy().astype(np.uint8, copy=False) + base_bytes_np = np.ascontiguousarray(base_bytes_np) + ls_np = np.ascontiguousarray(ls_np) + bd_np = np.ascontiguousarray(bd_np) + + mixer = lib.ctxmixer_new( + base_beta, agree_bonus, + within_threshold, within_beta, + word_threshold, word_beta, + open_table_bits, token_threshold_scale, order_stride, + ) + if not mixer: + raise RuntimeError("ctxmixer_new returned NULL") + try: + lib.ctxmixer_set_tokens( + mixer, + tokens_np.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)), + ctypes.c_int64(int(n_tok)), + ) + lib.ctxmixer_set_luts( + mixer, + base_bytes_np.ctypes.data_as(ctypes.POINTER(ctypes.c_int16)), + ls_np.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)), + bd_np.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)), + ) + positions = np.arange(1, n_tok, dtype=np.int64) + positions = np.ascontiguousarray(positions) + out_hints = np.full(n_tok - 1, -1, dtype=np.int32) + out_betas = np.zeros(n_tok - 1, dtype=np.float64) + lib.ctxmixer_get_hints_batch( + mixer, + positions.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)), + ctypes.c_int(int(n_tok - 1)), + out_hints.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + out_betas.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ) + finally: + lib.ctxmixer_delete(mixer) + self.hints_cpu[1:] = torch.from_numpy(out_hints) + self.betas_cpu[1:] = torch.from_numpy(out_betas) + elapsed = time.perf_counter() - t0 + n_hits = int((out_hints >= 0).sum()) + log( + f"ngram_tilt:precompute n_tok={n_tok} hints={n_hits} " + f"({100*n_hits/(n_tok-1):.2f}%) elapsed={elapsed:.1f}s " + f"base_beta={base_beta} within_beta={within_beta} agree_bonus={agree_bonus}" + ) + + # Move to device, broadcast from rank 0 + self.hints = self.hints_cpu.to(device=device, dtype=torch.int64) + self.betas = self.betas_cpu.to(device=device, dtype=torch.float64) + if world_size > 1: + dist.broadcast(self.hints, src=0) + dist.broadcast(self.betas, src=0) + + def tilt_nll( + self, + scored_nll: torch.Tensor, # [N] float64, per-position NLL from base softmax + scored_logits: torch.Tensor, # [N, V] float, logits at scored positions + target_ids: torch.Tensor, # [N] int64, true target tokens + global_positions: torch.Tensor, # [N] int64, position index into the val stream + ) -> torch.Tensor: + """Apply n-gram tilt to per-position NLL. + + Returns mixed_nll [N] float64. When hint is -1 the tilt is a no-op. + """ + hints = self.hints[global_positions] + betas = self.betas[global_positions] + has_hint = (hints >= 0).to(torch.float64) + + # Recover logsumexp from nll: nll = lse - logit_tgt => lse = nll + logit_tgt + logit_tgt = scored_logits.gather(-1, target_ids.unsqueeze(-1)).squeeze(-1).to(torch.float64) + safe_h = hints.clamp(min=0) + logit_hint = scored_logits.gather(-1, safe_h.unsqueeze(-1)).squeeze(-1).to(torch.float64) + lse = scored_nll + logit_tgt + p_hint = (logit_hint - lse).exp().clamp(0.0, 1.0) + Z = 1.0 + p_hint * (betas.exp() - 1.0) + is_hit = (target_ids == hints).to(torch.float64) + mixed_nll = scored_nll + has_hint * (Z.log() - betas * is_hit) + return mixed_nll diff --git a/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/submission.json b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/submission.json new file mode 100644 index 0000000000..da8c2e7092 --- /dev/null +++ b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/submission.json @@ -0,0 +1,90 @@ +{ + "name": "Record: SP8192 + Parallel Residuals + 3-Layer Recurrence + Token-Only N-gram Tilt \u2014 val_bpb 1.08091 (5-seed mean, causal-corrected)", + "val_bpb": 1.08091, + "val_loss": 2.7921, + "bytes_total": 15995572, + "blurb": "Causal-corrected 5-seed mean 1.08091 BPB (val_loss 2.79210 nats per token, std 0.00043). Beats PR #1394 (clarkkev, 1.08563) by +0.01219 nats per token, clearing the 0.005-nat record bar by 2.4x. Beats merged SOTA PR #1019 (1.11473) by +0.08736 nats per token. The originally-posted 1.07807 5-seed mean used a non-causal n-gram kernel inherited from PR #1420 (within_hint and word_hint gated emission on is_bnd[tokens_[p]]/is_ws[tokens_[p]], leaking 1-2 bits about the answer per scored position, Issue #1017 condition 2 violation). Fix matches @abaybektursun proposed patch in the PR #1420 thread: derive prev_is_bnd/prev_is_ws from tokens_[p-1] for hint gating, while updates use the actual target token via tok_is_bnd/tok_is_ws (causal because they happen after hint emission for that position). 5 seeds re-run from this submission folder with the patched kernel; the corrected number is the legitimate value. PR #1420 has the identical bug; @abaybektursun has acknowledged it. Pre-fix per-seed values preserved in seed_results_pre_fix for the public record.", + "author": "dexhunter", + "github_id": "dexhunter", + "date": "2026-04-07", + "seed_results": { + "0": { + "val_bpb": 1.08035, + "val_loss": 2.79067, + "steps": 4911, + "artifact_bytes": 15994644 + }, + "42": { + "val_bpb": 1.08097, + "val_loss": 2.79225, + "steps": 4906, + "artifact_bytes": 15995572 + }, + "1234": { + "val_bpb": 1.08127, + "val_loss": 2.79303, + "steps": 4915, + "artifact_bytes": 15993531 + }, + "1337": { + "val_bpb": 1.0806, + "val_loss": 2.79131, + "steps": 4905, + "artifact_bytes": 15988802 + }, + "2025": { + "val_bpb": 1.08135, + "val_loss": 2.79324, + "steps": 4911, + "artifact_bytes": 15993360 + } + }, + "lineage": [ + "PR #1394 (clarkkev) \u2014 sp8192 base", + "PR #1413 (dexhunter) \u2014 sp8192 + QK5 + legal score-first TTT", + "PR #1412 (Robby955) \u2014 parallel residuals on layers 7-10", + "PR #1420 (abaybektursun) \u2014 n-gram tilt mechanism + C++ kernel", + "PR #1145 (AnirudhRahul) \u2014 original normalized causal n-gram cache pattern", + "PR #549 (abaybektursun, merged) \u2014 score-first TTT precedent" + ], + "seed_results_pre_fix": { + "0": { + "val_bpb": 1.07751, + "val_loss": 2.78333, + "steps": 4918, + "artifact_bytes": 15992304 + }, + "42": { + "val_bpb": 1.07809, + "val_loss": 2.78481, + "steps": 4911, + "artifact_bytes": 15993733 + }, + "1234": { + "val_bpb": 1.07813, + "val_loss": 2.78492, + "steps": 4908, + "artifact_bytes": 15990539 + }, + "1337": { + "val_bpb": 1.07801, + "val_loss": 2.78461, + "steps": 4909, + "artifact_bytes": 15988039 + }, + "2025": { + "val_bpb": 1.07862, + "val_loss": 2.7862, + "steps": 4908, + "artifact_bytes": 15992215 + } + }, + "correction_note": { + "date": "2026-04-07", + "issue": "Issue #1017 condition 2 (causality)", + "root_cause": "fused_expert_kernel.cpp::get_hints_batch read tokens_[p] (target token) and used is_bnd[tok]/is_ws[tok] to gate within_hint/word_hint emission, leaking 1-2 bits per scored position", + "fix": "kernel patched to derive prev_is_bnd/prev_is_ws from tokens_[p-1] for hint gates only; updates still use current tok (causal because they happen after hint emission). Additionally NGRAM_WITHIN_BETA=0 NGRAM_WORD_BETA=0 disables within/word experts (they contribute negative BPB once causal). Only token_hint contributes (already causal).", + "leak_magnitude_nats": 0.00284, + "shared_with": "PR #1420 (acknowledged by @abaybektursun in PR #1420 thread, fix proposal at https://github.com/openai/parameter-golf/pull/1420#issuecomment-4199452189)" + } +} \ No newline at end of file diff --git a/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/train_gpt.py b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/train_gpt.py new file mode 100644 index 0000000000..97c9dbf21e --- /dev/null +++ b/records/track_10min_16mb/2026-04-07_SP8192_ParallelResid7_Loop35_NgramTilt/train_gpt.py @@ -0,0 +1,2 @@ +import lzma as L,base64 as B 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