diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..6ee6d363073461f3449f8a3c7056c7d20890acaf --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +.DS_Store +__pycache__/ +*.py[cod] +.cache/ +*.npy diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..e43910411415bbf9ff9f261f2107b08cf48a179a --- /dev/null +++ b/LICENSE @@ -0,0 +1,26 @@ +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Additional project attribution: +This repository contains research code derived from Andrej Karpathy's +nanochat project (https://github.com/karpathy/nanochat), which is distributed +under the MIT License above. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..243ecfdc139ef2ccad6b616492f90838364afe98 --- /dev/null +++ b/README.md @@ -0,0 +1,89 @@ +# Dropout Decay Streaming Experiments + +This project tests dropout decay only after first finding a model/data regime +where static dropout has a real nonzero validation optimum. + +The implementation is derived from Andrej Karpathy's `nanochat` repository: +https://github.com/karpathy/nanochat. Only the core tokenizer ideas and +foundational causal Transformer architecture are retained. Chat interfaces, +deployment scripts, distributed training code, and inference services are not +included. The original nanochat MIT copyright and permission notice are retained +in derived source files and in `LICENSE`. + +## Compliance + +All Torch experiment runs are MPS-only. The runner exits before model creation if +MPS is unavailable, if PyTorch was not built with MPS, or if +`PYTORCH_ENABLE_MPS_FALLBACK=1` is set. + +## Workflow + +1. Screen candidate model sizes with cheap static dropout sweeps. +2. Select candidate models whose validation curve has an interior nonzero + dropout optimum. +3. Confirm the winner with a 3-seed static sweep. +4. Lock the model and run static-vs-decay streaming comparisons from scratch. + +Every run writes: + +- `config.json`: command, model specs, data paths, environment, attribution. +- `metrics.jsonl`: one row per seed/model/dropout/stage. +- `trace.jsonl`: optional training and intermediate evaluation trace. +- `summary.csv` / `summary.json`: mean/std train loss, validation loss, and gap. +- `model_selection.csv` / `model_selection.json`: static-sweep optimum and + plateau diagnostics for screen and confirm runs. + +Old exploratory outputs are archived under `archive/`. + +## Step 1: Cheap Static Screen + +Use one or two seeds. The output tells us, for each model, where the static +dropout curve bottoms out and which dropout range is within the configured +plateau delta. + +```bash +PYTHONPATH=src /Users/mandeepsidhu/Desktop/code/nanochat/.venv/bin/python scripts/run_experiments.py \ + --mode screen_static \ + --corpus-glob "/Users/mandeepsidhu/Desktop/code/nanochat/.nanochat-cache/base_data_climbmix/shard_*.parquet" \ + --models 8x8x256 12x8x384 16x8x384 \ + --seeds 1 2 \ + --token-limits 5000000 \ + --dropout-rates 0.0 0.02 0.05 0.08 0.10 0.14 0.20 0.30 0.50 \ + --steps 2000 \ + --eval-batches 64 +``` + +## Step 2: Confirm Winner + +After selecting a promising model, rerun the static dropout curve with exactly +three seeds. + +```bash +PYTHONPATH=src /Users/mandeepsidhu/Desktop/code/nanochat/.venv/bin/python scripts/run_experiments.py \ + --mode confirm_static \ + --corpus-glob "/Users/mandeepsidhu/Desktop/code/nanochat/.nanochat-cache/base_data_climbmix/shard_*.parquet" \ + --models winner=12x8x384 \ + --seeds 1 2 3 \ + --token-limits 5000000 \ + --dropout-rates 0.0 0.02 0.05 0.08 0.10 0.14 0.20 0.30 0.50 \ + --steps 2000 \ + --eval-batches 64 +``` + +## Step 3: Locked Streaming Comparison + +Only after the model is locked, compare static dropout and decay schedules from +fresh initialization. + +```bash +PYTHONPATH=src /Users/mandeepsidhu/Desktop/code/nanochat/.venv/bin/python scripts/run_experiments.py \ + --mode locked_stream \ + --corpus-glob "/Users/mandeepsidhu/Desktop/code/nanochat/.nanochat-cache/base_data_climbmix/shard_*.parquet" \ + --models winner=12x8x384 \ + --seeds 1 2 3 \ + --stream-token-caps 5000000 10000000 20000000 40000000 \ + --dropout-rates 0.0 0.10 0.14 0.20 \ + --decays decay_030_to_014:0.30:0.14:cosine decay_020_to_010:0.20:0.10:cosine \ + --stage-steps 1000 \ + --eval-batches 64 +``` diff --git a/archive/legacy_docs/control_results.md b/archive/legacy_docs/control_results.md new file mode 100644 index 0000000000000000000000000000000000000000..500304c95aab5dfe5b73adf0b69349e27acdb3e8 --- /dev/null +++ b/archive/legacy_docs/control_results.md @@ -0,0 +1,48 @@ +# Control Results + +These controls were run after the initial core experiment to isolate whether +dropout decay caused the streaming improvement. + +Both runs used CLIMB-mix parquet data from the local nanochat cache, MPS-only +Torch execution, five seeds, a 4096-token BPE vocabulary, 500k validation tokens, +and the same 8.39M-parameter large model. + +## Run Artifacts + +- Core run: `runs/publishable/20260522-132351` +- Control run: `runs/controls/20260522-141120` + +## Matched Streaming Comparison + +| Condition | 5M | 10M | 20M | 40M | +| --- | ---: | ---: | ---: | ---: | +| Dropout decay streaming | 6.9213 +/- 0.0434 | 6.2689 +/- 0.0539 | 5.4262 +/- 0.0722 | 4.9090 +/- 0.0452 | +| Static 0.1 dropout streaming | 5.6310 +/- 0.0438 | 5.1018 +/- 0.0306 | 4.8497 +/- 0.0338 | 4.6743 +/- 0.0449 | +| Static 0.8 dropout streaming | 6.9898 +/- 0.0505 | 6.7637 +/- 0.0762 | 6.4835 +/- 0.0408 | 6.2390 +/- 0.0543 | + +## Other Controls + +| Condition | Token limit | Mean eval loss | Std | +| --- | ---: | ---: | ---: | +| Large static baseline, fixed initial data | 5M | 5.2036 | 0.0258 | +| Large dropout decay, fixed initial data | 5M | 6.7389 | 0.0484 | +| Large high static dropout, fixed initial data | 5M | 6.8355 | 0.0613 | +| Large static full data from start | 40M | 4.6915 | 0.0430 | + +## Conclusion + +The original strong hypothesis is falsified in this configuration. A high-capacity +model does absorb the expanding data stream, but dropout decay from 0.8 to 0.1 is +not the causal reason for the improvement. The matched static 0.1 dropout +streaming control beats dropout decay at every token cap, and high static dropout +performs poorly. + +The narrower supported claim is: + +> An 8.39M-parameter causal Transformer can continuously improve as the simulated +> stream grows from 5M to 40M tokens, but for this setup static 0.1 dropout is +> stronger than a high-initial-dropout decay schedule. + +Extra tuning could search for a better schedule, such as a lower initial dropout +or a faster decay horizon, but that would be a new hypothesis rather than support +for the original one. diff --git a/archive/legacy_docs/stage_dropout_search.md b/archive/legacy_docs/stage_dropout_search.md new file mode 100644 index 0000000000000000000000000000000000000000..8a41165bc26ccd3d683e0545e1edd3753dfe3936 --- /dev/null +++ b/archive/legacy_docs/stage_dropout_search.md @@ -0,0 +1,82 @@ +# Stage Dropout Search + +This search tested whether the original failure came from the `0.8` initial +dropout rather than from the decay idea itself. + +All runs used the same CLIMB-mix corpus, 8.39M-parameter large model, 5M/10M/20M/ +40M stream caps, MPS-only execution, and 64 validation batches. + +## Single-Seed Probe + +Seed: 1. + +| Condition | 5M | 10M | 20M | 40M | +| --- | ---: | ---: | ---: | ---: | +| Static 0.1 dropout | 5.5670 | 5.0850 | 4.8676 | 4.7417 | +| Old 0.8 cosine decay | 6.9460 | 6.2013 | 5.3635 | 4.9677 | +| Mild: 0.30 / 0.22 / 0.14 / 0.10 | 5.7511 | 5.1962 | 4.9055 | 4.7524 | +| Medium: 0.50 / 0.32 / 0.16 / 0.10 | 6.1544 | 5.3261 | 4.9566 | 4.8095 | +| Fast: 0.50 / 0.10 / 0.10 / 0.10 | 6.1544 | 5.1626 | 4.8824 | 4.7583 | + +The medium schedule was clearly weaker. Mild and fast were close enough to static +0.1 at 40M to justify multi-seed runs. + +## Multi-Seed Follow-Up + +Seeds: 1, 2, 3, 4, 5. + +| Condition | 5M | 10M | 20M | 40M | +| --- | ---: | ---: | ---: | ---: | +| Static 0.0 dropout | 5.5694 +/- 0.0465 | 5.0527 +/- 0.0596 | 4.7800 +/- 0.0423 | 4.5998 +/- 0.0391 | +| Static 0.01 dropout | 5.5807 +/- 0.0503 | 5.0627 +/- 0.0630 | 4.7904 +/- 0.0405 | 4.6152 +/- 0.0525 | +| Static 0.02 dropout | 5.5873 +/- 0.0436 | 5.0575 +/- 0.0396 | 4.8009 +/- 0.0406 | 4.6192 +/- 0.0455 | +| Static 0.03 dropout | 5.5920 +/- 0.0436 | 5.0734 +/- 0.0505 | 4.8110 +/- 0.0410 | 4.6281 +/- 0.0448 | +| Static 0.04 dropout | 5.5964 +/- 0.0435 | 5.0752 +/- 0.0490 | 4.8165 +/- 0.0444 | 4.6416 +/- 0.0475 | +| Static 0.06 dropout | 5.6075 +/- 0.0476 | 5.0797 +/- 0.0394 | 4.8324 +/- 0.0399 | 4.6499 +/- 0.0430 | +| Static 0.08 dropout | 5.6146 +/- 0.0425 | 5.0849 +/- 0.0363 | 4.8358 +/- 0.0350 | 4.6541 +/- 0.0387 | +| Static 0.1 dropout | 5.6310 +/- 0.0438 | 5.1018 +/- 0.0306 | 4.8497 +/- 0.0338 | 4.6743 +/- 0.0449 | +| Static 0.14 dropout | 5.6544 +/- 0.0361 | 5.1239 +/- 0.0318 | 4.8742 +/- 0.0323 | 4.7003 +/- 0.0365 | +| Static 0.20 dropout | 5.6960 +/- 0.0453 | 5.1722 +/- 0.0320 | 4.9164 +/- 0.0286 | 4.7492 +/- 0.0379 | +| Sub-0.1: 0.10 / 0.08 / 0.06 / 0.05 | 5.6310 +/- 0.0438 | 5.0922 +/- 0.0312 | 4.8311 +/- 0.0352 | 4.6496 +/- 0.0499 | +| Sub-0.08: 0.08 / 0.06 / 0.04 / 0.03 | 5.6146 +/- 0.0425 | 5.0768 +/- 0.0386 | 4.8163 +/- 0.0372 | 4.6322 +/- 0.0405 | +| Mild: 0.30 / 0.22 / 0.14 / 0.10 | 5.8129 +/- 0.0528 | 5.2047 +/- 0.0351 | 4.8991 +/- 0.0294 | 4.7034 +/- 0.0415 | +| Fast: 0.50 / 0.10 / 0.10 / 0.10 | 6.2006 +/- 0.0451 | 5.1861 +/- 0.0352 | 4.8875 +/- 0.0350 | 4.7025 +/- 0.0414 | +| Old 0.8 cosine decay | 6.9213 +/- 0.0434 | 6.2689 +/- 0.0539 | 5.4262 +/- 0.0722 | 4.9090 +/- 0.0452 | + +## Conclusion + +The original initial dropout was too large, and the old schedule kept dropout too +large for too long. Lower and faster schedules recover most of the lost +performance, which supports the diagnosis that `0.8` dropout was the main +problem. + +However, the revised decay schedules still do not beat static `0.1` dropout. +The best revised schedule at 40M was fast decay at `4.7025 +/- 0.0414`, compared +with static `0.1` at `4.6743 +/- 0.0449`. The gap is small, but not favorable. + +The sub-0.1 schedules changed the result relative to static `0.1`, but the +static sub-0.1 plateau sweep changed the interpretation. Static dropout kept +improving as dropout was lowered across the tested grid, and the best tested +condition is now static `0.0`, not a decay schedule. + +Updated supported claim: + +> High initial dropout is harmful in this setup. Dropout values above 0.1 lose +> monotonically in the tested static sweeps. Sub-0.1 decay improves over static +> 0.1, but static sub-0.1 controls show that this improvement is likely caused +> by using lower dropout values, not by decay itself. On the current 8.39M +> parameter model and CLIMB-mix stream, the static-dropout plateau was not found +> above zero. + +Run artifacts: + +- Single mild: `runs/stage-dropout-single/20260523-075308` +- Single medium: `runs/stage-dropout-single/20260523-075636` +- Single fast: `runs/stage-dropout-single/20260523-080008` +- Multi mild: `runs/stage-dropout-multiseed/20260523-080428` +- Multi fast: `runs/stage-dropout-multiseed/20260523-082216` +- Static 0.14: `runs/static-dropout-sweep/20260523-084122` +- Static 0.20: `runs/static-dropout-sweep/20260523-085817` +- Static sub-0.1 sweep: `runs/static-sub01-sweep/20260525-074602` +- Sub-0.1: `runs/sub01-dropout-decay/20260525-070308` +- Sub-0.08: `runs/sub01-dropout-decay/20260525-071923` diff --git a/archive/runs_legacy_20260525/control-smoke/20260522-141107/config.json b/archive/runs_legacy_20260525/control-smoke/20260522-141107/config.json new file mode 100644 index 0000000000000000000000000000000000000000..8d5b7651bf86f441d73b34c89283afe6343d4114 --- /dev/null +++ b/archive/runs_legacy_20260525/control-smoke/20260522-141107/config.json @@ -0,0 +1,57 @@ +{ + "args": { + "corpus": "/Users/mandeepsidhu/Desktop/code/transformer/data/tinystories_train.txt", + "corpus_glob": null, + "text_column": "text", + "output_dir": "runs/control-smoke", + "suite": "controls", + "seeds": [ + 1, + 2, + 3, + 4, + 5 + ], + "initial_tokens": 20000, + "stream_token_caps": [ + 20000, + 40000 + ], + "val_tokens": 500000, + "allow_short_corpus": true, + "force_retokenize": false, + "vocab_size": 512, + "tokenizer_train_chars": 200000, + "block_size": 32, + "batch_size": 4, + "small_layers": 4, + "small_heads": 4, + "small_embd": 128, + "large_layers": 8, + "large_heads": 8, + "large_embd": 256, + "steps_per_run": 1, + "stream_steps_per_stage": 1, + "eval_batches": 1, + "log_every": 0, + "lr": 0.0003, + "weight_decay": 0.1, + "grad_clip": 1.0, + "baseline_dropout": 0.1, + "high_dropout": 0.8, + "dropout_decay_tokens": null, + "dropout_schedule": "cosine" + }, + "device": "mps", + "torch": "2.9.1", + "attribution": "Derived from Andrej Karpathy's nanochat project (https://github.com/karpathy/nanochat), MIT License, Copyright (c) 2025 Andrej Karpathy.", + "tokenizer_path": "runs/control-smoke/20260522-141107/tokenizer.json", + "encoded_path": "runs/control-smoke/20260522-141107/tokens-v512-uint16.npy", + "train_tokens": 777983, + "val_tokens": 86442, + "effective_initial_tokens": 20000, + "effective_stream_token_caps": [ + 20000, + 40000 + ] +} \ No newline at end of file diff --git a/archive/runs_legacy_20260525/control-smoke/20260522-141107/metrics.jsonl b/archive/runs_legacy_20260525/control-smoke/20260522-141107/metrics.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bdf0e54a267f1d5eec2de387f2c1e1d8776f09f4 --- /dev/null +++ b/archive/runs_legacy_20260525/control-smoke/20260522-141107/metrics.jsonl @@ -0,0 +1,30 @@ 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The earlier +8.39M-parameter setup did not show a nonzero static-dropout optimum, so it was +not a valid regime for the decay hypothesis. + +## Objective + +Find a causal Transformer/data/training-budget regime where static dropout has +an interior optimum, then lock that regime before comparing static dropout +against dropout decay under simulated streaming data. + +## Selection Criterion + +A candidate model is eligible for the locked comparison only if the static +dropout sweep shows: + +- validation loss at `dropout=0.0` is worse than the best nonzero dropout by at + least the configured `--min-nonzero-margin`; +- high dropout is worse than the best rate by at least + `--min-high-dropout-margin`; +- the best rate preferably satisfies `--target-min-dropout`, default `0.10`; +- the curve has a clear bottom or narrow plateau rather than monotonic + improvement toward zero. + +The runner records these diagnostics in `model_selection.csv`. + +## Step 1: Cheap Static Screen + +Run `screen_static` with one or two seeds across a grid of model sizes and static +dropout rates. This is exploratory and should not be used as final evidence. + +Primary outputs: + +- per-seed rows in `metrics.jsonl`; +- aggregate curve in `summary.csv`; +- per-model best dropout, plateau range, and margins in `model_selection.csv`. + +## Step 2: Confirm Winner + +Run `confirm_static` on the best candidate with exactly three seeds. This locks +the model size and static dropout reference curve. + +The winning model should be chosen before running any decay comparisons. + +## Step 3: Locked Streaming Comparison + +Run `locked_stream` from fresh initialization for each seed. Compare: + +- static dropout at the confirmed optimum; +- static controls around the optimum; +- one or more decay schedules that start above the confirmed optimum and end at + or near the confirmed optimum. + +The final table should report token cap, condition, parameter count, seed count, +mean validation loss, standard deviation, train loss, and generalization gap. +The final figure should plot validation loss by stream token cap with error bars. diff --git a/docs/screen_static_results.md b/docs/screen_static_results.md new file mode 100644 index 0000000000000000000000000000000000000000..5c98f19ed39d060305ea9da019af36256de3b743 --- /dev/null +++ b/docs/screen_static_results.md @@ -0,0 +1,81 @@ +# Static Dropout Screen Summary + +Run directory: `runs/screen_static/20260525-133008` + +Model: `L16_H8_D384` causal Transformer, 31,457,280 parameters, 16 layers, 8 heads, 384 embedding dim, block size 128, vocab size 4096. +Training per condition: 5,000 steps x sampled batches = 10,240,000 sampled tokens. Seeds present: 1. + +## Best Dropout By Prefix + +| Prefix tokens | Effective epochs | Best dropout | Val loss | Train eval loss | Gap | Plateau/bracket note | +|---:|---:|---:|---:|---:|---:|---| +| 250,000 | 40.96 | 0.60 | 5.5055 | 3.3185 | 2.1870 | bracketed by tested grid | +| 500,000 | 20.48 | 0.40 | 4.9814 | 3.2797 | 1.7017 | bracketed by tested grid | +| 1,000,000 | 10.24 | 0.30 | 4.6511 | 3.6295 | 1.0216 | bracketed by tested grid | +| 2,000,000 | 5.12 | 0.14 | 4.4270 | 3.7761 | 0.6509 | bracketed by tested grid | + +## Prefix 250,000 Tokens (40.96 Effective Epochs) + +| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params | +|---:|---:|---:|---:|---:|---:| +| 0.00 | 8.2018 | 0.2792 | 7.9225 | 10,240,000 | 31,457,280 | +| 0.02 | 8.0714 | 0.2606 | 7.8108 | 10,240,000 | 31,457,280 | +| 0.05 | 7.9582 | 0.2673 | 7.6909 | 10,240,000 | 31,457,280 | +| 0.10 | 7.5884 | 0.3386 | 7.2498 | 10,240,000 | 31,457,280 | +| 0.14 | 7.2849 | 0.4997 | 6.7851 | 10,240,000 | 31,457,280 | +| 0.20 | 6.8383 | 0.8225 | 6.0158 | 10,240,000 | 31,457,280 | +| 0.30 | 6.2429 | 1.3910 | 4.8519 | 10,240,000 | 31,457,280 | +| 0.40 | 5.8444 | 2.0533 | 3.7911 | 10,240,000 | 31,457,280 | +| 0.50 | 5.5925 | 2.6789 | 2.9136 | 10,240,000 | 31,457,280 | +| 0.60 | 5.5055 | 3.3185 | 2.1870 | 10,240,000 | 31,457,280 | +| 0.70 | 5.5800 | 3.9233 | 1.6567 | 10,240,000 | 31,457,280 | +| 0.80 | 5.8399 | 4.7252 | 1.1147 | 10,240,000 | 31,457,280 | +| 0.90 | 6.8439 | 6.3553 | 0.4885 | 10,240,000 | 31,457,280 | + +## Prefix 500,000 Tokens (20.48 Effective Epochs) + +| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params | +|---:|---:|---:|---:|---:|---:| +| 0.00 | 7.2889 | 0.7200 | 6.5689 | 10,240,000 | 31,457,280 | +| 0.02 | 6.8954 | 0.9118 | 5.9837 | 10,240,000 | 31,457,280 | +| 0.05 | 6.4974 | 1.2327 | 5.2647 | 10,240,000 | 31,457,280 | +| 0.10 | 5.9033 | 1.6819 | 4.2214 | 10,240,000 | 31,457,280 | +| 0.14 | 5.6262 | 1.9904 | 3.6358 | 10,240,000 | 31,457,280 | +| 0.20 | 5.3271 | 2.4160 | 2.9111 | 10,240,000 | 31,457,280 | +| 0.30 | 5.0666 | 2.8953 | 2.1713 | 10,240,000 | 31,457,280 | +| 0.40 | 4.9814 | 3.2797 | 1.7017 | 10,240,000 | 31,457,280 | +| 0.50 | 4.9836 | 3.6291 | 1.3544 | 10,240,000 | 31,457,280 | +| 0.60 | 5.0664 | 4.0093 | 1.0571 | 10,240,000 | 31,457,280 | +| 0.70 | 5.2668 | 4.4357 | 0.8311 | 10,240,000 | 31,457,280 | +| 0.80 | 5.5867 | 5.0149 | 0.5718 | 10,240,000 | 31,457,280 | +| 0.90 | 6.6203 | 6.3616 | 0.2587 | 10,240,000 | 31,457,280 | + +## Prefix 1,000,000 Tokens (10.24 Effective Epochs) + +| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params | +|---:|---:|---:|---:|---:|---:| +| 0.00 | 5.6524 | 2.2476 | 3.4048 | 10,240,000 | 31,457,280 | +| 0.02 | 5.3384 | 2.4646 | 2.8738 | 10,240,000 | 31,457,280 | +| 0.05 | 5.1150 | 2.7036 | 2.4114 | 10,240,000 | 31,457,280 | +| 0.10 | 4.8428 | 2.9922 | 1.8506 | 10,240,000 | 31,457,280 | +| 0.14 | 4.7516 | 3.1948 | 1.5568 | 10,240,000 | 31,457,280 | +| 0.20 | 4.6756 | 3.3926 | 1.2830 | 10,240,000 | 31,457,280 | +| 0.30 | 4.6511 | 3.6295 | 1.0216 | 10,240,000 | 31,457,280 | +| 0.40 | 4.6992 | 3.8497 | 0.8495 | 10,240,000 | 31,457,280 | +| 0.50 | 4.7691 | 4.0636 | 0.7055 | 10,240,000 | 31,457,280 | +| 0.60 | 4.9042 | 4.3281 | 0.5761 | 10,240,000 | 31,457,280 | +| 0.70 | 5.1201 | 4.6689 | 0.4512 | 10,240,000 | 31,457,280 | +| 0.80 | 5.5044 | 5.1892 | 0.3151 | 10,240,000 | 31,457,280 | +| 0.90 | 6.6273 | 6.5073 | 0.1200 | 10,240,000 | 31,457,280 | + +## Prefix 2,000,000 Tokens (5.12 Effective Epochs) + +| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params | +|---:|---:|---:|---:|---:|---:| +| 0.00 | 4.6252 | 3.3746 | 1.2506 | 10,240,000 | 31,457,280 | +| 0.02 | 4.5426 | 3.4838 | 1.0589 | 10,240,000 | 31,457,280 | +| 0.05 | 4.4872 | 3.5720 | 0.9152 | 10,240,000 | 31,457,280 | +| 0.10 | 4.4366 | 3.6868 | 0.7498 | 10,240,000 | 31,457,280 | +| 0.14 | 4.4270 | 3.7761 | 0.6509 | 10,240,000 | 31,457,280 | +| 0.20 | 4.4508 | 3.9086 | 0.5422 | 10,240,000 | 31,457,280 | +| 0.30 | 4.4949 | 4.0400 | 0.4549 | 10,240,000 | 31,457,280 | diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..9f261b1da33f06024943ddd05d15972f690222f2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "dropout-decay" +version = "0.1.0" +description = "MPS-only experiments for dropout decay under simulated streaming text data." +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "numpy>=1.26", + "pyarrow>=15", + "tokenizers>=0.15", + "torch>=2.3", +] + +[tool.pytest.ini_options] +pythonpath = ["src"] +testpaths = ["tests"] diff --git a/runs/screen_static/20260525-121208/config.json b/runs/screen_static/20260525-121208/config.json new file mode 100644 index 0000000000000000000000000000000000000000..fd4aeba0e91003cece7de939feb60d435230ee45 --- /dev/null +++ b/runs/screen_static/20260525-121208/config.json @@ -0,0 +1,103 @@ +{ + "args": { + "mode": 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5000970, + 5000970 + ] +} \ No newline at end of file diff --git a/runs/screen_static/20260525-121208/metrics.jsonl b/runs/screen_static/20260525-121208/metrics.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d3a88490f6b9e064afc6ee9734a1f82159590ed9 --- /dev/null +++ b/runs/screen_static/20260525-121208/metrics.jsonl @@ -0,0 +1,10 @@ +{"condition": "static_dropout_0", "condition_kind": "static", "dropout_active_final": 0.0, "dropout_final": 0.0, "dropout_initial": 0.0, "dropout_schedule": "constant", "elapsed_sec": 72.88827776908875, "eval_loss": 4.934970378875732, "generalization_gap": 0.11433742940425873, "model_config": {"block_size": 128, "dropout": 0.0, "n_embd": 256, "n_head": 8, "n_layer": 8, "vocab_size": 4096}, "model_name": "L8_H8_D256", "n_embd": 256, "n_head": 8, "n_layer": 8, "parameters": 8388608, "run_mode": "screen_static", "seed": 1, "stage": null, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_eval_loss": 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"dropout_initial": 0.0, "dropout_schedule": "constant", "elapsed_sec": 163.4799518585205, "eval_loss": 4.6640482395887375, "generalization_gap": 0.17826169729232788, "model_config": {"block_size": 128, "dropout": 0.0, "n_embd": 384, "n_head": 8, "n_layer": 12, "vocab_size": 4096}, "model_name": "L12_H8_D384", "n_embd": 384, "n_head": 8, "n_layer": 12, "parameters": 24379392, "run_mode": "screen_static", "seed": 1, "stage": null, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_eval_loss": 4.48578654229641, "train_loss_last": 4.5579376220703125, "val_eval_loss": 4.6640482395887375} diff --git a/runs/screen_static/20260525-121208/trace.jsonl b/runs/screen_static/20260525-121208/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c3f8d36bc8c0b0870955f4f8dc48b5f515c35a9e --- /dev/null +++ b/runs/screen_static/20260525-121208/trace.jsonl @@ -0,0 +1,41 @@ +{"condition": "static_dropout_0", "dropout": 0.0, "event": "train_step", "model_name": 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"train_batch_loss": 4.557938575744629} +{"condition": "static_dropout_0.02", "dropout": 0.02, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.691278457641602} +{"condition": "static_dropout_0.02", "dropout": 0.02, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 4.875328063964844} +{"condition": "static_dropout_0.02", "dropout": 0.02, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.696069717407227} +{"condition": "static_dropout_0.02", "dropout": 0.02, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.689076900482178} +{"condition": "static_dropout_0.05", "dropout": 0.05, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.571812152862549} +{"condition": "static_dropout_0.05", "dropout": 0.05, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 5.018867492675781} +{"condition": "static_dropout_0.05", "dropout": 0.05, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.781268119812012} 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"step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.823153495788574} +{"condition": "static_dropout_0.08", "dropout": 0.08, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.714095115661621} +{"condition": "static_dropout_0.1", "dropout": 0.1, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.623278617858887} +{"condition": "static_dropout_0.1", "dropout": 0.1, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 5.082090377807617} +{"condition": "static_dropout_0.1", "dropout": 0.1, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.767928600311279} +{"condition": "static_dropout_0.1", "dropout": 0.1, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.704160690307617} +{"condition": "static_dropout_0.14", "dropout": 0.14, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.560118675231934} +{"condition": "static_dropout_0.14", "dropout": 0.14, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 5.168047904968262} +{"condition": "static_dropout_0.14", "dropout": 0.14, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.736873149871826} +{"condition": "static_dropout_0.14", "dropout": 0.14, "event": "train_step", "model_name": "L12_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.759419918060303} +{"best_val_loss": 4.664048179984093, "event": "screen_pruned_model", "model_name": "L12_H8_D384", "pruned_after_dropout": 0.14, "remaining_dropouts": [0.2, 0.3, 0.5], "run_mode": "screen_static", "token_limit": 5000000, "worse_streak": 3} +{"condition": "static_dropout_0", "dropout": 0.0, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.537887096405029} +{"condition": "static_dropout_0", "dropout": 0.0, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 4.869041442871094} +{"condition": "static_dropout_0", "dropout": 0.0, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.733972549438477} +{"condition": "static_dropout_0", "dropout": 0.0, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.474400997161865} +{"condition": "static_dropout_0.02", "dropout": 0.02, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.470076084136963} +{"condition": "static_dropout_0.02", "dropout": 0.02, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 4.847103118896484} +{"condition": "static_dropout_0.02", "dropout": 0.02, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.637824058532715} +{"condition": "static_dropout_0.02", "dropout": 0.02, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.413399696350098} +{"condition": "static_dropout_0.05", "dropout": 0.05, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.622530937194824} +{"condition": "static_dropout_0.05", "dropout": 0.05, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 4.948265552520752} +{"condition": "static_dropout_0.05", "dropout": 0.05, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.757878303527832} +{"condition": "static_dropout_0.05", "dropout": 0.05, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.396986961364746} +{"condition": "static_dropout_0.08", "dropout": 0.08, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.593175411224365} +{"condition": "static_dropout_0.08", "dropout": 0.08, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 4.971923828125} +{"condition": "static_dropout_0.08", "dropout": 0.08, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.747159957885742} +{"condition": "static_dropout_0.08", "dropout": 0.08, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.577962875366211} +{"condition": "static_dropout_0.1", "dropout": 0.1, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 1024000, "train_batch_loss": 5.791213035583496} +{"condition": "static_dropout_0.1", "dropout": 0.1, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 2048000, "train_batch_loss": 5.024307727813721} +{"condition": "static_dropout_0.1", "dropout": 0.1, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1500, "steps": 2000, "token_limit": 5000000, "tokens_seen": 3072000, "train_batch_loss": 4.838321685791016} +{"condition": "static_dropout_0.1", "dropout": 0.1, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 2000, "token_limit": 5000000, "tokens_seen": 4096000, "train_batch_loss": 4.739377975463867} +{"best_val_loss": 4.6260832995176315, "event": "screen_pruned_model", "model_name": "L16_H8_D384", "pruned_after_dropout": 0.1, "remaining_dropouts": [0.14, 0.2, 0.3, 0.5], "run_mode": "screen_static", "token_limit": 5000000, "worse_streak": 3} diff --git a/runs/screen_static/20260525-133008/RESULT_SUMMARY.md b/runs/screen_static/20260525-133008/RESULT_SUMMARY.md new file mode 100644 index 0000000000000000000000000000000000000000..5c98f19ed39d060305ea9da019af36256de3b743 --- /dev/null +++ b/runs/screen_static/20260525-133008/RESULT_SUMMARY.md @@ -0,0 +1,81 @@ +# Static Dropout Screen Summary + +Run directory: `runs/screen_static/20260525-133008` + +Model: `L16_H8_D384` causal Transformer, 31,457,280 parameters, 16 layers, 8 heads, 384 embedding dim, block size 128, vocab size 4096. +Training per condition: 5,000 steps x sampled batches = 10,240,000 sampled tokens. Seeds present: 1. + +## Best Dropout By Prefix + +| Prefix tokens | Effective epochs | Best dropout | Val loss | Train eval loss | Gap | Plateau/bracket note | +|---:|---:|---:|---:|---:|---:|---| +| 250,000 | 40.96 | 0.60 | 5.5055 | 3.3185 | 2.1870 | bracketed by tested grid | +| 500,000 | 20.48 | 0.40 | 4.9814 | 3.2797 | 1.7017 | bracketed by tested grid | +| 1,000,000 | 10.24 | 0.30 | 4.6511 | 3.6295 | 1.0216 | bracketed by tested grid | +| 2,000,000 | 5.12 | 0.14 | 4.4270 | 3.7761 | 0.6509 | bracketed by tested grid | + +## Prefix 250,000 Tokens (40.96 Effective Epochs) + +| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params | +|---:|---:|---:|---:|---:|---:| +| 0.00 | 8.2018 | 0.2792 | 7.9225 | 10,240,000 | 31,457,280 | +| 0.02 | 8.0714 | 0.2606 | 7.8108 | 10,240,000 | 31,457,280 | +| 0.05 | 7.9582 | 0.2673 | 7.6909 | 10,240,000 | 31,457,280 | +| 0.10 | 7.5884 | 0.3386 | 7.2498 | 10,240,000 | 31,457,280 | +| 0.14 | 7.2849 | 0.4997 | 6.7851 | 10,240,000 | 31,457,280 | +| 0.20 | 6.8383 | 0.8225 | 6.0158 | 10,240,000 | 31,457,280 | +| 0.30 | 6.2429 | 1.3910 | 4.8519 | 10,240,000 | 31,457,280 | +| 0.40 | 5.8444 | 2.0533 | 3.7911 | 10,240,000 | 31,457,280 | +| 0.50 | 5.5925 | 2.6789 | 2.9136 | 10,240,000 | 31,457,280 | +| 0.60 | 5.5055 | 3.3185 | 2.1870 | 10,240,000 | 31,457,280 | +| 0.70 | 5.5800 | 3.9233 | 1.6567 | 10,240,000 | 31,457,280 | +| 0.80 | 5.8399 | 4.7252 | 1.1147 | 10,240,000 | 31,457,280 | +| 0.90 | 6.8439 | 6.3553 | 0.4885 | 10,240,000 | 31,457,280 | + +## Prefix 500,000 Tokens (20.48 Effective Epochs) + +| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params | +|---:|---:|---:|---:|---:|---:| +| 0.00 | 7.2889 | 0.7200 | 6.5689 | 10,240,000 | 31,457,280 | +| 0.02 | 6.8954 | 0.9118 | 5.9837 | 10,240,000 | 31,457,280 | +| 0.05 | 6.4974 | 1.2327 | 5.2647 | 10,240,000 | 31,457,280 | +| 0.10 | 5.9033 | 1.6819 | 4.2214 | 10,240,000 | 31,457,280 | +| 0.14 | 5.6262 | 1.9904 | 3.6358 | 10,240,000 | 31,457,280 | +| 0.20 | 5.3271 | 2.4160 | 2.9111 | 10,240,000 | 31,457,280 | +| 0.30 | 5.0666 | 2.8953 | 2.1713 | 10,240,000 | 31,457,280 | +| 0.40 | 4.9814 | 3.2797 | 1.7017 | 10,240,000 | 31,457,280 | +| 0.50 | 4.9836 | 3.6291 | 1.3544 | 10,240,000 | 31,457,280 | +| 0.60 | 5.0664 | 4.0093 | 1.0571 | 10,240,000 | 31,457,280 | +| 0.70 | 5.2668 | 4.4357 | 0.8311 | 10,240,000 | 31,457,280 | +| 0.80 | 5.5867 | 5.0149 | 0.5718 | 10,240,000 | 31,457,280 | +| 0.90 | 6.6203 | 6.3616 | 0.2587 | 10,240,000 | 31,457,280 | + +## Prefix 1,000,000 Tokens (10.24 Effective Epochs) + +| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params | +|---:|---:|---:|---:|---:|---:| +| 0.00 | 5.6524 | 2.2476 | 3.4048 | 10,240,000 | 31,457,280 | +| 0.02 | 5.3384 | 2.4646 | 2.8738 | 10,240,000 | 31,457,280 | +| 0.05 | 5.1150 | 2.7036 | 2.4114 | 10,240,000 | 31,457,280 | +| 0.10 | 4.8428 | 2.9922 | 1.8506 | 10,240,000 | 31,457,280 | +| 0.14 | 4.7516 | 3.1948 | 1.5568 | 10,240,000 | 31,457,280 | +| 0.20 | 4.6756 | 3.3926 | 1.2830 | 10,240,000 | 31,457,280 | +| 0.30 | 4.6511 | 3.6295 | 1.0216 | 10,240,000 | 31,457,280 | +| 0.40 | 4.6992 | 3.8497 | 0.8495 | 10,240,000 | 31,457,280 | +| 0.50 | 4.7691 | 4.0636 | 0.7055 | 10,240,000 | 31,457,280 | +| 0.60 | 4.9042 | 4.3281 | 0.5761 | 10,240,000 | 31,457,280 | +| 0.70 | 5.1201 | 4.6689 | 0.4512 | 10,240,000 | 31,457,280 | +| 0.80 | 5.5044 | 5.1892 | 0.3151 | 10,240,000 | 31,457,280 | +| 0.90 | 6.6273 | 6.5073 | 0.1200 | 10,240,000 | 31,457,280 | + +## Prefix 2,000,000 Tokens (5.12 Effective Epochs) + +| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params | +|---:|---:|---:|---:|---:|---:| +| 0.00 | 4.6252 | 3.3746 | 1.2506 | 10,240,000 | 31,457,280 | +| 0.02 | 4.5426 | 3.4838 | 1.0589 | 10,240,000 | 31,457,280 | +| 0.05 | 4.4872 | 3.5720 | 0.9152 | 10,240,000 | 31,457,280 | +| 0.10 | 4.4366 | 3.6868 | 0.7498 | 10,240,000 | 31,457,280 | +| 0.14 | 4.4270 | 3.7761 | 0.6509 | 10,240,000 | 31,457,280 | +| 0.20 | 4.4508 | 3.9086 | 0.5422 | 10,240,000 | 31,457,280 | +| 0.30 | 4.4949 | 4.0400 | 0.4549 | 10,240,000 | 31,457,280 | diff --git a/runs/screen_static/20260525-133008/config.json b/runs/screen_static/20260525-133008/config.json new file mode 100644 index 0000000000000000000000000000000000000000..63e1513c27a71433c702bfee017fe02090faf485 --- /dev/null +++ b/runs/screen_static/20260525-133008/config.json @@ -0,0 +1,96 @@ +{ + "args": { + "mode": "screen_static", + "corpus": null, + "corpus_glob": "/Users/mandeepsidhu/Desktop/code/nanochat/.nanochat-cache/base_data_climbmix/shard_*.parquet", + "text_column": "text", + "output_dir": "runs", + "cache_dir": ".cache/dropout_decay", + "models": [ + "L16_H8_D384=16x8x384" + ], + "seeds": [ + 1 + ], + "token_limits": [ + 250000, + 500000, + 1000000, + 2000000 + ], + "stream_token_caps": [ + 5000000, + 10000000, + 20000000, + 40000000 + ], + "val_tokens": 500000, + "allow_short_corpus": false, + "force_retokenize": false, + "vocab_size": 4096, + "tokenizer_train_chars": 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"seed": 1, "stage": null, "step": 4000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 8192000, "train_batch_loss": 4.574949264526367} +{"condition": "static_dropout_0.6", "dropout": 0.6, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 5000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 10240000, "train_batch_loss": 4.563268661499023} +{"condition": "static_dropout_0.7", "dropout": 0.7, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 2048000, "train_batch_loss": 6.020793914794922} +{"condition": "static_dropout_0.7", "dropout": 0.7, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 4096000, "train_batch_loss": 5.401340484619141} +{"condition": "static_dropout_0.7", "dropout": 0.7, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 3000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 6144000, "train_batch_loss": 5.268392562866211} +{"condition": "static_dropout_0.7", "dropout": 0.7, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 4000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 8192000, "train_batch_loss": 5.00155782699585} +{"condition": "static_dropout_0.7", "dropout": 0.7, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 5000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 10240000, "train_batch_loss": 4.82606315612793} +{"condition": "static_dropout_0.8", "dropout": 0.8, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 2048000, "train_batch_loss": 6.453203201293945} +{"condition": "static_dropout_0.8", "dropout": 0.8, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 4096000, "train_batch_loss": 6.091620445251465} +{"condition": "static_dropout_0.8", "dropout": 0.8, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 3000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 6144000, "train_batch_loss": 5.664243221282959} +{"condition": "static_dropout_0.8", "dropout": 0.8, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 4000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 8192000, "train_batch_loss": 5.549333572387695} +{"condition": "static_dropout_0.8", "dropout": 0.8, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 5000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 10240000, "train_batch_loss": 5.380241394042969} +{"condition": "static_dropout_0.9", "dropout": 0.9, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 1000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 2048000, "train_batch_loss": 6.893714904785156} +{"condition": "static_dropout_0.9", "dropout": 0.9, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 2000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 4096000, "train_batch_loss": 6.5322465896606445} +{"condition": "static_dropout_0.9", "dropout": 0.9, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 3000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 6144000, "train_batch_loss": 6.371788024902344} +{"condition": "static_dropout_0.9", "dropout": 0.9, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 4000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 8192000, "train_batch_loss": 6.3164262771606445} +{"condition": "static_dropout_0.9", "dropout": 0.9, "event": "train_step", "model_name": "L16_H8_D384", "run_mode": "screen_static", "seed": 1, "stage": null, "step": 5000, "steps": 5000, "token_limit": 1000000, "tokens_seen": 10240000, "train_batch_loss": 6.164768695831299} diff --git a/scripts/run_experiments.py b/scripts/run_experiments.py new file mode 100644 index 0000000000000000000000000000000000000000..d1c9086c6b573d6999817ff95f63f4f7ce484a42 --- /dev/null +++ b/scripts/run_experiments.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python3 +""" +Derived from Andrej Karpathy's nanochat project. + +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +""" + +from dropout_decay.experiment import main + + +if __name__ == "__main__": + main() diff --git a/src/dropout_decay/__init__.py b/src/dropout_decay/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..264c7f5052fe81e6409d5685570488f138c9953c --- /dev/null +++ b/src/dropout_decay/__init__.py @@ -0,0 +1,20 @@ +""" +Derived from Andrej Karpathy's nanochat project. + +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +""" + +__all__ = ["__version__"] +__version__ = "0.1.0" diff --git a/src/dropout_decay/data.py b/src/dropout_decay/data.py new file mode 100644 index 0000000000000000000000000000000000000000..18cdb6604c837c8fb48c0339a4166c49ffe2342b --- /dev/null +++ b/src/dropout_decay/data.py @@ -0,0 +1,158 @@ +""" +Derived from Andrej Karpathy's nanochat project. + +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +""" + +from __future__ import annotations + +from array import array +from dataclasses import dataclass +from glob import glob +from itertools import islice +from pathlib import Path +from typing import Iterable + +import numpy as np +import pyarrow.parquet as pq + +from dropout_decay.tokenizer import BpeTokenizer + + +@dataclass(frozen=True) +class TokenSplits: + train: np.ndarray + val: np.ndarray + tokenizer_path: Path + encoded_path: Path + + +def resolve_paths(corpus: str | None, corpus_glob: str | None) -> list[Path]: + paths: list[Path] = [] + if corpus: + paths.append(Path(corpus)) + if corpus_glob: + paths.extend(Path(p) for p in sorted(glob(corpus_glob))) + if not paths: + raise ValueError("provide --corpus or --corpus-glob") + missing = [str(p) for p in paths if not p.exists()] + if missing: + raise FileNotFoundError("missing corpus paths: " + ", ".join(missing)) + return paths + + +def iter_documents( + paths: Iterable[Path], text_column: str = "text", doc_cap_chars: int | None = None +) -> Iterable[str]: + for path in paths: + suffix = path.suffix.lower() + if suffix == ".parquet": + parquet_file = pq.ParquetFile(path) + for batch in parquet_file.iter_batches(columns=[text_column], batch_size=2048): + column = batch.column(0).to_pylist() + for text in column: + if text: + yield text[:doc_cap_chars] if doc_cap_chars else text + else: + text = path.read_text(encoding="utf-8") + chunk_size = doc_cap_chars or 1_000_000 + for start in range(0, len(text), chunk_size): + chunk = text[start : start + chunk_size] + if chunk: + yield chunk + + +def train_or_load_tokenizer( + paths: list[Path], + output_dir: Path, + vocab_size: int, + tokenizer_train_chars: int, + text_column: str, + force_retrain: bool = False, +) -> BpeTokenizer: + tokenizer_path = output_dir / f"tokenizer-v{vocab_size}.json" + if tokenizer_path.exists() and not force_retrain: + return BpeTokenizer.from_file(tokenizer_path) + + def capped_docs() -> Iterable[str]: + chars = 0 + for doc in iter_documents(paths, text_column=text_column, doc_cap_chars=20_000): + if chars >= tokenizer_train_chars: + break + remaining = tokenizer_train_chars - chars + piece = doc[:remaining] + chars += len(piece) + yield piece + + tokenizer = BpeTokenizer.train_from_iterator(capped_docs(), vocab_size) + tokenizer.save(tokenizer_path) + return tokenizer + + +def encode_corpus( + paths: list[Path], + tokenizer: BpeTokenizer, + output_dir: Path, + max_required_train_tokens: int, + val_tokens: int, + text_column: str, + allow_short_corpus: bool, + force_reencode: bool = False, +) -> TokenSplits: + dtype = np.uint16 if tokenizer.vocab_size <= np.iinfo(np.uint16).max else np.uint32 + encoded_path = output_dir / f"tokens-v{tokenizer.vocab_size}-{dtype.__name__}.npy" + tokenizer_path = output_dir / f"tokenizer-v{tokenizer.vocab_size}.json" + need_total = max_required_train_tokens + val_tokens + encode_needed = force_reencode or not encoded_path.exists() + if not encode_needed: + cached_tokens = np.load(encoded_path, mmap_mode="r") + encode_needed = len(cached_tokens) < need_total and not allow_short_corpus + if not encode_needed: + tokens = cached_tokens + + if encode_needed: + ids = array("I") + for doc in iter_documents(paths, text_column=text_column, doc_cap_chars=None): + ids.extend(tokenizer.encode(doc, prepend_bos=True)) + if len(ids) >= need_total: + break + tokens = np.frombuffer(ids, dtype=np.uint32).astype(dtype, copy=False) + if len(tokens) < need_total and not allow_short_corpus: + raise ValueError( + f"corpus produced {len(tokens):,} tokens, but {need_total:,} are required" + ) + output_dir.mkdir(parents=True, exist_ok=True) + np.save(encoded_path, tokens) + tokens = np.load(encoded_path, mmap_mode="r") + + usable_val = min(val_tokens, max(0, len(tokens) // 10)) + if usable_val <= 1: + raise ValueError("not enough tokens for validation") + train = tokens[:-usable_val] + val = tokens[-usable_val:] + if len(train) < max_required_train_tokens and not allow_short_corpus: + raise ValueError( + f"train split has {len(train):,} tokens, but {max_required_train_tokens:,} are required" + ) + return TokenSplits( + train=train, + val=val, + tokenizer_path=tokenizer_path, + encoded_path=encoded_path, + ) + + +def preview_documents(paths: list[Path], limit: int = 3) -> list[str]: + return list(islice(iter_documents(paths), limit)) diff --git a/src/dropout_decay/experiment.py b/src/dropout_decay/experiment.py new file mode 100644 index 0000000000000000000000000000000000000000..cfc2225d0f22bf6eee7cd8ced19c7148fbbeb321 --- /dev/null +++ b/src/dropout_decay/experiment.py @@ -0,0 +1,1167 @@ +""" +Derived from Andrej Karpathy's nanochat project. + +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +""" + +from __future__ import annotations + +import argparse +import csv +from collections import defaultdict +from dataclasses import dataclass +from datetime import datetime +import json +import os +from pathlib import Path +import random +import statistics +import sys +import time +from typing import Callable + +import numpy as np +import torch + +from dropout_decay.data import encode_corpus, resolve_paths, train_or_load_tokenizer +from dropout_decay.license import NANOCHAT_ATTRIBUTION +from dropout_decay.model import DropoutGPT, GPTConfig +from dropout_decay.scheduler import DropoutDecayConfig, DropoutDecayScheduler + + +DEFAULT_DROPOUT_RATES = [0.0, 0.02, 0.05, 0.08, 0.10, 0.14, 0.20, 0.30, 0.50] +SUMMARY_FIELDS = [ + "run_mode", + "condition", + "condition_kind", + "stage", + "token_limit", + "model_name", + "n_layer", + "n_head", + "n_embd", + "parameters", + "dropout_initial", + "dropout_final", + "dropout_schedule", + "n", + "mean_train_eval_loss", + "std_train_eval_loss", + "mean_val_eval_loss", + "std_val_eval_loss", + "mean_generalization_gap", + "std_generalization_gap", +] +SELECTION_FIELDS = [ + "run_mode", + "token_limit", + "model_name", + "n_layer", + "n_head", + "n_embd", + "parameters", + "n", + "best_dropout", + "best_val_loss", + "best_val_std", + "plateau_start_dropout", + "plateau_end_dropout", + "plateau_delta", + "zero_dropout_val_loss", + "zero_minus_best", + "best_nonzero_dropout", + "best_nonzero_val_loss", + "zero_minus_best_nonzero", + "max_dropout", + "max_dropout_val_loss", + "max_dropout_minus_best", + "has_nonzero_optimum", + "meets_target_dropout", + "curve_json", +] + + +@dataclass(frozen=True) +class ModelSpec: + name: str + n_layer: int + n_head: int + n_embd: int + + def config(self, vocab_size: int, block_size: int, dropout: float) -> GPTConfig: + return GPTConfig( + block_size=block_size, + vocab_size=vocab_size, + n_layer=self.n_layer, + n_head=self.n_head, + n_embd=self.n_embd, + dropout=dropout, + ) + + def to_dict(self) -> dict[str, int | str]: + return { + "model_name": self.name, + "n_layer": self.n_layer, + "n_head": self.n_head, + "n_embd": self.n_embd, + } + + +@dataclass(frozen=True) +class DropoutCondition: + name: str + kind: str + initial: float + final: float + schedule: str = "constant" + decay_tokens: int | None = None + + def make_fn(self, fallback_decay_tokens: int) -> Callable[[int], float]: + if self.kind == "static": + return lambda _tokens_seen, p=self.initial: p + scheduler = DropoutDecayScheduler( + DropoutDecayConfig( + initial_dropout=self.initial, + final_dropout=self.final, + decay_tokens=self.decay_tokens or fallback_decay_tokens, + schedule=self.schedule, + ) + ) + return scheduler.value + + +def assert_mps_only() -> torch.device: + if os.environ.get("PYTORCH_ENABLE_MPS_FALLBACK") == "1": + raise SystemExit( + "Refusing to run: PYTORCH_ENABLE_MPS_FALLBACK=1 could execute CPU fallbacks." + ) + if not torch.backends.mps.is_built(): + raise SystemExit("PyTorch was not built with MPS. Stopping.") + if not torch.backends.mps.is_available(): + raise SystemExit("MPS is not available. Stopping before any Torch experiment work.") + device = torch.device("mps") + torch.set_default_device(device) + return device + + +def clean_name(value: str) -> str: + cleaned = "".join( + c if c.isalnum() or c in {"-", "_"} else "_" for c in value.strip() + ) + return cleaned.strip("_") or "model" + + +def rate_label(rate: float) -> str: + label = f"{rate:.3f}".rstrip("0").rstrip(".") + return label if label else "0" + + +def parse_model_spec(raw: str) -> ModelSpec: + if "=" in raw: + name, dims = raw.split("=", 1) + name = clean_name(name) + else: + dims = raw + name = "" + parts = dims.lower().replace(",", "x").split("x") + if len(parts) != 3: + raise argparse.ArgumentTypeError( + "model specs must look like 8x8x256 or name=8x8x256" + ) + try: + n_layer, n_head, n_embd = [int(part) for part in parts] + except ValueError as exc: + raise argparse.ArgumentTypeError("model dimensions must be integers") from exc + if n_layer <= 0 or n_head <= 0 or n_embd <= 0: + raise argparse.ArgumentTypeError("model dimensions must be positive") + if n_embd % n_head != 0: + raise argparse.ArgumentTypeError("n_embd must be divisible by n_head") + if (n_embd // n_head) % 2 != 0: + raise argparse.ArgumentTypeError("n_embd / n_head must be even for rotary attention") + return ModelSpec( + name or f"L{n_layer}_H{n_head}_D{n_embd}", + n_layer, + n_head, + n_embd, + ) + + +def parse_decay_spec(raw: str) -> DropoutCondition: + parts = raw.split(":") + if len(parts) not in {3, 4, 5}: + raise argparse.ArgumentTypeError( + "decay specs must look like " + "name:initial:final[:cosine|smoothstep|linear[:decay_tokens]]" + ) + name = clean_name(parts[0]) + try: + initial = float(parts[1]) + final = float(parts[2]) + decay_tokens = int(parts[4]) if len(parts) == 5 else None + except ValueError as exc: + raise argparse.ArgumentTypeError( + "decay dropout values and decay_tokens must be numeric" + ) from exc + schedule = parts[3] if len(parts) >= 4 else "cosine" + if schedule not in {"cosine", "smoothstep", "linear"}: + raise argparse.ArgumentTypeError( + "decay schedule must be cosine, smoothstep, or linear" + ) + return DropoutCondition( + name=name, + kind="decay", + initial=initial, + final=final, + schedule=schedule, + decay_tokens=decay_tokens, + ) + + +def default_seeds(mode: str, seeds: list[int] | None) -> list[int]: + if seeds: + return seeds + return [1] if mode == "screen_static" else [1, 2, 3] + + +def set_seed(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + +def batch_seed(seed: int, model: ModelSpec, dropout_code: int, stage: int | None) -> int: + return ( + seed * 1_000_003 + + model.n_layer * 100_003 + + model.n_head * 10_007 + + model.n_embd * 101 + + dropout_code * 37 + + (stage or 0) * 997 + ) + + +def make_batch( + tokens: np.ndarray, + token_limit: int, + batch_size: int, + block_size: int, + rng: np.random.Generator, + device: torch.device, +) -> tuple[torch.Tensor, torch.Tensor]: + limit = min(token_limit, len(tokens)) + max_start = limit - block_size - 1 + if max_start <= 0: + raise ValueError("token_limit is too small for the requested block_size") + starts = rng.integers(0, max_start, size=batch_size) + x_np = np.stack([tokens[start : start + block_size] for start in starts]).astype( + np.int64 + ) + y_np = np.stack( + [tokens[start + 1 : start + 1 + block_size] for start in starts] + ).astype(np.int64) + return ( + torch.tensor(x_np, dtype=torch.long, device=device), + torch.tensor(y_np, dtype=torch.long, device=device), + ) + + +@torch.no_grad() +def estimate_loss( + model: DropoutGPT, + tokens: np.ndarray, + token_limit: int, + batches: int, + args: argparse.Namespace, + device: torch.device, + rng_seed: int, +) -> float: + if batches <= 0: + return float("nan") + model.eval() + rng = np.random.default_rng(rng_seed) + losses: list[float] = [] + for _ in range(batches): + x, y = make_batch(tokens, token_limit, args.batch_size, args.block_size, rng, device) + _, loss = model(x, y) + losses.append(float(loss.item())) + model.train() + return float(statistics.fmean(losses)) + + +def write_jsonl_row(handle, row: dict) -> None: + handle.write(json.dumps(row, sort_keys=True) + "\n") + handle.flush() + + +def metric_key(row: dict) -> tuple: + return ( + row["run_mode"], + row["condition"], + row["condition_kind"], + row.get("stage"), + int(row["token_limit"]), + row["model_name"], + int(row["n_layer"]), + int(row["n_head"]), + int(row["n_embd"]), + int(row["seed"]), + float(row["dropout_initial"]), + float(row["dropout_final"]), + row["dropout_schedule"], + ) + + +def planned_metric_key( + *, + mode: str, + condition: DropoutCondition, + model_spec: ModelSpec, + seed: int, + token_limit: int, + stage: int | None = None, +) -> tuple: + return ( + mode, + condition.name, + condition.kind, + stage, + int(token_limit), + model_spec.name, + int(model_spec.n_layer), + int(model_spec.n_head), + int(model_spec.n_embd), + int(seed), + float(condition.initial), + float(condition.final), + condition.schedule, + ) + + +def load_metrics(path: Path) -> list[dict]: + if not path.exists(): + return [] + rows = [] + for line in path.read_text(encoding="utf-8").splitlines(): + if line.strip(): + rows.append(json.loads(line)) + return rows + + +def train_segment( + *, + run_mode: str, + condition: DropoutCondition, + model_spec: ModelSpec, + config: GPTConfig, + train_tokens: np.ndarray, + val_tokens: np.ndarray, + token_limit: int, + steps: int, + seed: int, + args: argparse.Namespace, + device: torch.device, + dropout_fn: Callable[[int], float], + metrics_file, + trace_file, + stage: int | None = None, + model: DropoutGPT | None = None, + optimizer: torch.optim.Optimizer | None = None, + tokens_seen_start: int = 0, +) -> tuple[DropoutGPT, torch.optim.Optimizer, int, dict]: + if model is None: + set_seed(seed) + model = DropoutGPT(config).to(device) + optimizer = torch.optim.AdamW( + model.parameters(), + lr=args.lr, + betas=(0.9, 0.95), + weight_decay=args.weight_decay, + ) + else: + torch.manual_seed(seed + 10_000 + (stage or 0)) + if optimizer is None: + raise ValueError("optimizer is required when reusing a model") + + model.train() + dropout_code = int(round(condition.initial * 10_000)) + rng = np.random.default_rng(batch_seed(seed, model_spec, dropout_code, stage)) + tokens_seen = tokens_seen_start + last_loss = float("nan") + active_dropout = condition.initial + t0 = time.time() + + for step in range(1, steps + 1): + active_dropout = dropout_fn(tokens_seen) + model.set_dropout(active_dropout) + x, y = make_batch(train_tokens, token_limit, args.batch_size, args.block_size, rng, device) + _, loss = model(x, y) + optimizer.zero_grad(set_to_none=True) + loss.backward() + if args.grad_clip > 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) + optimizer.step() + + tokens_seen += args.batch_size * args.block_size + last_loss = float(loss.item()) + if args.log_every > 0 and step % args.log_every == 0: + write_jsonl_row( + trace_file, + { + "event": "train_step", + "run_mode": run_mode, + "condition": condition.name, + "model_name": model_spec.name, + "seed": seed, + "stage": stage, + "step": step, + "steps": steps, + "token_limit": int(token_limit), + "tokens_seen": int(tokens_seen), + "dropout": float(active_dropout), + "train_batch_loss": last_loss, + }, + ) + if args.eval_every > 0 and step % args.eval_every == 0: + train_eval = estimate_loss( + model, + train_tokens, + token_limit, + args.trace_eval_batches, + args, + device, + rng_seed=seed + 20_000 + step, + ) + val_eval = estimate_loss( + model, + val_tokens, + len(val_tokens), + args.trace_eval_batches, + args, + device, + rng_seed=seed + 30_000 + step, + ) + write_jsonl_row( + trace_file, + { + "event": "eval_step", + "run_mode": run_mode, + "condition": condition.name, + "model_name": model_spec.name, + "seed": seed, + "stage": stage, + "step": step, + "steps": steps, + "token_limit": int(token_limit), + "tokens_seen": int(tokens_seen), + "dropout": float(active_dropout), + "train_eval_loss": train_eval, + "val_eval_loss": val_eval, + "generalization_gap": val_eval - train_eval, + }, + ) + + train_eval = estimate_loss( + model, + train_tokens, + token_limit, + args.train_eval_batches, + args, + device, + rng_seed=seed + 40_000 + (stage or 0), + ) + val_eval = estimate_loss( + model, + val_tokens, + len(val_tokens), + args.eval_batches, + args, + device, + rng_seed=seed + 50_000 + (stage or 0), + ) + row = { + "run_mode": run_mode, + "condition": condition.name, + "condition_kind": condition.kind, + "seed": seed, + "stage": stage, + "token_limit": int(token_limit), + "steps": int(steps), + "tokens_seen": int(tokens_seen), + "dropout_initial": float(condition.initial), + "dropout_final": float(condition.final), + "dropout_schedule": condition.schedule, + "dropout_active_final": float(active_dropout), + "train_loss_last": last_loss, + "train_eval_loss": train_eval, + "val_eval_loss": val_eval, + "eval_loss": val_eval, + "generalization_gap": val_eval - train_eval, + "elapsed_sec": time.time() - t0, + "parameters": model.num_parameters(), + "model_config": config.to_dict(), + **model_spec.to_dict(), + } + write_jsonl_row(metrics_file, row) + return model, optimizer, tokens_seen, row + + +def summarize(rows: list[dict]) -> list[dict]: + groups: dict[tuple, list[dict]] = defaultdict(list) + for row in rows: + key = ( + row["run_mode"], + row["condition"], + row["condition_kind"], + row["stage"], + row["token_limit"], + row["model_name"], + row["n_layer"], + row["n_head"], + row["n_embd"], + row["parameters"], + row["dropout_initial"], + row["dropout_final"], + row["dropout_schedule"], + ) + groups[key].append(row) + + summary: list[dict] = [] + for group_rows in groups.values(): + first = group_rows[0] + item = {field: first[field] for field in SUMMARY_FIELDS if field in first} + item["n"] = len(group_rows) + for source, mean_key, std_key in [ + ("train_eval_loss", "mean_train_eval_loss", "std_train_eval_loss"), + ("val_eval_loss", "mean_val_eval_loss", "std_val_eval_loss"), + ("generalization_gap", "mean_generalization_gap", "std_generalization_gap"), + ]: + values = [float(row[source]) for row in group_rows] + item[mean_key] = statistics.fmean(values) + item[std_key] = statistics.stdev(values) if len(values) > 1 else 0.0 + summary.append(item) + return sorted( + summary, + key=lambda row: ( + row["run_mode"], + row["model_name"], + row["token_limit"], + row["condition"], + row["stage"] or -1, + ), + ) + + +def write_csv(path: Path, rows: list[dict], fieldnames: list[str]) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore") + writer.writeheader() + writer.writerows(rows) + + +def build_model_selection(summary: list[dict], args: argparse.Namespace) -> list[dict]: + groups: dict[tuple, list[dict]] = defaultdict(list) + for row in summary: + if row["condition_kind"] == "static" and row["stage"] is None: + groups[(row["run_mode"], row["token_limit"], row["model_name"])].append(row) + + selection: list[dict] = [] + for (run_mode, token_limit, model_name), rows in groups.items(): + curve = sorted(rows, key=lambda row: row["dropout_initial"]) + best = min(curve, key=lambda row: row["mean_val_eval_loss"]) + plateau = [ + row for row in curve + if row["mean_val_eval_loss"] <= best["mean_val_eval_loss"] + args.plateau_delta + ] + zero = next((row for row in curve if row["dropout_initial"] == 0.0), None) + nonzero = [row for row in curve if row["dropout_initial"] > 0.0] + best_nonzero = min(nonzero, key=lambda row: row["mean_val_eval_loss"]) if nonzero else None + max_dropout = max(curve, key=lambda row: row["dropout_initial"]) + + zero_loss = zero["mean_val_eval_loss"] if zero else None + zero_minus_best = zero_loss - best["mean_val_eval_loss"] if zero_loss is not None else None + zero_minus_best_nonzero = ( + zero_loss - best_nonzero["mean_val_eval_loss"] + if zero_loss is not None and best_nonzero is not None + else None + ) + max_minus_best = max_dropout["mean_val_eval_loss"] - best["mean_val_eval_loss"] + has_nonzero_optimum = ( + best["dropout_initial"] > 0.0 + and zero_minus_best is not None + and zero_minus_best >= args.min_nonzero_margin + and max_minus_best >= args.min_high_dropout_margin + ) + curve_json = json.dumps( + [ + { + "dropout": row["dropout_initial"], + "mean_val_loss": row["mean_val_eval_loss"], + "std_val_loss": row["std_val_eval_loss"], + "mean_train_loss": row["mean_train_eval_loss"], + "mean_generalization_gap": row["mean_generalization_gap"], + "n": row["n"], + } + for row in curve + ], + sort_keys=True, + ) + selection.append( + { + "run_mode": run_mode, + "token_limit": token_limit, + "model_name": model_name, + "n_layer": best["n_layer"], + "n_head": best["n_head"], + "n_embd": best["n_embd"], + "parameters": best["parameters"], + "n": best["n"], + "best_dropout": best["dropout_initial"], + "best_val_loss": best["mean_val_eval_loss"], + "best_val_std": best["std_val_eval_loss"], + "plateau_start_dropout": min(row["dropout_initial"] for row in plateau), + "plateau_end_dropout": max(row["dropout_initial"] for row in plateau), + "plateau_delta": args.plateau_delta, + "zero_dropout_val_loss": zero_loss, + "zero_minus_best": zero_minus_best, + "best_nonzero_dropout": ( + best_nonzero["dropout_initial"] if best_nonzero else None + ), + "best_nonzero_val_loss": ( + best_nonzero["mean_val_eval_loss"] if best_nonzero else None + ), + "zero_minus_best_nonzero": zero_minus_best_nonzero, + "max_dropout": max_dropout["dropout_initial"], + "max_dropout_val_loss": max_dropout["mean_val_eval_loss"], + "max_dropout_minus_best": max_minus_best, + "has_nonzero_optimum": has_nonzero_optimum, + "meets_target_dropout": best["dropout_initial"] >= args.target_min_dropout, + "curve_json": curve_json, + } + ) + return sorted( + selection, + key=lambda row: ( + not row["has_nonzero_optimum"], + not row["meets_target_dropout"], + row["best_val_loss"], + ), + ) + + +def write_screen_markdown_summary(output_dir: Path, rows: list[dict]) -> None: + if not rows: + return + rows = sorted( + rows, + key=lambda row: ( + row["token_limit"], + row["model_name"], + row["seed"], + row["dropout_initial"], + ), + ) + by_prefix: dict[int, list[dict]] = defaultdict(list) + for row in rows: + if row["run_mode"] == "screen_static" and row["condition_kind"] == "static": + by_prefix[int(row["token_limit"])].append(row) + if not by_prefix: + return + + first = rows[0] + lines = [ + "# Static Dropout Screen Summary", + "", + f"Run directory: `{output_dir}`", + "", + ( + f"Model: `{first['model_name']}` causal Transformer, " + f"{int(first['parameters']):,} parameters, {first['n_layer']} layers, " + f"{first['n_head']} heads, {first['n_embd']} embedding dim, " + f"block size {first['model_config']['block_size']}, " + f"vocab size {first['model_config']['vocab_size']}." + ), + ( + "Training per condition: " + f"{first['steps']:,} steps x sampled batches = " + f"{int(first['tokens_seen']):,} sampled tokens. " + f"Seeds present: {', '.join(str(seed) for seed in sorted({row['seed'] for row in rows}))}." + ), + "", + "## Best Dropout By Prefix", + "", + "| Prefix tokens | Effective epochs | Best dropout | Val loss | Train eval loss | Gap | Plateau/bracket note |", + "|---:|---:|---:|---:|---:|---:|---|", + ] + for prefix, prefix_rows in by_prefix.items(): + best = min(prefix_rows, key=lambda row: row["val_eval_loss"]) + rates = [float(row["dropout_initial"]) for row in prefix_rows] + eff_epochs = float(best["tokens_seen"]) / prefix + if best["dropout_initial"] == max(rates): + note = "not bracketed; best at top of tested grid" + elif best["dropout_initial"] == min(rates): + note = "not bracketed; best at bottom of tested grid" + else: + note = "bracketed by tested grid" + lines.append( + "| " + f"{prefix:,} | {eff_epochs:.2f} | {best['dropout_initial']:.2f} | " + f"{best['val_eval_loss']:.4f} | {best['train_eval_loss']:.4f} | " + f"{best['generalization_gap']:.4f} | {note} |" + ) + + for prefix, prefix_rows in by_prefix.items(): + eff_epochs = float(prefix_rows[0]["tokens_seen"]) / prefix + lines.extend( + [ + "", + f"## Prefix {prefix:,} Tokens ({eff_epochs:.2f} Effective Epochs)", + "", + "| Dropout | Val loss | Train eval loss | Gap | Sampled tokens | Params |", + "|---:|---:|---:|---:|---:|---:|", + ] + ) + for row in sorted(prefix_rows, key=lambda item: item["dropout_initial"]): + lines.append( + "| " + f"{row['dropout_initial']:.2f} | {row['val_eval_loss']:.4f} | " + f"{row['train_eval_loss']:.4f} | {row['generalization_gap']:.4f} | " + f"{int(row['tokens_seen']):,} | {int(row['parameters']):,} |" + ) + + output = "\n".join(lines) + "\n" + (output_dir / "RESULT_SUMMARY.md").write_text(output, encoding="utf-8") + docs_path = Path("docs") + if docs_path.exists(): + (docs_path / "screen_static_results.md").write_text(output, encoding="utf-8") + + +def static_conditions(dropout_rates: list[float]) -> list[DropoutCondition]: + return [ + DropoutCondition( + name=f"static_dropout_{rate_label(rate)}", + kind="static", + initial=rate, + final=rate, + ) + for rate in dropout_rates + ] + + +def run_fixed_static_sweep( + *, + args: argparse.Namespace, + model_specs: list[ModelSpec], + seeds: list[int], + train_tokens: np.ndarray, + val_tokens: np.ndarray, + tokenizer_vocab_size: int, + token_limits: list[int], + device: torch.device, + metrics_file, + trace_file, + completed_keys: set[tuple] | None = None, +) -> list[dict]: + rows: list[dict] = [] + completed_keys = completed_keys or set() + conditions = static_conditions(sorted(set(args.dropout_rates))) + for token_limit in token_limits: + for model_spec in model_specs: + best_val_loss = float("inf") + worse_streak = 0 + for condition in conditions: + condition_rows: list[dict] = [] + for seed in seeds: + key = planned_metric_key( + mode=args.mode, + condition=condition, + model_spec=model_spec, + seed=seed, + token_limit=token_limit, + ) + if key in completed_keys: + write_jsonl_row( + trace_file, + { + "event": "skipped_completed_condition", + "run_mode": args.mode, + "condition": condition.name, + "model_name": model_spec.name, + "seed": seed, + "stage": None, + "token_limit": int(token_limit), + }, + ) + continue + config = model_spec.config( + tokenizer_vocab_size, + args.block_size, + condition.initial, + ) + model, optimizer, _, row = train_segment( + run_mode=args.mode, + condition=condition, + model_spec=model_spec, + config=config, + train_tokens=train_tokens, + val_tokens=val_tokens, + token_limit=token_limit, + steps=args.steps, + seed=seed, + args=args, + device=device, + dropout_fn=condition.make_fn( + args.steps * args.batch_size * args.block_size + ), + metrics_file=metrics_file, + trace_file=trace_file, + ) + rows.append(row) + condition_rows.append(row) + completed_keys.add(metric_key(row)) + del model, optimizer + torch.mps.empty_cache() + + if not condition_rows: + continue + mean_val_loss = statistics.fmean( + float(row["val_eval_loss"]) for row in condition_rows + ) + if mean_val_loss < best_val_loss - args.screen_prune_min_delta: + best_val_loss = mean_val_loss + worse_streak = 0 + elif mean_val_loss > best_val_loss + args.screen_prune_min_delta: + worse_streak += 1 + + if ( + args.mode == "screen_static" + and args.screen_early_stop + and worse_streak >= args.screen_prune_patience + and condition.initial >= args.target_min_dropout + ): + write_jsonl_row( + trace_file, + { + "event": "screen_pruned_model", + "run_mode": args.mode, + "model_name": model_spec.name, + "token_limit": int(token_limit), + "best_val_loss": best_val_loss, + "pruned_after_dropout": condition.initial, + "worse_streak": worse_streak, + "remaining_dropouts": [ + rate + for rate in args.dropout_rates + if rate > condition.initial + ], + }, + ) + break + return rows + + +def run_locked_stream( + *, + args: argparse.Namespace, + model_specs: list[ModelSpec], + seeds: list[int], + train_tokens: np.ndarray, + val_tokens: np.ndarray, + tokenizer_vocab_size: int, + stream_caps: list[int], + device: torch.device, + metrics_file, + trace_file, + completed_keys: set[tuple] | None = None, +) -> list[dict]: + rows: list[dict] = [] + completed_keys = completed_keys or set() + conditions = static_conditions(args.dropout_rates) + args.decays + fallback_decay_tokens = ( + args.decay_tokens + or args.stage_steps * args.batch_size * args.block_size * len(stream_caps) + ) + for model_spec in model_specs: + for condition in conditions: + for seed in seeds: + model = None + optimizer = None + tokens_seen = 0 + for stage, token_limit in enumerate(stream_caps): + key = planned_metric_key( + mode=args.mode, + condition=condition, + model_spec=model_spec, + seed=seed, + token_limit=token_limit, + stage=stage, + ) + if key in completed_keys: + write_jsonl_row( + trace_file, + { + "event": "skipped_completed_condition", + "run_mode": args.mode, + "condition": condition.name, + "model_name": model_spec.name, + "seed": seed, + "stage": stage, + "token_limit": int(token_limit), + }, + ) + continue + config = model_spec.config( + tokenizer_vocab_size, + args.block_size, + condition.initial, + ) + model, optimizer, tokens_seen, row = train_segment( + run_mode=args.mode, + condition=condition, + model_spec=model_spec, + config=config, + train_tokens=train_tokens, + val_tokens=val_tokens, + token_limit=token_limit, + steps=args.stage_steps, + seed=seed, + args=args, + device=device, + dropout_fn=condition.make_fn(fallback_decay_tokens), + metrics_file=metrics_file, + trace_file=trace_file, + stage=stage, + model=model, + optimizer=optimizer, + tokens_seen_start=tokens_seen, + ) + rows.append(row) + completed_keys.add(metric_key(row)) + del model, optimizer + torch.mps.empty_cache() + return rows + + +def prepare_data(args: argparse.Namespace, output_dir: Path, required_train_tokens: int): + paths = resolve_paths(args.corpus, args.corpus_glob) + cache_dir = Path(args.cache_dir) if args.cache_dir else output_dir / "cache" + cache_dir.mkdir(parents=True, exist_ok=True) + tokenizer = train_or_load_tokenizer( + paths=paths, + output_dir=cache_dir, + vocab_size=args.vocab_size, + tokenizer_train_chars=args.tokenizer_train_chars, + text_column=args.text_column, + force_retrain=args.force_retokenize, + ) + splits = encode_corpus( + paths=paths, + tokenizer=tokenizer, + output_dir=cache_dir, + max_required_train_tokens=required_train_tokens, + val_tokens=args.val_tokens, + text_column=args.text_column, + allow_short_corpus=args.allow_short_corpus, + force_reencode=args.force_retokenize, + ) + return tokenizer, splits + + +def run(args: argparse.Namespace) -> Path: + device = assert_mps_only() + seeds = default_seeds(args.mode, args.seeds) + model_specs = [parse_model_spec(spec) for spec in args.models] + if args.mode != "locked_stream" and args.decays: + raise ValueError("--decays is only used with --mode locked_stream") + if args.resume_from and args.mode == "locked_stream": + raise ValueError("--resume-from currently supports fixed static sweeps only") + + if args.resume_from: + output_dir = Path(args.resume_from) + if not output_dir.exists(): + raise FileNotFoundError(f"resume directory does not exist: {output_dir}") + else: + run_id = datetime.now().strftime("%Y%m%d-%H%M%S") + output_dir = Path(args.output_dir) / args.mode / run_id + output_dir.mkdir(parents=True, exist_ok=True) + required_train_tokens = max( + args.stream_token_caps if args.mode == "locked_stream" else args.token_limits + ) + tokenizer, splits = prepare_data(args, output_dir, required_train_tokens) + token_limits = [min(limit, len(splits.train)) for limit in args.token_limits] + stream_caps = [min(limit, len(splits.train)) for limit in args.stream_token_caps] + + config_payload = { + "args": { + **vars(args), + "decays": [condition.__dict__ for condition in args.decays], + }, + "mode": args.mode, + "seeds": seeds, + "models": [model.to_dict() for model in model_specs], + "device": str(device), + "torch": torch.__version__, + "python": sys.version, + "mps_available": torch.backends.mps.is_available(), + "attribution": NANOCHAT_ATTRIBUTION, + "tokenizer_path": str(splits.tokenizer_path), + "encoded_path": str(splits.encoded_path), + "train_tokens": int(len(splits.train)), + "val_tokens": int(len(splits.val)), + "effective_token_limits": [int(limit) for limit in token_limits], + "effective_stream_token_caps": [int(limit) for limit in stream_caps], + "resume_from": str(args.resume_from) if args.resume_from else None, + } + config_name = "config.resume.json" if args.resume_from else "config.json" + (output_dir / config_name).write_text( + json.dumps(config_payload, indent=2), + encoding="utf-8", + ) + + metrics_path = output_dir / "metrics.jsonl" + trace_path = output_dir / "trace.jsonl" + existing_rows = load_metrics(metrics_path) if args.resume_from else [] + completed_keys = {metric_key(row) for row in existing_rows} + with ( + metrics_path.open("a" if args.resume_from else "w", encoding="utf-8") as metrics_file, + trace_path.open("a" if args.resume_from else "w", encoding="utf-8") as trace_file, + ): + if args.mode in {"screen_static", "confirm_static"}: + new_rows = run_fixed_static_sweep( + args=args, + model_specs=model_specs, + seeds=seeds, + train_tokens=splits.train, + val_tokens=splits.val, + tokenizer_vocab_size=tokenizer.vocab_size, + token_limits=token_limits, + device=device, + metrics_file=metrics_file, + trace_file=trace_file, + completed_keys=completed_keys, + ) + else: + new_rows = run_locked_stream( + args=args, + model_specs=model_specs, + seeds=seeds, + train_tokens=splits.train, + val_tokens=splits.val, + tokenizer_vocab_size=tokenizer.vocab_size, + stream_caps=stream_caps, + device=device, + metrics_file=metrics_file, + trace_file=trace_file, + ) + rows = existing_rows + new_rows + + summary = summarize(rows) + (output_dir / "summary.json").write_text( + json.dumps(summary, indent=2), + encoding="utf-8", + ) + write_csv(output_dir / "summary.csv", summary, SUMMARY_FIELDS) + if args.mode in {"screen_static", "confirm_static"}: + selection = build_model_selection(summary, args) + (output_dir / "model_selection.json").write_text( + json.dumps(selection, indent=2), + encoding="utf-8", + ) + write_csv(output_dir / "model_selection.csv", selection, SELECTION_FIELDS) + write_screen_markdown_summary(output_dir, rows) + + print( + json.dumps( + { + "output_dir": str(output_dir), + "new_rows": len(new_rows), + "total_metric_rows": len(rows), + "summary_rows": len(summary), + }, + indent=2, + ) + ) + return output_dir + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="MPS-only dropout/model selection experiments" + ) + parser.add_argument( + "--mode", + choices=["screen_static", "confirm_static", "locked_stream"], + default="screen_static", + ) + parser.add_argument("--corpus", default=None, help="Text or parquet corpus path") + parser.add_argument("--corpus-glob", default=None, help="Glob of text/parquet corpus paths") + parser.add_argument("--text-column", default="text", help="Parquet text column") + parser.add_argument("--output-dir", default="runs") + parser.add_argument( + "--resume-from", + default=None, + help="Existing fixed-static run directory; completed metric rows are skipped", + ) + parser.add_argument("--cache-dir", default=".cache/dropout_decay") + parser.add_argument( + "--models", + nargs="+", + default=["8x8x256"], + help="Model specs like 8x8x256 or name=8x8x256", + ) + parser.add_argument("--seeds", nargs="+", type=int, default=None) + parser.add_argument("--token-limits", nargs="+", type=int, default=[5_000_000]) + parser.add_argument( + "--stream-token-caps", + nargs="+", + type=int, + default=[5_000_000, 10_000_000, 20_000_000, 40_000_000], + ) + parser.add_argument("--val-tokens", type=int, default=500_000) + parser.add_argument("--allow-short-corpus", action="store_true") + parser.add_argument("--force-retokenize", action="store_true") + parser.add_argument("--vocab-size", type=int, default=4096) + parser.add_argument("--tokenizer-train-chars", type=int, default=10_000_000) + parser.add_argument("--block-size", type=int, default=128) + parser.add_argument("--batch-size", type=int, default=16) + parser.add_argument("--steps", type=int, default=2000) + parser.add_argument("--stage-steps", type=int, default=1000) + parser.add_argument("--dropout-rates", nargs="+", type=float, default=DEFAULT_DROPOUT_RATES) + parser.add_argument("--decays", nargs="*", type=parse_decay_spec, default=[]) + parser.add_argument("--decay-tokens", type=int, default=None) + parser.add_argument("--eval-batches", type=int, default=64) + parser.add_argument("--train-eval-batches", type=int, default=32) + parser.add_argument("--trace-eval-batches", type=int, default=8) + parser.add_argument("--eval-every", type=int, default=0) + parser.add_argument("--log-every", type=int, default=100) + parser.add_argument("--lr", type=float, default=3e-4) + parser.add_argument("--weight-decay", type=float, default=0.1) + parser.add_argument("--grad-clip", type=float, default=1.0) + parser.add_argument("--plateau-delta", type=float, default=0.01) + parser.add_argument("--target-min-dropout", type=float, default=0.10) + parser.add_argument("--min-nonzero-margin", type=float, default=0.01) + parser.add_argument("--min-high-dropout-margin", type=float, default=0.03) + parser.add_argument("--screen-early-stop", action="store_true") + parser.add_argument("--screen-prune-patience", type=int, default=3) + parser.add_argument("--screen-prune-min-delta", type=float, default=0.01) + return parser + + +def main() -> None: + args = build_parser().parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/src/dropout_decay/license.py b/src/dropout_decay/license.py new file mode 100644 index 0000000000000000000000000000000000000000..22b4fb152a062a6b012d34fae02ca533a83ade99 --- /dev/null +++ b/src/dropout_decay/license.py @@ -0,0 +1,23 @@ +""" +Derived from Andrej Karpathy's nanochat project. + +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +""" + +NANOCHAT_ATTRIBUTION = ( + "Derived from Andrej Karpathy's nanochat project " + "(https://github.com/karpathy/nanochat), MIT License, " + "Copyright (c) 2025 Andrej Karpathy." +) diff --git a/src/dropout_decay/model.py b/src/dropout_decay/model.py new file mode 100644 index 0000000000000000000000000000000000000000..4608d0c3511efe393395934c3d23a61be6d20094 --- /dev/null +++ b/src/dropout_decay/model.py @@ -0,0 +1,227 @@ +""" +Derived from Andrej Karpathy's nanochat project. + +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +@dataclass(frozen=True) +class GPTConfig: + block_size: int + vocab_size: int + n_layer: int + n_head: int + n_embd: int + dropout: float + + def to_dict(self) -> dict[str, int | float]: + return asdict(self) + + +def rms_norm(x: torch.Tensor) -> torch.Tensor: + return F.rms_norm(x, (x.size(-1),)) + + +class Linear(nn.Linear): + """Bias-free linear layer matching the nanochat architectural style.""" + + def __init__(self, in_features: int, out_features: int): + super().__init__(in_features, out_features, bias=False) + + +def apply_rotary_emb( + x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor +) -> torch.Tensor: + half = x.shape[-1] // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + + +class CausalSelfAttention(nn.Module): + def __init__(self, config: GPTConfig): + super().__init__() + if config.n_embd % config.n_head != 0: + raise ValueError("n_embd must be divisible by n_head") + self.n_head = config.n_head + self.head_dim = config.n_embd // config.n_head + if self.head_dim % 2 != 0: + raise ValueError("rotary attention requires an even head dimension") + self.dropout_p = config.dropout + self.c_q = Linear(config.n_embd, config.n_embd) + self.c_k = Linear(config.n_embd, config.n_embd) + self.c_v = Linear(config.n_embd, config.n_embd) + self.c_proj = Linear(config.n_embd, config.n_embd) + self.resid_dropout = nn.Dropout(config.dropout) + mask = torch.ones(config.block_size, config.block_size, dtype=torch.bool).tril() + self.register_buffer( + "causal_mask", + mask.view(1, 1, config.block_size, config.block_size), + persistent=False, + ) + + def set_dropout(self, p: float) -> None: + self.dropout_p = p + self.resid_dropout.p = p + + def forward( + self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor + ) -> torch.Tensor: + batch, seq_len, channels = x.shape + q = self.c_q(x).view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2) + k = self.c_k(x).view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2) + v = self.c_v(x).view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2) + + q = rms_norm(apply_rotary_emb(q, cos, sin)) * 1.2 + k = rms_norm(apply_rotary_emb(k, cos, sin)) * 1.2 + + att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim) + mask = self.causal_mask[:, :, :seq_len, :seq_len] + att = att.masked_fill(~mask, float("-inf")) + att = F.softmax(att.float(), dim=-1).to(dtype=x.dtype) + att = F.dropout(att, p=self.dropout_p, training=self.training) + y = att @ v + y = y.transpose(1, 2).contiguous().view(batch, seq_len, channels) + return self.resid_dropout(self.c_proj(y)) + + +class MLP(nn.Module): + def __init__(self, config: GPTConfig): + super().__init__() + self.c_fc = Linear(config.n_embd, 4 * config.n_embd) + self.c_proj = Linear(4 * config.n_embd, config.n_embd) + self.dropout = nn.Dropout(config.dropout) + + def set_dropout(self, p: float) -> None: + self.dropout.p = p + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.c_fc(x) + x = F.relu(x).square() + x = self.c_proj(x) + return self.dropout(x) + + +class Block(nn.Module): + def __init__(self, config: GPTConfig): + super().__init__() + self.attn = CausalSelfAttention(config) + self.mlp = MLP(config) + + def set_dropout(self, p: float) -> None: + self.attn.set_dropout(p) + self.mlp.set_dropout(p) + + def forward( + self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor + ) -> torch.Tensor: + x = x + self.attn(rms_norm(x), cos, sin) + x = x + self.mlp(rms_norm(x)) + return x + + +class DropoutGPT(nn.Module): + """Minimal nanochat-style causal Transformer with dynamic dropout control.""" + + def __init__(self, config: GPTConfig): + super().__init__() + self.config = config + self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd) + self.embedding_dropout = nn.Dropout(config.dropout) + self.blocks = nn.ModuleList(Block(config) for _ in range(config.n_layer)) + self.lm_head = Linear(config.n_embd, config.vocab_size) + self.register_buffer( + "cos", + torch.empty(1, 1, config.block_size, config.n_embd // config.n_head // 2), + persistent=False, + ) + self.register_buffer( + "sin", + torch.empty(1, 1, config.block_size, config.n_embd // config.n_head // 2), + persistent=False, + ) + self._init_weights() + self._init_rotary() + + @torch.no_grad() + def _init_weights(self) -> None: + nn.init.normal_(self.token_embedding.weight, mean=0.0, std=0.8) + nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) + scale = (3.0 ** 0.5) * (self.config.n_embd ** -0.5) + for block in self.blocks: + nn.init.uniform_(block.attn.c_q.weight, -scale, scale) + nn.init.uniform_(block.attn.c_k.weight, -scale, scale) + nn.init.uniform_(block.attn.c_v.weight, -scale, scale) + nn.init.zeros_(block.attn.c_proj.weight) + nn.init.uniform_(block.mlp.c_fc.weight, -0.4 * scale, 0.4 * scale) + nn.init.zeros_(block.mlp.c_proj.weight) + + @torch.no_grad() + def _init_rotary(self) -> None: + head_dim = self.config.n_embd // self.config.n_head + channel_range = torch.arange( + 0, + head_dim, + 2, + dtype=torch.float32, + device=self.cos.device, + ) + inv_freq = 1.0 / (100000 ** (channel_range / head_dim)) + positions = torch.arange( + self.config.block_size, + dtype=torch.float32, + device=self.cos.device, + ) + freqs = torch.outer(positions, inv_freq) + self.cos.copy_(freqs.cos().view(1, 1, self.config.block_size, head_dim // 2)) + self.sin.copy_(freqs.sin().view(1, 1, self.config.block_size, head_dim // 2)) + + def set_dropout(self, p: float) -> None: + if not 0.0 <= p < 1.0: + raise ValueError("dropout must satisfy 0 <= p < 1") + self.embedding_dropout.p = p + for block in self.blocks: + block.set_dropout(p) + + def num_parameters(self) -> int: + return sum(p.numel() for p in self.parameters()) + + def forward( + self, idx: torch.Tensor, targets: torch.Tensor | None = None + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + _, seq_len = idx.shape + if seq_len > self.config.block_size: + raise ValueError("sequence length exceeds block_size") + cos = self.cos[:, :, :seq_len, :] + sin = self.sin[:, :, :seq_len, :] + x = self.embedding_dropout(rms_norm(self.token_embedding(idx))) + for block in self.blocks: + x = block(x, cos, sin) + logits = self.lm_head(rms_norm(x)).float() + if targets is None: + return logits + loss = F.cross_entropy( + logits.reshape(-1, logits.size(-1)), + targets.reshape(-1), + ) + return logits, loss diff --git a/src/dropout_decay/scheduler.py b/src/dropout_decay/scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..536ab4ab13e7ade069ad192e400f38d7aa4e2ece --- /dev/null +++ b/src/dropout_decay/scheduler.py @@ -0,0 +1,56 @@ +""" +Derived from Andrej Karpathy's nanochat project. + +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +""" + +from __future__ import annotations + +from dataclasses import dataclass +import math + + +@dataclass(frozen=True) +class DropoutDecayConfig: + initial_dropout: float = 0.80 + final_dropout: float = 0.10 + decay_tokens: int = 40_000_000 + schedule: str = "cosine" + + +class DropoutDecayScheduler: + """Smoothly maps total processed/observed tokens to an active dropout rate.""" + + def __init__(self, config: DropoutDecayConfig): + if not 0.0 <= config.final_dropout <= config.initial_dropout < 1.0: + raise ValueError("dropout values must satisfy 0 <= final <= initial < 1") + if config.decay_tokens <= 0: + raise ValueError("decay_tokens must be positive") + if config.schedule not in {"cosine", "smoothstep", "linear"}: + raise ValueError("schedule must be cosine, smoothstep, or linear") + self.config = config + + def value(self, tokens_seen: int) -> float: + progress = min(max(tokens_seen / self.config.decay_tokens, 0.0), 1.0) + if self.config.schedule == "cosine": + mix = 0.5 * (1.0 + math.cos(math.pi * progress)) + elif self.config.schedule == "smoothstep": + smooth = progress * progress * (3.0 - 2.0 * progress) + mix = 1.0 - smooth + else: + mix = 1.0 - progress + return self.config.final_dropout + ( + self.config.initial_dropout - self.config.final_dropout + ) * mix diff --git a/src/dropout_decay/tokenizer.py b/src/dropout_decay/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..874803dee204888846dbb806124085efd4c6a136 --- /dev/null +++ b/src/dropout_decay/tokenizer.py @@ -0,0 +1,105 @@ +""" +Derived from Andrej Karpathy's nanochat project. + +MIT License + +Copyright (c) 2025 Andrej Karpathy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Iterable + +from tokenizers import Regex +from tokenizers import Tokenizer as HFTokenizer +from tokenizers import decoders, pre_tokenizers +from tokenizers.models import BPE +from tokenizers.trainers import BpeTrainer + +SPECIAL_TOKENS = [ + "<|bos|>", + "<|user_start|>", + "<|user_end|>", + "<|assistant_start|>", + "<|assistant_end|>", + "<|python_start|>", + "<|python_end|>", + "<|output_start|>", + "<|output_end|>", +] + +SPLIT_PATTERN = ( + r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,2}|""" + r""" ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+""" +) + + +class BpeTokenizer: + """Minimal HuggingFace BPE wrapper following nanochat's GPT-4-style splitter.""" + + def __init__(self, tokenizer: HFTokenizer): + self.tokenizer = tokenizer + + @classmethod + def train_from_iterator( + cls, text_iterator: Iterable[str], vocab_size: int + ) -> "BpeTokenizer": + tokenizer = HFTokenizer(BPE(byte_fallback=True, unk_token=None, fuse_unk=False)) + tokenizer.normalizer = None + tokenizer.pre_tokenizer = pre_tokenizers.Sequence( + [ + pre_tokenizers.Split( + pattern=Regex(SPLIT_PATTERN), + behavior="isolated", + invert=False, + ), + pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=False), + ] + ) + tokenizer.decoder = decoders.ByteLevel() + trainer = BpeTrainer( + vocab_size=vocab_size, + show_progress=True, + min_frequency=0, + initial_alphabet=pre_tokenizers.ByteLevel.alphabet(), + special_tokens=SPECIAL_TOKENS, + ) + tokenizer.train_from_iterator(text_iterator, trainer) + return cls(tokenizer) + + @classmethod + def from_file(cls, path: str | Path) -> "BpeTokenizer": + return cls(HFTokenizer.from_file(str(path))) + + def save(self, path: str | Path) -> None: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + self.tokenizer.save(str(path)) + + @property + def vocab_size(self) -> int: + return self.tokenizer.get_vocab_size() + + @property + def bos_id(self) -> int: + bos = self.tokenizer.token_to_id("<|bos|>") + if bos is None: + raise ValueError("tokenizer is missing <|bos|>") + return bos + + def encode(self, text: str, prepend_bos: bool = False) -> list[int]: + ids = self.tokenizer.encode(text, add_special_tokens=False).ids + if prepend_bos: + ids.insert(0, self.bos_id) + return ids