--- license: mit tags: - training-logs - scaling-laws - hyperparameter-transfer - tpu - jax pretty_name: rig training logs --- # rig training logs Complete training logs for every GPT pretraining run in [honglu2875/rig](https://github.com/honglu2875/rig) — 177 runs across six studies, at full recorded resolution. Loss and learning-rate curves at every optimizer step; per-layer parameter, gradient, and update statistics at every diagnostic step. Nothing here is downsampled. The dashboards in the GitHub repository are thinned summaries of these files. What follows is that repository's audit of them, unchanged, so the two cannot drift apart. ## Layout ``` / / training.riglog loss, learning rate, gradient norm, per step diagnostics.riglog per-scope statistics, per diagnostic step result.json configuration, final metrics, provenance validation.csv held-out loss records.jsonl one ledger line per run snapshot.json.gz loss curves only, for the study browser ``` Run names state what varies: `500m-20tpp-bs128-lr2e-8-s1337` is the 500M tier at 20 tokens per parameter, batch 128, base learning rate 2^-8, seed 1337. ## A seed does not identify a run on its own The training stream *is* invariant under the process count. The global batch sequence is fixed by the seed alone, and each rank takes a slice of it: `_prepare_epoch` mixes only the seed and the epoch, and `next_batch` advances a global cursor by the whole global batch. Verified directly — 1, 4, and 8 processes produce byte-identical global batches. Results still differ across topologies. The same configuration and seed on 8 chips versus 16 lands 0.004 to 0.023 nats apart, which is the same size as the seed effect itself. The data is identical and so is the attention tile plan; both were checked. What differs is floating point: gradients are reduced across a different number of devices, so the sum is accumulated in a different order, and each chip holds a different number of sequences, which changes the shapes XLA compiles for. Neither is addressable by seeding, and both are the same non-associativity that makes any reduction order-dependent. So `--seed N` plus the configuration does not pin a number; the topology is part of it. Every run records `chip`, `data_processes` and `devices` in its study's `records.jsonl`, alongside the full `system` block in `result.json`, and the dashboards show them beside each run. | study | hardware | |---|---| | `batch-sweep-60M` | TPU v4 — 4 processes, 16 chips | | `lr-batch-sweep-125M` | TPU v4 — 4 processes, 16 chips | | `batch-sweep-250M` | TPU v4 — 4 processes, 16 chips | | `batch-sweep-500M` | **mixed**: 6 runs TPU v4 (4 proc, 16 chips), 6 runs TPU v6 lite (1 proc, 8 chips) | | `lr-transfer-250M` | TPU v4 — 4 processes, 16 chips | | `lr-sweep-8k-60M` | TPU v4 — 4 processes, 16 chips | | `moe-lr-sweep-8k` | TPU v4 — 4 processes, 16 chips | `batch-sweep-500M` is the only study spanning two chip types, and the split follows its 5- against 20-tokens-per-parameter arms. Those are separate experiments whose losses were never comparable, so the topology change does not cross a comparison that was being made. ## The format `.riglog` is a packed binary log: an 8-byte magic, a fixed header, a column table addressing each series by permanent integer ids, then fixed-width records. About 21x smaller than the long-form CSV it replaced, and it reads with one memory copy. ```python from huggingface_hub import hf_hub_download from rig import logpack path = hf_hub_download("quintic/rig-logs", "batch-sweep-60M/60m-5tpp-bs128-lr2e-8-s1337/training.riglog", repo_type="dataset") log = logpack.read_log(path) log.series("train_loss") # every optimizer step log.series("grad.l2_norm", "block", 7) # per-layer, from diagnostics ``` `logpack.layout_descriptor()` returns every offset and element type, derived from the definitions the writer uses, so a reader in another language can be built without reading the Python. --- Every dashboard here, the runs behind it, and the command that reproduces it. Commands are **demonstrative**: they use the current CLI and reproduce the *design*, not the exact invocation from the time. Seeds, tiers, and grids are exact. ## The logs live on HuggingFace **[huggingface.co/datasets/quintic/rig-logs](https://huggingface.co/datasets/quintic/rig-logs)** — 177 runs across six studies, laid out as `//`, at full recorded resolution. That is the archive of record; its [dataset card](https://huggingface.co/datasets/quintic/rig-logs/blob/main/README.md) is a copy of this file. The dashboards committed here are **summaries** of those logs, thinned so they stay portable. Nothing in them is a substitute for the logs: they are one rendering at one fidelity, and a thinned curve is indistinguishable on screen from a complete one. When a number matters, read it from the `.riglog`. ```python from huggingface_hub import hf_hub_download from rig import logpack path = hf_hub_download( "quintic/rig-logs", "batch-sweep-60M/60m-5tpp-bs128-lr2e-8-s1337/training.riglog", repo_type="dataset", ) log = logpack.read_log(path) log.series("train_loss") # every optimizer step ``` ## What "summary" means here Every series is thinned to at most **1,440 points**. Per-layer diagnostic charts additionally keep a bounded number of step frames — 8 for most studies, and more for the two where the per-layer behaviour is the subject rather than a by-product: | report | curve points | layer frames | size | |---|--:|--:|--:| | batch-size-sweep-60M | 1,440 | 400 | 44.3 MB | | batch-size-sweep-500M | 1,440 | 1,440 | 44.3 MB | | batch-size-sweep-250M | 1,440 | 8 | 15.4 MB | | lr-batch-sweep-125M | 1,440 | 8 | 8.2 MB | | 3-seed-gradient-spike | 1,440 | 8 | 6.6 MB | | 8k-lr-sweep-60M | 1,440 | 8 | 2.4 MB | | moe-lr-sweep-8k | 1,440 | 8 | 7.2 MB | The two large ones carry layer detail because gradient spikes are visible in it, and studying them is the point. This is deliberate discretion, not a default: keep it to a couple of files so the repository stays clonable. Charts resample against the visible span as you zoom, keeping each pixel bucket's minimum and maximum rather than one representative point — so a spike inside the embedded data stays visible at every zoom level. It cannot recover a sample that thinning already dropped. Charts are per-metric, and a metric no selected run recorded is not drawn at all — the panel is hidden rather than left as an empty frame. Routed runs record routing series a dense run never will, so most reports carry charts that do not apply to part of the selection, and a grid of empty frames would bury the ones that do. Which metrics get charted is a declared list in `rig/report.py`, separate from the metric registry, because how a quantity should be drawn is a judgement the registry cannot make. Everything so far is a line against the time axis; a distribution rather than a scalar — a routing histogram, say — wants bars against expert index and would arrive as a new chart kind rather than being bent into a timeline. ## The study browser [`study-browser.html`](study-browser.html) carries no data at all — 53 KB. It lists the studies, renders each one's card from the dataset, and fetches only that study's overview (0.05–0.30 MB) when you pick one. The full logs are a second, separately labelled click that states the size before it starts: 6.4 MB for the 8k sweep, 138 MB for the 500M one. Nothing downloads on load. Everything it fetches is an ordinary report payload, so the page never needs to understand the packed log format — the two only have to agree about JSON. ## Contents | report | runs | tier(s) | what varies | logs | |---|--:|---|---|---| | [batch-size-sweep-60M](batch-size-sweep-60M.html) | 75 | 60M | batch × LR × seed | [`batch-sweep-60M`](https://huggingface.co/datasets/quintic/rig-logs/tree/main/batch-sweep-60M) | | [lr-batch-sweep-125M](lr-batch-sweep-125M.html) | 27 | 125M | batch × LR × seed | [`lr-batch-sweep-125M`](https://huggingface.co/datasets/quintic/rig-logs/tree/main/lr-batch-sweep-125M) | | [batch-size-sweep-250M](batch-size-sweep-250M.html) | 36 | 250M | batch × LR × seed | [`batch-sweep-250M`](https://huggingface.co/datasets/quintic/rig-logs/tree/main/batch-sweep-250M) | | [batch-size-sweep-500M](batch-size-sweep-500M.html) | 12 | 500M | batch × LR × seed, 5 and 20 TPP | [`batch-sweep-500M`](https://huggingface.co/datasets/quintic/rig-logs/tree/main/batch-sweep-500M) | | [3-seed-gradient-spike](3-seed-gradient-spike.html) | 12 | 250M | LR × seed | [`lr-transfer-250M`](https://huggingface.co/datasets/quintic/rig-logs/tree/main/lr-transfer-250M) | | [8k-lr-sweep-60M](8k-lr-sweep-60M.html) | 15 | 60M | LR × seed at 8k context | [`lr-sweep-8k-60M`](https://huggingface.co/datasets/quintic/rig-logs/tree/main/lr-sweep-8k-60M) | | [moe-lr-sweep-8k](moe-lr-sweep-8k.html) | 18 | 60M/125M | LR × seed, top-2 of 8 experts | [`moe-lr-sweep-8k`](https://huggingface.co/datasets/quintic/rig-logs/tree/main/moe-lr-sweep-8k) | | [transfer-charts](transfer-charts.html) | — | — | derived figures, not a run dashboard | — | Each study also carries a `snapshot.json.gz` (loss curves only, 0.05–0.30 MB) and, for the two above, a `snapshot-diagnostics.json.gz` (1.0–3.5 MB). These are what the study browser loads before you ask it for anything larger. --- ## batch-size-sweep-60M.html 75 runs: **5 batches × 5 learning rates × 3 seeds** at 60M, 5 tokens per parameter, 1,024 context. The widest grid here, and what study 2 leans on. ```bash for bs in 32 64 128 256 512; do for lr in 0.015625 0.0078125 0.00390625 0.001953125 0.0009765625; do for seed in 1337 1338 1339; do rig run reference --cluster v4-32 --profile dev --track open \ --tier 60m --tokens-per-parameter 5 \ --study-batch-size "$bs" --base-learning-rate "$lr" --seed "$seed" \ --name "60m-bs${bs}-lr${lr}-s${seed}" done done done rig report --runs --max-points 1440 --layer-snapshots 400 \ --output docs/reports/batch-size-sweep-60M.html ``` ## lr-batch-sweep-125M.html 27 runs: **3 batches (64/128/256) × 3 learning rates (2^-7/2^-8/2^-9) × 3 seeds** at 125M, 5 TPP, 1,024 context. The grid is a batch × LR product, so either axis can be read as the subject. This replaces the former `batch-size-sweep-125M.html` and `lr-sweep-125M.html`, which were two renderings of these same 27 runs. ```bash for bs in 64 128 256; do for lr in 0.0078125 0.00390625 0.001953125; do for seed in 1337 1338 1339; do rig run reference --cluster v4-32 --profile dev --track open \ --tier 125m --tokens-per-parameter 5 \ --study-batch-size "$bs" --base-learning-rate "$lr" --seed "$seed" \ --name "125m-bs${bs}-lr${lr}-s${seed}" done done done ``` ## batch-size-sweep-250M.html 36 runs: **4 batches (64/128/256/512) × 3 learning rates × 3 seeds** at 250M, 5 TPP, 1,024 context. Three runs — `250m-5tpp-bs512-lr2e-7`, all three seeds — recorded diagnostics only from step 1920 onward. A report refuses a diagnostics log that does not start at step 1, because its axes would not line up with the training curve, so those three carry their partial series as `diagnostics-partial.riglog`: kept beside the run, not declared, read by nothing automatically. The runs still plot from their training curves rather than being dropped over it. ```bash for bs in 64 128 256 512; do for lr in 0.0078125 0.00390625 0.001953125; do for seed in 1337 1338 1339; do rig run reference --cluster v4-32 --profile dev --track open \ --tier 250m --tokens-per-parameter 5 \ --study-batch-size "$bs" --base-learning-rate "$lr" --seed "$seed" \ --name "250m-bs${bs}-lr${lr}-s${seed}" done done done ``` ## batch-size-sweep-500M.html 12 runs at two token budgets. Run names carry the budget (`500m-5tpp-…` against `500m-20tpp-…`) because the two are different experiments whose losses are not comparable to each other. This is study 3's dashboard. It replaces both the former `500M-20tpp-v6e.html` (three of these twelve) and `500M-20tpp-diagnostics.html`, which existed only because those three were once the only 500M runs whose diagnostics could be read. All twelve can now. ```bash # 5 TPP arm, batch bracket at the optimal LR for bs in 128 256; do for seed in 1337 1338 1339; do rig run reference --cluster v4-32 --profile dev --track open \ --tier 500m --tokens-per-parameter 5 \ --study-batch-size "$bs" --base-learning-rate 0.00390625 --seed "$seed" \ --name "500m-5tpp-bs${bs}-s${seed}" done done # 20 TPP arm on the v6e-8: batch bracket, then the LR bracket at batch 128 for bs in 64 128 256; do rig run reference --cluster v6e-8 --profile dev --track open \ --tier 500m --tokens-per-parameter 20 --checkpoint-policy none \ --study-batch-size "$bs" --base-learning-rate 0.00390625 --seed 1337 \ --name "500m-20tpp-bs${bs}-s1337" done for lr in 0.0078125 0.001953125; do rig run reference --cluster v6e-8 --profile dev --track open \ --tier 500m --tokens-per-parameter 20 --checkpoint-policy none \ --study-batch-size 128 --base-learning-rate "$lr" --seed 1337 \ --name "500m-20tpp-bs128-lr${lr}-s1337" done ``` ## 3-seed-gradient-spike.html 12 runs: **4 learning rates × 3 seeds** at 250M, batch 128, 5 TPP. Built to settle the 250M reseed in study 1, and the evidence base for [GRADIENT_SPIKES.md](../GRADIENT_SPIKES.md). Its diagnostics were unreadable long-form CSV until they were converted, so for a while the dashboard about gradient spikes contained no gradient statistics at all. ```bash for lr in 0.015625 0.0078125 0.00390625 0.001953125; do for seed in 1337 1338 1339; do rig run reference --cluster v4-32 --profile dev --track open \ --tier 250m --tokens-per-parameter 5 \ --study-batch-size 128 --base-learning-rate "$lr" --seed "$seed" \ --name "250m-lr${lr}-s${seed}" done done ``` ## 8k-lr-sweep-60M.html 15 runs: **5 learning rates × 3 seeds** of [`reference_8k`](../../recipes/reference_8k/) — 60M at 8,192 context with document masking, batch 16 so tokens per step and step count match the 1,024-context ladder exactly. This is study 4. ```bash for lr in 0.015625 0.0078125 0.00390625 0.001953125 0.0009765625; do for seed in 1337 1338 1339; do rig run reference_8k --cluster v4-32 --profile dev --track open \ --tier 60m --tokens-per-parameter 5 \ --base-learning-rate "$lr" --seed "$seed" --checkpoint-policy none \ --name "60m-bs16-lr${lr}-s${seed}" done done ``` ## moe-lr-sweep-8k.html 18 runs of [`reference_moe`](../../recipes/reference_moe/) — top-2 of 8 experts at 8,192 context, forked from the dense 8k ladder. 60M at five learning rates × three seeds, plus 125M spot runs at three learning rates. The routed ladder peaks at `2^-8`, the same learning rate the dense one does, and beats it at every learning rate by 0.07–0.12 nats at equal *active* parameters and matched compute, for about 1.7x the memory. No expert in any of the 12 layers finished below 1% of assignments in any of the 18 runs. This report carries six routing series the dense reports do not have: balance loss, busiest and idlest expert share, routing entropy, mean top-1 gate, and router logit RMS. They are recorded model-wide and per layer, with per-expert load for all 8 experts in all 12 layers, at every step. ```bash for lr in 0.015625 0.0078125 0.00390625 0.001953125 0.0009765625; do for seed in 1337 1338 1339; do rig run reference_moe --cluster v4-32 --profile dev --track open \ --tier 60m --tokens-per-parameter 5 \ --base-learning-rate "$lr" --seed "$seed" --checkpoint-policy none \ --name "60m-moe-lr${lr}-s${seed}" done done ``` ## transfer-charts.html Not a run dashboard. Derived figures built from recorded results by [`make_transfer_charts.py`](make_transfer_charts.py), committed beside it. ```bash uv run --frozen --no-sync python docs/reports/make_transfer_charts.py ``` --- ## Rebuilding Download a study from the dataset and point `rig report` at it: ```bash huggingface-cli download quintic/rig-logs --repo-type dataset \ --include 'batch-sweep-60M/*' --local-dir /tmp/rig-logs rig report --runs /tmp/rig-logs/batch-sweep-60M \ --max-points 1440 --layer-snapshots 400 \ --output docs/reports/batch-size-sweep-60M.html ``` `--max-points 0 --layer-snapshots 0` embeds every recorded sample. That is what the dataset holds; it makes a much larger file than anything committed here. ## Two runs that are not in the dataset - `20260816T213609.122328Z-…-37299d66` — a 500M run whose `stdout.log` was deleted while the process still held the descriptor, so no `result.json` was ever written. Its curves survive in the original archive but nothing records what it measured, so it cannot be placed on a chart. - A `studies` directory inside the 60M archive, which is not a run.