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README.md

@@ -9,11 +9,11 @@ tags:
 - scaling-laws
 ---
 
-# Alfredvc/chess-autocomplete-v1-checkpoints — training checkpoints (with optimizer state)
+# Alfredvc/chess-autocomplete-v1-checkpoints: training checkpoints (with optimizer state)
 
 34 checkpoints, 100 GB, across three model sizes and five token
 budgets. Each checkpoint is a `torch.save` payload carrying the full optimizer state
-(AdamW + Muon), the GradScaler and the dataset cursor, so training can be resumed from it.
+(AdamW + Muon), the GradScaler and the dataset cursor, so you can resume training from it.
 
 The models predict the move a human Lichess player of a given rating plays; Elo and time
 control are conditioning tokens. Weights-only inference repo (safetensors + ONNX,
@@ -31,7 +31,7 @@ control are conditioning tokens. Weights-only inference repo (safetensors + ONNX
 | `700m/` | 700M | **742.2M** | gpt2_gpt2large | 64k tok/s (283 TFLOP/s) |
 
 `params` is the exact count, embeddings included. The directory names are the GPT-2
-nicknames the runs were launched under; every number below uses `params`.
+nicknames we launched the runs under; every number below uses `params`.
 
 ## Layout
 
@@ -40,9 +40,9 @@ resumes the trunk at `0.8·T` and decays the LR to zero over the remaining `0.2
 it differs from the trunk config only in `max_iters` and `warmdown_iters`. Each budget `T`
 therefore has two checkpoints:
 
-* **pre-anneal** — the trunk checkpoint at `0.8·T`, LR still hot. Stored under `trunk/`
-  and referenced from `metrics.csv`, not duplicated under `anneal/`.
-* **post-anneal** — the branch's final checkpoint at `T-1`, LR decayed to zero.
+* **pre-anneal**: the trunk checkpoint at `0.8·T`, LR still hot. It lives under `trunk/`,
+  and `metrics.csv` points at it there rather than duplicating it under `anneal/`.
+* **post-anneal**: the branch's final checkpoint at `T-1`, LR decayed to zero.
 
 ```
 91m/
@@ -80,20 +80,21 @@ therefore has two checkpoints:
 700m/   (same layout; trunk still training, so no annealed 600k checkpoint yet)
 ```
 
-* `config.json` — the run's own config (model / train / hardware / tokenizers).
-* `training_config.yaml` — the merged effective YAML, `extends` chain resolved, including
+* `config.json`: the run's own config (model / train / hardware / tokenizers).
+* `training_config.yaml`: the merged effective YAML, `extends` chain resolved, including
   `data_config.shards`. `dataset_state.start_shard` is a bare index into that list.
-* `metrics.csv` / `metrics.json` — one row per checkpoint: its evals, tokens,
+* `metrics.csv` / `metrics.json`: one row per checkpoint, with its evals, tokens,
   `training_flops` (6ND), `gpu_hours`, and its path in this repo.
-* `index.json` — sha256 and byte size of every file.
-* `figures/` — the plots below, as PNG and SVG.
+* `index.json`: sha256 and byte size of every file.
+* `training-curves/`: the full W&B history of every run here, one CSV each.
+* `figures/`: the plots below, as PNG and SVG.
 
 ## Evals
 
 `top1_move_match_pct` on the Allie benchmark's 2022-blitz test set (884,049 positions,
 `maia3_table1` surface). **Δ acc** = post-anneal − pre-anneal.
 
-The LR is held at `max_lr` for the first 80% of a budget and annealed to zero over the
+We hold the LR at `max_lr` for the first 80% of a budget, then anneal it to zero over the
 final 20%, as `max_lr · (1 − √progress)`. The dashed line is the model before that anneal
 (the trunk at `0.8·T`, LR still at `max_lr`); the solid line is the same run after it (the
 branch final at `T`, LR at zero).
@@ -131,9 +132,110 @@ branch final at `T`, LR at zero).
 | 150,000 | 30.7B | 55.03 | **56.88** | +1.86 | 1.3031 | `700m/trunk/checkpoint-000120000.pt` | `700m/anneal/T150000/checkpoint-000149999.pt` |
 | 300,000 | 61.4B | 55.29 | **57.15** | +1.86 | 1.2917 | `700m/trunk/checkpoint-000240000.pt` | `700m/anneal/T300000/checkpoint-000299999.pt` |
 
+## Training curves
+
+The tables above give endpoints. `training-curves/` holds the runs behind them: every
+metric the training loop logged, for all 18 runs in this repo, as plain CSV.
+Nothing in the files is downsampled or smoothed, and the smoothing below happens only in
+the plots.
+
+![Validation loss over training](figures/training-loss.png)
+
+Each anneal branch resumes its trunk at `0.8·T` and decays the LR to zero. Every endpoint
+in the next plot is a checkpoint you can download:
+
+![Anneal branches leaving the trunk](figures/training-anneal.png)
+
+All three sizes run the same LR schedule, with Muon 5× hotter than AdamW. Below that, the
+gradient norms that schedule produces:
+
+![Learning-rate schedule](figures/training-lr.png)
+
+![Gradient norm before clipping](figures/training-grad-norm.png)
+
+Throughput on one `1x NVIDIA H100 NVL`. The wall-clock section below rests on these measurements.
+Bigger models use more of the GPU per step, so ranking the sizes by time and by FLOPs
+gives different answers:
+
+![Training throughput](figures/training-throughput.png)
+
+### What is in the CSVs
+
+Two logging cadences share one table: eval metrics every `eval_interval` steps, the rest
+every `log_interval` steps (500 and 100 for these runs). A cell is empty where the run
+logged nothing for that metric at that step, so read every column against `step` rather
+than against a row index.
+
+| column | cadence | meaning |
+| --- | --- | --- |
+| `step` | every logged step | optimizer step, absolute (a branch starts at 0.8·T) |
+| `tokens` | derived | `step × tokens_per_step`, the x-axis of every card curve |
+| `wall_clock_s` | every logged step | seconds since the run started |
+| `train_loss` | eval | next-token NLL on a held-out slice of the training shards |
+| `val_loss` | eval | next-token NLL on the held-out validation shard |
+| `valid_prob_mass` | eval | probability mass the model puts on legal moves |
+| `game_end_accuracy` | eval | accuracy on the game-terminating token |
+| `game_end_top1_accuracy` | eval | top-1 accuracy on the game-terminating token |
+| `batch_train_loss` | log | loss on the current microbatch, noisy by construction |
+| `lr` | log | AdamW learning rate |
+| `muon_lr` | log | Muon learning rate |
+| `unclipped_grad_norm` | log | ‖g‖ *before* `grad_clip = 1.0` is applied |
+| `tokens_per_second` | log | instantaneous, from the last iteration's duration |
+| `avg_tokens_per_second` | log | averaged over the interval since the last log |
+| `samples_per_second` | log | sequences/s, i.e. `tokens_per_second / 200` |
+| `t_flops` | log | the run's own FLOP/s estimate (TiFLOP/s, 1024⁴ scaling) |
+| `shard_idx` | log | index into `data_config.shards`, the dataset cursor |
+| `shard_sample_idx` | log | position within that shard |
+
+`step` is absolute. An anneal branch's history starts at `0.8·T` rather than at 0, because
+it continues its trunk.
+
+| run | steps logged | step range | tokens at the end | history |
+| --- | --- | --- | --- | --- |
+| 91M trunk | 6,001 | 0 → 600,000 | 122.9B | `training-curves/91m/trunk.csv` |
+| 91M anneal T=18,750 | 38 | 15,100 → 18,750 | 3.8B | `training-curves/91m/anneal-T018750.csv` |
+| 91M anneal T=37,500 | 75 | 30,100 → 37,500 | 7.7B | `training-curves/91m/anneal-T037500.csv` |
+| 91M anneal T=75,000 | 150 | 60,100 → 75,000 | 15.4B | `training-curves/91m/anneal-T075000.csv` |
+| 91M anneal T=150,000 | 300 | 120,100 → 150,000 | 30.7B | `training-curves/91m/anneal-T150000.csv` |
+| 91M anneal T=300,000 | 600 | 240,100 → 300,000 | 61.4B | `training-curves/91m/anneal-T300000.csv` |
+| 350M trunk | 6,001 | 0 → 600,000 | 122.9B | `training-curves/350m/trunk.csv` |
+| 350M anneal T=18,750 | 38 | 15,100 → 18,750 | 3.8B | `training-curves/350m/anneal-T018750.csv` |
+| 350M anneal T=37,500 | 75 | 30,100 → 37,500 | 7.7B | `training-curves/350m/anneal-T037500.csv` |
+| 350M anneal T=75,000 | 150 | 60,100 → 75,000 | 15.4B | `training-curves/350m/anneal-T075000.csv` |
+| 350M anneal T=150,000 | 300 | 120,100 → 150,000 | 30.7B | `training-curves/350m/anneal-T150000.csv` |
+| 350M anneal T=300,000 | 600 | 240,100 → 300,000 | 61.4B | `training-curves/350m/anneal-T300000.csv` |
+| 700M trunk | 5,204 | 0 → 520,400 | 106.6B | `training-curves/700m/trunk.csv` |
+| 700M anneal T=18,750 | 38 | 15,100 → 18,750 | 3.8B | `training-curves/700m/anneal-T018750.csv` |
+| 700M anneal T=37,500 | 75 | 30,100 → 37,500 | 7.7B | `training-curves/700m/anneal-T037500.csv` |
+| 700M anneal T=75,000 | 150 | 60,100 → 75,000 | 15.4B | `training-curves/700m/anneal-T075000.csv` |
+| 700M anneal T=150,000 | 300 | 120,100 → 150,000 | 30.7B | `training-curves/700m/anneal-T150000.csv` |
+| 700M anneal T=300,000 | 600 | 240,100 → 300,000 | 61.4B | `training-curves/700m/anneal-T300000.csv` |
+
+### Load them into your own W&B
+
+`training-curves/replay_to_wandb.py` loads these CSVs into your own W&B project, so you
+can plot your run against ours on the same axes. It needs `wandb` and nothing else:
+
+```bash
+pip install wandb
+python training-curves/replay_to_wandb.py --project my-project
+
+# or just the runs you want ("91m" means every 91M run)
+python training-curves/replay_to_wandb.py --project my-project --runs 91m/trunk 350m/anneal-T300000
+
+# log locally now, upload whenever
+WANDB_MODE=offline python training-curves/replay_to_wandb.py --project my-project
+wandb sync --sync-all
+```
+
+The script keeps the original step numbers and loads each run's `config.json` as its W&B
+config, so the run-comparison filters work the way they do on a run you trained yourself.
+It logs `tokens` as a metric too; pick that as the x-axis to compare by data seen rather
+than by step.
+
 ## Architecture
 
-All three are the same decoder-only transformer — discrete move-token embedding,
+All three share one decoder-only transformer: discrete move-token embedding,
 non-interleaved NeoX RoPE, SwiGLU MLP, LayerNorm, head dim 64, a 600-half-move context and
 a shared move+metadata vocabulary. They differ only in depth and width:
 
@@ -158,8 +260,8 @@ Cooled (post-anneal) points grouped into bands whose `C` agrees within 20%.
 | 1.26e+20 | 1.17× | 350M 56.78 · 700M 56.88 | **700M** |
 | 2.53e+20 | 1.17× | 350M 57.01 · 700M 57.15 | **700M** |
 
-The sweep grid is iso-*token*, not iso-FLOP: the bands span up to 1.17× in `C` (second
-column).
+The sweep grid is iso-*token* rather than iso-FLOP, so the bands span up to 1.17× in `C`
+(second column).
 
 ## Fitted surface
 
@@ -171,8 +273,8 @@ accuracy = 58.91 − 16.7·(N/1e6)^−0.455 − 4.49·(D/1e9)^−0.385
 R² = 0.99980
 ```
 
-Minimised under `6ND = C`: **N\* ∝ C^0.46**, **D\* ∝ C^0.54**. β is fit
-from five budgets per size, α from three sizes spanning 8×.
+Minimised under `6ND = C`: **N\* ∝ C^0.46**, **D\* ∝ C^0.54**. We fit β
+from five budgets per size and α from three sizes spanning 8×.
 
 ![Compute-optimal frontier and the N*/D* split](figures/frontier.png)
 
@@ -191,28 +293,29 @@ D\*/N\* ≈ 60.
 
 [`Alfredvc/chess-autocomplete-lichess`](https://huggingface.co/datasets/Alfredvc/chess-autocomplete-lichess) holds
 7.86B games = **528,651,645,230 move tokens** (528.7B), which is
-2.58M steps at 204,800 tokens per step. One pass, no repeats. Each
-size below is extrapolated to that `D` twice, by two fits that extend along different axes:
+2.58M steps at 204,800 tokens per step. One pass, no repeats. We
+extrapolate each size below to that `D` twice, with two fits that extend along different
+axes:
 
-* the **`L(N,D)` surface**, fit on all three sizes at once. It extends in **N**: it can be
-  evaluated at a size nobody trained (that is what the frontier table above does).
+* the **`L(N,D)` surface**, fit on all three sizes at once. It extends in **N**, so it
+  gives a number for a size nobody trained (the frontier table above does exactly that).
 * that size's **own `L(D)` curve** (`E + B·(D/1e9)^−β`, fit on its points alone). It
   extends in **D**, for that exact architecture, with no assumption tying it to the other
   sizes.
 
 ![Measured token curves extended to the full dataset](figures/projection.png)
 
-| model | largest run | accuracy there | full dataset is | on the full dataset — L(N,D) surface | on the full dataset — that size's own L(D) curve | C = 6ND | GPU-hours |
+| model | largest run | accuracy there | full dataset is | full dataset, L(N,D) surface | full dataset, that size's own L(D) curve | C = 6ND | GPU-hours |
 | --- | --- | --- | --- | --- | --- | --- | --- |
 | 91M | 122.9B tok | 56.05% | 4.3× that | 56.36% | 56.35% | 2.9e+20 | 349 |
 | 350M | 122.9B tok | 57.01% | 4.3× that | 57.29% | 57.32% | 1.0e+21 | 1,080 |
 | 700M | 61.4B tok | 57.15% | 8.6× that | 57.68% | 57.63% | 2.4e+21 | 2,313 |
 
-The largest run in this repo is 122.9B tokens, so both fits are read 4.3–8.6× beyond the
-tokens they were fit on. The surface's ceiling (N→∞, D→∞) is 58.91%.
+The largest run in this repo is 122.9B tokens, so you are reading both fits 4.3–8.6×
+beyond the tokens behind them. The surface's ceiling (N→∞, D→∞) is 58.91%.
 
-The token count above is the whole dataset; the training runs exclude the benchmark months
-(2019-12, 2023-12, all of 2022), which is not subtracted here.
+The token count above covers the whole dataset. The training runs exclude the benchmark
+months (2019-12, 2023-12, all of 2022), which we do not subtract here.
 
 The same compute, spent as the surface prefers:
 
@@ -225,20 +328,20 @@ The same compute, spent as the surface prefers:
 ## Wall-clock
 
 Accuracy per GPU-hour, from each size's measured `avg_tokens_per_second` on the same GPU
-(`1x NVIDIA H100 NVL`). Accuracy is interpolated along that size's own measured curve; a blank cell is
-a budget outside the range that was run.
+(`1x NVIDIA H100 NVL`). We interpolate accuracy along that size's own measured curve; a blank cell is
+a budget outside the range we ran.
 
 ![Accuracy against wall-clock hours on one GPU](figures/isotime.png)
 
 | GPU-hours (1× H100 NVL) | 91M | 350M | 700M |
 | --- | --- | --- | --- |
-| 5h | 54.71%  (8B tok) | — | — |
-| 10h | 55.20%  (15B tok) | 55.22%  (5B tok) | — |
+| 5h | 54.71%  (8B tok) |  |  |
+| 10h | 55.20%  (15B tok) | 55.22%  (5B tok) |  |
 | 20h | 55.55%  (30B tok) | 55.81%  (10B tok) | 55.58%  (5B tok) |
 | 40h | 55.84%  (61B tok) | 56.25%  (20B tok) | 56.17%  (9B tok) |
 | 80h | 56.05%  (121B tok) | 56.59%  (39B tok) | 56.60%  (18B tok) |
-| 160h | — | 56.86%  (78B tok) | 56.95%  (37B tok) |
-| 240h | — | 57.00%  (118B tok) | 57.11%  (55B tok) |
+| 160h |  | 56.86%  (78B tok) | 56.95%  (37B tok) |
+| 240h |  | 57.00%  (118B tok) | 57.11%  (55B tok) |
 
 ## The same surface fit on the pre-anneal points
 
@@ -289,15 +392,15 @@ in the shipped `training_config.yaml`.
 
 ## Metric definitions
 
-* `top1_move_match_pct` — % of positions where the model's argmax move equals the human's.
-* `mean_nll_legal` / `perplexity_legal` — NLL of the human move, renormalized over legal
+* `top1_move_match_pct`: % of positions where the model's argmax move equals the human's.
+* `mean_nll_legal` / `perplexity_legal`: NLL of the human move, renormalized over legal
   moves, on the Allie 2022-blitz test set.
-* `final_val_loss` — next-token NLL on the held-out validation shard, from the training
+* `final_val_loss`: next-token NLL on the held-out validation shard, from the training
   run's own final eval. A different distribution from `mean_nll_legal`. Pre-anneal rows
-  have no value: a mid-schedule checkpoint has no end-of-run eval block.
-* `training_flops` = `6 · N · D`; the attention term (~3–4% at sequence length 200) is
-  excluded.
-* All eval probability math runs in fp32, and every point was scored on one host with one
+  have no value, since a mid-schedule checkpoint has no end-of-run eval block.
+* `training_flops` = `6 · N · D`, which leaves out the attention term (~3–4% at sequence
+  length 200).
+* All eval probability math runs in fp32, and we scored every point on one host with one
   code version.
 
 ## Training data
@@ -306,9 +409,9 @@ Lichess standard rated games (`lichess_db_standard_rated_*`), most recent months
 `data_config.shards` in any `training_config.yaml` has the exact list and order. Sequence
 length 200 half-moves, batch size 1024, so **tokens = iter × 204,800**.
 
-These models are benchmarked against Maia-1/2/3 and Allie, so the months those test sets
-are drawn from are excluded from training: 2019-12, 2023-12, and all of 2022. The pipeline
-has no game-id holdout.
+We benchmark these models against Maia-1/2/3 and Allie, so training excludes the months
+those test sets draw from: 2019-12, 2023-12, and all of 2022. The pipeline has no game-id
+holdout.
 
 The full training corpus is at
 [`Alfredvc/chess-autocomplete-lichess`](https://huggingface.co/datasets/Alfredvc/chess-autocomplete-lichess); the evaluation

index.json

@@ -1,6 +1,6 @@
 {
   "repo_id": "Alfredvc/chess-autocomplete-v1-checkpoints",
-  "total_bytes": 99730475390,
+  "total_bytes": 99736679106,
   "files": [
     {
       "path": "350m/anneal/T018750/checkpoint-000018749.pt",
@@ -354,8 +354,8 @@
     },
     {
       "path": "README.md",
-      "size": 15278,
-      "sha256": "73b13b3eb9b2f06fd1a8be6630fa50791bdf7a513b9433d96289561fa8a3c818"
+      "size": 20962,
+      "sha256": "f3f3e151b8c816f6b77374c9361475a052d26da870c322dc6efd8433bce253ac"
     },
     {
       "path": "figures/anneal.png",
@@ -365,7 +365,7 @@
     {
       "path": "figures/anneal.svg",
       "size": 61148,
-      "sha256": "eb0c7cc639ddefc47bcc632bbe5220ab2f2175f5ac211745cfdd141390d87f62"
+      "sha256": "5742255e75a78c84b122d03b4135097994f0f2d4b42a0c262579b887303bd60b"
     },
     {
       "path": "figures/frontier.png",
@@ -375,7 +375,7 @@
     {
       "path": "figures/frontier.svg",
       "size": 120161,
-      "sha256": "1121eaf5cc834c7490635d59c609d6bbe85be65bbd84a8fda6c0e7dbad6cec5e"
+      "sha256": "e1bb1ca8f8a6e1f7dee261b7d2a1f89fe7b8edbb890d49e81e940e8120ef919b"
     },
     {
       "path": "figures/isoflop.png",
@@ -385,7 +385,7 @@
     {
       "path": "figures/isoflop.svg",
       "size": 67456,
-      "sha256": "bd3550144956a98c0ba40832d00df01b578d00205836ca0ba885c0984f0290a8"
+      "sha256": "f16ada10f6771d84ec3903a33f3b776927671518b82959d260eaedeee7db10bd"
     },
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       "path": "figures/isotime.png",
@@ -395,7 +395,7 @@
     {
       "path": "figures/isotime.svg",
       "size": 74233,
-      "sha256": "6bd4fb82302221181ec989380bcb6d905bd8bdb5e820cca2b0a96bd5630eda8f"
+      "sha256": "63fc2cc7e41b1793939f531cbc0cd7dd002115cc546447fa20a3e313625980ab"
     },
     {
       "path": "figures/projection.png",
@@ -405,7 +405,57 @@
     {
       "path": "figures/projection.svg",
       "size": 80775,
-      "sha256": "83359c17ee4138bce332f5f18a9d8fd71ae0e4c0e40e2bdd65d40d1cbe999882"
+      "sha256": "9aa3811a3001bb124a5b844b22bdea4dd7d5f9ef80c02e6e872c8b0a5948f999"
+    },
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+      "size": 172082,
+      "sha256": "040bebd9b5b4ab82308f9a199b53ed6621cc36e518c4fd457ae130b97e38fc16"
+    },
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+      "size": 179061,
+      "sha256": "5fa7a9cf5e15c79162f0048963338c6e5cea1c2bfafca0ef12e19ca9a4e04285"
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+      "sha256": "56b5e3b42416d1e82a099c313c61ed16c1c9bb20103afa1b62b946e104ed8f57"
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+      "sha256": "4de88fc608028173d293398e48dd84552377f260b12ce474208bad8951571a15"
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+      "size": 183964,
+      "sha256": "49aaa61019b242361fdf7fcc2927186d470336342ea6753c26f421dea5eca94d"
+    },
+    {
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+      "size": 281513,
+      "sha256": "8f1acab85d3e0d8c2da07a90680ac17b573fa844aff1ca57b5f2d875f849944f"
+    },
+    {
+      "path": "figures/training-lr.png",
+      "size": 100602,
+      "sha256": "03ffdc41fb2604fd5295e2da23f0d1b8b6fdde947180d39b76d5fdd774d67024"
+    },
+    {
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+      "size": 71508,
+      "sha256": "a4d3f6fbaa810c67a4c4568ed5bbfd02054fa51be014b580c7e6760cf752ef3b"
+    },
+    {
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+      "size": 128397,
+      "sha256": "b4570bc1aa1c6b3e4be22a5b30a6aa882c14fca777fa71b698f1f2d356f0b3a6"
+    },
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+      "size": 191838,
+      "sha256": "6850de57f92f576f196e68a170629bb53794501388806e887893cfd4f314ed90"
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@@ -416,6 +466,101 @@
       "path": "metrics.json",
       "size": 16527,
       "sha256": "2a52419794a2926936b4a9389dab5417ea6990d7279a0aed15bb5f91e52c22d6"
+    },
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+      "size": 8654,
+      "sha256": "40fb3d13a321d8d3abfb690f99a9a4f10ec9c41327cd6f6c8f8e6003096dd5de"
+    },
+    {
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training-curves/91m/anneal-T018750.csv

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training-curves/91m/anneal-T037500.csv

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training-curves/replay_to_wandb.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+"""Replay the published training curves into your own Weights & Biases project.
+
+The CSVs next to this script are the full W&B history of every run in this repo. This
+turns them back into real W&B runs in *your* account, so you can put your own training
+next to ours on the same axes instead of eyeballing a PNG.
+
+    pip install wandb
+    python replay_to_wandb.py --project my-project
+
+    # just the runs you care about ("91m" means every 91M run)
+    python replay_to_wandb.py --project my-project --runs 91m/trunk 350m/anneal-T300000
+    python replay_to_wandb.py --project my-project --runs 91m
+
+    # log locally first, upload when you feel like it
+    WANDB_MODE=offline python replay_to_wandb.py --project my-project
+    wandb sync --sync-all
+
+Each replayed run keeps its original step numbers, so an anneal branch starts at 0.8·T
+rather than at 0 — it is a continuation of its trunk, not a fresh run. `tokens` is logged
+as a metric too: pick it as the x-axis in the W&B UI to compare runs by data seen rather
+than by step.
+
+If you cloned the whole repo (not just `training-curves/`), each run's `config.json` is
+found automatically and becomes the run's W&B config, so the config panel and W&B's
+run-comparison filters work the way they would on a run you trained yourself.
+
+No dependency but `wandb` — this is deliberately standalone, so it keeps working after
+the rest of this repo has gone stale.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import sys
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+STEP_COLUMN = "step"
+
+
+def discover(curves_dir: Path) -> list[tuple[str, Path]]:
+    """Every published history, as (run key, csv path). Key is "<model>/<branch>"."""
+    found = []
+    for path in sorted(curves_dir.glob("*/*.csv")):
+        found.append((f"{path.parent.name}/{path.stem}", path))
+    return found
+
+
+def selected(runs: list[tuple[str, Path]], wanted: list[str] | None) -> list[tuple[str, Path]]:
+    if not wanted:
+        return runs
+    keep = [(key, path) for key, path in runs if key in wanted or key.split("/")[0] in wanted]
+    unknown = set(wanted) - {k for k, _ in runs} - {k.split("/")[0] for k, _ in runs}
+    if unknown:
+        raise SystemExit(f"unknown run(s): {', '.join(sorted(unknown))}")
+    return keep
+
+
+def config_for(repo_root: Path, key: str) -> dict:
+    """The run's own config.json, if this is a full checkout of the repo.
+
+    `91m/trunk` lives at `91m/trunk/`, `91m/anneal-T018750` at `91m/anneal/T018750/` —
+    the CSV name flattens the checkpoint tree's two levels into one.
+    """
+    model, branch = key.split("/", 1)
+    if branch == "trunk":
+        path = repo_root / model / "trunk" / "config.json"
+    else:
+        path = repo_root / model / "anneal" / branch.removeprefix("anneal-") / "config.json"
+    if not path.is_file():
+        return {}
+    raw = json.loads(path.read_text(encoding="utf-8"))
+    # Flatten the config sections into one namespace, as the training loop logged it.
+    flat: dict = {}
+    for section in ("model_config", "train_config", "hardware_config", "tokenizers_config"):
+        flat.update(raw.get(section, {}))
+    return flat
+
+
+def rows_of(path: Path) -> list[dict]:
+    """History rows, with unlogged cells dropped rather than sent as empty strings.
+
+    Two cadences share one table (evals every 500 steps, the rest every 100), so most
+    rows carry only the log-cadence metrics. Logging the blanks would put nulls in W&B
+    where the run simply did not measure anything.
+    """
+    out = []
+    with path.open(newline="", encoding="utf-8") as handle:
+        for raw in csv.DictReader(handle):
+            row = {key: float(value) for key, value in raw.items() if value not in ("", None)}
+            if STEP_COLUMN in row:
+                out.append(row)
+    return out
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("--project", required=True, help="W&B project to log into.")
+    parser.add_argument("--entity", help="W&B entity (default: your default entity).")
+    parser.add_argument(
+        "--curves-dir", type=Path, default=HERE, help="Where the CSVs are (default: here)."
+    )
+    parser.add_argument(
+        "--runs",
+        nargs="+",
+        metavar="MODEL[/BRANCH]",
+        help='Limit to these runs, e.g. "91m/trunk" or "91m" for all 91M runs.',
+    )
+    parser.add_argument(
+        "--name-prefix",
+        default="",
+        help='Prepended to each run name, e.g. "ref-" -> "ref-91m-trunk".',
+    )
+    parser.add_argument(
+        "--dry-run", action="store_true", help="List what would be logged, log nothing."
+    )
+    args = parser.parse_args(argv)
+
+    runs = selected(discover(args.curves_dir), args.runs)
+    if not runs:
+        raise SystemExit(f"no curve CSVs under {args.curves_dir}")
+
+    if not args.dry_run:
+        import wandb
+
+    repo_root = args.curves_dir.parent
+    for key, path in runs:
+        rows = rows_of(path)
+        name = args.name_prefix + key.replace("/", "-")
+        print(f"  {key:26} {len(rows):>6,} steps -> {name}", flush=True)
+        if args.dry_run:
+            continue
+        with wandb.init(
+            entity=args.entity,
+            project=args.project,
+            name=name,
+            config=config_for(repo_root, key),
+            tags=["chess-autocomplete-v1", "replay"],
+        ) as run:
+            for row in rows:
+                step = int(row.pop(STEP_COLUMN))
+                run.log(row, step=step)
+
+    print(f"\n{len(runs)} run(s) {'listed' if args.dry_run else 'logged'}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())