Add rig training logs: 177 runs across six studies, full resolution (part 4)
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- .gitattributes +43 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1338/diagnostics.riglog +3 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1338/metrics.json +238 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1338/result.json +238 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1338/training.riglog +3 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1338/validation.csv +2 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1339/diagnostics.riglog +3 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1339/metrics.json +238 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1339/result.json +238 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1339/training.riglog +3 -0
- lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1339/validation.csv +2 -0
- lr-batch-sweep-125M/records.jsonl +0 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/diagnostics.riglog +3 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/metrics.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/result.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/stderr.log +284 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/stdout.log +1 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/training.riglog +0 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/validation.csv +2 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/diagnostics.riglog +3 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/metrics.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/result.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/stderr.log +284 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/stdout.log +1 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/training.riglog +0 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/validation.csv +2 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/diagnostics.riglog +3 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/metrics.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/result.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/stderr.log +284 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/stdout.log +1 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/training.riglog +0 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/validation.csv +2 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/diagnostics.riglog +3 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/metrics.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/result.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/stderr.log +284 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/stdout.log +1 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/training.riglog +0 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/validation.csv +2 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/diagnostics.riglog +3 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/metrics.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/result.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/stderr.log +284 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/stdout.log +1 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/training.riglog +0 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/validation.csv +2 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1339/diagnostics.riglog +3 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1339/metrics.json +239 -0
- lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1339/result.json +239 -0
.gitattributes
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lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1338/diagnostics.riglog
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lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1338/metrics.json
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{
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"artifacts": {
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"diagnostics": "diagnostics.csv",
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"training_curve": "training.csv",
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"validation_curve": "validation.csv"
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},
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"checkpoint": null,
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"contract": {
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"dataset_id": "fineweb-8b-gpt2",
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"model": {
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"d_model": 640,
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"heads": 10,
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"layers": 12,
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lr-batch-sweep-125M/125m-5tpp-bs64-lr2e-9-s1338/result.json
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@@ -0,0 +1,238 @@
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"train_seconds": 211.65018371900078,
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"training_data_epochs": 0.037928020133706226,
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"training_data_sharding": "rank_disjoint_shuffled_windows",
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| 175 |
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"training_sampling": "shuffled_epochs",
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| 176 |
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"training_steps": 2286,
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| 177 |
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"training_token_budget": 299630592,
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"training_usable_tokens_per_epoch": 7899979776,
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"validation_loss": 4.224437057971954,
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"validation_tokens": 1048576
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| 183 |
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},
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"profile": "dev",
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| 185 |
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"schema_version": 1,
|
| 186 |
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"seed": 1337,
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| 187 |
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"status": "ok",
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| 188 |
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"system": {
|
| 189 |
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"controller_process_index": 1,
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| 190 |
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"device_count": 16,
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| 191 |
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"device_ids": [
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| 192 |
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| 193 |
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| 194 |
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|
| 195 |
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| 196 |
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| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
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|
| 202 |
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|
| 203 |
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|
| 204 |
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12,
|
| 205 |
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|
| 206 |
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14,
|
| 207 |
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15
|
| 208 |
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|
| 209 |
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"device_kinds": [
|
| 210 |
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"TPU v4"
|
| 211 |
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],
|
| 212 |
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"jax_version": "0.11.0",
|
| 213 |
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"jaxlib_version": "0.11.0",
|
| 214 |
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"libtpu_version": "0.0.44.1",
|
| 215 |
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|
| 216 |
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|
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| 225 |
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| 226 |
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|
| 227 |
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|
| 228 |
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|
| 229 |
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|
| 230 |
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|
| 231 |
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|
| 232 |
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|
| 233 |
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|
| 234 |
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3
|
| 235 |
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|
| 236 |
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"python_version": "3.12.13"
|
| 237 |
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},
|
| 238 |
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"track": "open"
|
| 239 |
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}
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/stderr.log
ADDED
|
@@ -0,0 +1,284 @@
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| 1 |
+
E0818 09:39:49.314286 2181591 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 2 |
+
E0818 09:39:49.315668 2228828 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 3 |
+
E0818 09:39:49.315799 2272703 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 4 |
+
E0818 09:39:49.316637 2941551 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 5 |
+
|
| 6 |
+
◆ GPT TPU RIG reference / jax
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
● Attention tile preflight resolving the shipped lookup or shape heuristic
|
| 10 |
+
╭─ run configuration ────────────────────────────────────────────────────────╮
|
| 11 |
+
│ experiment config config.yaml · dev · sha256:e3a9103e6016 │
|
| 12 |
+
│ devices 16 × TPU v4 │
|
| 13 |
+
│ JAX processes 4 (this rank 1) │
|
| 14 |
+
│ mesh data=16 (replicated model) │
|
| 15 |
+
│ dataset 79 train + 1 val shard(s) │
|
| 16 |
+
│ train / val tokens 7,900,000,000 / 100,000,000 │
|
| 17 |
+
│ downstream not requested │
|
| 18 |
+
│ model 60m · L12 D384 H6 RoPE RMSNorm GELU MLP×4 │
|
| 19 |
+
│ parameters 59.92M │
|
| 20 |
+
│ parameterization complete_d_p · mN=1 · mL=1 · mD=1 │
|
| 21 |
+
│ global batch 16 × 8192 tokens │
|
| 22 |
+
│ train sampling shuffled epochs · 7,899,979,776 unique targets/epoch │
|
| 23 |
+
│ compute bfloat16 │
|
| 24 |
+
│ attention tpu_flash │
|
| 25 |
+
│ attention tuning heuristic · key ec38db343130 │
|
| 26 |
+
│ attention fwd q512 · kv512/256 │
|
| 27 |
+
│ attention dK/dV q512/256 · kv512/256 │
|
| 28 |
+
│ attention dQ q256 · kv512/256 │
|
| 29 |
+
│ output loss tiled CE (semantic 50,304, tile 2,048) │
|
| 30 |
+
│ diagnostics step 1 / every 10 / final │
|
| 31 |
+
│ duration 2,286 steps │
|
| 32 |
+
│ train tokens 299.63M │
|
| 33 |
+
│ traced FLOPs 221029.69T │
|
| 34 |
+
│ FLOP breakdown dot_general 2,332,173,533,184 (38.6%) · tpu_flash_ca… │
|
| 35 |
+
│ XProf disabled │
|
| 36 |
+
╰────────────────────────────────────────────────────────────────────────────╯
|
| 37 |
+
|
| 38 |
+
● Compiling train step compilation is outside train_seconds
|
| 39 |
+
|
| 40 |
+
● Compiling sparse diagnostics separate executable; compilation is outside train_seconds
|
| 41 |
+
|
| 42 |
+
● Compiling evaluation reused by probes and final validation
|
| 43 |
+
|
| 44 |
+
● Training train compiled in 30.37s, eval in 7.23s; periodic validation disabled
|
| 45 |
+
1/2286 ────────────────── loss 10.9027 lr 4.26e-06 |g| 3.605 699.07K tok/s
|
| 46 |
+
10/2286 ────────────────── loss 10.3238 lr 4.26e-05 |g| 1.756 1.16M tok/s
|
| 47 |
+
20/2286 ────────────────── loss 10.0021 lr 8.53e-05 |g| 1.626 1.27M tok/s
|
| 48 |
+
30/2286 ────────────────── loss 9.5818 lr 1.28e-04 |g| 1.606 1.32M tok/s
|
| 49 |
+
40/2286 ────────────────── loss 9.0293 lr 1.71e-04 |g| 1.553 1.34M tok/s
|
| 50 |
+
50/2286 ────────────────── loss 8.3912 lr 2.13e-04 |g| 1.442 1.36M tok/s
|
| 51 |
+
60/2286 ────────────────── loss 7.9151 lr 2.56e-04 |g| 1.100 1.37M tok/s
|
| 52 |
+
70/2286 ━───────────────── loss 7.5653 lr 2.99e-04 |g| 0.646 1.37M tok/s
|
| 53 |
+
80/2286 ━───────────────── loss 7.3001 lr 3.41e-04 |g| 0.842 1.38M tok/s
|
| 54 |
+
90/2286 ━───────────────── loss 7.1431 lr 3.84e-04 |g| 0.351 1.38M tok/s
|
| 55 |
+
100/2286 ━───────────────── loss 6.8978 lr 4.26e-04 |g| 0.628 1.39M tok/s
|
| 56 |
+
110/2286 ━───────────────── loss 6.8967 lr 4.69e-04 |g| 0.420 1.39M tok/s
|
| 57 |
+
120/2286 ━───────────────── loss 6.6230 lr 5.12e-04 |g| 0.510 1.39M tok/s
|
| 58 |
+
130/2286 ━───────────────── loss 6.4973 lr 5.54e-04 |g| 0.442 1.39M tok/s
|
| 59 |
+
140/2286 ━───────────────── loss 6.5044 lr 5.97e-04 |g| 0.432 1.39M tok/s
|
| 60 |
+
150/2286 ━───────────────── loss 6.4400 lr 6.40e-04 |g| 0.633 1.40M tok/s
|
| 61 |
+
160/2286 ━───────────────── loss 6.3818 lr 6.82e-04 |g| 0.921 1.40M tok/s
|
| 62 |
+
170/2286 ━───────────────── loss 6.2080 lr 7.25e-04 |g| 0.435 1.40M tok/s
|
| 63 |
+
180/2286 ━───────────────── loss 6.2707 lr 7.68e-04 |g| 0.457 1.40M tok/s
|
| 64 |
+
190/2286 ━───────────────── loss 6.2748 lr 8.10e-04 |g| 0.644 1.40M tok/s
|
| 65 |
+
200/2286 ━━──────────────── loss 6.1271 lr 8.53e-04 |g| 0.971 1.40M tok/s
|
| 66 |
+
210/2286 ━━──────────────── loss 6.0287 lr 8.96e-04 |g| 0.534 1.40M tok/s
|
| 67 |
+
220/2286 ━━──────────────── loss 5.9796 lr 9.38e-04 |g| 0.390 1.40M tok/s
|
| 68 |
+
230/2286 ━━──────────────── loss 6.0303 lr 9.77e-04 |g| 0.731 1.40M tok/s
|
| 69 |
+
240/2286 ━━──────────────── loss 5.9754 lr 9.77e-04 |g| 0.818 1.40M tok/s
|
| 70 |
+
250/2286 ━━──────────────── loss 5.9474 lr 9.76e-04 |g| 0.419 1.40M tok/s
|
| 71 |
+
260/2286 ━━──────────────── loss 5.9624 lr 9.76e-04 |g| 0.740 1.40M tok/s
|
| 72 |
+
270/2286 ━━──────────────── loss 5.7900 lr 9.76e-04 |g| 0.599 1.41M tok/s
|
| 73 |
+
280/2286 ━━──────────────── loss 5.7643 lr 9.75e-04 |g| 0.736 1.41M tok/s
|
| 74 |
+
290/2286 ━━──────────────── loss 5.6298 lr 9.75e-04 |g| 0.597 1.41M tok/s
|
| 75 |
+
300/2286 ━━──────────────── loss 5.6607 lr 9.74e-04 |g| 0.539 1.41M tok/s
|
| 76 |
+
310/2286 ━━──────────────── loss 5.5967 lr 9.73e-04 |g| 0.568 1.41M tok/s
|
| 77 |
+
320/2286 ━━━─────────────── loss 5.5921 lr 9.72e-04 |g| 0.411 1.41M tok/s
|
| 78 |
+
330/2286 ━━━─────────────── loss 5.5941 lr 9.71e-04 |g| 0.498 1.41M tok/s
|
| 79 |
+
340/2286 ━━━─────────────── loss 5.5336 lr 9.70e-04 |g| 0.747 1.41M tok/s
|
| 80 |
+
350/2286 ━━━─────────────── loss 5.6452 lr 9.69e-04 |g| 0.507 1.41M tok/s
|
| 81 |
+
360/2286 ━━━─────────────── loss 5.5515 lr 9.68e-04 |g| 0.585 1.41M tok/s
|
| 82 |
+
370/2286 ━━━─────────────── loss 5.4333 lr 9.66e-04 |g| 0.498 1.41M tok/s
|
| 83 |
+
380/2286 ━━━─────────────── loss 5.3973 lr 9.65e-04 |g| 0.469 1.41M tok/s
|
| 84 |
+
390/2286 ━━━─────────────── loss 5.5683 lr 9.63e-04 |g| 0.479 1.41M tok/s
|
| 85 |
+
400/2286 ━━━─────────────── loss 5.4585 lr 9.62e-04 |g| 0.429 1.41M tok/s
|
| 86 |
+
410/2286 ━━━─────────────── loss 5.3981 lr 9.60e-04 |g| 0.571 1.41M tok/s
|
| 87 |
+
420/2286 ━━━─────────────── loss 5.3049 lr 9.58e-04 |g| 0.502 1.41M tok/s
|
| 88 |
+
430/2286 ━━━─────────────── loss 5.4952 lr 9.56e-04 |g| 0.419 1.41M tok/s
|
| 89 |
+
440/2286 ━━━─────────────── loss 5.3877 lr 9.54e-04 |g| 0.483 1.41M tok/s
|
| 90 |
+
450/2286 ━━━━────────────── loss 5.3125 lr 9.52e-04 |g| 0.476 1.41M tok/s
|
| 91 |
+
460/2286 ━━━━────────────── loss 5.3086 lr 9.49e-04 |g| 0.465 1.41M tok/s
|
| 92 |
+
470/2286 ━━━━────────────── loss 5.2779 lr 9.47e-04 |g| 0.533 1.41M tok/s
|
| 93 |
+
480/2286 ━━━━────────────── loss 5.2777 lr 9.45e-04 |g| 0.533 1.41M tok/s
|
| 94 |
+
490/2286 ━━━━────────────── loss 5.2570 lr 9.42e-04 |g| 0.436 1.41M tok/s
|
| 95 |
+
500/2286 ━━━━────────────── loss 5.2142 lr 9.39e-04 |g| 0.481 1.41M tok/s
|
| 96 |
+
510/2286 ━━━━────────────── loss 5.1703 lr 9.37e-04 |g| 0.433 1.41M tok/s
|
| 97 |
+
520/2286 ━━━━────────────── loss 5.1913 lr 9.34e-04 |g| 0.505 1.41M tok/s
|
| 98 |
+
530/2286 ━━━━────────────── loss 5.1929 lr 9.31e-04 |g| 0.493 1.41M tok/s
|
| 99 |
+
540/2286 ━━━━────────────── loss 5.1604 lr 9.28e-04 |g| 0.538 1.41M tok/s
|
| 100 |
+
550/2286 ━━━━────────────── loss 5.1093 lr 9.25e-04 |g| 0.373 1.41M tok/s
|
| 101 |
+
560/2286 ━━━━────────────── loss 5.0640 lr 9.22e-04 |g| 0.415 1.41M tok/s
|
| 102 |
+
570/2286 ━━━━────────────── loss 5.1135 lr 9.18e-04 |g| 0.459 1.41M tok/s
|
| 103 |
+
580/2286 ━━━━━───────────── loss 5.1120 lr 9.15e-04 |g| 0.506 1.41M tok/s
|
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1850/2286 ━━━━━━━━━━━━━━━─── loss 4.3253 lr 1.92e-04 |g| 0.293 1.42M tok/s
|
| 231 |
+
1860/2286 ━━━━━━━━━━━━━━━─── loss 4.3023 lr 1.87e-04 |g| 0.369 1.42M tok/s
|
| 232 |
+
1870/2286 ━━━━━━━━━━━━━━━─── loss 4.1935 lr 1.83e-04 |g| 0.311 1.42M tok/s
|
| 233 |
+
1880/2286 ━━━━━━━━━━━━━━━─── loss 4.3056 lr 1.79e-04 |g| 0.345 1.42M tok/s
|
| 234 |
+
1890/2286 ━━━━━━━━━━━━━━━─── loss 4.2715 lr 1.76e-04 |g| 0.361 1.42M tok/s
|
| 235 |
+
1900/2286 ━━━━━━━━━━━━━━━─── loss 4.2063 lr 1.72e-04 |g| 0.348 1.42M tok/s
|
| 236 |
+
1910/2286 ━━━━━━━━━━━━━━━─── loss 4.2998 lr 1.68e-04 |g| 0.359 1.42M tok/s
|
| 237 |
+
1920/2286 ━━━━━━━━━━━━━━━─── loss 4.2930 lr 1.65e-04 |g| 0.368 1.42M tok/s
|
| 238 |
+
1930/2286 ━━━━━━━━━━━━━━━─── loss 4.3180 lr 1.61e-04 |g| 0.325 1.42M tok/s
|
| 239 |
+
1940/2286 ━━━━━━━━━━━━━━━─── loss 4.2747 lr 1.58e-04 |g| 0.358 1.42M tok/s
|
| 240 |
+
1950/2286 ━━━━━━━━━━━━━━━─── loss 4.2490 lr 1.54e-04 |g| 0.324 1.42M tok/s
|
| 241 |
+
1960/2286 ━━━━━━━━━━━━━━━─── loss 4.3026 lr 1.51e-04 |g| 0.328 1.42M tok/s
|
| 242 |
+
1970/2286 ━━━━━━━━━━━━━━━━── loss 4.1930 lr 1.48e-04 |g| 0.335 1.42M tok/s
|
| 243 |
+
1980/2286 ━━━━━━━━━━━━━━━━── loss 4.2861 lr 1.45e-04 |g| 0.331 1.42M tok/s
|
| 244 |
+
1990/2286 ━━━━━━━━━━━━━━━━── loss 4.2556 lr 1.42e-04 |g| 0.361 1.42M tok/s
|
| 245 |
+
2000/2286 ━━━━━━━━━━━━━━━━── loss 4.2066 lr 1.39e-04 |g| 0.324 1.42M tok/s
|
| 246 |
+
2010/2286 ━━━━━━━━━━━━━━━━── loss 4.2912 lr 1.36e-04 |g| 0.307 1.42M tok/s
|
| 247 |
+
2020/2286 ━━━━━━━━━━━━━━━━── loss 4.2651 lr 1.33e-04 |g| 0.314 1.42M tok/s
|
| 248 |
+
2030/2286 ━━━━━━━━━━━━━━━━── loss 4.2446 lr 1.31e-04 |g| 0.360 1.42M tok/s
|
| 249 |
+
2040/2286 ━━━━━━━━━━━━━━━━── loss 4.2905 lr 1.28e-04 |g| 0.327 1.42M tok/s
|
| 250 |
+
2050/2286 ━━━━━━━━━━━━━━━━── loss 4.1146 lr 1.26e-04 |g| 0.384 1.42M tok/s
|
| 251 |
+
2060/2286 ━━━━━━━━━━━━━━━━── loss 4.2357 lr 1.24e-04 |g| 0.308 1.42M tok/s
|
| 252 |
+
2070/2286 ━━━━━━━━━━━━━━━━── loss 4.1904 lr 1.21e-04 |g| 0.343 1.42M tok/s
|
| 253 |
+
2080/2286 ━━━━━━━━━━━━━━━━── loss 4.3284 lr 1.19e-04 |g| 0.364 1.42M tok/s
|
| 254 |
+
2090/2286 ━━━━━━━━━━━━━━━━── loss 4.2149 lr 1.17e-04 |g| 0.322 1.42M tok/s
|
| 255 |
+
2100/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2177 lr 1.15e-04 |g| 0.323 1.42M tok/s
|
| 256 |
+
2110/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2583 lr 1.13e-04 |g| 0.334 1.42M tok/s
|
| 257 |
+
2120/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2362 lr 1.12e-04 |g| 0.329 1.42M tok/s
|
| 258 |
+
2130/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2967 lr 1.10e-04 |g| 0.350 1.42M tok/s
|
| 259 |
+
2140/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2376 lr 1.09e-04 |g| 0.339 1.42M tok/s
|
| 260 |
+
2150/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2345 lr 1.07e-04 |g| 0.345 1.42M tok/s
|
| 261 |
+
2160/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2396 lr 1.06e-04 |g| 0.331 1.42M tok/s
|
| 262 |
+
2170/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1619 lr 1.05e-04 |g| 0.334 1.42M tok/s
|
| 263 |
+
2180/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2383 lr 1.03e-04 |g| 0.454 1.42M tok/s
|
| 264 |
+
2190/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2531 lr 1.02e-04 |g| 0.331 1.42M tok/s
|
| 265 |
+
2200/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3344 lr 1.01e-04 |g| 0.373 1.42M tok/s
|
| 266 |
+
2210/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3195 lr 1.01e-04 |g| 0.324 1.42M tok/s
|
| 267 |
+
2220/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2331 lr 9.99e-05 |g| 0.329 1.42M tok/s
|
| 268 |
+
2230/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2016 lr 9.93e-05 |g| 0.330 1.42M tok/s
|
| 269 |
+
2240/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2402 lr 9.87e-05 |g| 0.338 1.42M tok/s
|
| 270 |
+
2250/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1518 lr 9.83e-05 |g| 0.338 1.42M tok/s
|
| 271 |
+
2260/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1651 lr 9.80e-05 |g| 0.313 1.42M tok/s
|
| 272 |
+
2270/2286 ━━━━━━━━━━━━━━━━━━ loss 4.3755 lr 9.78e-05 |g| 0.317 1.42M tok/s
|
| 273 |
+
2280/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2800 lr 9.77e-05 |g| 0.342 1.42M tok/s
|
| 274 |
+
2286/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2869 lr 9.77e-05 |g| 0.325 1.42M tok/s
|
| 275 |
+
|
| 276 |
+
● Canonical validation 8 deterministic batches outside train_seconds
|
| 277 |
+
|
| 278 |
+
● Fresh-domain validation skipped; no downstream data supplied
|
| 279 |
+
|
| 280 |
+
● Artifacts training.riglog + validation.csv + diagnostics.riglog
|
| 281 |
+
|
| 282 |
+
✓ synchronized training 211.650s (compilation excluded)
|
| 283 |
+
validation loss 4.2244 in 0.283s
|
| 284 |
+
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/stdout.log
ADDED
|
@@ -0,0 +1 @@
|
|
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|
| 1 |
+
RIG_RESULT={"artifacts":{"diagnostics":"diagnostics.riglog","training_curve":"training.riglog","validation_curve":"validation.csv"},"checkpoint":null,"contract":{"dataset_id":"fineweb-8b-gpt2","model":{"d_model":384,"heads":6,"layers":12,"mlp_activation":"gelu","mlp_mult":4,"normalization":"rms_norm","parameterization":"complete_d_p","position_encoding":"rope_base_10000","semantic_vocab_size":50304,"tied_embeddings":false,"tier":"60m","vocab_size":50304},"model_id":"reference-gpt-v3-family","sequence_length":8192,"tokenizer_id":"gpt2"},"evaluations":{"fineweb":{"canonical":true,"loss":4.224437057971954,"perplexity":68.33602349889868,"scored_tokens":1048576,"seconds":0.2825207129935734}},"implementation":{"attention_backend":"tpu_flash","attention_tuning":{"key_digest":"ec38db3431308f3e9f99acf40c7e3bb85ca0695b0d97491e44a93bc7adfada00","resolution_source":"heuristic","tiles":{"block_kv":512,"block_kv_compute":256,"block_kv_dkv":512,"block_kv_dkv_compute":256,"block_kv_dq":512,"block_kv_dq_compute":256,"block_q":512,"block_q_dkv":512,"block_q_dkv_compute":256,"block_q_dq":256},"tune_seconds":0.0},"configuration":{"overrides":{"tokens_per_parameter_micros":5000000},"path":"config.yaml","profile":"dev","resolved":{"evaluation":{"eval_batches":8,"val_every":0,"val_probe_batches":8},"kernels":{"attention_backend":"tpu_flash","loss_backend":"tiled","vocab_tile_size":2048},"logging":{"diagnostics_every":10,"log_every":10},"model":{"d_model":384,"heads":6,"layers":12,"mlp_activation":"gelu","mlp_mult":4,"normalization":"rms_norm","parameterization":"complete_d_p","position_encoding":"rope_base_10000","semantic_vocab_size":50304,"tied_embeddings":false,"tier":"60m","vocab_size":50304},"optimizer":{"adam_epsilon":1e-08,"beta1":0.9,"beta2":0.95,"effective":{"adam_epsilon_horizon_multiplier":1.0,"beta1":0.9,"beta2":0.95,"global_peak_learning_rate":0.0009765625,"weight_decay_horizon_multiplier":1.0},"grad_clip":0.0,"learning_rate":0.0009765625,"min_lr_ratio":0.1,"warmup_steps":229,"weight_decay":0.1},"parameterization":{"attention_scale":"inverse_head_dim","base_depth":12,"base_width":384,"batch_multiplier":1.0,"data_multiplier":1.0,"depth_alpha":1.0,"depth_multiplier":1.0,"embeddings":"untied","init_std":0.02,"name":"complete_d_p","width_multiplier":1.0},"training":{"batch_size":16,"dtype":"bfloat16","sampling":"shuffled_epochs","seq_len":8192,"steps":2286,"tokens_per_parameter":5.000660099848113,"train_tokens":299630592}},"schema_version":2,"sha256":"e3a9103e60160c720239a391afbec5aca213eaf0cecd971a2fb45fe709b764cd"},"loss_backend":"tiled","vocab_tile_size":2048},"metrics":{"achieved_tflops":1044.3160910380807,"attention_tune_seconds":0.0,"base_learning_rate":0.0009765625,"diagnostic_compile_seconds":58.35763206798583,"diagnostic_point_count":230,"diagnostics_every":10,"early_stopping_step":null,"estimated_total_flops":221029692528918528,"eval_compile_seconds":7.229049086978193,"final_validation_seconds":0.2825207129935734,"flop_accounting":{"by_site":{"dot_general":2332173533184,"tpu_flash_causal_attention_bwd_dkv":1236950581248,"tpu_flash_causal_attention_bwd_dq":1236950581248,"tpu_flash_causal_attention_fwd":1236950581248},"elementwise_per_sequence":9192636658,"matmul_per_sequence":6043025276928,"method":"traced-jaxpr","warnings":[]},"flops_per_token":737673984,"mfu_estimate":0.23734456614501834,"model_tier":"60m","parameter_count":59918208,"parameters":59918208,"schedule_steps":2286,"tokens_per_parameter":5.000660099848113,"tokens_per_second":1415687.8427179027,"tokens_processed":299630592,"total_compile_seconds":95.95862559298985,"train_compile_seconds":30.37194443802582,"train_loss":4.286937713623047,"train_seconds":211.65018371900078,"training_data_epochs":0.037928020133706226,"training_data_sharding":"rank_disjoint_shuffled_windows","training_sampling":"shuffled_epochs","training_steps":2286,"training_token_budget":299630592,"training_usable_tokens_per_epoch":7899979776,"validation_loss":4.224437057971954,"validation_probe_count":0,"validation_probe_seconds":0.0,"validation_tokens":1048576},"profile":"dev","schema_version":1,"seed":1337,"status":"ok","system":{"controller_process_index":1,"device_count":16,"device_ids":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"device_kinds":["TPU v4"],"jax_version":"0.11.0","jaxlib_version":"0.11.0","libtpu_version":"0.0.44.1","local_device_count":4,"platform":"tpu","process_count":4,"process_indices":[0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3],"python_version":"3.12.13"},"track":"open"}
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/training.riglog
ADDED
|
Binary file (36.7 kB). View file
|
|
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1337/validation.csv
ADDED
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@@ -0,0 +1,2 @@
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|
| 1 |
+
step,tokens_processed,kind,domain,validation_tokens,validation_loss,perplexity,validation_seconds,canonical
|
| 2 |
+
2286,299630592,fineweb,fineweb,1048576,4.224437057971954,68.33602349889868,0.2825207129935734,true
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/diagnostics.riglog
ADDED
|
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:c5de3da0757ba9550c444292df2da0c6b75ad4e2bc8329a6fc7728bd32ca83bd
|
| 3 |
+
size 272824
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/metrics.json
ADDED
|
@@ -0,0 +1,239 @@
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|
| 1 |
+
{
|
| 2 |
+
"artifacts": {
|
| 3 |
+
"diagnostics": "diagnostics.riglog",
|
| 4 |
+
"training_curve": "training.riglog",
|
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/result.json
ADDED
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@@ -0,0 +1,239 @@
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/stderr.log
ADDED
|
@@ -0,0 +1,284 @@
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| 1 |
+
E0818 09:45:18.814914 2234399 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 2 |
+
E0818 09:45:18.815528 2947975 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 3 |
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E0818 09:45:18.816400 2187102 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 4 |
+
E0818 09:45:18.817239 2278301 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 5 |
+
|
| 6 |
+
◆ GPT TPU RIG reference / jax
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
● Attention tile preflight resolving the shipped lookup or shape heuristic
|
| 10 |
+
╭─ run configuration ────────────────────────────────────────────────────────╮
|
| 11 |
+
│ experiment config config.yaml · dev · sha256:e3a9103e6016 │
|
| 12 |
+
│ devices 16 × TPU v4 │
|
| 13 |
+
│ JAX processes 4 (this rank 1) │
|
| 14 |
+
│ mesh data=16 (replicated model) │
|
| 15 |
+
│ dataset 79 train + 1 val shard(s) │
|
| 16 |
+
│ train / val tokens 7,900,000,000 / 100,000,000 │
|
| 17 |
+
│ downstream not requested │
|
| 18 |
+
│ model 60m · L12 D384 H6 RoPE RMSNorm GELU MLP×4 │
|
| 19 |
+
│ parameters 59.92M │
|
| 20 |
+
│ parameterization complete_d_p · mN=1 · mL=1 · mD=1 │
|
| 21 |
+
│ global batch 16 × 8192 tokens │
|
| 22 |
+
│ train sampling shuffled epochs · 7,899,979,776 unique targets/epoch │
|
| 23 |
+
│ compute bfloat16 │
|
| 24 |
+
│ attention tpu_flash │
|
| 25 |
+
│ attention tuning heuristic · key ec38db343130 │
|
| 26 |
+
│ attention fwd q512 · kv512/256 │
|
| 27 |
+
│ attention dK/dV q512/256 · kv512/256 │
|
| 28 |
+
│ attention dQ q256 · kv512/256 │
|
| 29 |
+
│ output loss tiled CE (semantic 50,304, tile 2,048) │
|
| 30 |
+
│ diagnostics step 1 / every 10 / final │
|
| 31 |
+
│ duration 2,286 steps │
|
| 32 |
+
│ train tokens 299.63M │
|
| 33 |
+
│ traced FLOPs 221029.69T │
|
| 34 |
+
│ FLOP breakdown dot_general 2,332,173,533,184 (38.6%) · tpu_flash_ca… │
|
| 35 |
+
│ XProf disabled │
|
| 36 |
+
╰────────────────────────────────────────────────────────────────────────────╯
|
| 37 |
+
|
| 38 |
+
● Compiling train step compilation is outside train_seconds
|
| 39 |
+
|
| 40 |
+
● Compiling sparse diagnostics separate executable; compilation is outside train_seconds
|
| 41 |
+
|
| 42 |
+
● Compiling evaluation reused by probes and final validation
|
| 43 |
+
|
| 44 |
+
● Training train compiled in 28.72s, eval in 7.46s; periodic validation disabled
|
| 45 |
+
1/2286 ────────────────── loss 10.8827 lr 4.26e-06 |g| 3.359 573.69K tok/s
|
| 46 |
+
10/2286 ────────────────── loss 10.3394 lr 4.26e-05 |g| 1.697 1.12M tok/s
|
| 47 |
+
20/2286 ────────────────── loss 9.9985 lr 8.53e-05 |g| 1.627 1.25M tok/s
|
| 48 |
+
30/2286 ────────────────── loss 9.5801 lr 1.28e-04 |g| 1.630 1.30M tok/s
|
| 49 |
+
40/2286 ────────────────── loss 9.0121 lr 1.71e-04 |g| 1.559 1.33M tok/s
|
| 50 |
+
50/2286 ────────────────── loss 8.3458 lr 2.13e-04 |g| 1.463 1.35M tok/s
|
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1480/2286 ━━━━━━━━━━━━────── loss 4.4083 lr 3.91e-04 |g| 0.378 1.41M tok/s
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1490/2286 ━━━━━━━━━━━━────── loss 4.4842 lr 3.84e-04 |g| 0.343 1.41M tok/s
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1500/2286 ━━━━━━━━━━━━────── loss 4.4146 lr 3.78e-04 |g| 0.324 1.41M tok/s
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1520/2286 ━━━━━━━━━━━━────── loss 4.3929 lr 3.66e-04 |g| 0.300 1.41M tok/s
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1530/2286 ━━━━━━━━━━━━────── loss 4.3932 lr 3.59e-04 |g| 0.368 1.41M tok/s
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1590/2286 ━━━━━━━━━━━━━───── loss 4.3998 lr 3.23e-04 |g| 0.374 1.41M tok/s
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1800/2286 ━━━━━━━━━━━━━━──── loss 4.2883 lr 2.13e-04 |g| 0.343 1.42M tok/s
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1810/2286 ━━━━━━━━━━━━━━──── loss 4.4622 lr 2.09e-04 |g| 0.365 1.42M tok/s
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1820/2286 ━━━━━━━━━━━━━━──── loss 4.3586 lr 2.04e-04 |g| 0.351 1.42M tok/s
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1830/2286 ━━━━━━━━━━━━━━──── loss 4.2714 lr 2.00e-04 |g| 0.315 1.42M tok/s
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1840/2286 ━━━━━━━━━━━━━━──── loss 4.3576 lr 1.96e-04 |g| 0.376 1.42M tok/s
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1850/2286 ━━━━━━━━━━━━━━━─── loss 4.3790 lr 1.92e-04 |g| 0.380 1.42M tok/s
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1860/2286 ━━━━━━━━━━━━━━━─── loss 4.3559 lr 1.87e-04 |g| 0.358 1.42M tok/s
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1870/2286 ━━━━━━━━━━━━━━━─── loss 4.2140 lr 1.83e-04 |g| 0.370 1.42M tok/s
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1880/2286 ━━━━━━━━━━━━━━━─── loss 4.3002 lr 1.79e-04 |g| 0.326 1.42M tok/s
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1890/2286 ━━━━━━━━━━━━━━━─── loss 4.2769 lr 1.76e-04 |g| 0.352 1.42M tok/s
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1900/2286 ━━━━━━━━━━━━━━━─── loss 4.3118 lr 1.72e-04 |g| 0.329 1.42M tok/s
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1910/2286 ━━━━━━━━━━━━━━━─── loss 4.3536 lr 1.68e-04 |g| 0.776 1.42M tok/s
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1920/2286 ━━━━━━━━━━━━━━━─── loss 4.2996 lr 1.65e-04 |g| 0.336 1.42M tok/s
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1930/2286 ━━━━━━━━━━━━━━━─── loss 4.3733 lr 1.61e-04 |g| 0.339 1.42M tok/s
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1940/2286 ━━━━━━━━━━━━━━━─── loss 4.3496 lr 1.58e-04 |g| 0.330 1.42M tok/s
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1950/2286 ━━━━━━━━━━━━━━━─── loss 4.2904 lr 1.54e-04 |g| 0.331 1.42M tok/s
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1960/2286 ━━━━━━━━━━━━━━━─── loss 4.3831 lr 1.51e-04 |g| 0.334 1.42M tok/s
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1970/2286 ━━━━━━━━━━━━━━━━── loss 4.3324 lr 1.48e-04 |g| 0.348 1.42M tok/s
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1980/2286 ━━━━━━━━━━━━━━━━── loss 4.3137 lr 1.45e-04 |g| 0.332 1.42M tok/s
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1990/2286 ━━━━━━━━━━━━━━━━── loss 4.2593 lr 1.42e-04 |g| 0.303 1.42M tok/s
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2000/2286 ━━━━━━━━━━━━━━━━── loss 4.2909 lr 1.39e-04 |g| 0.340 1.42M tok/s
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2010/2286 ━━━━━━━━━━━━━━━━── loss 4.2766 lr 1.36e-04 |g| 0.435 1.42M tok/s
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2020/2286 ━━━━━━━━━━━━━━━━── loss 4.3067 lr 1.33e-04 |g| 0.339 1.42M tok/s
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2030/2286 ━━━━━━━━━━━━━━━━── loss 4.3135 lr 1.31e-04 |g| 0.359 1.42M tok/s
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2040/2286 ━━━━━━━━━━━━━━━━── loss 4.3325 lr 1.28e-04 |g| 0.295 1.42M tok/s
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| 250 |
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2050/2286 ━━━━━━━━━━━━━━━━── loss 4.3089 lr 1.26e-04 |g| 0.358 1.42M tok/s
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| 251 |
+
2060/2286 ━━━━━━━━━━━━━━━━── loss 4.4130 lr 1.24e-04 |g| 0.356 1.42M tok/s
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2070/2286 ━━━━━━━━━━━━━━━━── loss 4.2807 lr 1.21e-04 |g| 0.328 1.42M tok/s
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2080/2286 ━━━━━━━━━━━━━━━━── loss 4.2055 lr 1.19e-04 |g| 0.323 1.42M tok/s
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| 254 |
+
2090/2286 ━━━━━━━━━━━━━━━━── loss 4.3559 lr 1.17e-04 |g| 0.305 1.42M tok/s
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2100/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1746 lr 1.15e-04 |g| 0.329 1.42M tok/s
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2110/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3666 lr 1.13e-04 |g| 0.335 1.42M tok/s
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| 257 |
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2120/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3062 lr 1.12e-04 |g| 0.390 1.42M tok/s
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2130/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3446 lr 1.10e-04 |g| 0.313 1.42M tok/s
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2140/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2768 lr 1.09e-04 |g| 0.322 1.42M tok/s
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| 260 |
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2150/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3831 lr 1.07e-04 |g| 0.332 1.42M tok/s
|
| 261 |
+
2160/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3090 lr 1.06e-04 |g| 0.360 1.42M tok/s
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| 262 |
+
2170/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2993 lr 1.05e-04 |g| 0.365 1.42M tok/s
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| 263 |
+
2180/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2563 lr 1.03e-04 |g| 0.348 1.42M tok/s
|
| 264 |
+
2190/2286 ━━━━━━━━━━━━━━━━━─ loss 4.4104 lr 1.02e-04 |g| 0.343 1.42M tok/s
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| 265 |
+
2200/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2736 lr 1.01e-04 |g| 0.308 1.42M tok/s
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| 266 |
+
2210/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2553 lr 1.01e-04 |g| 0.332 1.42M tok/s
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| 267 |
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2220/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2736 lr 9.99e-05 |g| 0.334 1.42M tok/s
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| 268 |
+
2230/2286 ━━━━━━━━━━━━━━━━━━ loss 4.3300 lr 9.93e-05 |g| 0.339 1.42M tok/s
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| 269 |
+
2240/2286 ━━━━━━━━━━━━━━━━━━ loss 4.3097 lr 9.87e-05 |g| 0.321 1.42M tok/s
|
| 270 |
+
2250/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2861 lr 9.83e-05 |g| 0.321 1.42M tok/s
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| 271 |
+
2260/2286 ━━━━━━━━━━━━━━━━━━ loss 4.3087 lr 9.80e-05 |g| 0.313 1.42M tok/s
|
| 272 |
+
2270/2286 ━━━━━━━━━━━━━━━━━━ loss 4.3035 lr 9.78e-05 |g| 0.304 1.42M tok/s
|
| 273 |
+
2280/2286 ━���━━━━━━━━━━━━━━━━ loss 4.2655 lr 9.77e-05 |g| 0.329 1.42M tok/s
|
| 274 |
+
2286/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2927 lr 9.77e-05 |g| 0.386 1.42M tok/s
|
| 275 |
+
|
| 276 |
+
● Canonical validation 8 deterministic batches outside train_seconds
|
| 277 |
+
|
| 278 |
+
● Fresh-domain validation skipped; no downstream data supplied
|
| 279 |
+
|
| 280 |
+
● Artifacts training.riglog + validation.csv + diagnostics.riglog
|
| 281 |
+
|
| 282 |
+
✓ synchronized training 211.667s (compilation excluded)
|
| 283 |
+
validation loss 4.2728 in 0.271s
|
| 284 |
+
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/stdout.log
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1338/validation.csv
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/metrics.json
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| 225 |
+
1,
|
| 226 |
+
1,
|
| 227 |
+
2,
|
| 228 |
+
2,
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| 229 |
+
2,
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| 231 |
+
3,
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| 232 |
+
3,
|
| 233 |
+
3,
|
| 234 |
+
3
|
| 235 |
+
],
|
| 236 |
+
"python_version": "3.12.13"
|
| 237 |
+
},
|
| 238 |
+
"track": "open"
|
| 239 |
+
}
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/stderr.log
ADDED
|
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| 1 |
+
E0818 09:50:51.353817 2240033 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 2 |
+
E0818 09:50:51.354958 2192737 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 3 |
+
E0818 09:50:51.355111 2954406 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 4 |
+
E0818 09:50:51.355179 2284006 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 5 |
+
|
| 6 |
+
◆ GPT TPU RIG reference / jax
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
● Attention tile preflight resolving the shipped lookup or shape heuristic
|
| 10 |
+
╭─ run configuration ────────────────────────────────────────────────────────╮
|
| 11 |
+
│ experiment config config.yaml · dev · sha256:e3a9103e6016 │
|
| 12 |
+
│ devices 16 × TPU v4 │
|
| 13 |
+
│ JAX processes 4 (this rank 1) │
|
| 14 |
+
│ mesh data=16 (replicated model) │
|
| 15 |
+
│ dataset 79 train + 1 val shard(s) │
|
| 16 |
+
│ train / val tokens 7,900,000,000 / 100,000,000 │
|
| 17 |
+
│ downstream not requested │
|
| 18 |
+
│ model 60m · L12 D384 H6 RoPE RMSNorm GELU MLP×4 │
|
| 19 |
+
│ parameters 59.92M │
|
| 20 |
+
│ parameterization complete_d_p · mN=1 · mL=1 · mD=1 │
|
| 21 |
+
│ global batch 16 × 8192 tokens │
|
| 22 |
+
│ train sampling shuffled epochs · 7,899,979,776 unique targets/epoch │
|
| 23 |
+
│ compute bfloat16 │
|
| 24 |
+
│ attention tpu_flash │
|
| 25 |
+
│ attention tuning heuristic · key ec38db343130 │
|
| 26 |
+
│ attention fwd q512 · kv512/256 │
|
| 27 |
+
│ attention dK/dV q512/256 · kv512/256 │
|
| 28 |
+
│ attention dQ q256 · kv512/256 │
|
| 29 |
+
│ output loss tiled CE (semantic 50,304, tile 2,048) │
|
| 30 |
+
│ diagnostics step 1 / every 10 / final │
|
| 31 |
+
│ duration 2,286 steps │
|
| 32 |
+
│ train tokens 299.63M │
|
| 33 |
+
│ traced FLOPs 221029.69T │
|
| 34 |
+
│ FLOP breakdown dot_general 2,332,173,533,184 (38.6%) · tpu_flash_ca… │
|
| 35 |
+
│ XProf disabled │
|
| 36 |
+
╰────────────────────────────────────────────────────────────────────────────╯
|
| 37 |
+
|
| 38 |
+
● Compiling train step compilation is outside train_seconds
|
| 39 |
+
|
| 40 |
+
● Compiling sparse diagnostics separate executable; compilation is outside train_seconds
|
| 41 |
+
|
| 42 |
+
● Compiling evaluation reused by probes and final validation
|
| 43 |
+
|
| 44 |
+
● Training train compiled in 28.26s, eval in 7.16s; periodic validation disabled
|
| 45 |
+
1/2286 ────────────────── loss 10.9093 lr 4.26e-06 |g| 3.749 580.21K tok/s
|
| 46 |
+
10/2286 ────────────────── loss 10.3500 lr 4.26e-05 |g| 1.708 1.13M tok/s
|
| 47 |
+
20/2286 ────────────────── loss 10.0082 lr 8.53e-05 |g| 1.631 1.26M tok/s
|
| 48 |
+
30/2286 ────────────────── loss 9.5869 lr 1.28e-04 |g| 1.597 1.31M tok/s
|
| 49 |
+
40/2286 ────────────────── loss 9.0041 lr 1.71e-04 |g| 1.539 1.33M tok/s
|
| 50 |
+
50/2286 ────────────────── loss 8.4136 lr 2.13e-04 |g| 1.369 1.35M tok/s
|
| 51 |
+
60/2286 ────────────────── loss 7.9001 lr 2.56e-04 |g| 1.085 1.36M tok/s
|
| 52 |
+
70/2286 ━───────────────── loss 7.5706 lr 2.99e-04 |g| 0.615 1.37M tok/s
|
| 53 |
+
80/2286 ━───────────────── loss 7.3732 lr 3.41e-04 |g| 0.571 1.37M tok/s
|
| 54 |
+
90/2286 ━───────────────── loss 7.1297 lr 3.84e-04 |g| 0.494 1.38M tok/s
|
| 55 |
+
100/2286 ━───────────────── loss 6.9905 lr 4.26e-04 |g| 0.495 1.38M tok/s
|
| 56 |
+
110/2286 ━───────────────── loss 6.7845 lr 4.69e-04 |g| 0.477 1.38M tok/s
|
| 57 |
+
120/2286 ━───────────────── loss 6.6553 lr 5.12e-04 |g| 0.553 1.39M tok/s
|
| 58 |
+
130/2286 ━───────────────── loss 6.5609 lr 5.54e-04 |g| 0.586 1.39M tok/s
|
| 59 |
+
140/2286 ━───────────────── loss 6.4935 lr 5.97e-04 |g| 0.360 1.39M tok/s
|
| 60 |
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150/2286 ━───────────────── loss 6.4183 lr 6.40e-04 |g| 0.388 1.39M tok/s
|
| 61 |
+
160/2286 ━───────────────── loss 6.4292 lr 6.82e-04 |g| 0.428 1.39M tok/s
|
| 62 |
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170/2286 ━───────────────── loss 6.3356 lr 7.25e-04 |g| 0.483 1.40M tok/s
|
| 63 |
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180/2286 ━───────────────── loss 6.2413 lr 7.68e-04 |g| 0.680 1.40M tok/s
|
| 64 |
+
190/2286 ━───────────────── loss 6.1640 lr 8.10e-04 |g| 0.595 1.40M tok/s
|
| 65 |
+
200/2286 ━━──────────────── loss 6.0855 lr 8.53e-04 |g| 0.481 1.40M tok/s
|
| 66 |
+
210/2286 ━━──────────────── loss 6.1591 lr 8.96e-04 |g| 0.472 1.40M tok/s
|
| 67 |
+
220/2286 ━━──────────────── loss 5.9942 lr 9.38e-04 |g| 0.755 1.40M tok/s
|
| 68 |
+
230/2286 ━━──────────────── loss 5.9106 lr 9.77e-04 |g| 0.413 1.40M tok/s
|
| 69 |
+
240/2286 ━━──────────────── loss 5.9910 lr 9.77e-04 |g| 0.579 1.40M tok/s
|
| 70 |
+
250/2286 ━━──────────────── loss 5.8627 lr 9.76e-04 |g| 0.508 1.40M tok/s
|
| 71 |
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260/2286 ━━──────────────── loss 5.8001 lr 9.76e-04 |g| 0.521 1.40M tok/s
|
| 72 |
+
270/2286 ━━──────────────── loss 5.7983 lr 9.76e-04 |g| 0.634 1.40M tok/s
|
| 73 |
+
280/2286 ━━──────────────── loss 5.7113 lr 9.75e-04 |g| 0.555 1.40M tok/s
|
| 74 |
+
290/2286 ━━──────────────── loss 5.7640 lr 9.75e-04 |g| 0.486 1.40M tok/s
|
| 75 |
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300/2286 ━━──────────────── loss 5.6734 lr 9.74e-04 |g| 0.459 1.40M tok/s
|
| 76 |
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310/2286 ━━──────────────── loss 5.5952 lr 9.73e-04 |g| 0.457 1.41M tok/s
|
| 77 |
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320/2286 ━━━─────────────── loss 5.7090 lr 9.72e-04 |g| 0.531 1.41M tok/s
|
| 78 |
+
330/2286 ━━━─────────────── loss 5.6055 lr 9.71e-04 |g| 0.515 1.41M tok/s
|
| 79 |
+
340/2286 ━━━─────────────── loss 5.6518 lr 9.70e-04 |g| 0.407 1.41M tok/s
|
| 80 |
+
350/2286 ━━━─────────────── loss 5.5626 lr 9.69e-04 |g| 0.425 1.41M tok/s
|
| 81 |
+
360/2286 ━━━─────────────── loss 5.3949 lr 9.68e-04 |g| 0.410 1.41M tok/s
|
| 82 |
+
370/2286 ━━━─────────────── loss 5.4401 lr 9.66e-04 |g| 0.445 1.41M tok/s
|
| 83 |
+
380/2286 ━━━─────────────── loss 5.5448 lr 9.65e-04 |g| 0.502 1.41M tok/s
|
| 84 |
+
390/2286 ━━━─────────────── loss 5.4420 lr 9.63e-04 |g| 0.397 1.41M tok/s
|
| 85 |
+
400/2286 ━━━─────────────── loss 5.4218 lr 9.62e-04 |g| 0.417 1.41M tok/s
|
| 86 |
+
410/2286 ━━━─────────────── loss 5.3800 lr 9.60e-04 |g| 0.526 1.41M tok/s
|
| 87 |
+
420/2286 ━━━─────────────── loss 5.2608 lr 9.58e-04 |g| 0.487 1.41M tok/s
|
| 88 |
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430/2286 ━━━─────────────── loss 5.4518 lr 9.56e-04 |g| 0.351 1.41M tok/s
|
| 89 |
+
440/2286 ━━━─────────────── loss 5.3779 lr 9.54e-04 |g| 0.559 1.41M tok/s
|
| 90 |
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450/2286 ━━━━────────────── loss 5.2576 lr 9.52e-04 |g| 0.473 1.41M tok/s
|
| 91 |
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460/2286 ━━━━────────────── loss 5.2809 lr 9.49e-04 |g| 0.390 1.41M tok/s
|
| 92 |
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470/2286 ━━━━────────────── loss 5.3027 lr 9.47e-04 |g| 0.416 1.41M tok/s
|
| 93 |
+
480/2286 ━━━━────────────── loss 5.3812 lr 9.45e-04 |g| 0.527 1.41M tok/s
|
| 94 |
+
490/2286 ━━━━────────────── loss 5.4033 lr 9.42e-04 |g| 0.445 1.41M tok/s
|
| 95 |
+
500/2286 ━━━━────────────── loss 5.2901 lr 9.39e-04 |g| 0.661 1.41M tok/s
|
| 96 |
+
510/2286 ━━━━────────────── loss 5.2307 lr 9.37e-04 |g| 0.427 1.41M tok/s
|
| 97 |
+
520/2286 ━━━━────────────── loss 5.2053 lr 9.34e-04 |g| 0.380 1.41M tok/s
|
| 98 |
+
530/2286 ━━━━────────────── loss 5.2400 lr 9.31e-04 |g| 0.515 1.41M tok/s
|
| 99 |
+
540/2286 ━━━━────────────── loss 5.1709 lr 9.28e-04 |g| 0.377 1.41M tok/s
|
| 100 |
+
550/2286 ━━━━────────────── loss 5.0454 lr 9.25e-04 |g| 0.403 1.41M tok/s
|
| 101 |
+
560/2286 ━━━━────────────── loss 5.0390 lr 9.22e-04 |g| 0.416 1.41M tok/s
|
| 102 |
+
570/2286 ━━━━────────────── loss 5.0930 lr 9.18e-04 |g| 0.368 1.41M tok/s
|
| 103 |
+
580/2286 ━━━━━───────────── loss 5.2370 lr 9.15e-04 |g| 0.558 1.41M tok/s
|
| 104 |
+
590/2286 ━━━━━───────────── loss 5.1610 lr 9.11e-04 |g| 0.475 1.41M tok/s
|
| 105 |
+
600/2286 ━━━━━───────────── loss 5.0777 lr 9.08e-04 |g| 0.448 1.41M tok/s
|
| 106 |
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610/2286 ━━━━━───────────── loss 5.0234 lr 9.04e-04 |g| 0.350 1.41M tok/s
|
| 107 |
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620/2286 ━━━━━───────────── loss 5.1189 lr 9.01e-04 |g| 0.524 1.41M tok/s
|
| 108 |
+
630/2286 ━━━━━───────────── loss 5.1264 lr 8.97e-04 |g| 0.392 1.41M tok/s
|
| 109 |
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640/2286 ━━━━━───────────── loss 5.0189 lr 8.93e-04 |g| 0.430 1.41M tok/s
|
| 110 |
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| 111 |
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| 112 |
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670/2286 ━━━━━───────────── loss 5.0060 lr 8.81e-04 |g| 0.442 1.41M tok/s
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| 113 |
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680/2286 ━━━━━───────────── loss 5.0201 lr 8.76e-04 |g| 0.386 1.41M tok/s
|
| 114 |
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690/2286 ━━━━━───────────── loss 4.9448 lr 8.72e-04 |g| 0.420 1.41M tok/s
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| 115 |
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|
| 116 |
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|
| 117 |
+
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| 118 |
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| 119 |
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| 120 |
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|
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1980/2286 ━━━━━━━━━━━━━━━━── loss 4.3568 lr 1.45e-04 |g| 0.330 1.42M tok/s
|
| 244 |
+
1990/2286 ━━━━━━━━━━━━━━━━── loss 4.2469 lr 1.42e-04 |g| 0.327 1.42M tok/s
|
| 245 |
+
2000/2286 ━━━━━━━━━━━━━━━━── loss 4.4277 lr 1.39e-04 |g| 0.341 1.42M tok/s
|
| 246 |
+
2010/2286 ━━━━━━━━━━━━━━━━── loss 4.4337 lr 1.36e-04 |g| 0.333 1.42M tok/s
|
| 247 |
+
2020/2286 ━━━━━━━━━━━━━━━━── loss 4.2260 lr 1.33e-04 |g| 0.343 1.42M tok/s
|
| 248 |
+
2030/2286 ━━━━━━━━━━━━━━━━── loss 4.1577 lr 1.31e-04 |g| 0.348 1.42M tok/s
|
| 249 |
+
2040/2286 ━━━━━━━━━━━━━━━━── loss 4.2604 lr 1.28e-04 |g| 0.309 1.42M tok/s
|
| 250 |
+
2050/2286 ━━━━━━━━━━━━━━━━── loss 4.2580 lr 1.26e-04 |g| 0.312 1.42M tok/s
|
| 251 |
+
2060/2286 ━━━━━━━━━━━━━━━━── loss 4.3101 lr 1.24e-04 |g| 0.320 1.42M tok/s
|
| 252 |
+
2070/2286 ━━━━━━━━━━━━━━━━── loss 4.2841 lr 1.21e-04 |g| 0.343 1.42M tok/s
|
| 253 |
+
2080/2286 ━━━━━━━━━━━━━━━━── loss 4.2764 lr 1.19e-04 |g| 0.359 1.42M tok/s
|
| 254 |
+
2090/2286 ━━━━━━━━━━━━━━━━── loss 4.3147 lr 1.17e-04 |g| 0.312 1.42M tok/s
|
| 255 |
+
2100/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3176 lr 1.15e-04 |g| 0.296 1.42M tok/s
|
| 256 |
+
2110/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2716 lr 1.13e-04 |g| 0.320 1.42M tok/s
|
| 257 |
+
2120/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1900 lr 1.12e-04 |g| 0.316 1.42M tok/s
|
| 258 |
+
2130/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2568 lr 1.10e-04 |g| 0.324 1.42M tok/s
|
| 259 |
+
2140/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1823 lr 1.09e-04 |g| 0.289 1.42M tok/s
|
| 260 |
+
2150/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2328 lr 1.07e-04 |g| 0.318 1.42M tok/s
|
| 261 |
+
2160/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2847 lr 1.06e-04 |g| 0.338 1.42M tok/s
|
| 262 |
+
2170/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2633 lr 1.05e-04 |g| 0.308 1.42M tok/s
|
| 263 |
+
2180/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2364 lr 1.03e-04 |g| 0.306 1.42M tok/s
|
| 264 |
+
2190/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1992 lr 1.02e-04 |g| 0.355 1.42M tok/s
|
| 265 |
+
2200/2286 ━━━━━━━━━━━━━━━━━─ loss 4.3483 lr 1.01e-04 |g| 0.337 1.42M tok/s
|
| 266 |
+
2210/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1503 lr 1.01e-04 |g| 0.339 1.42M tok/s
|
| 267 |
+
2220/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2184 lr 9.99e-05 |g| 0.338 1.42M tok/s
|
| 268 |
+
2230/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1683 lr 9.93e-05 |g| 0.317 1.42M tok/s
|
| 269 |
+
2240/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2502 lr 9.87e-05 |g| 0.317 1.42M tok/s
|
| 270 |
+
2250/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2154 lr 9.83e-05 |g| 0.326 1.42M tok/s
|
| 271 |
+
2260/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1469 lr 9.80e-05 |g| 0.316 1.42M tok/s
|
| 272 |
+
2270/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1263 lr 9.78e-05 |g| 0.336 1.42M tok/s
|
| 273 |
+
2280/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2815 lr 9.77e-05 |g| 0.325 1.42M tok/s
|
| 274 |
+
2286/2286 ━━━━━━━━━━━━━━━━━━ loss 4.0992 lr 9.77e-05 |g| 0.325 1.42M tok/s
|
| 275 |
+
|
| 276 |
+
● Canonical validation 8 deterministic batches outside train_seconds
|
| 277 |
+
|
| 278 |
+
● Fresh-domain validation skipped; no downstream data supplied
|
| 279 |
+
|
| 280 |
+
● Artifacts training.riglog + validation.csv + diagnostics.riglog
|
| 281 |
+
|
| 282 |
+
✓ synchronized training 211.637s (compilation excluded)
|
| 283 |
+
validation loss 4.2401 in 0.273s
|
| 284 |
+
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/stdout.log
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
RIG_RESULT={"artifacts":{"diagnostics":"diagnostics.riglog","training_curve":"training.riglog","validation_curve":"validation.csv"},"checkpoint":null,"contract":{"dataset_id":"fineweb-8b-gpt2","model":{"d_model":384,"heads":6,"layers":12,"mlp_activation":"gelu","mlp_mult":4,"normalization":"rms_norm","parameterization":"complete_d_p","position_encoding":"rope_base_10000","semantic_vocab_size":50304,"tied_embeddings":false,"tier":"60m","vocab_size":50304},"model_id":"reference-gpt-v3-family","sequence_length":8192,"tokenizer_id":"gpt2"},"evaluations":{"fineweb":{"canonical":true,"loss":4.240055501461029,"perplexity":69.4117041828435,"scored_tokens":1048576,"seconds":0.2725372739951126}},"implementation":{"attention_backend":"tpu_flash","attention_tuning":{"key_digest":"ec38db3431308f3e9f99acf40c7e3bb85ca0695b0d97491e44a93bc7adfada00","resolution_source":"heuristic","tiles":{"block_kv":512,"block_kv_compute":256,"block_kv_dkv":512,"block_kv_dkv_compute":256,"block_kv_dq":512,"block_kv_dq_compute":256,"block_q":512,"block_q_dkv":512,"block_q_dkv_compute":256,"block_q_dq":256},"tune_seconds":0.0},"configuration":{"overrides":{"tokens_per_parameter_micros":5000000},"path":"config.yaml","profile":"dev","resolved":{"evaluation":{"eval_batches":8,"val_every":0,"val_probe_batches":8},"kernels":{"attention_backend":"tpu_flash","loss_backend":"tiled","vocab_tile_size":2048},"logging":{"diagnostics_every":10,"log_every":10},"model":{"d_model":384,"heads":6,"layers":12,"mlp_activation":"gelu","mlp_mult":4,"normalization":"rms_norm","parameterization":"complete_d_p","position_encoding":"rope_base_10000","semantic_vocab_size":50304,"tied_embeddings":false,"tier":"60m","vocab_size":50304},"optimizer":{"adam_epsilon":1e-08,"beta1":0.9,"beta2":0.95,"effective":{"adam_epsilon_horizon_multiplier":1.0,"beta1":0.9,"beta2":0.95,"global_peak_learning_rate":0.0009765625,"weight_decay_horizon_multiplier":1.0},"grad_clip":0.0,"learning_rate":0.0009765625,"min_lr_ratio":0.1,"warmup_steps":229,"weight_decay":0.1},"parameterization":{"attention_scale":"inverse_head_dim","base_depth":12,"base_width":384,"batch_multiplier":1.0,"data_multiplier":1.0,"depth_alpha":1.0,"depth_multiplier":1.0,"embeddings":"untied","init_std":0.02,"name":"complete_d_p","width_multiplier":1.0},"training":{"batch_size":16,"dtype":"bfloat16","sampling":"shuffled_epochs","seq_len":8192,"steps":2286,"tokens_per_parameter":5.000660099848113,"train_tokens":299630592}},"schema_version":2,"sha256":"e3a9103e60160c720239a391afbec5aca213eaf0cecd971a2fb45fe709b764cd"},"loss_backend":"tiled","vocab_tile_size":2048},"metrics":{"achieved_tflops":1044.3788635962658,"attention_tune_seconds":0.0,"base_learning_rate":0.0009765625,"diagnostic_compile_seconds":58.37414735497441,"diagnostic_point_count":230,"diagnostics_every":10,"early_stopping_step":null,"estimated_total_flops":221029692528918528,"eval_compile_seconds":7.1594817860168405,"final_validation_seconds":0.2725372739951126,"flop_accounting":{"by_site":{"dot_general":2332173533184,"tpu_flash_causal_attention_bwd_dkv":1236950581248,"tpu_flash_causal_attention_bwd_dq":1236950581248,"tpu_flash_causal_attention_fwd":1236950581248},"elementwise_per_sequence":9192636658,"matmul_per_sequence":6043025276928,"method":"traced-jaxpr","warnings":[]},"flops_per_token":737673984,"mfu_estimate":0.23735883263551494,"model_tier":"60m","parameter_count":59918208,"parameters":59918208,"schedule_steps":2286,"tokens_per_parameter":5.000660099848113,"tokens_per_second":1415772.9379761694,"tokens_processed":299630592,"total_compile_seconds":93.79263106297003,"train_compile_seconds":28.25900192197878,"train_loss":4.099155902862549,"train_seconds":211.63746245094808,"training_data_epochs":0.037928020133706226,"training_data_sharding":"rank_disjoint_shuffled_windows","training_sampling":"shuffled_epochs","training_steps":2286,"training_token_budget":299630592,"training_usable_tokens_per_epoch":7899979776,"validation_loss":4.240055501461029,"validation_probe_count":0,"validation_probe_seconds":0.0,"validation_tokens":1048576},"profile":"dev","schema_version":1,"seed":1339,"status":"ok","system":{"controller_process_index":1,"device_count":16,"device_ids":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"device_kinds":["TPU v4"],"jax_version":"0.11.0","jaxlib_version":"0.11.0","libtpu_version":"0.0.44.1","local_device_count":4,"platform":"tpu","process_count":4,"process_indices":[0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3],"python_version":"3.12.13"},"track":"open"}
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/training.riglog
ADDED
|
Binary file (36.7 kB). View file
|
|
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-10-s1339/validation.csv
ADDED
|
@@ -0,0 +1,2 @@
|
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|
|
|
|
|
|
|
|
| 1 |
+
step,tokens_processed,kind,domain,validation_tokens,validation_loss,perplexity,validation_seconds,canonical
|
| 2 |
+
2286,299630592,fineweb,fineweb,1048576,4.240055501461029,69.4117041828435,0.2725372739951126,true
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/diagnostics.riglog
ADDED
|
@@ -0,0 +1,3 @@
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:54286374b09e1faaa16fabaa8a145525a3f0fdc2adba59280068c3b41db028f7
|
| 3 |
+
size 272824
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/metrics.json
ADDED
|
@@ -0,0 +1,239 @@
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|
| 1 |
+
{
|
| 2 |
+
"artifacts": {
|
| 3 |
+
"diagnostics": "diagnostics.riglog",
|
| 4 |
+
"training_curve": "training.riglog",
|
| 5 |
+
"validation_curve": "validation.csv"
|
| 6 |
+
},
|
| 7 |
+
"checkpoint": null,
|
| 8 |
+
"contract": {
|
| 9 |
+
"dataset_id": "fineweb-8b-gpt2",
|
| 10 |
+
"model": {
|
| 11 |
+
"d_model": 384,
|
| 12 |
+
"heads": 6,
|
| 13 |
+
"layers": 12,
|
| 14 |
+
"mlp_activation": "gelu",
|
| 15 |
+
"mlp_mult": 4,
|
| 16 |
+
"normalization": "rms_norm",
|
| 17 |
+
"parameterization": "complete_d_p",
|
| 18 |
+
"position_encoding": "rope_base_10000",
|
| 19 |
+
"semantic_vocab_size": 50304,
|
| 20 |
+
"tied_embeddings": false,
|
| 21 |
+
"tier": "60m",
|
| 22 |
+
"vocab_size": 50304
|
| 23 |
+
},
|
| 24 |
+
"model_id": "reference-gpt-v3-family",
|
| 25 |
+
"sequence_length": 8192,
|
| 26 |
+
"tokenizer_id": "gpt2"
|
| 27 |
+
},
|
| 28 |
+
"evaluations": {
|
| 29 |
+
"fineweb": {
|
| 30 |
+
"canonical": true,
|
| 31 |
+
"loss": 4.131175935268402,
|
| 32 |
+
"perplexity": 62.25108315466655,
|
| 33 |
+
"scored_tokens": 1048576,
|
| 34 |
+
"seconds": 0.27447028300957754
|
| 35 |
+
}
|
| 36 |
+
},
|
| 37 |
+
"implementation": {
|
| 38 |
+
"attention_backend": "tpu_flash",
|
| 39 |
+
"attention_tuning": {
|
| 40 |
+
"key_digest": "ec38db3431308f3e9f99acf40c7e3bb85ca0695b0d97491e44a93bc7adfada00",
|
| 41 |
+
"resolution_source": "heuristic",
|
| 42 |
+
"tiles": {
|
| 43 |
+
"block_kv": 512,
|
| 44 |
+
"block_kv_compute": 256,
|
| 45 |
+
"block_kv_dkv": 512,
|
| 46 |
+
"block_kv_dkv_compute": 256,
|
| 47 |
+
"block_kv_dq": 512,
|
| 48 |
+
"block_kv_dq_compute": 256,
|
| 49 |
+
"block_q": 512,
|
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@@ -0,0 +1,239 @@
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/stderr.log
ADDED
|
@@ -0,0 +1,284 @@
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| 1 |
+
E0818 08:34:15.044412 2161908 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 2 |
+
E0818 08:34:15.045740 2114397 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 3 |
+
E0818 08:34:15.046303 2859246 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 4 |
+
E0818 08:34:15.047447 2205271 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
|
| 5 |
+
|
| 6 |
+
◆ GPT TPU RIG reference / jax
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
● Attention tile preflight resolving the shipped lookup or shape heuristic
|
| 10 |
+
╭─ run configuration ────────────────────────────────────────────────────────╮
|
| 11 |
+
│ experiment config config.yaml · dev · sha256:e3a9103e6016 │
|
| 12 |
+
│ devices 16 × TPU v4 │
|
| 13 |
+
│ JAX processes 4 (this rank 1) │
|
| 14 |
+
│ mesh data=16 (replicated model) │
|
| 15 |
+
│ dataset 79 train + 1 val shard(s) │
|
| 16 |
+
│ train / val tokens 7,900,000,000 / 100,000,000 │
|
| 17 |
+
│ downstream not requested │
|
| 18 |
+
│ model 60m · L12 D384 H6 RoPE RMSNorm GELU MLP×4 │
|
| 19 |
+
│ parameters 59.92M │
|
| 20 |
+
│ parameterization complete_d_p · mN=1 · mL=1 · mD=1 │
|
| 21 |
+
│ global batch 16 × 8192 tokens │
|
| 22 |
+
│ train sampling shuffled epochs · 7,899,979,776 unique targets/epoch │
|
| 23 |
+
│ compute bfloat16 │
|
| 24 |
+
│ attention tpu_flash │
|
| 25 |
+
│ attention tuning heuristic · key ec38db343130 │
|
| 26 |
+
│ attention fwd q512 · kv512/256 │
|
| 27 |
+
│ attention dK/dV q512/256 · kv512/256 │
|
| 28 |
+
│ attention dQ q256 · kv512/256 │
|
| 29 |
+
│ output loss tiled CE (semantic 50,304, tile 2,048) │
|
| 30 |
+
│ diagnostics step 1 / every 10 / final │
|
| 31 |
+
│ duration 2,286 steps │
|
| 32 |
+
│ train tokens 299.63M │
|
| 33 |
+
│ traced FLOPs 221029.69T │
|
| 34 |
+
│ FLOP breakdown dot_general 2,332,173,533,184 (38.6%) · tpu_flash_ca… │
|
| 35 |
+
│ XProf disabled │
|
| 36 |
+
╰────────────────────────────────────────────────────────────────────────────╯
|
| 37 |
+
|
| 38 |
+
● Compiling train step compilation is outside train_seconds
|
| 39 |
+
|
| 40 |
+
● Compiling sparse diagnostics separate executable; compilation is outside train_seconds
|
| 41 |
+
|
| 42 |
+
● Compiling evaluation reused by probes and final validation
|
| 43 |
+
|
| 44 |
+
● Training train compiled in 27.98s, eval in 7.08s; periodic validation disabled
|
| 45 |
+
1/2286 ────────────────── loss 10.9027 lr 6.82e-05 |g| 3.605 903.94K tok/s
|
| 46 |
+
10/2286 ────────────────── loss 9.2554 lr 6.82e-04 |g| 1.588 1.22M tok/s
|
| 47 |
+
20/2286 ────────────────── loss 8.8554 lr 1.36e-03 |g| 178.694 1.31M tok/s
|
| 48 |
+
30/2286 ────────────────── loss 7.7357 lr 2.05e-03 |g| 0.971 1.34M tok/s
|
| 49 |
+
40/2286 ────────────────── loss 7.8096 lr 2.73e-03 |g| 0.434 1.36M tok/s
|
| 50 |
+
50/2286 ────────────────── loss 7.6845 lr 3.41e-03 |g| 0.262 1.37M tok/s
|
| 51 |
+
60/2286 ────────────────── loss 7.6751 lr 4.09e-03 |g| 0.211 1.38M tok/s
|
| 52 |
+
70/2286 ━───────────────── loss 7.5807 lr 4.78e-03 |g| 0.298 1.39M tok/s
|
| 53 |
+
80/2286 ━───────────────── loss 7.4604 lr 5.46e-03 |g| 0.243 1.39M tok/s
|
| 54 |
+
90/2286 ━───────────────── loss 7.5177 lr 6.14e-03 |g| 0.757 1.39M tok/s
|
| 55 |
+
100/2286 ━───────────────── loss 7.2497 lr 6.82e-03 |g| 0.451 1.39M tok/s
|
| 56 |
+
110/2286 ━───────────────── loss 7.3044 lr 7.51e-03 |g| 0.294 1.40M tok/s
|
| 57 |
+
120/2286 ━───────────────── loss 7.0768 lr 8.19e-03 |g| 0.428 1.40M tok/s
|
| 58 |
+
130/2286 ━───────────────── loss 7.1436 lr 8.87e-03 |g| 0.421 1.40M tok/s
|
| 59 |
+
140/2286 ━───────────────── loss 7.0949 lr 9.55e-03 |g| 0.295 1.40M tok/s
|
| 60 |
+
150/2286 ━───────────────── loss 7.2613 lr 1.02e-02 |g| 0.505 1.40M tok/s
|
| 61 |
+
160/2286 ━───────────────── loss 7.0549 lr 1.09e-02 |g| 0.301 1.40M tok/s
|
| 62 |
+
170/2286 ━───────────────── loss 6.8672 lr 1.16e-02 |g| 0.259 1.40M tok/s
|
| 63 |
+
180/2286 ━───────────────── loss 6.8943 lr 1.23e-02 |g| 0.209 1.40M tok/s
|
| 64 |
+
190/2286 ━───────────────── loss 6.8456 lr 1.30e-02 |g| 0.212 1.41M tok/s
|
| 65 |
+
200/2286 ━━──────────────── loss 6.7405 lr 1.36e-02 |g| 0.316 1.41M tok/s
|
| 66 |
+
210/2286 ━━──────────────── loss 6.6456 lr 1.43e-02 |g| 0.159 1.41M tok/s
|
| 67 |
+
220/2286 ━━──────────────── loss 6.6766 lr 1.50e-02 |g| 0.275 1.41M tok/s
|
| 68 |
+
230/2286 ━━──────────────── loss 6.6807 lr 1.56e-02 |g| 0.178 1.41M tok/s
|
| 69 |
+
240/2286 ━━──────────────── loss 6.6355 lr 1.56e-02 |g| 0.246 1.41M tok/s
|
| 70 |
+
250/2286 ━━──────────────── loss 6.6233 lr 1.56e-02 |g| 0.151 1.41M tok/s
|
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1610/2286 ━━━━━━━━━━━━━───── loss 4.2475 lr 4.99e-03 |g| 0.088 1.42M tok/s
|
| 207 |
+
1620/2286 ━━━━━━━━━━━━━───── loss 4.2192 lr 4.90e-03 |g| 0.078 1.42M tok/s
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1720/2286 ━━━━━━━━━━━━━━──── loss 4.1784 lr 4.03e-03 |g| 0.081 1.42M tok/s
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1740/2286 ━━━━━━━━━━━━━━──── loss 4.3344 lr 3.87e-03 |g| 0.094 1.42M tok/s
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1750/2286 ━━━━━━━━━━━━━━──── loss 4.2346 lr 3.79e-03 |g| 0.075 1.42M tok/s
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1790/2286 ━━━━━━━━━━━━━━──── loss 4.1595 lr 3.49e-03 |g| 0.081 1.42M tok/s
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1800/2286 ━━━━━━━━━━━━━━──── loss 4.2592 lr 3.41e-03 |g| 0.090 1.42M tok/s
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1810/2286 ━━━━━━━━━━━━━━──── loss 4.1610 lr 3.34e-03 |g| 0.069 1.42M tok/s
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1820/2286 ━━━━━━━━━━━━━━──── loss 4.2892 lr 3.27e-03 |g| 0.083 1.42M tok/s
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1830/2286 ━━━━━━━━━━━━━━──── loss 4.2977 lr 3.20e-03 |g| 0.070 1.42M tok/s
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1840/2286 ━━━━━━━━━━━━━━──── loss 4.3188 lr 3.13e-03 |g| 0.100 1.42M tok/s
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1850/2286 ━━━━━━━━━━━━━━━─── loss 4.2705 lr 3.06e-03 |g| 0.078 1.42M tok/s
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| 231 |
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1860/2286 ━━━━━━━━━━━━━━━─── loss 4.2377 lr 3.00e-03 |g| 0.072 1.42M tok/s
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1870/2286 ━━━━━━━━━━━━━━━─── loss 4.1480 lr 2.93e-03 |g| 0.079 1.42M tok/s
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1880/2286 ━━━━━━━━━━━━━━━─── loss 4.2531 lr 2.87e-03 |g| 0.081 1.42M tok/s
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| 234 |
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1890/2286 ━━━━━━━━━━━━━━━─── loss 4.2077 lr 2.81e-03 |g| 0.075 1.42M tok/s
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1900/2286 ━━━━━━━━━━━━━━━─── loss 4.1266 lr 2.75e-03 |g| 0.078 1.42M tok/s
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1910/2286 ━━━━━━━━━━━━━━━─── loss 4.2356 lr 2.69e-03 |g| 0.089 1.42M tok/s
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1920/2286 ━━━━━━━━━━━━━━━─── loss 4.2295 lr 2.63e-03 |g| 0.082 1.42M tok/s
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1930/2286 ━━━━━━━━━━━━━━━─── loss 4.2642 lr 2.58e-03 |g| 0.094 1.42M tok/s
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| 239 |
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1940/2286 ━━━━━━━━━━━━━━━─── loss 4.2077 lr 2.52e-03 |g| 0.080 1.42M tok/s
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| 240 |
+
1950/2286 ━━━━━━━━━━━━━━━─── loss 4.1870 lr 2.47e-03 |g| 0.071 1.42M tok/s
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| 241 |
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1960/2286 ━━━━━━━━━━━━━━━─── loss 4.2425 lr 2.42e-03 |g| 0.074 1.42M tok/s
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| 242 |
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1970/2286 ━━━━━━━━━━━━━━━━── loss 4.1153 lr 2.37e-03 |g| 0.085 1.42M tok/s
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| 243 |
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1980/2286 ━━━━━━━━━━━━━━━━── loss 4.2093 lr 2.32e-03 |g| 0.071 1.42M tok/s
|
| 244 |
+
1990/2286 ━━━━━━━━━━━━━━━━── loss 4.1767 lr 2.27e-03 |g| 0.080 1.42M tok/s
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| 245 |
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2000/2286 ━━━━━━━━━━━━━━━━── loss 4.1339 lr 2.22e-03 |g| 0.079 1.42M tok/s
|
| 246 |
+
2010/2286 ━━━━━━━━━━━━━━━━── loss 4.2212 lr 2.18e-03 |g| 0.081 1.42M tok/s
|
| 247 |
+
2020/2286 ━━━━━━━━━━━━━━━━── loss 4.2000 lr 2.13e-03 |g| 0.074 1.42M tok/s
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| 248 |
+
2030/2286 ━━━━━━━━━━━━━━━━── loss 4.1550 lr 2.09e-03 |g| 0.076 1.42M tok/s
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| 249 |
+
2040/2286 ━━━━━━━━━━━━━━━━── loss 4.2039 lr 2.05e-03 |g| 0.071 1.42M tok/s
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| 250 |
+
2050/2286 ━━━━━━━━━━━━━━━━── loss 4.0454 lr 2.01e-03 |g| 0.089 1.42M tok/s
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| 251 |
+
2060/2286 ━━━━━━━━━━━━━━━━── loss 4.1681 lr 1.98e-03 |g| 0.090 1.42M tok/s
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| 252 |
+
2070/2286 ━━━━━━━━━━━━━━━━── loss 4.1188 lr 1.94e-03 |g| 0.085 1.42M tok/s
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| 253 |
+
2080/2286 ━━━━━━━━━━━━━━━━── loss 4.2280 lr 1.91e-03 |g| 0.084 1.42M tok/s
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| 254 |
+
2090/2286 ━━━━━━━━━━━━━━━━── loss 4.1286 lr 1.88e-03 |g| 0.079 1.42M tok/s
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| 255 |
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2100/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1245 lr 1.84e-03 |g| 0.087 1.42M tok/s
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| 256 |
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2110/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1633 lr 1.81e-03 |g| 0.076 1.42M tok/s
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| 257 |
+
2120/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1501 lr 1.79e-03 |g| 0.072 1.42M tok/s
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| 258 |
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2130/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1876 lr 1.76e-03 |g| 0.091 1.42M tok/s
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| 259 |
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2140/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1559 lr 1.74e-03 |g| 0.074 1.42M tok/s
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| 260 |
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2150/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1396 lr 1.71e-03 |g| 0.081 1.42M tok/s
|
| 261 |
+
2160/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1621 lr 1.69e-03 |g| 0.086 1.42M tok/s
|
| 262 |
+
2170/2286 ━━━━━━━━━━━━━━━━━─ loss 4.0730 lr 1.67e-03 |g| 0.069 1.42M tok/s
|
| 263 |
+
2180/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1396 lr 1.65e-03 |g| 0.104 1.42M tok/s
|
| 264 |
+
2190/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1609 lr 1.64e-03 |g| 0.076 1.42M tok/s
|
| 265 |
+
2200/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2484 lr 1.62e-03 |g| 0.073 1.42M tok/s
|
| 266 |
+
2210/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2257 lr 1.61e-03 |g| 0.076 1.42M tok/s
|
| 267 |
+
2220/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1532 lr 1.60e-03 |g| 0.102 1.42M tok/s
|
| 268 |
+
2230/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1057 lr 1.59e-03 |g| 0.078 1.42M tok/s
|
| 269 |
+
2240/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1536 lr 1.58e-03 |g| 0.084 1.42M tok/s
|
| 270 |
+
2250/2286 ━━━━━━━━━━━━━━━━━━ loss 4.0462 lr 1.57e-03 |g| 0.082 1.42M tok/s
|
| 271 |
+
2260/2286 ━━━━━━━━━━━━━━━━━━ loss 4.0857 lr 1.57e-03 |g| 0.084 1.42M tok/s
|
| 272 |
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2270/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2909 lr 1.56e-03 |g| 0.075 1.42M tok/s
|
| 273 |
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2280/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1997 lr 1.56e-03 |g| 0.072 1.42M tok/s
|
| 274 |
+
2286/2286 ━━━━━━━━━━━━━━━━━━ loss 4.2024 lr 1.56e-03 |g| 0.074 1.42M tok/s
|
| 275 |
+
|
| 276 |
+
● Canonical validation 8 deterministic batches outside train_seconds
|
| 277 |
+
|
| 278 |
+
● Fresh-domain validation skipped; no downstream data supplied
|
| 279 |
+
|
| 280 |
+
● Artifacts training.riglog + validation.csv + diagnostics.riglog
|
| 281 |
+
|
| 282 |
+
✓ synchronized training 211.590s (compilation excluded)
|
| 283 |
+
validation loss 4.1312 in 0.274s
|
| 284 |
+
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/stdout.log
ADDED
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@@ -0,0 +1 @@
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| 1 |
+
RIG_RESULT={"artifacts":{"diagnostics":"diagnostics.riglog","training_curve":"training.riglog","validation_curve":"validation.csv"},"checkpoint":null,"contract":{"dataset_id":"fineweb-8b-gpt2","model":{"d_model":384,"heads":6,"layers":12,"mlp_activation":"gelu","mlp_mult":4,"normalization":"rms_norm","parameterization":"complete_d_p","position_encoding":"rope_base_10000","semantic_vocab_size":50304,"tied_embeddings":false,"tier":"60m","vocab_size":50304},"model_id":"reference-gpt-v3-family","sequence_length":8192,"tokenizer_id":"gpt2"},"evaluations":{"fineweb":{"canonical":true,"loss":4.131175935268402,"perplexity":62.25108315466655,"scored_tokens":1048576,"seconds":0.27447028300957754}},"implementation":{"attention_backend":"tpu_flash","attention_tuning":{"key_digest":"ec38db3431308f3e9f99acf40c7e3bb85ca0695b0d97491e44a93bc7adfada00","resolution_source":"heuristic","tiles":{"block_kv":512,"block_kv_compute":256,"block_kv_dkv":512,"block_kv_dkv_compute":256,"block_kv_dq":512,"block_kv_dq_compute":256,"block_q":512,"block_q_dkv":512,"block_q_dkv_compute":256,"block_q_dq":256},"tune_seconds":0.0},"configuration":{"overrides":{"tokens_per_parameter_micros":5000000},"path":"config.yaml","profile":"dev","resolved":{"evaluation":{"eval_batches":8,"val_every":0,"val_probe_batches":8},"kernels":{"attention_backend":"tpu_flash","loss_backend":"tiled","vocab_tile_size":2048},"logging":{"diagnostics_every":10,"log_every":10},"model":{"d_model":384,"heads":6,"layers":12,"mlp_activation":"gelu","mlp_mult":4,"normalization":"rms_norm","parameterization":"complete_d_p","position_encoding":"rope_base_10000","semantic_vocab_size":50304,"tied_embeddings":false,"tier":"60m","vocab_size":50304},"optimizer":{"adam_epsilon":1e-08,"beta1":0.9,"beta2":0.95,"effective":{"adam_epsilon_horizon_multiplier":1.0,"beta1":0.9,"beta2":0.95,"global_peak_learning_rate":0.015625,"weight_decay_horizon_multiplier":1.0},"grad_clip":0.0,"learning_rate":0.015625,"min_lr_ratio":0.1,"warmup_steps":229,"weight_decay":0.1},"parameterization":{"attention_scale":"inverse_head_dim","base_depth":12,"base_width":384,"batch_multiplier":1.0,"data_multiplier":1.0,"depth_alpha":1.0,"depth_multiplier":1.0,"embeddings":"untied","init_std":0.02,"name":"complete_d_p","width_multiplier":1.0},"training":{"batch_size":16,"dtype":"bfloat16","sampling":"shuffled_epochs","seq_len":8192,"steps":2286,"tokens_per_parameter":5.000660099848113,"train_tokens":299630592}},"schema_version":2,"sha256":"e3a9103e60160c720239a391afbec5aca213eaf0cecd971a2fb45fe709b764cd"},"loss_backend":"tiled","vocab_tile_size":2048},"metrics":{"achieved_tflops":1044.6128796473636,"attention_tune_seconds":0.0,"base_learning_rate":0.015625,"diagnostic_compile_seconds":58.26712679397315,"diagnostic_point_count":230,"diagnostics_every":10,"early_stopping_step":null,"estimated_total_flops":221029692528918528,"eval_compile_seconds":7.075891792017501,"final_validation_seconds":0.27447028300957754,"flop_accounting":{"by_site":{"dot_general":2332173533184,"tpu_flash_causal_attention_bwd_dkv":1236950581248,"tpu_flash_causal_attention_bwd_dq":1236950581248,"tpu_flash_causal_attention_fwd":1236950581248},"elementwise_per_sequence":9192636658,"matmul_per_sequence":6043025276928,"method":"traced-jaxpr","warnings":[]},"flops_per_token":737673984,"mfu_estimate":0.23741201810167353,"model_tier":"60m","parameter_count":59918208,"parameters":59918208,"schedule_steps":2286,"tokens_per_parameter":5.000660099848113,"tokens_per_second":1416090.1730368787,"tokens_processed":299630592,"total_compile_seconds":93.32246213499457,"train_compile_seconds":27.979443549003918,"train_loss":4.2023773193359375,"train_seconds":211.59005104698008,"training_data_epochs":0.037928020133706226,"training_data_sharding":"rank_disjoint_shuffled_windows","training_sampling":"shuffled_epochs","training_steps":2286,"training_token_budget":299630592,"training_usable_tokens_per_epoch":7899979776,"validation_loss":4.131175935268402,"validation_probe_count":0,"validation_probe_seconds":0.0,"validation_tokens":1048576},"profile":"dev","schema_version":1,"seed":1337,"status":"ok","system":{"controller_process_index":1,"device_count":16,"device_ids":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"device_kinds":["TPU v4"],"jax_version":"0.11.0","jaxlib_version":"0.11.0","libtpu_version":"0.0.44.1","local_device_count":4,"platform":"tpu","process_count":4,"process_indices":[0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3],"python_version":"3.12.13"},"track":"open"}
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/training.riglog
ADDED
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Binary file (36.7 kB). View file
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1337/validation.csv
ADDED
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step,tokens_processed,kind,domain,validation_tokens,validation_loss,perplexity,validation_seconds,canonical
|
| 2 |
+
2286,299630592,fineweb,fineweb,1048576,4.131175935268402,62.25108315466655,0.27447028300957754,true
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/diagnostics.riglog
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:7e6f403fb03ed1cd166be74ed9eb5c046d7cd5f4ab15460c5e1300b64b92618b
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| 3 |
+
size 272824
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/metrics.json
ADDED
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/result.json
ADDED
|
@@ -0,0 +1,239 @@
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/stderr.log
ADDED
|
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E0818 08:39:42.019689 2210880 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
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E0818 08:39:42.019897 2167481 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
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E0818 08:39:42.019976 2119998 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
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E0818 08:39:42.023069 2867451 hugepage_text.cc:340] RAW: File offset incorrectly aligned for file-backed THP: (600000 & ~ffffffffffe00000) = 0 != 1000 = (201000 & ~ffffffffffe00000)
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◆ GPT TPU RIG reference / jax
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● Attention tile preflight resolving the shipped lookup or shape heuristic
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╭─ run configuration ────────────────────────────────────────────────────────╮
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│ experiment config config.yaml · dev · sha256:e3a9103e6016 │
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│ devices 16 × TPU v4 │
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│ JAX processes 4 (this rank 1) │
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│ mesh data=16 (replicated model) │
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│ dataset 79 train + 1 val shard(s) │
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│ train / val tokens 7,900,000,000 / 100,000,000 │
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│ downstream not requested │
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│ model 60m · L12 D384 H6 RoPE RMSNorm GELU MLP×4 │
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│ parameters 59.92M │
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│ parameterization complete_d_p · mN=1 · mL=1 · mD=1 │
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│ global batch 16 × 8192 tokens │
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│ train sampling shuffled epochs · 7,899,979,776 unique targets/epoch │
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│ compute bfloat16 │
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| 24 |
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│ attention tpu_flash │
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| 25 |
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│ attention tuning heuristic · key ec38db343130 │
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│ attention fwd q512 · kv512/256 │
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│ attention dK/dV q512/256 · kv512/256 │
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│ attention dQ q256 · kv512/256 │
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│ output loss tiled CE (semantic 50,304, tile 2,048) │
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│ diagnostics step 1 / every 10 / final │
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│ duration 2,286 steps │
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| 32 |
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│ train tokens 299.63M │
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| 33 |
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│ traced FLOPs 221029.69T │
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| 34 |
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│ FLOP breakdown dot_general 2,332,173,533,184 (38.6%) · tpu_flash_ca… │
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│ XProf disabled │
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╰────────────────────────────────────────────────────────────────────────────╯
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| 37 |
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● Compiling train step compilation is outside train_seconds
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● Compiling sparse diagnostics separate executable; compilation is outside train_seconds
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● Compiling evaluation reused by probes and final validation
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● Training train compiled in 30.67s, eval in 7.08s; periodic validation disabled
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| 167 |
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1220/2286 ━━━━━━━━━━──────── loss 4.5716 lr 9.00e-03 |g| 0.068 1.40M tok/s
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| 168 |
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1230/2286 ━━━━━━━━━━──────── loss 4.4565 lr 8.89e-03 |g| 0.127 1.40M tok/s
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| 169 |
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1240/2286 ━━━━━━━━━━──────── loss 4.5300 lr 8.78e-03 |g| 0.083 1.40M tok/s
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| 170 |
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1250/2286 ━━━━━━━━━━──────── loss 4.5069 lr 8.67e-03 |g| 0.109 1.40M tok/s
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| 171 |
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1260/2286 ━━━━━━━━━━──────── loss 4.5395 lr 8.57e-03 |g| 0.105 1.40M tok/s
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| 172 |
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1270/2286 ━━━━━━━━━━──────── loss 4.3709 lr 8.46e-03 |g| 0.085 1.40M tok/s
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| 173 |
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1280/2286 ━━━━━━━━━━──────── loss 4.4332 lr 8.35e-03 |g| 0.072 1.40M tok/s
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| 174 |
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1290/2286 ━━━━━━━━━━──────── loss 4.5269 lr 8.24e-03 |g| 0.077 1.40M tok/s
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| 175 |
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1300/2286 ━━━━━━━━━━──────── loss 4.4468 lr 8.14e-03 |g| 0.072 1.40M tok/s
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| 176 |
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1310/2286 ━━━━━━━━━━──────── loss 4.3359 lr 8.03e-03 |g| 0.076 1.40M tok/s
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| 177 |
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1320/2286 ━━━━━━━━━━──────── loss 4.4112 lr 7.92e-03 |g| 0.075 1.40M tok/s
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| 178 |
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1330/2286 ━━━━━━━━━━──────── loss 4.4192 lr 7.82e-03 |g| 0.080 1.40M tok/s
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| 179 |
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1340/2286 ━━━━━━━━━━━─────── loss 4.4522 lr 7.71e-03 |g| 0.081 1.40M tok/s
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| 180 |
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1350/2286 ━━━━━━━━━━━─────── loss 4.4163 lr 7.60e-03 |g| 0.099 1.40M tok/s
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1360/2286 ━━━━━━━━━━━─────── loss 4.6591 lr 7.50e-03 |g| 0.116 1.40M tok/s
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| 182 |
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1370/2286 ━━━━━━━━━━━─────── loss 4.5130 lr 7.39e-03 |g| 0.065 1.40M tok/s
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| 183 |
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1380/2286 ━━━━━━━━━━━─────── loss 4.3897 lr 7.29e-03 |g| 0.086 1.40M tok/s
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| 184 |
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1390/2286 ━━━━━━━━━━━─────── loss 4.3638 lr 7.18e-03 |g| 0.072 1.40M tok/s
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| 185 |
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1400/2286 ━━━━━━━━━━━─────── loss 4.3624 lr 7.08e-03 |g| 0.076 1.40M tok/s
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| 186 |
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1410/2286 ━━━━━━━��━━━─────── loss 4.3315 lr 6.97e-03 |g| 0.070 1.40M tok/s
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| 187 |
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1420/2286 ━━━━━━━━━━━─────── loss 4.4066 lr 6.87e-03 |g| 0.062 1.40M tok/s
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1430/2286 ━━━━━━━━━━━─────── loss 4.3213 lr 6.76e-03 |g| 0.098 1.40M tok/s
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1440/2286 ━━━━━━━━━━━─────── loss 4.4279 lr 6.66e-03 |g| 0.078 1.40M tok/s
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1450/2286 ━━━━━━━━━━━─────── loss 4.3058 lr 6.56e-03 |g| 0.070 1.40M tok/s
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1460/2286 ━━━━━━━━━━━─────── loss 4.3155 lr 6.45e-03 |g| 0.105 1.40M tok/s
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1470/2286 ━━━━━━━━━━━━────── loss 4.2425 lr 6.35e-03 |g| 0.069 1.40M tok/s
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1480/2286 ━━━━━━━━━━━━────── loss 4.2235 lr 6.25e-03 |g| 0.082 1.40M tok/s
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1490/2286 ━━━━━━━━━━━━────── loss 4.3539 lr 6.15e-03 |g| 0.074 1.40M tok/s
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| 195 |
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1500/2286 ━━━━━━━━━━━━────── loss 4.2710 lr 6.05e-03 |g| 0.070 1.40M tok/s
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1510/2286 ━━━━━━━━━━━━────── loss 4.3358 lr 5.95e-03 |g| 0.068 1.40M tok/s
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1520/2286 ━━━━━━━━━━━━────── loss 4.2522 lr 5.85e-03 |g| 0.068 1.40M tok/s
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1530/2286 ━━━━━━━━━━━━────── loss 4.2566 lr 5.75e-03 |g| 0.068 1.40M tok/s
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1540/2286 ━━━━━━━━━━━━────── loss 4.2851 lr 5.65e-03 |g| 0.068 1.40M tok/s
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1550/2286 ━━━━━━━━━━━━────── loss 4.2256 lr 5.56e-03 |g| 0.070 1.40M tok/s
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1560/2286 ━━━━━━━━━━━━────── loss 4.3425 lr 5.46e-03 |g| 0.080 1.40M tok/s
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1570/2286 ━━━━━━━━━━━━────── loss 4.2672 lr 5.36e-03 |g| 0.078 1.40M tok/s
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1580/2286 ━━━━━━━━━━━━────── loss 4.3317 lr 5.27e-03 |g| 0.074 1.40M tok/s
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1590/2286 ━━━━━━━━━━━━━───── loss 4.2638 lr 5.17e-03 |g| 0.081 1.40M tok/s
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1600/2286 ━━━━━━━━━━━━━───── loss 4.3115 lr 5.08e-03 |g| 0.080 1.40M tok/s
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1610/2286 ━━━━━━━━━━━━━───── loss 4.2273 lr 4.99e-03 |g| 0.069 1.40M tok/s
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1620/2286 ━━━━━━━━━━━━━───── loss 4.2111 lr 4.90e-03 |g| 0.071 1.40M tok/s
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1630/2286 ━━━━━━━━━━━━━───── loss 4.2675 lr 4.81e-03 |g| 0.070 1.40M tok/s
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1640/2286 ━━━━━━━━━━━━━───── loss 4.2498 lr 4.72e-03 |g| 0.076 1.40M tok/s
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1650/2286 ━━━━━━━━━━━━━───── loss 4.2909 lr 4.63e-03 |g| 0.075 1.40M tok/s
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1660/2286 ━━━━━━━━━━━━━───── loss 4.2698 lr 4.54e-03 |g| 0.075 1.40M tok/s
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1670/2286 ━━━━━━━━━━━━━───── loss 4.3063 lr 4.45e-03 |g| 0.070 1.40M tok/s
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1680/2286 ━━━━━━━━━━━━━───── loss 4.1946 lr 4.37e-03 |g| 0.072 1.40M tok/s
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1690/2286 ━━━━━━━━━━━━━───── loss 4.3218 lr 4.28e-03 |g| 0.077 1.40M tok/s
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1700/2286 ━━━━━━━━━━━━━───── loss 4.3137 lr 4.20e-03 |g| 0.102 1.40M tok/s
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1710/2286 ━━━━━━━━━━━━━───── loss 4.2632 lr 4.11e-03 |g| 0.086 1.40M tok/s
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| 217 |
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1720/2286 ━━━━━━━━━━━━━━──── loss 4.2337 lr 4.03e-03 |g| 0.083 1.40M tok/s
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1730/2286 ━━━━━━━━━━━━━━──── loss 4.1804 lr 3.95e-03 |g| 0.071 1.40M tok/s
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1740/2286 ━━━━━━━━━━━━━━──── loss 4.3227 lr 3.87e-03 |g| 0.080 1.40M tok/s
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| 220 |
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1750/2286 ━━━━━━━━━━━━━━──── loss 4.2736 lr 3.79e-03 |g| 0.084 1.40M tok/s
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1760/2286 ━━━━━━━━━━━━━━──── loss 4.3453 lr 3.71e-03 |g| 0.091 1.40M tok/s
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| 222 |
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1770/2286 ━━━━━━━━━━━━━━──── loss 4.1788 lr 3.64e-03 |g| 0.067 1.40M tok/s
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1780/2286 ━━━━━━━━━━━━━━──── loss 4.2374 lr 3.56e-03 |g| 0.074 1.40M tok/s
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| 224 |
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1790/2286 ━━━━━━━━━━━━━━──── loss 4.1908 lr 3.49e-03 |g| 0.089 1.40M tok/s
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| 225 |
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1800/2286 ━━━━━━━━━━━━━━──── loss 4.1480 lr 3.41e-03 |g| 0.091 1.40M tok/s
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| 226 |
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1810/2286 ━━━━━━━━━━━━━━──── loss 4.3003 lr 3.34e-03 |g| 0.074 1.40M tok/s
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| 227 |
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1820/2286 ━━━━━━━━━━━━━━──── loss 4.2093 lr 3.27e-03 |g| 0.081 1.40M tok/s
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| 228 |
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1830/2286 ━━━━━━━━━━━━━━──── loss 4.1269 lr 3.20e-03 |g| 0.064 1.40M tok/s
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| 229 |
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1840/2286 ━━━━━━━━━━━━━━──── loss 4.1588 lr 3.13e-03 |g| 0.090 1.40M tok/s
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| 230 |
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1850/2286 ━━━━━━━━━━━━━━━─── loss 4.1935 lr 3.06e-03 |g| 0.073 1.40M tok/s
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| 231 |
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1860/2286 ━━━━━━━━━━━━━━━─── loss 4.1902 lr 3.00e-03 |g| 0.073 1.40M tok/s
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| 232 |
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1870/2286 ━━━━━━━━━━━━━━━─── loss 4.0626 lr 2.93e-03 |g| 0.077 1.40M tok/s
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| 233 |
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1880/2286 ━━━━━━━━━━━━━━━─── loss 4.1548 lr 2.87e-03 |g| 0.076 1.40M tok/s
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| 234 |
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1890/2286 ━━━━━━━━━━━━━━━─── loss 4.1112 lr 2.81e-03 |g| 0.066 1.40M tok/s
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| 235 |
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1900/2286 ━━━━━━━━━━━━━━━─── loss 4.1575 lr 2.75e-03 |g| 0.070 1.40M tok/s
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| 236 |
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1910/2286 ━━━━━━━━━━━━━━━─── loss 4.1927 lr 2.69e-03 |g| 0.189 1.40M tok/s
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| 237 |
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1920/2286 ━━━━━━━━━━━━━━━─── loss 4.1268 lr 2.63e-03 |g| 0.067 1.40M tok/s
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1930/2286 ━━━━━━━━━━━━━━━─── loss 4.1832 lr 2.58e-03 |g| 0.074 1.40M tok/s
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| 239 |
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1940/2286 ━━━━━━━━━━━━━━━─── loss 4.1851 lr 2.52e-03 |g| 0.069 1.40M tok/s
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| 240 |
+
1950/2286 ━━━━━━━━━━━━━━━─── loss 4.1214 lr 2.47e-03 |g| 0.075 1.40M tok/s
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| 241 |
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1960/2286 ━━━━━━━━━━━━━━━─── loss 4.2190 lr 2.42e-03 |g| 0.066 1.40M tok/s
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| 242 |
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1970/2286 ━━━━━━━━━━━━━━━━── loss 4.1866 lr 2.37e-03 |g| 0.070 1.40M tok/s
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| 243 |
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1980/2286 ━━━━━━━━━━━━━━━━── loss 4.1513 lr 2.32e-03 |g| 0.084 1.40M tok/s
|
| 244 |
+
1990/2286 ━━━━━━━━━━━━━━━━── loss 4.0884 lr 2.27e-03 |g| 0.077 1.40M tok/s
|
| 245 |
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2000/2286 ━━━━━━━━━━━━━━━━── loss 4.1123 lr 2.22e-03 |g| 0.073 1.40M tok/s
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| 246 |
+
2010/2286 ━━━━━━━━━━━━━━━━── loss 4.1146 lr 2.18e-03 |g| 0.093 1.40M tok/s
|
| 247 |
+
2020/2286 ━━━━━━━━━━━━━━━━── loss 4.1475 lr 2.13e-03 |g| 0.072 1.40M tok/s
|
| 248 |
+
2030/2286 ━━━━━━━━━━━━━━━━── loss 4.1350 lr 2.09e-03 |g| 0.074 1.40M tok/s
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| 249 |
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2040/2286 ━━━━━━━━━━━━━━━━── loss 4.1503 lr 2.05e-03 |g| 0.078 1.40M tok/s
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| 250 |
+
2050/2286 ━━━━━━━━━━━━━━━━── loss 4.1306 lr 2.01e-03 |g| 0.074 1.40M tok/s
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| 251 |
+
2060/2286 ━━━━━━━━━━━━━━━━── loss 4.1931 lr 1.98e-03 |g| 0.071 1.40M tok/s
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+
2070/2286 ━━━━━━━━━━━━━━━━── loss 4.1142 lr 1.94e-03 |g| 0.075 1.40M tok/s
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| 253 |
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2080/2286 ━━━━━━━━━━━━━━━━── loss 4.0320 lr 1.91e-03 |g| 0.073 1.40M tok/s
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| 254 |
+
2090/2286 ━━━━━━━━━━━━━━━━── loss 4.1826 lr 1.88e-03 |g| 0.067 1.40M tok/s
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| 255 |
+
2100/2286 ━━━━━━━━━━━━━━━━━─ loss 4.0205 lr 1.84e-03 |g| 0.069 1.40M tok/s
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| 256 |
+
2110/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1994 lr 1.81e-03 |g| 0.069 1.40M tok/s
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+
2120/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1116 lr 1.79e-03 |g| 0.078 1.40M tok/s
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+
2130/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1780 lr 1.76e-03 |g| 0.066 1.40M tok/s
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| 259 |
+
2140/2286 ━━━━━━━━━━━━━━━━━─ loss 4.0924 lr 1.74e-03 |g| 0.077 1.40M tok/s
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| 260 |
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2150/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2062 lr 1.71e-03 |g| 0.078 1.40M tok/s
|
| 261 |
+
2160/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1445 lr 1.69e-03 |g| 0.074 1.40M tok/s
|
| 262 |
+
2170/2286 ━━━━━━━━━━━━━━━━━─ loss 4.0919 lr 1.67e-03 |g| 0.083 1.40M tok/s
|
| 263 |
+
2180/2286 ━━━━━━━━━━━━━━━━━─ loss 4.0619 lr 1.65e-03 |g| 0.080 1.40M tok/s
|
| 264 |
+
2190/2286 ━━━━━━━━━━━━━━━━━─ loss 4.2425 lr 1.64e-03 |g| 0.075 1.40M tok/s
|
| 265 |
+
2200/2286 ━━━━━━━━━━━━━━━━━─ loss 4.1282 lr 1.62e-03 |g| 0.070 1.40M tok/s
|
| 266 |
+
2210/2286 ━━━━━━━━━━━━━━━━━─ loss 4.0682 lr 1.61e-03 |g| 0.074 1.40M tok/s
|
| 267 |
+
2220/2286 ━━━━━━━━━━━━━━━━━─ loss 4.0951 lr 1.60e-03 |g| 0.072 1.41M tok/s
|
| 268 |
+
2230/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1594 lr 1.59e-03 |g| 0.079 1.41M tok/s
|
| 269 |
+
2240/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1322 lr 1.58e-03 |g| 0.086 1.41M tok/s
|
| 270 |
+
2250/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1054 lr 1.57e-03 |g| 0.071 1.41M tok/s
|
| 271 |
+
2260/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1230 lr 1.57e-03 |g| 0.070 1.41M tok/s
|
| 272 |
+
2270/2286 ━━━━━━━━━━━━━━━━━━ loss 4.1245 lr 1.56e-03 |g| 0.072 1.41M tok/s
|
| 273 |
+
2280/2286 ━━━━━━━━━━━━━━━━━━ loss 4.0946 lr 1.56e-03 |g| 0.071 1.41M tok/s
|
| 274 |
+
2286/2286 ━━━━━━━━━━━━━━━━━━ loss 4.0840 lr 1.56e-03 |g| 0.078 1.41M tok/s
|
| 275 |
+
|
| 276 |
+
● Canonical validation 8 deterministic batches outside train_seconds
|
| 277 |
+
|
| 278 |
+
● Fresh-domain validation skipped; no downstream data supplied
|
| 279 |
+
|
| 280 |
+
● Artifacts training.riglog + validation.csv + diagnostics.riglog
|
| 281 |
+
|
| 282 |
+
✓ synchronized training 213.224s (compilation excluded)
|
| 283 |
+
validation loss 4.0921 in 0.269s
|
| 284 |
+
|
lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/stdout.log
ADDED
|
@@ -0,0 +1 @@
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|
|
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/training.riglog
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1338/validation.csv
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step,tokens_processed,kind,domain,validation_tokens,validation_loss,perplexity,validation_seconds,canonical
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1339/diagnostics.riglog
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size 272824
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lr-sweep-8k-60M/60m-5tpp-bs16-lr2e-6-s1339/metrics.json
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| 1 |
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1,
|
| 224 |
+
1,
|
| 225 |
+
1,
|
| 226 |
+
1,
|
| 227 |
+
2,
|
| 228 |
+
2,
|
| 229 |
+
2,
|
| 230 |
+
2,
|
| 231 |
+
3,
|
| 232 |
+
3,
|
| 233 |
+
3,
|
| 234 |
+
3
|
| 235 |
+
],
|
| 236 |
+
"python_version": "3.12.13"
|
| 237 |
+
},
|
| 238 |
+
"track": "open"
|
| 239 |
+
}
|