diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/README.md b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/README.md new file mode 100644 index 0000000000..1b00fe39f5 --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/README.md @@ -0,0 +1,203 @@ +# Non-record: Causal N-gram Logit Blend — Legal, Bug-Free, and Quantitatively Shown Not to Scale + +**Author:** @himanshudongre · **Date:** 2026-04-15 · **Track:** Non-record research (sp1024 + sp8192 scaling study) + +## TL;DR + +This PR is a **rigorous negative result**. It demonstrates that a legal, bug-free causal n-gram additive-logit contribution — the technique that every closed `ngram`-titled record PR in this repo was attempting — does not scale to strong models, and is unlikely to yield a record on top of [#1493](https://github.com/openai/parameter-golf/pull/1493) or any similarly trained SOTA stack. + +Why this is useful to the community: + +1. **Clean legal reference implementation.** Every previous n-gram PR was closed for a C1/C2/C3/C4 violation per Issue [#1017](https://github.com/openai/parameter-golf/issues/1017). Ours is verified against the specific closures — [#993](https://github.com/openai/parameter-golf/pull/993) (hashed caches), [#1185](https://github.com/openai/parameter-golf/pull/1185) (full-vocab renormalization), and [#959](https://github.com/openai/parameter-golf/pull/959) (two-pass rescoring) — with an automated 8-probe legality harness. +2. **Quantitative scaling curve across 6 model configurations** (2L/4L, 128d/256d, 800/2000/2500/4000 steps, sp1024/sp8192) showing the peak BPB improvement collapses from 0.0515 BPB on a very weak baseline to 0.00018 BPB on the strongest model tested. The extrapolation to real SOTA is clearly sub-threshold. +3. **Localized (bucketed) delta analysis** showing where the marginal gain actually comes from — 100% from "long-range cache hits outside the 2048-token attention window" — and why this architectural floor doesn't save the approach at scale. +4. **Reusable scaffolding.** The legality harness, integration test suite, and localized delta analysis can be applied to any future eval-time adaptation technique (SLOT variants, per-document LoRA, memory-augmented approaches, etc.). + +I hope this saves other participants from running the same experiment and submitting the same bugged variants, and gives the reviewer team a clearer picture of what the legal version of this approach actually delivers. + +--- + +## The four legality conditions (from Issue #1017) and how we satisfy each + +| Condition | Our implementation | Proof | +|---|---|---| +| **C1 Strict causal** — `p_t(·)` depends only on `A` + `x_1..x_{t-1}` | Frozen count snapshot is taken at chunk start. Lookups read only the frozen snapshot. Updates to the live snapshot happen *after* all windows in a chunk are scored. | `legality_harness.py::test_c1_strict_causal` mutates future tokens and asserts lookups are bit-identical. | +| **C2 Full normalized distribution** — sum to 1 over full vocab Σ, independent of `x_t` | N-gram returns a full V-dim log-prob vector via order-K→2 backoff with add-δ smoothing. Final logits = `neural_logits + α · log_p_ngram` passed through a standard softmax. Blend is a softmax-invariant shift on "no-hit" contexts. | `legality_harness.py::test_c2_full_vocab_normalization` + `::test_c2_xt_independence`. | +| **C3 Score-before-update** — score `p_t(x_t)` first, update state second | The eval loop in `ngram_eval.py::eval_val_ttt_with_ngram` performs all scoring inside `torch.no_grad()` using the frozen snapshot; only after the chunk's scored positions are collected does `ngram.add_token()` run. Re-freeze at chunk boundary. | `legality_harness.py::test_c3_score_before_update` runs a reference cache that never sees chunk tokens and asserts the scoring lookups match. | +| **C4 Single left-to-right pass** | Evaluation is a single traversal of window_starts. No rescore, no second pass, no APIs for retrospective revision. | `legality_harness.py::test_c4_single_pass` asserts no `rescore`/`rebuild`/`two_pass` methods exist on the class. | + +**Extra checks against specific reviewer closures:** + +- **[#993](https://github.com/openai/parameter-golf/pull/993) "Hashed n-gram models in this way are disallowed"** → `test_no_hashing` asserts count keys are Python `tuple` objects, not integer hash buckets. We use `collections.defaultdict(Counter)` indexed by the exact context tuple. +- **[#1185](https://github.com/openai/parameter-golf/pull/1185) "calculate and renormalize over the whole vocab size"** → we return the full-V log-prob vector on every call, then add to neural logits, then apply one softmax. We never compute a blended probability only for the observed token. See `ngram_eval.py::CausalNGram._lookup_log_probs`. +- **[#959](https://github.com/openai/parameter-golf/pull/959) "two-pass rescoring methods ... leaks eval tokens"** → we do a single left-to-right traversal with frozen-snapshot-per-chunk semantics. No second pass. + +**All 8 legality probes pass:** +``` +$ python3 legality_harness.py --verbose + PASS C1 strict causal + PASS C2 full-vocab normalization + PASS C2 x_t independence + PASS C3 score-before-update + PASS C4 single pass + PASS no-hashing (ruling #993) + PASS blend non-negative + finite + PASS backoff fallthrough to unigram +8/8 tests passed +``` + +**All 4 integration tests pass on both CPU and CUDA** (A40): +``` +$ python3 test_integration.py +--- regression (alpha=0) --- PASS (bit-identical to baseline) +--- stability (alpha>0 sweep) --- PASS (monotonic drop on repeating pattern) +--- legality preserved --- PASS +--- update-after-score ordering --- PASS +4/4 tests passed +``` + +The `regression (alpha=0)` test is the important one: when α=0 the blend short-circuits and BPB must be bit-identical to the unmodified `eval_val_ttt` path. This caught one class of integration bug early. + +--- + +## The scaling curve + +All experiments use a GPT-style decoder (`TinyGPT` in `code/tiny_train.py`), the additive-logit blend from `code/ngram_eval.py`, and order-4 causal n-gram with add-δ smoothing (`δ = 0.5`, `min_context_count = 2`). Training data is a prefix of the sp1024/sp8192 val shard (this is not a competition-valid training setup — it's a relative-delta measurement, and the ngram cache itself is strictly eval-time, see `code/ngram_eval.py`). Eval is on a held-out slice at the end of the same shard. + +| # | Tokenizer | Model | Steps | Baseline BPT (nats/tok) | **Peak Δ BPT** | **Peak α** | **Peak Δ BPB** | vs 0.0072 record threshold | +|---|---|---|---:|---:|---:|---:|---:|:---:| +| 1 | sp1024 | 2L 128d | 800 | 4.665 | **−0.0860** | 0.5 | **0.0515** | **7.1× above** | +| 2 | sp1024 | 2L 128d | 2500 | 4.145 | **−0.0374** | 0.3 | **0.0224** | 3.1× above | +| 3 | sp1024 | 4L 256d | 2000 | 3.811 | **−0.0132** | 0.2 | **0.0079** | 1.1× above | +| 4 | sp1024 | 4L 256d | 4000 | 3.640 | **−0.00569** | 0.15 | **0.00341** | 0.47× — below | +| 5 | **sp8192** | 4L 256d | 2000 | 5.625 | **−0.00566** | 0.10 | **0.00223** | **0.31× — below** | +| 6 | **sp8192** | 4L 256d | 4000 | 5.114 | **−0.000457** | 0.05 | **0.00018** | **0.025× — below** | + +(Δ BPT values converted from `bits/tok` to `nats/tok` via `÷ log₂ e` in the table above; raw `bits/tok` numbers live in `results/results_*.json`.) + +**Shrinkage per unit baseline improvement is accelerating toward zero:** + +| Transition | ΔBaseline (bpt) | ΔPeak (bpt) | Shrink ratio | +|---|---:|---:|---:| +| Run 1 → 2 (2L 128d, 800 → 2500 steps) | −0.52 | +0.0486 | 9.3% | +| Run 2 → 3 (2L 128d → 4L 256d, 2000 steps) | −0.33 | +0.0242 | 7.3% | +| Run 3 → 4 (4L 256d, 2000 → 4000 steps) | −0.17 | +0.0074 | 4.3% | +| Run 5 → 6 (sp8192 4L 256d, 2000 → 4000 steps) | −0.50 | +0.00512 | 1.0% | + +A strict-monotonic linear regression would put the per-unit shrink ratio near 0% in the limit, consistent with a non-zero floor — but that floor is clearly well under the 0.0072 BPB threshold, and the shrink is still happening at every measured step. + +### Why the "long-range architectural floor" argument I was optimistic about doesn't save it + +My overnight prediction was: "the gain comes from contexts first seen > 2048 tokens ago, which are literally invisible to the neural model's attention window, so it should persist regardless of model strength." + +**The localized delta analysis** (`code/localized_delta.py`, `results/results_localized.json`) bucket-decomposes the total delta by (range × doc_position) on the 4L 256d sp1024 model: + +| Range bucket × doc position | N | Δ bpt | **Δ × N** (weighted) | +|---|---:|---:|---:| +| `out_of_window × 0-2047` | 38,341 | −0.050 | **−1929** | +| `out_of_window × 2048-4095` | 7,267 | −0.076 | −554 | +| `out_of_window × 4096+` | 13,053 | −0.149 | **−1942** | +| `in_window × all` | 81 | — | −10 | +| `no_hit × 0-2047` | 99,326 | +0.008 | +787 | +| `no_hit × 2048-4095` | 17,350 | +0.004 | +70 | +| `no_hit × 4096+` | 24,581 | −0.007 | −175 | + +**100% of the net benefit comes from `out_of_window`.** That's the good news for the "architectural floor" argument. + +The bad news (which I didn't anticipate): **the `out_of_window` fraction shrinks with sp8192**. Sp8192 tokens are longer (mean 3.66 bytes/tok vs 2.41 for sp1024), so a 2048-token attention window covers ~52% more bytes of each document. The fraction of positions that are "physically invisible to attention" drops from ~25.4% of tokens (sp1024) to an estimated ~14% (sp8192). At the same time, stronger models also get better at the in-window positions, which doesn't change the out-of-window fraction but does reduce the *baseline uncertainty* at those positions, shrinking the n-gram's lossless-recall advantage. Both effects compound. + +This is a **useful insight for other techniques that rely on "outside the attention window" as their source of headroom**: the sp8192 → sp16384 tokenizer migration that's been happening will make that class of techniques less effective, not more. + +--- + +## What about Track B legality of this exact approach? + +Issue #1017's Track B section explicitly permits "Causal n-gram caches that accumulate statistics only from already-scored tokens." That's what we built. The concern isn't legality — the concern is that the legal version gives a sub-threshold improvement. + +valerio-oai's [#1185 comment](https://github.com/openai/parameter-golf/pull/1185) suggests the legal form "would be more inclined to be treated as legal." Based on the empirical scaling shown here, I believe the canonical response the community should now have is: *it's legal, it's been done cleanly, and it's ~0.0002 BPB on a properly trained model — so don't expect it to produce a record.* + +If reviewers would like, I can separately ping the specific closed PRs (#993, #1026, #1185) to point to this PR as the legal reference. Happy to do that after this lands. + +--- + +## Per-rule compliance statement + +This is a **non-record** submission. It does not claim a leaderboard position. All code provided is for reproduction of the reported negative result, not for a competitive BPB score. + +- Artifact size: **not applicable** (no artifact — this is a research submission) +- Training time: the compressed reproduction recipe runs in ~20 minutes on a single A40 (Phase 1-A) or ~10 minutes on Mac M4 MPS (local runs 1-4) +- Eval time: ~10 minutes for full alpha sweep +- Data: sp1024 + sp8192 shards from `willdepueoai/parameter-golf` and `kevclark/parameter-golf` respectively + +No network calls, no external downloads at eval time, no runtime side information. The n-gram state is built entirely from already-scored eval tokens per Track B semantics. + +--- + +## What's in this folder + +``` +2026-04-15_Causal_NGram_Null_Result/ +├── README.md ← this file +├── submission.json ← metadata +├── code/ +│ ├── causal_ngram.py ← reference CausalNGram class (module docstring documents legality invariants) +│ ├── ngram_eval.py ← production integration: eval_val_ttt_with_ngram +│ ├── legality_harness.py ← 8 automated legality probes +│ ├── test_integration.py ← 4 integration tests (α=0 regression, stability, legality preserved, update ordering) +│ ├── kill_switch_analysis.py ← val-set repetition analysis (doc lengths, long-range hit rates per order) +│ ├── extended_analysis.py ← bigram-proxy alpha sweep, global vs per-doc cache comparison +│ ├── tiny_train.py ← end-to-end train-then-eval pipeline with sweeps +│ └── localized_delta.py ← per-bucket (range × doc_pos) delta decomposition +├── results/ +│ ├── results_tiny_train.json ← Run 1: 2L 128d sp1024 800 steps +│ ├── results_tiny_long.json ← Run 2: 2L 128d sp1024 2500 steps +│ ├── results_tiny_bigger.json ← Run 3: 4L 256d sp1024 2000 steps +│ ├── results_tiny_bigger_long.json ← Run 4: 4L 256d sp1024 4000 steps +│ └── results_localized.json ← bucket analysis (4L 256d sp1024 2000 steps) +└── training_logs/ + ├── results_a40_sp8192_phase1a.log ← Run 5: 4L 256d sp8192 2000 steps (A40) + ├── results_a40_sp8192_phase1b.log ← Run 6: 4L 256d sp8192 4000 steps (A40) + └── results_extended_analysis.log ← bigram-proxy global vs per-doc alpha sweep (2M tokens) +``` + +--- + +## Reproduction + +### Legality + integration tests (≤ 10 seconds, any CPU) +```bash +python3 code/legality_harness.py +python3 code/test_integration.py +``` +Expected: `8/8 tests passed` and `4/4 tests passed`. + +### Val-set repetition analysis (~3 minutes, any CPU, sp1024 val shard needed) +```bash +python3 code/kill_switch_analysis.py --val /path/to/fineweb_val_000000.bin --orders 3,4,5 +``` + +### Tiny training + eval sweep on MPS/CUDA (~5-10 min) +```bash +python3 code/tiny_train.py \ + --val /path/to/fineweb_val_000000.bin \ + --dim 256 --layers 4 --heads 4 \ + --steps 4000 --batch 32 --seq-len 512 \ + --eval-cap 120000 --eval-chunk-tokens 16384 \ + --orders 4 --alphas 0,0.05,0.1,0.15,0.2,0.25,0.3 \ + --out results_my_run.json +``` +For sp8192, add `--vocab-size 8192` and point `--val` at the sp8192 shard. + +### Localized delta analysis (~5 min on MPS) +```bash +python3 code/localized_delta.py --dim 256 --layers 4 --steps 2000 --order 4 --alpha 0.2 +``` + +--- + +## Acknowledgements + +- valerio-oai for the definitive legality rulings on #993, #1185, #959 — without those closures I would have shipped the same buggy variant. +- @clarkkev and @bigbag for the #1394 and #1493 stacks that define the current SOTA and provided the integration target. +- @NoesisGenesis (@HKati) for Issue #1017 and the formal four-condition framework. +- @SPThole for #1602's autopsy framework — this PR follows its convention of rigorously documenting a negative result so others don't repeat the work. diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/causal_ngram.py b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/causal_ngram.py new file mode 100644 index 0000000000..95ccff9f7d --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/causal_ngram.py @@ -0,0 +1,231 @@ +""" +Causal N-gram Cache — eval-time additive logit contribution. + +LEGALITY (per Issue #1017 Four Conditions + valerio-oai rulings #993, #1185, #959): + +1. EXACT non-hashed counting (counters a Python dict of dict; NO hash buckets). + valerio-oai closed #993 for "hashed n-gram models in this way are disallowed". + +2. FULL-VOCAB LOG-PROB tensor over Sigma is produced and added to neural logits + BEFORE softmax, so the blend is an additive-logit shift and the final softmax + is a valid normalized distribution over Sigma, independent of x_t. + valerio-oai closed #1185 for computing the blend only for the target token. + +3. UPDATE-AFTER-SCORE discipline: counts are frozen at the start of a scoring + region. Only after all windows in the region are scored may counts be updated + with tokens that were just scored. No token influences its own probability. + +4. SINGLE left-to-right pass: the scoring region is processed once, no rescoring. + +5. Alpha is a fixed scalar baked into the artifact. No x_t-dependent mixing. + +DATA STRUCTURE: + counts[k] is a dict mapping context tuple (length k-1) -> Counter of token ids. + counts[1] is the unigram Counter (context = empty tuple). + We store order 1..K. Backoff walks K, K-1, ..., 1. + +SCORING: + For a context c of max length K-1 at position t: + - Walk from order K down: if c[-(k-1):] in counts[k], return the smoothed + log-prob vector for that context. + - Else back off. + - Order 1 (unigram) always has a defined distribution (uniform prior smoothing). + + Smoothing is add-delta (delta=0.5) applied within each order's lookup — no + cross-order mixing, so the distribution is well-defined and normalized. +""" + +from __future__ import annotations +import math +from collections import Counter, defaultdict +from typing import List, Optional +import numpy as np + +try: + import torch +except ImportError: + torch = None + + +class CausalNGram: + """Exact non-hashed causal n-gram with backoff. See module docstring.""" + + def __init__(self, vocab_size: int, order: int = 4, delta: float = 0.5, + min_context_count: int = 2): + """ + Args: + vocab_size: size of Sigma (token alphabet). + order: max n-gram order (K). Backoff goes K -> K-1 -> ... -> 1. + delta: add-delta smoothing parameter. + min_context_count: minimum total observations of a context before we + trust it (else back off to shorter order). Helps avoid the + degenerate order-82 failure mode of closed PRs. + """ + assert order >= 1 + assert vocab_size > 0 + self.V = vocab_size + self.K = order + self.delta = delta + self.min_ctx = min_context_count + + # counts[k] maps context tuple of length k-1 -> Counter of next tokens. + # counts[1] uses the empty tuple () as its only key. + self.counts = {k: defaultdict(Counter) for k in range(1, order + 1)} + + # Totals per context (for normalization without re-summing the counter). + self.totals = {k: defaultdict(int) for k in range(1, order + 1)} + + # Frozen snapshot (for update-after-score): + # After call to `freeze()`, lookups use the snapshot; subsequent `add()` + # calls update the live counts only. `thaw()` re-points lookups to live. + self._frozen_counts = None + self._frozen_totals = None + + # Cached log-prob vectors, invalidated when a new snapshot is taken. + self._cache: dict = {} + + # ------------------------------------------------------------------ + # Bookkeeping + # ------------------------------------------------------------------ + + def add_token(self, history: List[int], token: int) -> None: + """Accumulate one (history, token) observation into LIVE counts. + + history[-(k-1):] is used as the context for order k. Updates unigram + through order K in one shot. + """ + assert 0 <= token < self.V + for k in range(1, self.K + 1): + # context is the last (k-1) tokens of history + ctx_len = k - 1 + if ctx_len == 0: + ctx = () + else: + if len(history) < ctx_len: + continue # not enough history for this order + ctx = tuple(history[-ctx_len:]) + self.counts[k][ctx][token] += 1 + self.totals[k][ctx] += 1 + + def add_sequence(self, tokens: List[int]) -> None: + """Add a whole sequence. Equivalent to `add_token` called left-to-right.""" + for i, tok in enumerate(tokens): + self.add_token(tokens[:i], tok) + + def freeze(self) -> None: + """Snapshot current counts. Subsequent lookups use this snapshot. + + This is how we implement update-after-score: freeze before scoring, + then `add_token`/`add_sequence` to the live counts during/after scoring, + then `thaw()` to swap. + """ + # Deep copy is O(N) — fine for bounded cache sizes. Python dict copy + # is shallow but Counter copy via Counter(c) re-allocates. + self._frozen_counts = {k: {ctx: Counter(c) for ctx, c in d.items()} + for k, d in self.counts.items()} + self._frozen_totals = {k: dict(d) for k, d in self.totals.items()} + self._cache.clear() + + def thaw(self) -> None: + """Swap live counts into the "scoring" slot. Used at chunk boundary. + + Policy: at the end of a scoring region, the accumulated updates become + the new frozen snapshot for the NEXT region. Equivalent to calling + freeze() again but on the LIVE counts. + """ + self.freeze() + + # ------------------------------------------------------------------ + # Lookup (reads frozen snapshot, not live) + # ------------------------------------------------------------------ + + def _get_frozen(self, k: int, ctx: tuple): + if self._frozen_counts is None: + src = self.counts + tot = self.totals + else: + src = self._frozen_counts + tot = self._frozen_totals + return src[k].get(ctx, None), tot[k].get(ctx, 0) + + def log_probs(self, history: List[int]) -> np.ndarray: + """Return log_p(v | history) for all v in Sigma. Length = V. + + Walks backoff from order K down. First order where the context has + at least `min_ctx` observations is used. Unigram always available (we + fall through to a uniform if even unigram has no mass, which shouldn't + happen after any real data). + + Output is a FULL normalized log-distribution: exp(log_probs).sum() == 1. + """ + cache_key = tuple(history[-(self.K - 1):]) if self.K > 1 else () + if cache_key in self._cache: + return self._cache[cache_key] + + log_p = None + for k in range(self.K, 0, -1): + ctx_len = k - 1 + if ctx_len == 0: + ctx = () + elif len(history) < ctx_len: + continue + else: + ctx = tuple(history[-ctx_len:]) + counter, total = self._get_frozen(k, ctx) + if total >= self.min_ctx: + # Add-delta smoothing on full vocab + denom = total + self.delta * self.V + vec = np.full(self.V, self.delta / denom, dtype=np.float64) + if counter is not None: + for tok, c in counter.items(): + vec[tok] = (c + self.delta) / denom + log_p = np.log(vec) + break + if log_p is None: + # Uniform fallback (e.g., empty cache) + log_p = np.full(self.V, -math.log(self.V), dtype=np.float64) + + self._cache[cache_key] = log_p + return log_p + + # ------------------------------------------------------------------ + # Batch API for the eval loop + # ------------------------------------------------------------------ + + def batch_log_probs(self, context_tensor, device=None): + """Given a (B, T) tensor of token ids where position t in each row is the + context for predicting position t+1, return a (B, T, V) tensor of + log-probs. Only the FROZEN snapshot is used. + + Implementation: O(B*T) Python loop over positions. Acceptable for + prototype/small-model runs. For the 8xH100 competition eval we'll + need to port this to a GPU kernel (or at least cache per-context). + """ + assert torch is not None, "torch required for batch_log_probs" + B, T = context_tensor.shape + out = torch.empty((B, T, self.V), dtype=torch.float32, + device=device or context_tensor.device) + ctx_cpu = context_tensor.detach().cpu().tolist() + for b in range(B): + row = ctx_cpu[b] + for t in range(T): + # history = row[:t+1] (tokens 0..t inclusive become context for t+1) + hist = row[max(0, t + 1 - (self.K - 1)):t + 1] + lp = self.log_probs(hist) + out[b, t] = torch.from_numpy(lp).to(out.dtype) + return out + + # ------------------------------------------------------------------ + # Stats + # ------------------------------------------------------------------ + + def size_bytes(self) -> int: + """Rough estimate of Python memory used by count tables.""" + total = 0 + for k in range(1, self.K + 1): + total += sum(len(c) * 32 for c in self.counts[k].values()) # ~32B per entry + total += len(self.counts[k]) * 80 # dict overhead + return total + + def unique_contexts(self) -> dict: + return {k: len(self.counts[k]) for k in range(1, self.K + 1)} diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/extended_analysis.py b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/extended_analysis.py new file mode 100644 index 0000000000..ae3c295b6e --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/extended_analysis.py @@ -0,0 +1,283 @@ +""" +Extended analysis — compares: + +(1) PER-DOC cache (resets at each document boundary — what kill_switch measured) +(2) GLOBAL cache (accumulates across all docs — closer to what eval_val_ttt + actually does, since the val stream is a single concatenated sequence) + +Plus: an alpha sweep simulation using a FROZEN BIGRAM proxy for the "neural" +model. This is a cheap approximation — it tells us RELATIVE gain (ngram vs no +ngram for the same model), not absolute BPB. Gives us an alpha-sensitivity +curve without training anything. + +Metric: measured BPB reduction from adding the n-gram on top of bigram. + +This is a LOCAL, ZERO-COST experiment. Running overnight. +""" +from __future__ import annotations +import argparse +import math +import sys +import time +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + + +# ---------- data loading ---------- + +def load_val_tokens(path: Path): + header_bytes = 256 * 4 + tokens = np.fromfile(path, dtype=' start: + docs.append(tokens[start:b]) + start = b + 1 + if start < len(tokens): + docs.append(tokens[start:]) + return docs + + +# ---------- bigram "neural" proxy ---------- + +class BigramLM: + """Simple add-1 bigram LM trained ONCE on the val set itself before + evaluation. This is NOT legal for a real submission — it's only a stand-in + for the "neural" model so we can measure the RELATIVE gain of the n-gram + addition. + + We then evaluate its BPB WITH and WITHOUT an additive n-gram contribution. + """ + + def __init__(self, vocab_size: int): + self.V = vocab_size + self.counts = defaultdict(Counter) # prev_token -> Counter(next_token) + self.totals = defaultdict(int) + self._log_probs = None # (V, V) tensor after fit + + def fit(self, tokens: np.ndarray): + """Fit unconditionally on all tokens. For a fair comparison this is a + cheat (it sees the val tokens), but we're only using the DIFFERENCE + with and without n-gram, so the absolute BPB doesn't matter.""" + for i in range(len(tokens) - 1): + prev = int(tokens[i]) + nxt = int(tokens[i + 1]) + self.counts[prev][nxt] += 1 + self.totals[prev] += 1 + # Precompute log-prob matrix with add-1 smoothing + self._log_probs = np.full((self.V, self.V), -math.log(self.V), + dtype=np.float32) + for prev, counter in self.counts.items(): + total = self.totals[prev] + denom = total + self.V + for tok in range(self.V): + c = counter.get(tok, 0) + self._log_probs[prev, tok] = math.log((c + 1) / denom) + + def log_probs(self, prev_token: int) -> np.ndarray: + return self._log_probs[prev_token] + + +# ---------- n-gram cache (for BOTH per-doc and global modes) ---------- + +class ExactNGramCache: + """Exact counts with add-delta smoothing and order-K backoff.""" + + def __init__(self, vocab_size: int, order: int, delta: float = 0.5, + min_ctx: int = 2): + self.V = vocab_size + self.K = order + self.delta = delta + self.min_ctx = min_ctx + self.counts = {k: defaultdict(Counter) for k in range(1, order + 1)} + self.totals = {k: defaultdict(int) for k in range(1, order + 1)} + + def add(self, history: list, tok: int): + for k in range(1, self.K + 1): + ctx = tuple(history[-(k - 1):]) if k > 1 else () + if k > 1 and len(history) < k - 1: + continue + self.counts[k][ctx][tok] += 1 + self.totals[k][ctx] += 1 + + def clear(self): + self.counts = {k: defaultdict(Counter) for k in range(1, self.K + 1)} + self.totals = {k: defaultdict(int) for k in range(1, self.K + 1)} + + def log_probs(self, history: list) -> np.ndarray: + """Full-vocab log-prob vector via backoff from K -> 1.""" + for k in range(self.K, 0, -1): + ctx_len = k - 1 + if ctx_len == 0: + ctx = () + elif len(history) < ctx_len: + continue + else: + ctx = tuple(history[-ctx_len:]) + total = self.totals[k].get(ctx, 0) + if total >= self.min_ctx: + counter = self.counts[k].get(ctx) + denom = total + self.delta * self.V + vec = np.full(self.V, self.delta / denom, dtype=np.float32) + if counter: + for tok, c in counter.items(): + vec[tok] = (c + self.delta) / denom + return np.log(vec) + return np.full(self.V, -math.log(self.V), dtype=np.float32) + + +# ---------- experiments ---------- + +def simulate_bpb(tokens: np.ndarray, bigram: BigramLM, + ngram: ExactNGramCache | None, + alpha: float, + mode: str = "per_doc", + doc_boundaries: list | None = None, + update_after_score: bool = True, + verbose_every: int = 0) -> dict: + """Measure per-token NLL under the blend `bigram + alpha * ngram`. + + Mode: + "per_doc": reset the n-gram cache at each document boundary + "global": never reset + "none": no n-gram, just bigram + + Returns a dict with nll_sum, token_count, and derived mean loss + BPB. + Note: since we're working on tokens not bytes, "BPB" here is actually + bits-per-TOKEN. Useful for relative comparison only. + """ + nll_sum = 0.0 + token_count = 0 + + running_hist = [] # rolling context for n-gram lookups + if ngram is not None: + ngram.clear() + + N = len(tokens) + for t in range(1, N): + prev = int(tokens[t - 1]) + tgt = int(tokens[t]) + + # Reset n-gram cache on doc boundary (per_doc mode) + if mode == "per_doc" and doc_boundaries is not None and t in doc_boundaries: + ngram.clear() if ngram is not None else None + running_hist = [] + + # Compute log-prob of target under the blend + log_p_bigram = bigram.log_probs(prev) + log_p_bigram_shifted = log_p_bigram - log_p_bigram.max() # for stability + if ngram is not None and alpha != 0.0: + log_p_ng = ngram.log_probs(running_hist) + # Blend as ADDITIVE LOGITS: logit = log_p_bigram + alpha*log_p_ngram + # then softmax. We approximate logits ≈ log_p since bigram already + # outputs log-probs. + blended = log_p_bigram_shifted + alpha * log_p_ng + # Softmax to get normalized distribution + blended -= blended.max() + e = np.exp(blended) + p = e / e.sum() + nll = -math.log(max(p[tgt], 1e-30)) + else: + nll = -log_p_bigram[tgt] + + nll_sum += nll + token_count += 1 + + # Update the n-gram AFTER scoring (respects C3) + if ngram is not None and update_after_score: + ngram.add(running_hist, tgt) + + running_hist.append(tgt) + if len(running_hist) > ngram.K - 1 if ngram is not None else 0: + running_hist = running_hist[-(ngram.K - 1):] + + if verbose_every and token_count % verbose_every == 0: + current = nll_sum / token_count / math.log(2) + print(f" ... {token_count}/{N - 1} bits/tok={current:.4f}", + file=sys.stderr) + + mean_nll = nll_sum / max(token_count, 1) + return { + "nll_sum": nll_sum, + "token_count": token_count, + "mean_nll": mean_nll, + "bits_per_tok": mean_nll / math.log(2), + } + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--val", type=Path, + default=Path("data/datasets/fineweb10B_sp1024/fineweb_val_000000.bin")) + ap.add_argument("--max-tokens", type=int, default=2_000_000, + help="Limit tokens for speed (2M default, full run is 62M)") + ap.add_argument("--orders", type=str, default="4,5") + ap.add_argument("--alphas", type=str, default="0,0.1,0.3,0.5,1.0,1.5") + args = ap.parse_args() + + print(f"Loading {args.val}...", file=sys.stderr) + tokens = load_val_tokens(args.val) + if args.max_tokens and args.max_tokens < len(tokens): + tokens = tokens[:args.max_tokens] + print(f" using {len(tokens):,} tokens", file=sys.stderr) + + # Segment doc boundaries (indices into tokens where a new doc starts) + bos = 1 + doc_starts = set() + for i, t in enumerate(tokens): + if t == bos: + doc_starts.add(i + 1) + + vocab_size = 1024 + print("Fitting bigram baseline...", file=sys.stderr) + t0 = time.time() + bg = BigramLM(vocab_size=vocab_size) + bg.fit(tokens) + print(f" bigram fit in {time.time() - t0:.1f}s", file=sys.stderr) + + # Baseline: bigram only, no n-gram contribution + print("\n=== BASELINE: bigram only (no n-gram) ===", file=sys.stderr) + t0 = time.time() + base = simulate_bpb(tokens, bg, ngram=None, alpha=0.0, mode="none", + verbose_every=500_000) + print(f" time {time.time() - t0:.1f}s bits/tok={base['bits_per_tok']:.5f}") + baseline_bits = base["bits_per_tok"] + + orders = [int(x) for x in args.orders.split(",")] + alphas = [float(x) for x in args.alphas.split(",")] + + print("\n=== PER-DOC CACHE vs GLOBAL CACHE — alpha sweep ===") + rows = [] + rows.append(f"{'order':>5} {'mode':>8} {'alpha':>6} {'bits/tok':>10} {'delta':>10}") + rows.append("-" * 50) + for order in orders: + for mode in ["per_doc", "global"]: + for alpha in alphas: + if alpha == 0.0 and mode != "global": + continue # alpha=0 is mode-independent + ng = ExactNGramCache(vocab_size=vocab_size, order=order, + delta=0.5, min_ctx=2) + t0 = time.time() + res = simulate_bpb(tokens, bg, ngram=ng, alpha=alpha, + mode=mode, doc_boundaries=doc_starts, + update_after_score=True, verbose_every=0) + dt = time.time() - t0 + delta = res['bits_per_tok'] - baseline_bits + rows.append(f"{order:>5} {mode:>8} {alpha:>6.2f} {res['bits_per_tok']:>10.5f} {delta:>+10.5f}") + print(f" order={order} {mode} alpha={alpha:.2f} " + f"bits/tok={res['bits_per_tok']:.5f} delta={delta:+.5f} ({dt:.0f}s)", + file=sys.stderr) + for r in rows: + print(r) + + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/kill_switch_analysis.py b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/kill_switch_analysis.py new file mode 100644 index 0000000000..72b71cb72c --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/kill_switch_analysis.py @@ -0,0 +1,238 @@ +""" +Kill-switch analysis for the causal n-gram approach. + +Answers: does the sp1024 FineWeb val set have enough long-range n-gram +repetition to justify an eval-time cache, or should we pivot? + +Numerical GO/NO-GO gates (my thresholds): + GO: at least 5% of scored tokens are in positions where a confident + order-4 match exists in history AND that match is > 2048 tokens back + (i.e., outside the neural attention window). + GO: theoretical BPB upper bound (assuming cache predicts those positions + perfectly) > 0.003 nats. + NO-GO: otherwise. + +Reads sp1024 val shard directly; no torch, no model, no pod. +""" +from __future__ import annotations +import argparse +import math +import sys +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + + +def load_val_tokens(path: Path) -> np.ndarray: + """Load a challenge .bin shard: 256 int32 header + uint16 tokens.""" + header_bytes = 256 * 4 + hdr = np.fromfile(path, dtype=' list[np.ndarray]: + """Split the token stream by EOS. Returns list of document token arrays + (without the trailing EOS).""" + boundaries = np.nonzero(tokens == eos_id)[0] + docs = [] + start = 0 + for b in boundaries: + if b > start: + docs.append(tokens[start:b]) + start = b + 1 + if start < len(tokens): + docs.append(tokens[start:]) + return docs + + +def analyze_doc_lengths(docs: list[np.ndarray]) -> dict: + lens = np.array([len(d) for d in docs]) + total_tokens = int(lens.sum()) + return { + "num_docs": len(docs), + "total_tokens": total_tokens, + "mean_len": float(lens.mean()), + "median_len": float(np.median(lens)), + "p90_len": float(np.percentile(lens, 90)), + "p99_len": float(np.percentile(lens, 99)), + "max_len": int(lens.max()), + "tokens_in_docs_gt_2048": int(lens[lens > 2048].sum()), + "frac_tokens_in_docs_gt_2048": float(lens[lens > 2048].sum() / total_tokens), + "tokens_in_docs_gt_4096": int(lens[lens > 4096].sum()), + "frac_tokens_in_docs_gt_4096": float(lens[lens > 4096].sum() / total_tokens), + "tokens_beyond_2048_in_long_docs": int(np.maximum(lens - 2048, 0).sum()), + "frac_tokens_beyond_2048": float(np.maximum(lens - 2048, 0).sum() / total_tokens), + } + + +def analyze_ngram_repetition(docs: list[np.ndarray], order: int, + max_docs: int | None = None, + report_every: int = 5000) -> dict: + """For each scored token position (t) in each doc, check whether the order-K + context (x_{t-K+1}..x_{t-1}) has been seen before at position p < t, and + whether the (context, x_t) pair was observed (i.e., would the cache predict + x_t exactly). + + Metrics: + - hit_rate: % of positions where order-K context was seen earlier + - correct_hit_rate: % of positions where order-K context was seen AND + the majority predicted token matches x_t (cache would be "right") + - longrange_hit_rate: % of positions where context was seen earlier + AND the earliest match is > 2048 tokens back (outside neural window) + - longrange_correct_rate: longrange hit AND majority matches x_t + - mass_on_target: sum of p_cache(x_t) across all positions (a proxy + for the theoretical BPB upper bound gain) + """ + if max_docs is not None: + docs = docs[:max_docs] + + positions_total = 0 + hit_positions = 0 + correct_hits = 0 + longrange_positions = 0 + longrange_hits = 0 + longrange_correct = 0 + # Sum of log p_cache(x_t) when cache had a hit (smoothed) + sum_log_p_cache = 0.0 + # Sum of log p_uniform(x_t) across the same positions for baseline + vocab_size_approx = 1024 # sp1024 + log_uniform = -math.log(vocab_size_approx) + + for d_idx, doc in enumerate(docs): + # Per-doc cache: counts[context_tuple] -> Counter({next_token: count, ...}) + # Also store the FIRST position where the context was seen (for long-range check) + cache: dict = {} + first_pos: dict = {} + dl = len(doc) + for t in range(dl): + positions_total += 1 + if t < order - 1: + continue # not enough history for the context + ctx = tuple(int(x) for x in doc[t - (order - 1):t]) + tgt = int(doc[t]) + if ctx in cache: + hit_positions += 1 + counter = cache[ctx] + total = sum(counter.values()) + # MLE prediction with add-1 smoothing + c = counter.get(tgt, 0) + p_tgt = (c + 1) / (total + vocab_size_approx) + sum_log_p_cache += math.log(p_tgt) + + most_common = counter.most_common(1)[0][0] + if most_common == tgt: + correct_hits += 1 + + # Long-range check: earliest observation of this context + earliest = first_pos[ctx] + if (t - earliest) > 2048: + longrange_positions += 1 + if most_common == tgt: + longrange_correct += 1 + # Update the cache with this (context, token) observation + if ctx not in cache: + cache[ctx] = Counter() + first_pos[ctx] = t + cache[ctx][tgt] += 1 + + if (d_idx + 1) % report_every == 0: + print(f" ... processed {d_idx + 1}/{len(docs)} docs " + f"({positions_total} positions, hits={hit_positions})", + file=sys.stderr) + + # Average log-prob for hit positions + mean_log_p_cache_hits = (sum_log_p_cache / hit_positions) if hit_positions else 0.0 + + # Theoretical BPB upper bound assuming cache always correct on "correct hits" + # ... this is imprecise because we don't know the neural model's p at those + # positions. Report the RAW entropy reduction available as a proxy: + # BPB saved ~= (hit_positions / positions_total) * (mean_log_p_cache_hits - log_uniform) / log(2) + # This is nats converted to bits-per-token. + + if hit_positions: + bpt_savings_vs_uniform = ( + (hit_positions / positions_total) * + (mean_log_p_cache_hits - log_uniform) / math.log(2) + ) + else: + bpt_savings_vs_uniform = 0.0 + + return { + "order": order, + "positions_total": positions_total, + "hit_positions": hit_positions, + "hit_rate": hit_positions / max(positions_total, 1), + "correct_hits": correct_hits, + "correct_hit_rate": correct_hits / max(positions_total, 1), + "correct_rate_given_hit": correct_hits / max(hit_positions, 1), + "longrange_positions": longrange_positions, + "longrange_rate": longrange_positions / max(positions_total, 1), + "longrange_correct": longrange_correct, + "longrange_correct_rate": longrange_correct / max(positions_total, 1), + "mean_log_p_cache_on_hit": mean_log_p_cache_hits, + "bpt_upper_bound_vs_uniform_bits": bpt_savings_vs_uniform, + } + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--val", type=Path, + default=Path("data/datasets/fineweb10B_sp1024/fineweb_val_000000.bin")) + ap.add_argument("--max-docs", type=int, default=None, + help="Limit docs for faster iteration") + ap.add_argument("--orders", type=str, default="3,4,5", + help="Comma-separated orders to check") + args = ap.parse_args() + + if not args.val.exists(): + print(f"Missing val shard: {args.val}", file=sys.stderr) + sys.exit(2) + + print(f"Loading val shard {args.val}...", file=sys.stderr) + tokens, hdr = load_val_tokens(args.val) + print(f" {len(tokens):,} tokens", file=sys.stderr) + + # FineWeb .bin uses BOS (id 1) as the document separator — empirical check + # showed id=1 appears ~870x per 1M tokens (~1148-token docs, matches FineWeb + # median) while id=2 () has zero occurrences. + bos = 1 + docs = segment_documents(tokens, eos_id=bos) + print(f" {len(docs):,} documents", file=sys.stderr) + + doc_stats = analyze_doc_lengths(docs) + print("\n=== DOCUMENT LENGTH STATS ===") + for k, v in doc_stats.items(): + if isinstance(v, float) and "frac" in k: + print(f" {k}: {v:.4%}") + elif isinstance(v, float): + print(f" {k}: {v:,.1f}") + else: + print(f" {k}: {v:,}") + + orders = [int(x) for x in args.orders.split(",")] + for order in orders: + print(f"\n=== ORDER-{order} REPETITION ANALYSIS (per-doc cache) ===") + stats = analyze_ngram_repetition(docs, order=order, max_docs=args.max_docs) + for k, v in stats.items(): + if "rate" in k or "frac" in k: + print(f" {k}: {v:.4%}") + elif isinstance(v, float): + print(f" {k}: {v:.6f}") + else: + print(f" {k}: {v:,}") + + # GO/NO-GO interpretation + print() + go_crit_1 = stats["longrange_rate"] >= 0.05 + go_crit_2 = stats["bpt_upper_bound_vs_uniform_bits"] >= 0.003 + print(f" GO criterion 1 (longrange_rate >= 5%): " + f"{'PASS' if go_crit_1 else 'FAIL'} ({stats['longrange_rate']:.2%})") + print(f" GO criterion 2 (bpt upper bound vs uniform >= 0.003): " + f"{'PASS' if go_crit_2 else 'FAIL'} " + f"({stats['bpt_upper_bound_vs_uniform_bits']:.6f} bits)") + + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/legality_harness.py b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/legality_harness.py new file mode 100644 index 0000000000..8253d6e82e --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/legality_harness.py @@ -0,0 +1,287 @@ +""" +Legality harness for CausalNGram + additive-logit blend. + +Tests the four conditions from Issue #1017 empirically. Each test is a small +adversarial probe — if the code is legal, all tests pass. If any test fails, +STOP and fix before any further spend. + +Usage: + python legality_harness.py # runs all tests + python legality_harness.py --verbose # prints per-test details +""" + +from __future__ import annotations +import sys +import math +import random +import numpy as np + +# Repo-local import +sys.path.insert(0, ".") +from causal_ngram import CausalNGram + + +def _blend_logits(neural_logits: np.ndarray, ngram_log_p: np.ndarray, + alpha: float) -> np.ndarray: + """The production blend: additive logits then softmax. + + Returns the full normalized distribution (not log, just probs).""" + logits = neural_logits + alpha * ngram_log_p + logits -= logits.max() + e = np.exp(logits) + return e / e.sum() + + +def test_c1_strict_causal(): + """Condition 1: p_t depends only on history x_1..x_{t-1}, never on x_t or later. + + Adversarial probe: build the cache with one sequence, query position t, then + flip x_t and x_{t+1} to arbitrary values, re-query position t. Result must + be bit-identical. + """ + V = 32 + rng = random.Random(0) + seq = [rng.randrange(V) for _ in range(500)] + ng = CausalNGram(vocab_size=V, order=4) + # Populate from the whole sequence (simulating "cache built from all tokens + # scored so far"). Freeze to lock the snapshot. + ng.add_sequence(seq) + ng.freeze() + + t = 200 + history_before = seq[:t] + lp_before = ng.log_probs(history_before).copy() + + # Flip the future (tokens after t). Re-query — must be identical. + seq_mutated = seq[:t] + [(x + 7) % V for x in seq[t:]] + lp_after = ng.log_probs(seq_mutated[:t]) + + assert np.allclose(lp_before, lp_after), \ + "C1 violation: lookup depends on tokens at or after position t" + return True + + +def test_c2_full_vocab_normalization(): + """Condition 2: blend is a full distribution over Sigma that sums to 1. + + Adversarial probe: compute blend probs for 50 random contexts and assert + (a) sum == 1, (b) all entries >= 0, (c) shape == (V,). + """ + V = 64 + rng = random.Random(1) + seq = [rng.randrange(V) for _ in range(1000)] + ng = CausalNGram(vocab_size=V, order=4) + ng.add_sequence(seq) + ng.freeze() + + failures = [] + for trial in range(50): + t = rng.randrange(5, len(seq) - 1) + hist = seq[:t] + lp = ng.log_probs(hist) + assert lp.shape == (V,), f"n-gram log-prob shape wrong: {lp.shape}" + assert np.all(np.isfinite(lp)), "n-gram log-probs have nan/inf" + assert np.allclose(np.exp(lp).sum(), 1.0, atol=1e-9), \ + f"n-gram distribution not normalized: sum={np.exp(lp).sum()}" + + # Now blend with a random neural logits vector + neural = np.asarray([rng.gauss(0, 2) for _ in range(V)]) + blend = _blend_logits(neural, lp, alpha=0.5) + assert blend.shape == (V,) + assert np.allclose(blend.sum(), 1.0, atol=1e-9), \ + f"blend not normalized: sum={blend.sum()}" + assert np.all(blend >= 0), "blend has negative probs" + + return True + + +def test_c2_xt_independence(): + """Condition 2 (subtler): p_t(v) for any v must be computable WITHOUT knowing x_t. + + Adversarial probe: compute the full blend, then for each target v, verify + it equals what you'd get if you computed the blend "as if the answer were v". + If the mechanism short-circuits on the observed token, this catches it. + """ + V = 32 + rng = random.Random(2) + seq = [rng.randrange(V) for _ in range(500)] + ng = CausalNGram(vocab_size=V, order=4) + ng.add_sequence(seq) + ng.freeze() + + t = 100 + hist = seq[:t] + lp = ng.log_probs(hist) + neural = np.asarray([rng.gauss(0, 2) for _ in range(V)]) + blend_full = _blend_logits(neural, lp, alpha=0.5) + + # For our additive-logit design, there's no x_t in the compute path at all. + # This is trivially true — we just assert the blend was computed without + # reference to any single token, by computing it twice with "different + # assumed targets" and checking identity. + blend_full_again = _blend_logits(neural, lp, alpha=0.5) + assert np.allclose(blend_full, blend_full_again), \ + "blend is non-deterministic (suggests hidden state dependency on x_t)" + return True + + +def test_c3_score_before_update(): + """Condition 3: scoring at position t must use a state that was NOT updated + with x_t yet. + + Adversarial probe: simulate a chunk of 10 tokens. Freeze the cache, compute + scores for all 10 using the frozen snapshot, THEN add those 10 tokens. + Assert: the log-probs used during scoring are identical to the log-probs + that would be returned by a fresh cache state that has NEVER seen those + tokens. + """ + V = 32 + rng = random.Random(3) + prior = [rng.randrange(V) for _ in range(200)] + chunk = [rng.randrange(V) for _ in range(10)] + + ng = CausalNGram(vocab_size=V, order=4) + ng.add_sequence(prior) + ng.freeze() # snapshot reflects only `prior` + + # Reference: a parallel cache that also only has `prior`, never updated. + ref = CausalNGram(vocab_size=V, order=4) + ref.add_sequence(prior) + ref.freeze() + + # Score all chunk positions using the snapshot + scored_log_probs = [] + for i in range(len(chunk)): + hist = prior + chunk[:i] + scored_log_probs.append(ng.log_probs(hist)) + + # Update the live counts with the chunk tokens (simulating add-after-score) + for i, tok in enumerate(chunk): + ng.add_token(prior + chunk[:i], tok) + # Note: we do NOT re-freeze yet — the snapshot is still the pre-chunk one. + + # Compare: the scored log-probs should match what ref returns (ref never + # saw any of the chunk tokens). + for i, lp in enumerate(scored_log_probs): + hist = prior + chunk[:i] + ref_lp = ref.log_probs(hist) + assert np.allclose(lp, ref_lp), \ + f"C3 violation: scoring position {i} used state that reflects x_t" + + return True + + +def test_c4_single_pass(): + """Condition 4: no rescoring. + + Adversarial probe: simulate two passes over the same token stream. Second + pass should NOT be allowed to use state built from the first. We enforce + this by structure: the eval loop is single-pass by construction. This test + just documents that no "refresh cache" or "second pass" API exists on the + CausalNGram class. + """ + attrs = dir(CausalNGram) + forbidden = {"rescore", "rebuild", "reset_for_second_pass", "two_pass"} + overlap = set(attrs) & forbidden + assert not overlap, f"Forbidden APIs present: {overlap}" + return True + + +def test_no_hashing(): + """Extra: #993 rule — no hashed cache. Verify counts are keyed by exact + context tuples, not by a hash function. + """ + ng = CausalNGram(vocab_size=16, order=3) + ng.add_sequence([1, 2, 3, 4, 5, 1, 2, 3, 4, 5]) + # Order-3 context for predicting token at position 3 is (1, 2). + # Order-3 context for position 4 is (2, 3). These must be DISTINCT keys. + ctx12 = (1, 2) + ctx23 = (2, 3) + assert ctx12 in ng.counts[3], "expected exact context key missing" + assert ctx23 in ng.counts[3], "expected exact context key missing" + # Sanity: Python dict keys are tuples, not integers from a hash + for k in ng.counts[3].keys(): + assert isinstance(k, tuple), f"non-tuple key {k!r} — might be hashed" + return True + + +def test_blend_nonneg_and_finite(): + """Sanity: blend never produces negative or non-finite probabilities.""" + V = 128 + rng = random.Random(4) + seq = [rng.randrange(V) for _ in range(2000)] + ng = CausalNGram(vocab_size=V, order=5) + ng.add_sequence(seq) + ng.freeze() + + for trial in range(100): + t = rng.randrange(10, len(seq) - 1) + hist = seq[:t] + lp = ng.log_probs(hist) + neural = np.asarray([rng.gauss(0, 3) for _ in range(V)]) + for alpha in [0.0, 0.1, 0.5, 1.0, 2.0]: + blend = _blend_logits(neural, lp, alpha=alpha) + assert np.all(np.isfinite(blend)) + assert np.all(blend >= 0) + assert abs(blend.sum() - 1.0) < 1e-9 + return True + + +def test_backoff_fallthrough_unigram(): + """Order K context not seen -> back off to K-1, then K-2, ..., unigram always + available. Verify the walk behaves correctly. + """ + V = 16 + ng = CausalNGram(vocab_size=V, order=4, min_context_count=2) + # Only put one unigram-level observation + ng.add_token([], 3) + ng.add_token([3], 5) # order-2 context (3,) -> token 5 + ng.freeze() + + # Query with a totally unseen order-3 context + lp = ng.log_probs([1, 2, 3]) # order-3 context would be (1,2,3) — not seen + # After backoff, it should land on order-1 (unigram) or a fallback + assert lp.shape == (V,) + assert np.allclose(np.exp(lp).sum(), 1.0) + return True + + +def main(verbose=False): + tests = [ + ("C1 strict causal", test_c1_strict_causal), + ("C2 full-vocab normalization", test_c2_full_vocab_normalization), + ("C2 x_t independence", test_c2_xt_independence), + ("C3 score-before-update", test_c3_score_before_update), + ("C4 single pass", test_c4_single_pass), + ("no-hashing (ruling #993)", test_no_hashing), + ("blend non-negative + finite", test_blend_nonneg_and_finite), + ("backoff fallthrough to unigram", test_backoff_fallthrough_unigram), + ] + passed = 0 + failed = [] + for name, fn in tests: + try: + fn() + passed += 1 + if verbose: + print(f" PASS {name}") + except AssertionError as e: + failed.append((name, str(e))) + print(f" FAIL {name}: {e}") + except Exception as e: + failed.append((name, repr(e))) + print(f" ERROR {name}: {e!r}") + + print(f"\n{passed}/{len(tests)} tests passed") + if failed: + print("\nFAILURES — DO NOT proceed to training until these are fixed:") + for name, msg in failed: + print(f" - {name}: {msg}") + return 1 + print("All legality conditions verified. Safe to proceed.") + return 0 + + +if __name__ == "__main__": + verbose = "--verbose" in sys.argv or "-v" in sys.argv + sys.exit(main(verbose=verbose)) diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/localized_delta.py b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/localized_delta.py new file mode 100644 index 0000000000..ead92a1d82 --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/localized_delta.py @@ -0,0 +1,301 @@ +""" +Localized-delta analysis: break the n-gram BPB improvement down by where +the scored token is in its document (early vs late) and whether the context +was seen recently (<2048 back) or long-range (>2048 back). + +This tells us WHERE the gain is coming from. If most of the delta is in the +"long-range" bucket, that's the signal that this technique is bringing in +information the neural model literally cannot see — which is what we want +(and what makes the delta robust at scale). + +If most of the delta is in the "short-range" bucket, the delta will likely +vanish on a well-trained model (which already captures short-range via +attention), and we should pivot. + +Implementation: a lightweight eval loop that uses a fixed frozen model and +tags each scored position with its doc-position and cache-range-class, then +computes per-bucket BPB with and without the n-gram contribution. +""" +from __future__ import annotations +import argparse +import json +import math +import os +import sys +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from ngram_eval import CausalNGram +from tiny_train import TinyGPT, load_tokens, pick_device + + +def run_localized_analysis(val_path: Path, held_out_frac: float, + eval_cap: int, seq_len: int, stride: int, + chunk_tokens: int, dim: int, layers: int, + steps: int, batch: int, lr: float, + order: int, alpha: float, seed: int): + """Train a tiny model, then run ONE eval pass with fine-grained per-position + bucketing. Returns per-bucket nll sums and token counts for both the + baseline and the n-gram blend.""" + device = pick_device() + torch.manual_seed(seed) + + tokens = load_tokens(val_path) + split = int(len(tokens) * (1 - held_out_frac)) + train_tokens = tokens[:split][:4_000_000] + eval_tokens = tokens[split:split + eval_cap] + vocab_size = 1024 + + # --- Train --- + model = TinyGPT(vocab_size=vocab_size, dim=dim, n_layers=layers, + n_heads=4, seq_len=seq_len).to(device) + opt = torch.optim.AdamW(model.parameters(), lr=lr, betas=(0.9, 0.95), + weight_decay=0.01) + rng = np.random.default_rng(seed) + model.train() + for step in range(steps): + starts = rng.integers(0, len(train_tokens) - seq_len - 1, size=batch) + x = np.stack([train_tokens[s:s + seq_len] for s in starts]).astype(np.int64) + y = np.stack([train_tokens[s + 1:s + seq_len + 1] for s in starts]).astype(np.int64) + x_t = torch.from_numpy(x).to(device) + y_t = torch.from_numpy(y).to(device) + loss = model(x_t, y_t) + opt.zero_grad(set_to_none=True) + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + opt.step() + model.eval() + + # --- Eval with per-position bucketing --- + # Buckets: + # Range: "in_window" (cache hit at distance ≤ seq_len-1) vs + # "out_of_window" (distance > seq_len-1) vs + # "no_hit" (cache miss, context unseen) + # Doc position: measured as position_in_doc in {0-2047, 2048-4095, 4096+} + # + # For each bucket, accumulate: sum_nll_baseline, sum_nll_blend, count. + from collections import defaultdict + buckets = defaultdict(lambda: {"n": 0, "nll_base": 0.0, "nll_blend": 0.0}) + + context_size = seq_len - stride + total_tokens = len(eval_tokens) - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if ws + context_size < total_tokens] + + # Track global position -> doc boundaries + bos = 1 # FineWeb uses BOS as doc separator + doc_start_of = np.zeros(len(eval_tokens), dtype=np.int32) + last_start = 0 + for i, t in enumerate(eval_tokens): + if int(t) == bos: + last_start = i + 1 + doc_start_of[i] = last_start + + ng = CausalNGram(vocab_size=vocab_size, order=order, delta=0.5, + min_context_count=2) + ng.freeze() + + # We also keep a separate "first-seen distance" map: ctx_tuple -> last_pos + from collections import defaultdict as _dd + first_seen = {} # ctx -> global position of FIRST observation (for "range" classification at scoring) + + tokens_cpu = torch.from_numpy(eval_tokens).long() + num_chunks = max(1, (total_tokens + chunk_tokens - 1) // chunk_tokens) + chunk_windows = [[] for _ in range(num_chunks)] + for ws in window_starts: + wlen = min(ws + seq_len, total_tokens) - ws + s = 0 if ws == 0 else context_size + scored_start = ws + s + ci = min(scored_start // chunk_tokens, num_chunks - 1) + chunk_windows[ci].append(ws) + + with torch.no_grad(): + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_scored = [] + for bi in range(0, len(windows), batch): + batch_ws = windows[bi:bi + batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64) + wlens = [] + for i, ws in enumerate(batch_ws): + we = min(ws + seq_len, total_tokens) + wlen = we - ws + wlens.append(wlen) + chunk_tok = tokens_cpu[ws:we + 1] + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + x_batch_dev = x_batch.to(device) + y_batch_dev = y_batch.to(device) + + logits = model.forward_logits(x_batch_dev) + ngram_log_p = ng.batch_log_probs_torch(x_batch_dev).to(logits.dtype) + blended = logits + alpha * ngram_log_p + + nll_base = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch_dev.reshape(-1), reduction='none' + ).reshape(bsz, seq_len).detach().cpu().to(torch.float64) + nll_blend = F.cross_entropy( + blended.reshape(-1, blended.size(-1)).float(), + y_batch_dev.reshape(-1), reduction='none' + ).reshape(bsz, seq_len).detach().cpu().to(torch.float64) + + x_batch_np = x_batch.numpy().astype(np.int64) + y_batch_np = y_batch.numpy().astype(np.int64) + + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else context_size + for t in range(s, wlen): + gpos = ws + t # global position + # n-gram context for predicting y[t] is x[t-K+2:t+1] + ctx_start = max(0, t - (order - 1) + 1) + ctx_tail = tuple(int(x) for x in x_batch_np[i, ctx_start:t + 1]) + + # Range class: how far back was this context first seen? + if ctx_tail not in first_seen: + range_cls = "no_hit" + else: + dist = gpos - first_seen[ctx_tail] + if dist <= seq_len: + range_cls = "in_window" + else: + range_cls = "out_of_window" + + # Doc-position class + doc_start = int(doc_start_of[gpos]) + pos_in_doc = gpos - doc_start + if pos_in_doc < 2048: + dp_cls = "0-2047" + elif pos_in_doc < 4096: + dp_cls = "2048-4095" + else: + dp_cls = "4096+" + + key = (range_cls, dp_cls) + b = buckets[key] + b["n"] += 1 + b["nll_base"] += float(nll_base[i, t]) + b["nll_blend"] += float(nll_blend[i, t]) + + # Record for post-scoring update + chunk_scored.append((gpos, ctx_tail, int(y_batch_np[i, t]))) + + # Update n-gram AND first_seen after scoring the chunk + chunk_scored.sort() + for gpos, ctx_tail, tok in chunk_scored: + # Update first_seen: we record the position where this context + # was *already seen* before — which is any prior observation. + # For simplicity we use the position where we STORE the context + # (i.e., when we add this tok with THIS context). + if ctx_tail not in first_seen: + first_seen[ctx_tail] = gpos + # Update the n-gram + running = list(ctx_tail) + ng.add_token(tuple(running), tok) + ng.freeze() + + return buckets + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--val", type=Path, + default=Path("data/datasets/fineweb10B_sp1024/fineweb_val_000000.bin")) + ap.add_argument("--eval-cap", type=int, default=80_000) + ap.add_argument("--seq-len", type=int, default=256) + ap.add_argument("--stride", type=int, default=64) + ap.add_argument("--chunk-tokens", type=int, default=8192) + ap.add_argument("--dim", type=int, default=128) + ap.add_argument("--layers", type=int, default=2) + ap.add_argument("--steps", type=int, default=2500) + ap.add_argument("--batch", type=int, default=16) + ap.add_argument("--lr", type=float, default=3e-3) + ap.add_argument("--order", type=int, default=4) + ap.add_argument("--alpha", type=float, default=0.3) + ap.add_argument("--held-out-frac", type=float, default=0.2) + ap.add_argument("--seed", type=int, default=42) + ap.add_argument("--out", type=Path, default=Path("results_localized_delta.json")) + args = ap.parse_args() + + print(f"Training (dim={args.dim}, layers={args.layers}, steps={args.steps}) then " + f"running localized analysis @ order={args.order}, alpha={args.alpha}", + file=sys.stderr) + buckets = run_localized_analysis( + val_path=args.val, + held_out_frac=args.held_out_frac, + eval_cap=args.eval_cap, + seq_len=args.seq_len, + stride=args.stride, + chunk_tokens=args.chunk_tokens, + dim=args.dim, + layers=args.layers, + steps=args.steps, + batch=args.batch, + lr=args.lr, + order=args.order, + alpha=args.alpha, + seed=args.seed, + ) + + # Compute per-bucket deltas and totals + print(f"\n{'range':>15} {'doc_pos':>10} {'N':>8} {'bpt_base':>10} {'bpt_blend':>11} {'delta':>10} {'delta_x_N':>12}") + print("-" * 80) + + total_nll_base = 0.0 + total_nll_blend = 0.0 + total_n = 0 + + # Sort by range then doc_pos + for key in sorted(buckets.keys()): + b = buckets[key] + n = b["n"] + if n == 0: + continue + bpt_base = b["nll_base"] / n / math.log(2) + bpt_blend = b["nll_blend"] / n / math.log(2) + delta = bpt_blend - bpt_base + delta_weighted = delta * n + print(f"{key[0]:>15} {key[1]:>10} {n:>8} " + f"{bpt_base:>10.5f} {bpt_blend:>11.5f} {delta:>+10.5f} " + f"{delta_weighted:>+12.2f}") + total_nll_base += b["nll_base"] + total_nll_blend += b["nll_blend"] + total_n += n + + print("-" * 80) + overall_bpt_base = total_nll_base / total_n / math.log(2) + overall_bpt_blend = total_nll_blend / total_n / math.log(2) + overall_delta = overall_bpt_blend - overall_bpt_base + print(f"{'OVERALL':>15} {'':>10} {total_n:>8} {overall_bpt_base:>10.5f} " + f"{overall_bpt_blend:>11.5f} {overall_delta:>+10.5f}") + + result = { + "overall": { + "n": total_n, + "bpt_base": overall_bpt_base, + "bpt_blend": overall_bpt_blend, + "delta": overall_delta, + }, + "buckets": {f"{k[0]}__{k[1]}": { + "n": v["n"], + "nll_base": v["nll_base"], + "nll_blend": v["nll_blend"], + "bpt_base": v["nll_base"] / v["n"] / math.log(2) if v["n"] else 0, + "bpt_blend": v["nll_blend"] / v["n"] / math.log(2) if v["n"] else 0, + } for k, v in buckets.items()}, + } + args.out.write_text(json.dumps(result, indent=2)) + print(f"\nWrote {args.out}") + + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/ngram_eval.py b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/ngram_eval.py new file mode 100644 index 0000000000..4383eafad6 --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/ngram_eval.py @@ -0,0 +1,420 @@ +""" +Causal N-gram Eval Integration for #1493 stack. + +Provides `eval_val_ttt_with_ngram` — a drop-in replacement for `eval_val_ttt` +that injects a causal n-gram cache as an additive-logit contribution to the +neural model's output. + +LEGALITY (matches causal_ngram.py module docstring): + C1 strict causal: n-gram state at scoring time t reflects only tokens < t. + C2 full normalized: blend is `softmax(logits_neural + alpha * log_p_ngram)` + over full vocab. Normalization holds over actual tokens. + C3 score-before-update: cache is frozen at chunk start, scored under + inference_mode, updated only after all windows in the chunk have been + scored. + C4 single pass: one left-to-right traversal, no rescoring. + +INTEGRATION POINT: after `compiled_logits(x_batch)` and before +`F.cross_entropy`, we compute `log_p_ngram` for every (b, t) position and add +`alpha * log_p_ngram` to the neural logits. The softmax inside cross-entropy +then produces a valid normalized distribution. + +PERFORMANCE: + - Prototype path: pure Python context-tuple lookup, slow but correct. Used + for local prototype and small-model tests. + - Fast path (TODO for A40/H100): pre-compute per-unique-context log-prob + tensors and gather. Only rebuild when cache is updated (between chunks). +""" +from __future__ import annotations +import math +import os +import sys +import time +from collections import Counter, defaultdict +from typing import Optional + +import numpy as np +import torch +import torch.nn.functional as F + +# Same module-local CausalNGram class. To keep the record-submission inlining +# simple we keep everything in one file. + + +class CausalNGram: + """Exact non-hashed causal n-gram with backoff. + + State model: two count tables, `counts` (live) and `frozen_counts` + (immutable snapshot used for lookups). `freeze()` snapshots live -> frozen. + Lookups always read from frozen. Updates always write to live. + + Legal usage pattern for eval_val_ttt: + ng = CausalNGram(vocab_size, order=5, delta=0.5) + ng.freeze() # initial empty frozen state + for chunk in chunks: + # Score the chunk against the CURRENT frozen state + score_chunk(chunk, ng) + # After scoring, add the chunk's scored tokens to live counts + ng.add_many(chunk_history, chunk_tokens) + # Re-freeze live into frozen for the NEXT chunk + ng.freeze() + """ + + def __init__(self, vocab_size: int, order: int = 5, delta: float = 0.5, + min_context_count: int = 2): + assert order >= 1 and vocab_size > 0 + self.V = vocab_size + self.K = order + self.delta = delta + self.min_ctx = min_context_count + # Live counts + self.counts = {k: defaultdict(Counter) for k in range(1, order + 1)} + self.totals = {k: defaultdict(int) for k in range(1, order + 1)} + # Frozen snapshot (None until first freeze()) + self._frozen_counts = None + self._frozen_totals = None + # Log-prob vector cache (torch tensor per context tuple), invalidated + # on every freeze(). + self._lp_cache: dict = {} + + def add_token(self, history_tail: tuple, token: int) -> None: + """Update live counts. history_tail is the last K-1 tokens (as tuple). + If history_tail is shorter than K-1, shorter orders still update.""" + for k in range(1, self.K + 1): + ctx_len = k - 1 + if ctx_len == 0: + ctx = () + else: + if len(history_tail) < ctx_len: + continue + ctx = history_tail[-ctx_len:] + self.counts[k][ctx][token] += 1 + self.totals[k][ctx] += 1 + + def add_many(self, tokens: list[int], history_prefix: tuple = ()) -> None: + """Update live counts with a whole subsequence. `history_prefix` is the + tokens that came before tokens[0] (for context-lookup on the first few + positions). Typical usage: the context from the window's prefix.""" + running = list(history_prefix)[-(self.K - 1):] if self.K > 1 else [] + for tok in tokens: + self.add_token(tuple(running), int(tok)) + running.append(int(tok)) + if len(running) > (self.K - 1): + running = running[-(self.K - 1):] + + def freeze(self) -> None: + """Snapshot live counts as the immutable frozen state. Invalidates the + log-prob cache (since the frozen state has changed).""" + self._frozen_counts = {k: {ctx: Counter(c) for ctx, c in d.items()} + for k, d in self.counts.items()} + self._frozen_totals = {k: dict(d) for k, d in self.totals.items()} + self._lp_cache.clear() + + def _lookup_log_probs(self, ctx_tail: tuple) -> np.ndarray: + """Walk backoff from order K down. Return full-vocab log-prob vector. + Reads ONLY the frozen snapshot. + + IMPORTANT: we now back off only to order >= 2 (bigram). If even bigram + has no observation for the context, we return a FLAT uniform vector. + This is important because a flat uniform contribution is a logit + SHIFT, which softmax is invariant to — meaning positions with no real + cache hit get zero effective n-gram contribution, avoiding the small + positive drag observed in the localized-delta analysis. + + The min_bigram_for_hit threshold (backoff stops if order 2 has < this + many observations) is a principled way to require a "real hit" before + contributing anything. + """ + if ctx_tail in self._lp_cache: + return self._lp_cache[ctx_tail] + src = self._frozen_counts + tot = self._frozen_totals + V = self.V + uniform = np.full(V, -math.log(V), dtype=np.float32) + + if src is None: + self._lp_cache[ctx_tail] = uniform + return uniform + + log_p = None + # Walk K -> 2 (NOT down to unigram — unigram is no-op vs neural) + min_k = 2 + for k in range(self.K, min_k - 1, -1): + ctx_len = k - 1 + if ctx_len == 0: + ctx = () + elif len(ctx_tail) < ctx_len: + continue + else: + ctx = ctx_tail[-ctx_len:] + total = tot[k].get(ctx, 0) + if total >= self.min_ctx: + counter = src[k].get(ctx) + denom = total + self.delta * V + vec = np.full(V, self.delta / denom, dtype=np.float32) + if counter: + for tok, c in counter.items(): + vec[tok] = (c + self.delta) / denom + log_p = np.log(vec) + break + if log_p is None: + # No bigram-or-higher hit → flat uniform → softmax-invariant, + # zero effective contribution to the blended distribution. + log_p = uniform + self._lp_cache[ctx_tail] = log_p + return log_p + + def batch_log_probs_torch(self, x_batch: torch.Tensor) -> torch.Tensor: + """Given x_batch of shape (B, T), return (B, T, V) log-probs from the + frozen cache. + + Performance notes: + - Builds a CPU numpy (B,T,V) buffer in one pass via bulk fills, + then does ONE CPU->device transfer at the end (not B*T transfers). + - Unique-context caching: many adjacent positions share the same + context tuple — we collect unique contexts first, look up each + once, then scatter into the output. + """ + B, T = x_batch.shape + V = self.V + x_cpu = x_batch.detach().cpu().numpy().astype(np.int32) + Ksub = self.K - 1 # context length (number of previous tokens) + + # Build a CPU buffer of shape (B, T, V) filled with per-position log-probs. + # Use float32 numpy for speed, then transfer once. + out_np = np.empty((B, T, V), dtype=np.float32) + + # Collect (b, t) positions grouped by context tuple, so we only look + # up each unique context once per batch. + groups: dict = {} + for b in range(B): + row = x_cpu[b] + for t in range(T): + start = max(0, t - Ksub + 1) + ctx_tail = tuple(int(x) for x in row[start:t + 1]) + if ctx_tail in groups: + groups[ctx_tail].append((b, t)) + else: + groups[ctx_tail] = [(b, t)] + + # Lookup each unique context once, then scatter + for ctx_tail, positions in groups.items(): + lp = self._lookup_log_probs(ctx_tail) # numpy (V,) + for b, t in positions: + out_np[b, t] = lp + + # Single transfer to target device + return torch.from_numpy(out_np).to(device=x_batch.device) + + # --- stats --- + def unique_contexts(self) -> dict: + return {k: len(self.counts[k]) for k in range(1, self.K + 1)} + + +def eval_val_ttt_with_ngram(h, device, val_data, base_model, + ngram: CausalNGram, + alpha: float, + batch_seqs: int = 32, + enable_ttt: bool = True): + """Drop-in replacement for eval_val_ttt that additively blends a causal + n-gram log-prob contribution into the neural logits at scoring time, then + updates the n-gram with the scored tokens after each chunk. + + Args: + h: Hyperparameters (same as #1493). + device: torch device. + val_data: ValidationData (with base_bytes_lut etc.) + base_model: the compiled neural model (must expose forward_logits). + ngram: CausalNGram instance. Should be fresh (empty) at call time. + alpha: fixed scalar blend weight on log_p_ngram. Baked into the + artifact — NOT eval-token dependent. + batch_seqs: batch size for window scoring. + enable_ttt: whether to also run SGD TTT in addition to n-gram. + """ + import torch.distributed as dist + rank = h.rank + world_size = h.world_size + seq_len = h.eval_seq_len + stride = h.eval_stride + total_tokens = val_data.val_tokens.numel() - 1 + ttt_chunk = h.ttt_chunk_tokens + context_size = seq_len - stride + + # Pre-compute window starts and chunk assignment (same as #1493) + window_starts = [ws for ws in range(0, total_tokens, stride) + if ws + context_size < total_tokens] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows = [[] for _ in range(num_chunks)] + for ws in window_starts: + wlen = min(ws + seq_len, total_tokens) - ws + s = 0 if ws == 0 else context_size + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + + print(f"ngram_ttt:start chunks={num_chunks} alpha={alpha} order={ngram.K}", + file=sys.stderr) + + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) \ + if device.type == 'cuda' else base_model.forward_logits + + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + + ttt_params = [p for p in base_model.parameters()] + if enable_ttt: + for p in ttt_params: + p.requires_grad_(True) + optimizer = torch.optim.SGD(ttt_params, lr=h.ttt_lr, momentum=h.ttt_momentum) + else: + optimizer = None + + # Initial freeze: empty cache → uniform log-probs everywhere + ngram.freeze() + + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = len(windows) * rank // world_size + my_e = len(windows) * (rank + 1) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + + # Track which tokens get scored in this chunk (for n-gram update) + chunk_scored_positions = [] # list of (global_position, token_id) + + with torch.no_grad(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens = [] + for i, ws in enumerate(batch_ws): + we = min(ws + seq_len, total_tokens) + wlen = we - ws + wlens.append(wlen) + chunk_tok = val_data.val_tokens[ws:we + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + + # 1. Compute neural logits + if device.type == 'cuda': + with torch.autocast(device_type='cuda', dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + else: + logits = compiled_logits(x_batch) + + # 2. Compute n-gram log-probs (frozen cache). CPU-based lookup. + # Shape: (bsz, seq_len, V), same dtype as logits + if alpha != 0.0: + ngram_log_p = ngram.batch_log_probs_torch(x_batch).to(logits.dtype) + # 3. Additive logit blend (legal: softmax produces a valid + # normalized distribution over Σ, independent of x_t) + blended_logits = logits + alpha * ngram_log_p + else: + blended_logits = logits + + # 4. Compute nll from blended logits + nll = F.cross_entropy( + blended_logits.reshape(-1, blended_logits.size(-1)).float(), + y_batch.reshape(-1), reduction='none' + ).reshape(bsz, seq_len) + + # 5. Score + byte counting (verbatim from #1493) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else context_size + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = val_data.base_bytes_lut[tgt].to(torch.float64) + tb += (val_data.has_leading_space_lut[tgt] & ~val_data.is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + + # Record scored tokens for post-chunk n-gram update. + # The scored tokens are y_batch[i, s:wlen] at global + # positions (ws+s .. ws+wlen-1). Their contexts are + # x_batch[i, :s] (window prefix that leads up to s). + scored_toks = y_batch[i, s:wlen].cpu().numpy().astype(np.int64) + context_prefix = x_batch[i, :s].cpu().numpy().astype(np.int64) + # We record absolute positions so the update step is + # deterministic regardless of parallelism. + chunk_scored_positions.append( + (int(ws + s), context_prefix, scored_toks) + ) + + # --- End of scoring window loop for this chunk --- + # 6. N-GRAM UPDATE (after all scoring is complete for this chunk). + # This is the update-after-score discipline. Sort by global position + # to maintain a left-to-right update order. + chunk_scored_positions.sort(key=lambda t: t[0]) + for gpos, ctx_prefix, toks in chunk_scored_positions: + # Rolling context while updating. Start from the last K-1 tokens + # of ctx_prefix (which came from the window prefix, already + # previously scored in earlier windows/chunks). + running = list(int(x) for x in ctx_prefix[-(ngram.K - 1):]) if ngram.K > 1 else [] + for tok in toks: + ngram.add_token(tuple(running), int(tok)) + if ngram.K > 1: + running.append(int(tok)) + if len(running) > ngram.K - 1: + running = running[-(ngram.K - 1):] + # Re-freeze: live -> frozen, for use by the NEXT chunk + ngram.freeze() + + # --- Optional SGD TTT (same as #1493) --- + is_last_chunk = ci == num_chunks - 1 + if enable_ttt and not is_last_chunk and h.ttt_epochs > 0 and optimizer is not None: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = h.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = chunk_seqs * rank // world_size + my_seq_e = chunk_seqs * (rank + 1) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(h.ttt_epochs): + for bs in range(0, my_chunk_seqs, batch_seqs): + be = min(bs + batch_seqs, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_data.val_tokens.numel(): + continue + local = val_data.val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + if device.type == 'cuda': + with torch.autocast(device_type='cuda', dtype=torch.bfloat16): + loss = base_model(x, y) + else: + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, 1.0) + optimizer.step() + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + if enable_ttt: + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + return val_loss, val_bpb diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/test_integration.py b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/test_integration.py new file mode 100644 index 0000000000..437c524fe9 --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/test_integration.py @@ -0,0 +1,343 @@ +""" +Integration tests for `ngram_eval.eval_val_ttt_with_ngram`. + +Runs against a RANDOM-INIT GPT-style model (no training needed) to verify: + +1. Regression: alpha=0 must produce BPB bit-identical to baseline eval + (since the n-gram contribution is zero and scoring path is otherwise + mathematically identical). +2. Stability: alpha > 0 produces finite, non-nan BPB values. +3. Legality preservation: the four conditions still hold after integration. +4. Update-after-score discipline: freezing ordering is correct (tested via a + dry-run that records cache state at each chunk boundary and verifies it + only grows monotonically with prior-chunk tokens). + +Because we don't want to depend on flash_attn_3 or CUDA, we use a minimal +TinyGPT stand-in with the same `forward_logits` / `forward(input_ids, target_ids)` +interface that #1493's eval loop expects. + +Device: CPU (portable, slow but correct). +""" +from __future__ import annotations +import math +import os +import sys +from dataclasses import dataclass, field +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from causal_ngram import CausalNGram as CNG # for legality cross-checks +from ngram_eval import CausalNGram, eval_val_ttt_with_ngram + + +class TinyGPT(nn.Module): + """Minimal decoder-only LM for the integration test.""" + + def __init__(self, vocab_size: int, dim: int = 64, n_layers: int = 2, + seq_len: int = 128): + super().__init__() + self.tok_emb = nn.Embedding(vocab_size, dim) + self.pos_emb = nn.Embedding(seq_len, dim) + self.blocks = nn.ModuleList([nn.TransformerEncoderLayer( + d_model=dim, nhead=4, dim_feedforward=dim * 4, + batch_first=True, dropout=0.0, activation='gelu', + norm_first=True, + ) for _ in range(n_layers)]) + self.ln_f = nn.LayerNorm(dim) + self.head = nn.Linear(dim, vocab_size, bias=False) + self.seq_len = seq_len + + def forward_logits(self, input_ids: torch.Tensor) -> torch.Tensor: + B, T = input_ids.shape + pos = torch.arange(T, device=input_ids.device).unsqueeze(0).expand(B, -1) + x = self.tok_emb(input_ids) + self.pos_emb(pos) + # Causal mask + mask = torch.triu(torch.full((T, T), float('-inf'), device=x.device), diagonal=1) + for blk in self.blocks: + x = blk(x, src_mask=mask, is_causal=True) + x = self.ln_f(x) + return self.head(x) + + def forward(self, input_ids, target_ids): + logits = self.forward_logits(input_ids) + return F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + target_ids.reshape(-1), reduction='mean', + ) + + +@dataclass +class TinyHparams: + rank: int = 0 + world_size: int = 1 + eval_seq_len: int = 128 + eval_stride: int = 16 + vocab_size: int = 256 + ttt_chunk_tokens: int = 512 + ttt_lr: float = 0.0 # disable TTT SGD for legality isolation + ttt_epochs: int = 0 + ttt_momentum: float = 0.9 + + +class FakeValData: + """Stand-in for ValidationData — provides val_tokens and the byte LUTs.""" + + def __init__(self, tokens: torch.Tensor, vocab_size: int, device): + self.val_tokens = tokens # 1-D tensor of token IDs, CPU + # Synthetic LUTs: every token is 4 bytes, no leading space, no boundary. + # This keeps BPB computation simple and deterministic. + self.base_bytes_lut = torch.full((vocab_size,), 4, dtype=torch.int16, + device=device) + self.has_leading_space_lut = torch.zeros((vocab_size,), dtype=torch.bool, + device=device) + self.is_boundary_token_lut = torch.zeros((vocab_size,), dtype=torch.bool, + device=device) + + +def eval_val_ttt_baseline(h, device, val_data, base_model, batch_seqs: int = 8): + """Stripped-down copy of #1493 eval_val_ttt with TTT SGD disabled. Used as + the regression baseline for alpha=0.""" + rank = h.rank + world_size = h.world_size + seq_len = h.eval_seq_len + stride = h.eval_stride + total_tokens = val_data.val_tokens.numel() - 1 + ttt_chunk = h.ttt_chunk_tokens + context_size = seq_len - stride + + window_starts = [ws for ws in range(0, total_tokens, stride) + if ws + context_size < total_tokens] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows = [[] for _ in range(num_chunks)] + for ws in window_starts: + wlen = min(ws + seq_len, total_tokens) - ws + s = 0 if ws == 0 else context_size + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + + base_model.eval() + with torch.no_grad(): + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + my_windows = windows # world_size=1 + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens = [] + for i, ws in enumerate(batch_ws): + we = min(ws + seq_len, total_tokens) + wlen = we - ws + wlens.append(wlen) + chunk_tok = val_data.val_tokens[ws:we + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction='none' + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else context_size + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = val_data.base_bytes_lut[tgt].to(torch.float64) + tb += (val_data.has_leading_space_lut[tgt] & ~val_data.is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + return val_loss, val_bpb + + +# ============================================================================= +# Tests +# ============================================================================= + +def make_fake_val(vocab_size: int = 256, n_tokens: int = 4096, seed: int = 0): + g = torch.Generator().manual_seed(seed) + return torch.randint(0, vocab_size, (n_tokens,), dtype=torch.int64, generator=g) + + +def test_regression_alpha_zero(): + """alpha=0 must give BPB bit-identical to baseline eval (modulo floating + point within 1e-10).""" + torch.manual_seed(42) + device = torch.device('cpu') + vocab_size = 256 + h = TinyHparams(vocab_size=vocab_size) + model = TinyGPT(vocab_size=vocab_size, dim=32, n_layers=2, seq_len=h.eval_seq_len) + model.eval() + tokens = make_fake_val(vocab_size=vocab_size, n_tokens=4096) + val_data = FakeValData(tokens, vocab_size, device) + + _, bpb_baseline = eval_val_ttt_baseline(h, device, val_data, model, batch_seqs=8) + + # Now with alpha=0. Even creating a CausalNGram and going through the + # blend path must reproduce the baseline (since alpha=0 short-circuits). + ng = CausalNGram(vocab_size=vocab_size, order=5) + _, bpb_ngram = eval_val_ttt_with_ngram(h, device, val_data, model, + ngram=ng, alpha=0.0, + batch_seqs=8, enable_ttt=False) + delta = abs(bpb_baseline - bpb_ngram) + assert delta < 1e-8, \ + f"alpha=0 regression failed: baseline={bpb_baseline:.12f} ngram={bpb_ngram:.12f} delta={delta}" + return bpb_baseline, bpb_ngram + + +def test_stability_alpha_positive(): + """alpha > 0 produces finite, non-nan BPB values across a sweep.""" + torch.manual_seed(43) + device = torch.device('cpu') + vocab_size = 256 + h = TinyHparams(vocab_size=vocab_size) + model = TinyGPT(vocab_size=vocab_size, dim=32, n_layers=2, seq_len=h.eval_seq_len) + model.eval() + # Use a structured sequence (not random) so the n-gram has something to + # learn. Repeat a short pattern. + pattern = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] + tokens = torch.tensor(pattern * 400, dtype=torch.int64)[:4096] + val_data = FakeValData(tokens, vocab_size, device) + + results = {} + for alpha in [0.0, 0.1, 0.3, 0.5, 1.0, 2.0]: + ng = CausalNGram(vocab_size=vocab_size, order=5) + _, bpb = eval_val_ttt_with_ngram(h, device, val_data, model, ngram=ng, + alpha=alpha, batch_seqs=8, enable_ttt=False) + assert math.isfinite(bpb), f"alpha={alpha}: non-finite BPB={bpb}" + assert bpb > 0, f"alpha={alpha}: non-positive BPB={bpb}" + results[alpha] = bpb + + # For a repeating pattern, higher alpha should EVENTUALLY reduce BPB + # (assuming the cache learns the pattern). The alpha=0 baseline is random + # so we expect alpha>0 to win by a margin. + assert results[1.0] < results[0.0], \ + f"n-gram did not help on repeating pattern: {results}" + + return results + + +def test_legality_preserved(): + """The integrated eval path must still pass the legality harness's probes + on its CausalNGram instance.""" + # The harness in legality_harness.py operates on the causal_ngram.CausalNGram + # (slightly different class). The ngram_eval.CausalNGram is structurally + # the same — same freeze/add/lookup contract. Run a quick adversarial probe. + ng = CausalNGram(vocab_size=32, order=4) + # Build from one sequence + import random + rng = random.Random(0) + seq = [rng.randrange(32) for _ in range(500)] + running = [] + for tok in seq: + ng.add_token(tuple(running), tok) + running.append(tok) + if len(running) > 3: + running = running[-3:] + ng.freeze() + + # C1: mutate tokens >= position 200 and verify lookup at position 200 is identical + hist_before = tuple(seq[197:200]) + lp1 = ng._lookup_log_probs(hist_before).copy() + # The frozen cache should not change when we mutate the live counts with + # mutated data (live updates don't affect frozen lookups) + ng.add_many([999 % 32] * 50) # junk data into LIVE only + lp2 = ng._lookup_log_probs(hist_before) + assert np.allclose(lp1, lp2), "C1 violated: frozen cache changed due to live updates" + + # C2: distribution sums to 1 + prob = np.exp(lp2) + assert abs(prob.sum() - 1.0) < 1e-6, f"C2 violated: sum={prob.sum()}" + return True + + +def test_update_after_score_ordering(): + """Verify that in eval_val_ttt_with_ngram, the cache state used for scoring + a chunk is the state at chunk_start (not anything updated mid-chunk). + + We instrument this by providing a structured sequence and a small model, + then comparing the measured n-gram log-probs at scoring time against a + parallel reference cache that's manually frozen at the right point. + """ + torch.manual_seed(44) + device = torch.device('cpu') + vocab_size = 16 + h = TinyHparams(vocab_size=vocab_size, ttt_chunk_tokens=256) + model = TinyGPT(vocab_size=vocab_size, dim=16, n_layers=1, seq_len=h.eval_seq_len) + model.eval() + tokens = torch.tensor([(i % vocab_size) for i in range(2048)], dtype=torch.int64) + val_data = FakeValData(tokens, vocab_size, device) + + ng = CausalNGram(vocab_size=vocab_size, order=4) + _, bpb = eval_val_ttt_with_ngram(h, device, val_data, model, ngram=ng, + alpha=0.5, batch_seqs=4, enable_ttt=False) + + # After a full eval, the FROZEN cache should contain statistics from ALL + # scored tokens (not more, not less). We verify by counting order-1 total + # against the number of scored tokens expected from the eval loop. + total_tokens = val_data.val_tokens.numel() - 1 + stride = h.eval_stride + seq_len = h.eval_seq_len + context_size = seq_len - stride + window_starts = [ws for ws in range(0, total_tokens, stride) + if ws + context_size < total_tokens] + expected_scored = 0 + for ws in window_starts: + wlen = min(ws + seq_len, total_tokens) - ws + s = 0 if ws == 0 else context_size + expected_scored += wlen - s + + unigram_total = ng._frozen_totals[1].get((), 0) + # The frozen state is re-snapshotted after EACH chunk update, so at the end + # of eval the frozen state should reflect all scored tokens. + assert unigram_total == expected_scored, \ + f"Cache didn't update correctly: unigram total={unigram_total} expected={expected_scored}" + return unigram_total, expected_scored + + +def main(): + results = {} + for name, fn in [ + ("regression (alpha=0)", test_regression_alpha_zero), + ("stability (alpha>0 sweep)", test_stability_alpha_positive), + ("legality preserved", test_legality_preserved), + ("update-after-score ordering", test_update_after_score_ordering), + ]: + print(f"\n--- {name} ---") + try: + out = fn() + print(f" PASS {out}") + results[name] = ("pass", out) + except Exception as e: + import traceback + traceback.print_exc() + print(f" FAIL {e}") + results[name] = ("fail", str(e)) + + fails = [n for n, (s, _) in results.items() if s == "fail"] + if fails: + print(f"\n{len(fails)}/{len(results)} tests FAILED:") + for n in fails: + print(f" - {n}") + sys.exit(1) + print(f"\n{len(results)}/{len(results)} tests passed") + + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/tiny_train.py b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/tiny_train.py new file mode 100644 index 0000000000..80e93844e5 --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/code/tiny_train.py @@ -0,0 +1,379 @@ +""" +Tiny local training + eval pipeline. + +Trains a small sp1024 LM on a fraction of the val shard (we don't have train +shards locally — downloading them would be ~8GB), then evaluates BPB with and +without a causal n-gram additive contribution on a held-out slice. + +This is a SANITY MEASUREMENT, not a real competition run. Absolute BPB will be +much worse than the 1.08 competition SOTA because (1) the model is tiny, (2) +we're training on val data (cheating absolute but fine for relative delta), +(3) only a few hundred steps. + +What it tells us: whether the n-gram additive contribution gives a POSITIVE +delta when stacked on a trained neural model, and how much. This is the last +cheap signal we can get without spending on a pod. + +Device: MPS if available, else CPU. Seq_len 256, batch 16, 2L 128d model. +""" +from __future__ import annotations +import argparse +import json +import math +import os +import sys +import time +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from ngram_eval import CausalNGram + + +def pick_device(): + if torch.cuda.is_available(): + return torch.device('cuda') + if torch.backends.mps.is_available(): + return torch.device('mps') + return torch.device('cpu') + + +# ----------------------------------------------------------------------------- +# Model +# ----------------------------------------------------------------------------- + +class TinyGPT(nn.Module): + def __init__(self, vocab_size: int, dim: int = 128, n_layers: int = 2, + n_heads: int = 4, seq_len: int = 256, mlp_mult: int = 4): + super().__init__() + self.dim = dim + self.seq_len = seq_len + self.vocab_size = vocab_size + self.tok_emb = nn.Embedding(vocab_size, dim) + self.pos_emb = nn.Embedding(seq_len, dim) + self.blocks = nn.ModuleList([ + nn.TransformerEncoderLayer( + d_model=dim, nhead=n_heads, dim_feedforward=dim * mlp_mult, + batch_first=True, dropout=0.0, activation='gelu', + norm_first=True, + ) for _ in range(n_layers) + ]) + self.ln_f = nn.LayerNorm(dim) + self.head = nn.Linear(dim, vocab_size, bias=False) + # Tie input+output embeddings for efficiency + self.head.weight = self.tok_emb.weight + + def forward_logits(self, input_ids): + B, T = input_ids.shape + pos = torch.arange(T, device=input_ids.device).unsqueeze(0).expand(B, -1) + x = self.tok_emb(input_ids) + self.pos_emb(pos) + mask = torch.triu(torch.full((T, T), float('-inf'), device=x.device), + diagonal=1) + for blk in self.blocks: + x = blk(x, src_mask=mask, is_causal=True) + x = self.ln_f(x) + return self.head(x) + + def forward(self, input_ids, target_ids): + logits = self.forward_logits(input_ids) + return F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + target_ids.reshape(-1), reduction='mean' + ) + + +# ----------------------------------------------------------------------------- +# Data: we split the val shard into a TRAIN portion (first 80%) and a HELDOUT +# portion (last 20%) for eval. Training on part of val is a cheat for absolute +# numbers but fine for RELATIVE measurement (with vs without n-gram, same +# trained model, same held-out eval). +# ----------------------------------------------------------------------------- + +def load_tokens(path: Path) -> np.ndarray: + header_bytes = 256 * 4 + return np.fromfile(path, dtype=' 1 else [] + for tok in toks: + ng.add_token(tuple(running), int(tok)) + if ng.K > 1: + running.append(int(tok)) + if len(running) > ng.K - 1: + running = running[-(ng.K - 1):] + ng.freeze() + + mean_nll = nll_sum / max(n_scored, 1) + return { + "nll_sum": nll_sum, + "n_scored": n_scored, + "mean_nll_nats": mean_nll, + "bits_per_tok": mean_nll / math.log(2), + "unique_ctx": ng.unique_contexts() if ngram_enabled else None, + } + + +# ----------------------------------------------------------------------------- +# Main: train then eval +# ----------------------------------------------------------------------------- + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--val", type=Path, + default=Path("data/datasets/fineweb10B_sp1024/fineweb_val_000000.bin")) + ap.add_argument("--dim", type=int, default=128) + ap.add_argument("--layers", type=int, default=2) + ap.add_argument("--heads", type=int, default=4) + ap.add_argument("--seq-len", type=int, default=256) + ap.add_argument("--batch", type=int, default=16) + ap.add_argument("--steps", type=int, default=800) + ap.add_argument("--lr", type=float, default=3e-3) + ap.add_argument("--eval-stride", type=int, default=64) + ap.add_argument("--eval-chunk-tokens", type=int, default=8192) + ap.add_argument("--held-out-frac", type=float, default=0.2, + help="Fraction of val shard reserved for eval") + ap.add_argument("--train-cap", type=int, default=4_000_000, + help="Cap tokens used for training (for speed)") + ap.add_argument("--eval-cap", type=int, default=200_000, + help="Cap tokens used for eval") + ap.add_argument("--orders", type=str, default="3,4,5") + ap.add_argument("--alphas", type=str, default="0,0.1,0.2,0.3,0.5,0.7,1.0") + ap.add_argument("--seed", type=int, default=42) + ap.add_argument("--out", type=Path, default=Path("results_tiny_train.json")) + ap.add_argument("--vocab-size", type=int, default=None, + help="Override vocab size (auto-detected from val path if not set)") + args = ap.parse_args() + + device = pick_device() + print(f"Device: {device}", file=sys.stderr) + + torch.manual_seed(args.seed) + rng = np.random.default_rng(args.seed) + + print(f"Loading {args.val}...", file=sys.stderr) + tokens = load_tokens(args.val) + print(f" {len(tokens):,} tokens", file=sys.stderr) + + # Determine vocab size: CLI override > auto-detect from path > default 1024 + if args.vocab_size is not None: + vocab_size = args.vocab_size + else: + path_str = str(args.val) + if "sp8192" in path_str: + vocab_size = 8192 + elif "sp4096" in path_str: + vocab_size = 4096 + elif "sp1024" in path_str: + vocab_size = 1024 + else: + vocab_size = 1024 + print(f" vocab_size: {vocab_size}", file=sys.stderr) + split = int(len(tokens) * (1 - args.held_out_frac)) + train_tokens = tokens[:split][:args.train_cap] + eval_tokens = tokens[split:split + args.eval_cap] + print(f" train: {len(train_tokens):,} eval: {len(eval_tokens):,}", + file=sys.stderr) + + model = TinyGPT(vocab_size=vocab_size, dim=args.dim, n_layers=args.layers, + n_heads=args.heads, seq_len=args.seq_len).to(device) + n_params = sum(p.numel() for p in model.parameters()) + print(f" model: {n_params:,} params", file=sys.stderr) + + opt = torch.optim.AdamW(model.parameters(), lr=args.lr, + betas=(0.9, 0.95), weight_decay=0.01) + + # Training loop + model.train() + t0 = time.time() + last_loss = None + for step, (x_np, y_np) in enumerate( + iter_batches(train_tokens, args.seq_len, args.batch, args.steps, rng) + ): + x = torch.from_numpy(x_np).to(device) + y = torch.from_numpy(y_np).to(device) + loss = model(x, y) + opt.zero_grad(set_to_none=True) + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + opt.step() + last_loss = loss.item() + if (step + 1) % 50 == 0: + elapsed = time.time() - t0 + print(f" step {step + 1}/{args.steps} loss={last_loss:.4f} " + f"({elapsed:.0f}s, {(step + 1) / elapsed:.1f} steps/s)", + file=sys.stderr) + + train_time = time.time() - t0 + print(f"Training done: {train_time:.0f}s, final loss {last_loss:.4f}", + file=sys.stderr) + + # Eval sweep: for each order in --orders and each alpha in --alphas, run + # the eval and record BPT. Start with alpha=0 baseline for the reference. + orders = [int(x) for x in args.orders.split(",")] + alphas = sorted({float(x) for x in args.alphas.split(",")}) + + results = { + "config": {k: str(v) for k, v in vars(args).items()}, + "device": str(device), + "n_params": n_params, + "train_tokens": int(len(train_tokens)), + "eval_tokens": int(len(eval_tokens)), + "train_time_s": train_time, + "final_train_loss": last_loss, + "baseline": None, + "runs": [], + } + + print("\n--- EVAL SWEEP ---", file=sys.stderr) + # Baseline (no n-gram) + t0 = time.time() + base = eval_sliding(model, eval_tokens, vocab_size, args.seq_len, + args.eval_stride, device, alpha=0.0, ngram_order=3, + ngram_enabled=False, + chunk_tokens=args.eval_chunk_tokens) + base_time = time.time() - t0 + base["eval_time_s"] = base_time + results["baseline"] = base + print(f" BASELINE (no ngram): bpt={base['bits_per_tok']:.5f} " + f"({base_time:.0f}s)", file=sys.stderr) + + for order in orders: + for alpha in alphas: + t0 = time.time() + res = eval_sliding(model, eval_tokens, vocab_size, args.seq_len, + args.eval_stride, device, alpha=alpha, + ngram_order=order, ngram_enabled=True, + chunk_tokens=args.eval_chunk_tokens) + et = time.time() - t0 + delta = res['bits_per_tok'] - base['bits_per_tok'] + res["eval_time_s"] = et + res["order"] = order + res["alpha"] = alpha + res["delta_vs_baseline_bpt"] = delta + results["runs"].append(res) + print(f" order={order} alpha={alpha:.2f} " + f"bpt={res['bits_per_tok']:.5f} delta={delta:+.5f} " + f"({et:.0f}s)", file=sys.stderr) + + # Write summary + args.out.write_text(json.dumps(results, indent=2, default=str)) + print(f"\nWrote {args.out}", file=sys.stderr) + + # Print a compact table + print("\n=== SUMMARY ===") + print(f"{'order':>5} {'alpha':>6} {'bits/tok':>10} {'delta':>10}") + print(f"{'base':>5} {'---':>6} {base['bits_per_tok']:>10.5f} {0.0:>+10.5f}") + for r in results["runs"]: + print(f"{r['order']:>5} {r['alpha']:>6.2f} {r['bits_per_tok']:>10.5f} " + f"{r['delta_vs_baseline_bpt']:>+10.5f}") + + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/results/results_localized.json b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/results/results_localized.json new file mode 100644 index 0000000000..933e61bcaa --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/results/results_localized.json @@ -0,0 +1,73 @@ +{ + "overall": { + "n": 199999, + "bpt_base": 5.638564158716114, + "bpt_blend": 5.61979369713518, + "delta": -0.01877046158093343 + }, + "buckets": { + "no_hit__0-2047": { + "n": 99326, + "nll_base": 393384.34549938684, + "nll_blend": 393929.5503704393, + "bpt_base": 5.7138477781780805, + "bpt_blend": 5.721766795995529 + }, + "in_window__0-2047": { + "n": 67, + "nll_base": 276.0072292536497, + "nll_blend": 270.15112894773483, + "bpt_base": 5.94319792378722, + "bpt_blend": 5.817099910797791 + }, + "out_of_window__0-2047": { + "n": 38341, + "nll_base": 143486.9701188384, + "nll_blend": 142149.79052303688, + "bpt_base": 5.3991273107803925, + "bpt_blend": 5.348811920685175 + }, + "no_hit__2048-4095": { + "n": 17350, + "nll_base": 68114.04950744228, + "nll_blend": 68162.33384739805, + "bpt_base": 5.663850227046243, + "bpt_blend": 5.667865188303119 + }, + "out_of_window__2048-4095": { + "n": 7267, + "nll_base": 28183.524529413087, + "nll_blend": 27799.1167679308, + "bpt_base": 5.5951879831232585, + "bpt_blend": 5.518872698801018 + }, + "no_hit__4096+": { + "n": 24581, + "nll_base": 97957.27112942957, + "nll_blend": 97836.04203665539, + "bpt_base": 5.74925630679971, + "bpt_blend": 5.742141193055079 + }, + "out_of_window__4096+": { + "n": 13053, + "nll_base": 50208.41081364022, + "nll_blend": 48862.177950605284, + "bpt_base": 5.549331593637827, + "bpt_blend": 5.400537946554225 + }, + "in_window__2048-4095": { + "n": 13, + "nll_base": 53.65305010974407, + "nll_blend": 52.89734047651291, + "bpt_base": 5.954229947838064, + "bpt_blend": 5.870363906283093 + }, + "in_window__4096+": { + "n": 1, + "nll_base": 2.8295717239379883, + "nll_blend": 2.8759899139404297, + "bpt_base": 4.082209093964971, + "bpt_blend": 4.149176386488534 + } + } +} \ No newline at end of file diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/results/results_tiny_bigger.json b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/results/results_tiny_bigger.json new file mode 100644 index 0000000000..a1ef1874f9 --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/results/results_tiny_bigger.json @@ -0,0 +1,117 @@ +{ + "config": { + "val": "data/datasets/fineweb10B_sp1024/fineweb_val_000000.bin", + "dim": "256", + "layers": "4", + "heads": "4", + "seq_len": "256", + "batch": "16", + "steps": "2000", + "lr": "0.003", + "eval_stride": "64", + "eval_chunk_tokens": "8192", + "held_out_frac": "0.2", + "train_cap": "4000000", + "eval_cap": "80000", + "orders": "4", + "alphas": "0,0.1,0.2,0.3,0.5", + "seed": "42", + "out": "results_tiny_bigger.json" + }, + "device": "mps", + "n_params": 3487232, + "train_tokens": 4000000, + "eval_tokens": 80000, + "train_time_s": 158.64974689483643, + "final_train_loss": 3.7918806076049805, + "baseline": { + "nll_sum": 304812.12101226073, + "n_scored": 79999, + "mean_nll_nats": 3.810199140142511, + "bits_per_tok": 5.496955404282993, + "unique_ctx": null, + "eval_time_s": 2.3374569416046143 + }, + "runs": [ + { + "nll_sum": 304812.12101226073, + "n_scored": 79999, + "mean_nll_nats": 3.810199140142511, + "bits_per_tok": 5.496955404282993, + "unique_ctx": { + "1": 1, + "2": 786, + "3": 25849, + "4": 55205 + }, + "eval_time_s": 2.996046781539917, + "order": 4, + "alpha": 0.0, + "delta_vs_baseline_bpt": 0.0 + }, + { + "nll_sum": 304029.04289705446, + "n_scored": 79999, + "mean_nll_nats": 3.8004105413449474, + "bits_per_tok": 5.482833441340497, + "unique_ctx": { + "1": 1, + "2": 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Builds the legal reference implementation of an eval-time causal n-gram additive-logit blend (verified against valerio-oai closures #993/#1185/#959 with an 8-probe automated legality harness), then demonstrates across 6 model configurations (2L/4L, 128d/256d, 800\u20134000 steps, sp1024/sp8192) that the peak BPB improvement collapses from 0.0515 on a very weak baseline to 0.00018 on the strongest model tested \u2014 well below the 0.0072 BPB record threshold. Includes a localized delta decomposition showing 100% of the gain comes from context lookups whose first observation is outside the 2048-token attention window, and explains why that architectural floor does not save the approach at scale on larger tokenizers.", + "date": "2026-04-15", + "track": "non_record_16mb", + "result_type": "negative", + "scaling_runs": [ + {"tokenizer": "sp1024", "model": "2L 128d", "steps": 800, "baseline_bpt_nats": 4.665, "peak_delta_bpb": 0.0515, "peak_alpha": 0.5}, + {"tokenizer": "sp1024", "model": "2L 128d", "steps": 2500, "baseline_bpt_nats": 4.145, "peak_delta_bpb": 0.0224, "peak_alpha": 0.3}, + {"tokenizer": "sp1024", "model": "4L 256d", "steps": 2000, "baseline_bpt_nats": 3.811, "peak_delta_bpb": 0.0079, "peak_alpha": 0.2}, + {"tokenizer": "sp1024", "model": "4L 256d", "steps": 4000, "baseline_bpt_nats": 3.640, "peak_delta_bpb": 0.00341, "peak_alpha": 0.15}, + {"tokenizer": "sp8192", "model": "4L 256d", "steps": 2000, "baseline_bpt_nats": 5.625, "peak_delta_bpb": 0.00223, "peak_alpha": 0.10}, + {"tokenizer": "sp8192", "model": "4L 256d", "steps": 4000, "baseline_bpt_nats": 5.114, "peak_delta_bpb": 0.00018, "peak_alpha": 0.05} + ], + "legality_tests_passed": "8/8", + "integration_tests_passed": "4/4", + "integration_target": "PR #1493 eval_val_ttt path", + "rulings_cross_checked": ["#993", "#1185", "#959", "#1017"], + "hardware": ["1x Mac M4 (MPS, runs 1-4)", "1x NVIDIA A40 48GB (runs 5-6)"], + "total_compute_cost_usd": "~0.15" +} diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_a40_sp8192_phase1a.log b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_a40_sp8192_phase1a.log new file mode 100644 index 0000000000..7f8e4fc95a --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_a40_sp8192_phase1a.log @@ -0,0 +1,57 @@ +Device: cuda +Loading data/datasets/fineweb10B_sp8192/fineweb_val_000000.bin... + 40,540,803 tokens + vocab_size: 8192 + train: 4,000,000 eval: 300,000 + model: 5,387,776 params + step 50/2000 loss=10.4684 (3s, 14.6 steps/s) + step 100/2000 loss=7.3164 (6s, 15.5 steps/s) + step 150/2000 loss=7.1052 (9s, 15.9 steps/s) + step 200/2000 loss=6.8763 (12s, 16.1 steps/s) + step 250/2000 loss=6.8983 (15s, 16.2 steps/s) + step 300/2000 loss=6.8450 (18s, 16.3 steps/s) + step 350/2000 loss=6.7265 (21s, 16.3 steps/s) + step 400/2000 loss=6.7280 (25s, 16.3 steps/s) + step 450/2000 loss=6.5343 (28s, 16.3 steps/s) + step 500/2000 loss=6.5446 (31s, 16.4 steps/s) + step 550/2000 loss=6.4909 (34s, 16.4 steps/s) + step 600/2000 loss=6.3600 (37s, 16.4 steps/s) + step 650/2000 loss=6.3713 (40s, 16.4 steps/s) + step 700/2000 loss=6.3219 (43s, 16.4 steps/s) + step 750/2000 loss=6.1647 (46s, 16.4 steps/s) + step 800/2000 loss=6.2913 (49s, 16.4 steps/s) + step 850/2000 loss=6.2756 (52s, 16.4 steps/s) + step 900/2000 loss=6.1529 (55s, 16.4 steps/s) + step 950/2000 loss=6.1601 (58s, 16.4 steps/s) + step 1000/2000 loss=6.1052 (61s, 16.4 steps/s) + step 1050/2000 loss=6.0882 (64s, 16.4 steps/s) + step 1100/2000 loss=6.0726 (67s, 16.4 steps/s) + step 1150/2000 loss=6.0605 (70s, 16.4 steps/s) + step 1200/2000 loss=5.9195 (73s, 16.4 steps/s) + step 1250/2000 loss=5.9867 (76s, 16.4 steps/s) + step 1300/2000 loss=5.9567 (79s, 16.4 steps/s) + step 1350/2000 loss=5.9186 (82s, 16.4 steps/s) + step 1400/2000 loss=5.7486 (85s, 16.4 steps/s) + step 1450/2000 loss=5.6696 (89s, 16.4 steps/s) + step 1500/2000 loss=5.7640 (92s, 16.4 steps/s) + step 1550/2000 loss=5.6253 (95s, 16.4 steps/s) + step 1600/2000 loss=5.5381 (98s, 16.4 steps/s) + step 1650/2000 loss=5.6459 (101s, 16.4 steps/s) + step 1700/2000 loss=5.4538 (104s, 16.4 steps/s) + step 1750/2000 loss=5.5710 (107s, 16.4 steps/s) + step 1800/2000 loss=5.5721 (110s, 16.3 steps/s) + step 1850/2000 loss=5.5313 (113s, 16.3 steps/s) + step 1900/2000 loss=5.3968 (116s, 16.3 steps/s) + step 1950/2000 loss=5.5032 (119s, 16.3 steps/s) + step 2000/2000 loss=5.4469 (122s, 16.3 steps/s) +Training done: 122s, final loss 5.4469 + +--- EVAL SWEEP --- + BASELINE (no ngram): bpt=8.12311 (4s) + order=4 alpha=0.00 bpt=8.12311 delta=+0.00000 (16s) + order=4 alpha=0.05 bpt=8.11736 delta=-0.00575 (56s) + order=4 alpha=0.10 bpt=8.11494 delta=-0.00817 (56s) + order=4 alpha=0.15 bpt=8.11586 delta=-0.00725 (56s) + order=4 alpha=0.20 bpt=8.12008 delta=-0.00303 (56s) + order=4 alpha=0.25 bpt=8.12756 delta=+0.00445 (56s) + order=4 alpha=0.30 bpt=8.13822 delta=+0.01511 (55s) diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_a40_sp8192_phase1b.log b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_a40_sp8192_phase1b.log new file mode 100644 index 0000000000..57ac2e4fef --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_a40_sp8192_phase1b.log @@ -0,0 +1,93 @@ +Device: cuda +Loading data/datasets/fineweb10B_sp8192/fineweb_val_000000.bin... + 40,540,803 tokens + vocab_size: 8192 + train: 4,000,000 eval: 300,000 + model: 5,387,776 params + step 50/4000 loss=10.4690 (3s, 15.3 steps/s) + step 100/4000 loss=7.2377 (6s, 15.9 steps/s) + step 150/4000 loss=7.1083 (9s, 16.1 steps/s) + step 200/4000 loss=6.8967 (12s, 16.2 steps/s) + step 250/4000 loss=6.8668 (15s, 16.3 steps/s) + step 300/4000 loss=6.8521 (18s, 16.3 steps/s) + step 350/4000 loss=6.7211 (21s, 16.4 steps/s) + step 400/4000 loss=6.7363 (24s, 16.4 steps/s) + step 450/4000 loss=6.5254 (27s, 16.4 steps/s) + step 500/4000 loss=6.5587 (31s, 16.4 steps/s) + step 550/4000 loss=6.4740 (34s, 16.4 steps/s) + step 600/4000 loss=6.3496 (37s, 16.4 steps/s) + step 650/4000 loss=6.3548 (40s, 16.4 steps/s) + step 700/4000 loss=6.3071 (43s, 16.4 steps/s) + step 750/4000 loss=6.1592 (46s, 16.4 steps/s) + step 800/4000 loss=6.2770 (49s, 16.4 steps/s) + step 850/4000 loss=6.2636 (52s, 16.4 steps/s) + step 900/4000 loss=6.1373 (55s, 16.4 steps/s) + step 950/4000 loss=6.1451 (58s, 16.4 steps/s) + step 1000/4000 loss=6.0787 (61s, 16.4 steps/s) + step 1050/4000 loss=6.0786 (64s, 16.4 steps/s) + step 1100/4000 loss=6.0541 (67s, 16.4 steps/s) + step 1150/4000 loss=6.0310 (70s, 16.4 steps/s) + step 1200/4000 loss=5.8983 (73s, 16.3 steps/s) + step 1250/4000 loss=5.9693 (76s, 16.3 steps/s) + step 1300/4000 loss=5.9328 (80s, 16.3 steps/s) + step 1350/4000 loss=5.9017 (83s, 16.3 steps/s) + step 1400/4000 loss=5.7240 (86s, 16.3 steps/s) + step 1450/4000 loss=5.6342 (89s, 16.3 steps/s) + step 1500/4000 loss=5.7340 (92s, 16.3 steps/s) + step 1550/4000 loss=5.6005 (95s, 16.3 steps/s) + step 1600/4000 loss=5.5144 (98s, 16.3 steps/s) + step 1650/4000 loss=5.6368 (101s, 16.3 steps/s) + step 1700/4000 loss=5.4371 (104s, 16.3 steps/s) + step 1750/4000 loss=5.5546 (107s, 16.3 steps/s) + step 1800/4000 loss=5.5611 (110s, 16.3 steps/s) + step 1850/4000 loss=5.5028 (113s, 16.3 steps/s) + step 1900/4000 loss=5.3757 (117s, 16.3 steps/s) + step 1950/4000 loss=5.4839 (120s, 16.3 steps/s) + step 2000/4000 loss=5.4337 (123s, 16.3 steps/s) + step 2050/4000 loss=5.3654 (126s, 16.3 steps/s) + step 2100/4000 loss=5.4414 (129s, 16.3 steps/s) + step 2150/4000 loss=5.3132 (132s, 16.3 steps/s) + step 2200/4000 loss=5.2869 (135s, 16.3 steps/s) + step 2250/4000 loss=5.3136 (138s, 16.3 steps/s) + step 2300/4000 loss=5.2696 (141s, 16.3 steps/s) + step 2350/4000 loss=5.2269 (144s, 16.3 steps/s) + step 2400/4000 loss=5.2834 (147s, 16.3 steps/s) + step 2450/4000 loss=5.1872 (150s, 16.3 steps/s) + step 2500/4000 loss=5.1374 (153s, 16.3 steps/s) + step 2550/4000 loss=5.2403 (157s, 16.3 steps/s) + step 2600/4000 loss=5.1581 (160s, 16.3 steps/s) + step 2650/4000 loss=5.1898 (163s, 16.3 steps/s) + step 2700/4000 loss=5.0685 (166s, 16.3 steps/s) + step 2750/4000 loss=5.0195 (169s, 16.3 steps/s) + step 2800/4000 loss=5.2073 (172s, 16.3 steps/s) + step 2850/4000 loss=5.1364 (175s, 16.3 steps/s) + step 2900/4000 loss=5.1481 (178s, 16.3 steps/s) + step 2950/4000 loss=5.0720 (181s, 16.3 steps/s) + step 3000/4000 loss=5.0624 (184s, 16.3 steps/s) + step 3050/4000 loss=5.0006 (187s, 16.3 steps/s) + step 3100/4000 loss=5.0436 (190s, 16.3 steps/s) + step 3150/4000 loss=5.0510 (194s, 16.3 steps/s) + step 3200/4000 loss=5.0055 (197s, 16.3 steps/s) + step 3250/4000 loss=4.8384 (200s, 16.3 steps/s) + step 3300/4000 loss=4.9358 (203s, 16.3 steps/s) + step 3350/4000 loss=4.8018 (206s, 16.3 steps/s) + step 3400/4000 loss=4.8805 (209s, 16.3 steps/s) + step 3450/4000 loss=4.9622 (212s, 16.3 steps/s) + step 3500/4000 loss=4.9727 (215s, 16.3 steps/s) + step 3550/4000 loss=4.8461 (218s, 16.3 steps/s) + step 3600/4000 loss=4.9072 (221s, 16.3 steps/s) + step 3650/4000 loss=4.7935 (224s, 16.3 steps/s) + step 3700/4000 loss=4.8694 (227s, 16.3 steps/s) + step 3750/4000 loss=4.8371 (231s, 16.3 steps/s) + step 3800/4000 loss=4.6756 (234s, 16.3 steps/s) + step 3850/4000 loss=4.7183 (237s, 16.3 steps/s) + step 3900/4000 loss=4.8153 (240s, 16.3 steps/s) + step 3950/4000 loss=4.7919 (243s, 16.3 steps/s) + step 4000/4000 loss=4.7227 (246s, 16.3 steps/s) +Training done: 246s, final loss 4.7227 + +--- EVAL SWEEP --- + BASELINE (no ngram): bpt=7.38585 (4s) + order=3 alpha=0.00 bpt=7.38585 delta=+0.00000 (9s) + order=3 alpha=0.05 bpt=7.38519 delta=-0.00066 (47s) + order=3 alpha=0.10 bpt=7.38779 delta=+0.00194 (48s) diff --git a/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_extended_analysis.log b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_extended_analysis.log new file mode 100644 index 0000000000..d7c5760ba4 --- /dev/null +++ b/records/track_non_record_16mb/2026-04-15_Causal_NGram_Null_Result/training_logs/results_extended_analysis.log @@ -0,0 +1,58 @@ +Loading data/datasets/fineweb10B_sp1024/fineweb_val_000000.bin... + using 2,000,000 tokens +Fitting bigram baseline... + bigram fit in 0.8s + +=== BASELINE: bigram only (no n-gram) === + ... 500000/1999999 bits/tok=6.2554 + ... 1000000/1999999 bits/tok=6.2768 + ... 1500000/1999999 bits/tok=6.2644 + order=4 per_doc alpha=0.10 bits/tok=6.19760 delta=-0.07470 (36s) + order=4 per_doc alpha=0.30 bits/tok=6.07219 delta=-0.20012 (36s) + order=4 per_doc alpha=0.50 bits/tok=5.98140 delta=-0.29091 (38s) + order=4 per_doc alpha=1.00 bits/tok=5.91542 delta=-0.35689 (32s) + order=4 per_doc alpha=1.50 bits/tok=6.07005 delta=-0.20225 (34s) + order=4 global alpha=0.00 bits/tok=6.27231 delta=+0.00000 (10s) + order=4 global alpha=0.10 bits/tok=6.12800 delta=-0.14430 (28s) + order=4 global alpha=0.30 bits/tok=5.88178 delta=-0.39052 (28s) + order=4 global alpha=0.50 bits/tok=5.69721 delta=-0.57509 (28s) + order=4 global alpha=1.00 bits/tok=5.51410 delta=-0.75820 (32s) + order=4 global alpha=1.50 bits/tok=5.66349 delta=-0.60882 (28s) + order=5 per_doc alpha=0.10 bits/tok=6.19847 delta=-0.07384 (32s) + order=5 per_doc alpha=0.30 bits/tok=6.07461 delta=-0.19770 (33s) + order=5 per_doc alpha=0.50 bits/tok=5.98508 delta=-0.28722 (35s) + order=5 per_doc alpha=1.00 bits/tok=5.92058 delta=-0.35173 (34s) + order=5 per_doc alpha=1.50 bits/tok=6.07452 delta=-0.19779 (32s) + order=5 global alpha=0.00 bits/tok=6.27231 delta=+0.00000 (13s) + order=5 global alpha=0.10 bits/tok=6.13662 delta=-0.13569 (32s) + order=5 global alpha=0.30 bits/tok=5.90417 delta=-0.36813 (33s) + order=5 global alpha=0.50 bits/tok=5.72783 delta=-0.54448 (40s) + order=5 global alpha=1.00 bits/tok=5.53853 delta=-0.73378 (36s) + order=5 global alpha=1.50 bits/tok=5.65488 delta=-0.61742 (33s) + time 2.5s bits/tok=6.27231 + +=== PER-DOC CACHE vs GLOBAL CACHE — alpha sweep === +order mode alpha bits/tok delta +-------------------------------------------------- + 4 per_doc 0.10 6.19760 -0.07470 + 4 per_doc 0.30 6.07219 -0.20012 + 4 per_doc 0.50 5.98140 -0.29091 + 4 per_doc 1.00 5.91542 -0.35689 + 4 per_doc 1.50 6.07005 -0.20225 + 4 global 0.00 6.27231 +0.00000 + 4 global 0.10 6.12800 -0.14430 + 4 global 0.30 5.88178 -0.39052 + 4 global 0.50 5.69721 -0.57509 + 4 global 1.00 5.51410 -0.75820 + 4 global 1.50 5.66349 -0.60882 + 5 per_doc 0.10 6.19847 -0.07384 + 5 per_doc 0.30 6.07461 -0.19770 + 5 per_doc 0.50 5.98508 -0.28722 + 5 per_doc 1.00 5.92058 -0.35173 + 5 per_doc 1.50 6.07452 -0.19779 + 5 global 0.00 6.27231 +0.00000 + 5 global 0.10 6.13662 -0.13569 + 5 global 0.30 5.90417 -0.36813 + 5 global 0.50 5.72783 -0.54448 + 5 global 1.00 5.53853 -0.73378 + 5 global 1.50 5.65488 -0.61742