diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/README.md b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/README.md new file mode 100644 index 0000000000..a5a9ea1dd8 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/README.md @@ -0,0 +1,193 @@ +# exp105a: Meta-TTT Ablation — FOMAML Off (from exp101) + +**Parent**: 11L XSA-all · BigramHash 4096×64 pos-conditional (ws/non-ws split) · trigram · VE7-10 · FOMAML every=4 · SGD+cosine TTT · int6 GPTQ+lzma (legal_ttt **1.11588**) +**Single change**: `META_TTT_ENABLED=1 → 0` +**Result**: legal_ttt = **1.11624** | int6 = **1.13956** | model = **14.94 MB** (15.66 MB w/ code) + +--- + +## 1. Motivation + +### Experiment lineage + +``` +BigramHash 10240×128 · VE9-10 · FOMAML every=8 (first meta-TTT attempt) → legal_ttt 1.1156 +BigramHash 4096×64 · VE7-10 · FOMAML every=4 · TTT AdamW+flat (size-opt) → legal_ttt 1.1169 ← worse +BigramHash 4096×64 · VE7-10 · FOMAML every=4 · pos-cond bigram + trigram (ours) → legal_ttt 1.1159 ← current parent ++ copy head wired into FOMAML outer loop → legal_ttt 1.1214 ← much worse +``` + +The pattern "more meta-TTT intensity → worse bpb" appeared three times but was never +tested causally. All comparisons confounded meta-TTT with other architectural changes. + +**This experiment** isolates meta-TTT: identical architecture, identical schedule, one +flag changed. Every other hyperparameter is byte-identical to exp101 (including +`TRIGRAM=0`, which was the exp101 variant that achieved 1.1159). + +### What meta-TTT is doing in exp101 + +Meta-TTT (FOMAML) runs every 4 training steps. For each meta-step it: +1. Copies the current bank parameters into detached clones `banks'` +2. Runs one SGD inner step on the current batch: `banks' ← banks - α·∇L(banks; x)` +3. Evaluates the outer loss with adapted banks: `L_meta = L(banks'; x)` (same batch) +4. Accumulates `∇_banks L_meta` into the regular bank gradient + +The goal: shape bank initializations so that a single TTT step at eval time moves them +further toward the test distribution. + +The cost: ~3% extra compute per training step (one extra forward + backward + parameter +clone every 4 steps). + +--- + +## 2. Maths + +The FOMAML objective as implemented in exp101: + +$$ +\theta' = \theta - \alpha \nabla_\theta \mathcal{L}(\theta;\, x_\text{batch}) +$$ + +$$ +\mathcal{L}_\text{meta} = \mathcal{L}(\theta';\, x_\text{batch}) +$$ + +$$ +g_\text{meta} = \nabla_\theta \mathcal{L}_\text{meta} +\approx \nabla_{\theta'} \mathcal{L}(\theta';\, x_\text{batch}) +\quad \text{(first-order: Jacobian of inner step dropped)} +$$ + +$g_\text{meta}$ is added (scaled by `META_TTT_LOSS_WEIGHT=0.5`) to the standard +gradient before the Muon/Adam update. + +Note that **inner and outer use the same batch $x_\text{batch}$**. This is the key +design flaw that exp106 addresses. + +--- + +## 3. Implementation + +Single change in `run.sh`: + +```bash +# exp101 +export META_TTT_ENABLED=1 + +# exp105a (this experiment) +export META_TTT_ENABLED=0 +``` + +All other env vars are unchanged. The `META_TTT_INNER_LR`, `META_TTT_EVERY`, +`META_TTT_LOSS_WEIGHT`, `META_TTT_FREEZE_BLOCKS` vars are still exported but have +no effect when `META_TTT_ENABLED=0`. + +No `train_gpt.py` changes — the env var guard is already in exp101's codebase. + +--- + +## 4. Analysis + +### Results table + +| Metric | exp101 (meta-TTT ON) | exp105a (meta-TTT OFF) | Δ | +|---|---|---|---| +| Steps completed | 7020 / 7500 | 7226 / 7500 | — | +| val_bpb @ step 3000 | 1.2254 | 1.2264 | +0.0010 | +| val_bpb @ step 6000 | 1.1474 | 1.1524 | +0.0050 | +| val_bpb @ final step | 1.1349 | 1.1351 | +0.0002 | +| Post-EMA val_bpb | 1.1352 | 1.1353 | +0.0001 | +| **Int6 val_bpb (exact)** | **1.13930** | **1.13956** | **+0.0003** | +| **legal_ttt val_bpb (exact)** | **1.11588** | **1.11624** | **+0.00036** | +| TTT delta (int6 → TTT) | −0.02342 | −0.02331 | +0.00011 | +| Model size (int6+lzma) | 15,689,152 B (14.97 MB) | 15,659,520 B (14.94 MB) | — | +| Total submission size | 15,804,196 B (15.08 MB) | 15,774,564 B (15.05 MB) | — | +| Peak GPU memory | 23,044 MiB | 23,043 MiB | — | +| late_qat fired at step | 5384 | 5557 | — | +| SWA started at step | 5600 | 5750 | — | + +*Submission size = int6+lzma weights + train_gpt.py code (122,683 B).* + +### Key observations + +**1. Training-time loss: identical.** +Post-EMA bpb 1.1352 vs 1.1353 — difference of 0.0001, well within seed noise. +Meta-TTT does not impair or improve training convergence. + +**2. TTT delta: identical.** +Both models improve by ~0.0233 bpb from int6 baseline to legal_ttt (0.02342 vs 0.02331). +The meta-training did not cause the banks to generalize better under TTT. + +**3. Net meta-TTT value: +0.00036 bpb at ~3% compute cost.** +This is noise-level (sub-0.001 bpb). The ablation verdict: **meta-TTT in its exp101 +formulation adds no meaningful value.** + +**4. exp105a is actually slightly faster per step.** +Without the FOMAML overhead, exp105a completed 7226 steps vs exp101's 7020 in the same +80-minute wallclock — 206 extra steps (~3% more training) from eliminating meta-TTT. + +### Why the FOMAML signal is ineffective + +The root cause is the **same-batch inner/outer** design: + +- Inner step: `banks' ← banks - α·∇L(banks; x_batch)` adapts to `x_batch` +- Outer evaluation: `L(banks'; x_batch)` also evaluated on `x_batch` + +The meta-gradient is rewarding banks that can "recover" from one SGD step on a batch +they just saw. This is trivially solved by having banks with small gradient norms — +i.e., banks that are *already* well-converged on the training distribution. The +FOMAML signal is not asking banks to generalize to new data; it's asking them not to +move much under SGD. + +At eval time, TTT adapts to a new test chunk the model has never seen. The meta- +training objective does not match this deployment regime. + +### Weight-space analysis (exp101 vs exp105a) + +See `../META_TTT_ANALYSIS.md` for the full 5-analysis comparison. Summary: + +| Analysis | Finding | +|---|---| +| Weight deltas | Banks near-orthogonal element-wise (rel_L2 ≈ 1.37, cosine ≈ 0.07) — Muon trajectories diverged | +| Quantization sensitivity | Essentially identical (ratio 0.9989) — meta-TTT does NOT reduce quant error | +| Spectral regularizer | Condition number −8.2% (5.6 vs 6.1) — only real signal from meta-TTT | +| Subspace overlap | kv_bank avg cos 0.955 — same principal subspace despite orthogonal element-wise weights | +| Linear mode connectivity | Midpoint norm ratio 0.799 — borderline different basins | + +--- + +## 5. Conclusion + +Meta-TTT as formulated in exp101 (FOMAML, same-batch inner/outer) provides **+0.0003 +bpb** post-TTT improvement at **~3% training compute overhead**. The ablation +verdict is clear: the current formulation is not worth the cost. + +The fundamental issue is objective misalignment: same-batch FOMAML trains banks to +resist SGD updates on seen data, not to adapt to unseen test-time data. The two regimes +(training distribution vs test distribution) are different enough that the meta-signal +is near-zero. + +**This motivates exp106**, which addresses three concrete redesign points: +- **(A)** Cross-chunk split: inner/outer use different documents from the batch +- **(B)** Δ-loss outer: explicitly reward improvement from the inner step +- **(C)** MetaSGD: learn per-layer-per-bank inner-loop LR scales (~66 params, excluded from export) + +--- + +## Run + +```bash +bash records/phase3/exp105a_no-metattt_from_exp101/run.sh +``` + +Hardware: **1× H100 80 GB SXM**, `MAX_WALLCLOCK_SECONDS=4800` (80-minute cap). +A single H100 running for 80 minutes = 4800 GPU-seconds, matching the throughput +of the competition's standard 8×H100 @ 10-minute budget at substantially lower cost. +Steps completed: **7226 / 7500** — 206 more steps than exp101 because eliminating +FOMAML overhead freed ~3% compute per step. + +--- + +## TL;DR + +Disabling FOMAML meta-TTT entirely changes legal_ttt by only +0.00036 bpb (1.11624 vs exp101's 1.11588) — noise level. The meta-training objective in exp101 was fundamentally misaligned: same-batch inner/outer FOMAML trains banks to resist SGD updates on data they've already seen, not to generalize to unseen test chunks. This ablation confirms meta-TTT at its current formulation adds no meaningful value, and motivates the three-part redesign in exp106. The run used a single H100 for 80 minutes (= 4800 GPU-seconds, iso-compute with the competition's 8×H100 @ 10-min budget) and completed 7226 steps — 206 more than exp101 due to the eliminated FOMAML overhead. diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/logs_seed42.txt b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/logs_seed42.txt new file mode 100644 index 0000000000..b4cb27ad9f --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/logs_seed42.txt @@ -0,0 +1,184 @@ +logs/exp105a_no-metattt_from_exp101_seed42.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26960991 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:1 grad_accum_steps:8 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +ema:initialized decay=0.998 update_every=10 decay_eff=0.980179 +step:0/7500 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.01ms +step:1/7500 train_loss:6.9298 train_time:658ms step_avg:658.09ms +step:2/7500 train_loss:8.3907 train_time:1245ms step_avg:622.73ms +step:3/7500 train_loss:7.4660 train_time:1910ms step_avg:636.60ms +step:4/7500 train_loss:7.6125 train_time:2564ms step_avg:640.92ms +step:5/7500 train_loss:7.4386 train_time:3219ms step_avg:643.83ms +step:6/7500 train_loss:7.1132 train_time:3878ms step_avg:646.40ms +step:7/7500 train_loss:6.7981 train_time:4534ms step_avg:647.71ms +step:8/7500 train_loss:6.6367 train_time:5193ms step_avg:649.12ms +step:9/7500 train_loss:6.4074 train_time:5892ms step_avg:654.64ms +step:10/7500 train_loss:6.0814 train_time:6548ms step_avg:654.84ms +step:500/7500 train_loss:2.3127 train_time:331074ms step_avg:662.15ms +step:1000/7500 train_loss:2.2630 train_time:662462ms step_avg:662.46ms +step:1500/7500 train_loss:2.1337 train_time:993886ms step_avg:662.59ms +step:2000/7500 train_loss:2.0518 train_time:1325657ms step_avg:662.83ms +adaptive_warmdown:triggered step:2200 loss_ema:2.114333 improvement:-0.000150 +step:2500/7500 train_loss:2.0959 train_time:1657815ms step_avg:663.13ms +step:3000/7500 train_loss:2.0748 train_time:1989567ms step_avg:663.19ms +step:3000/7500 val_loss:2.0708 val_bpb:1.2264 train_time:1989632ms step_avg:663.21ms +step:3500/7500 train_loss:2.0620 train_time:2321048ms step_avg:663.16ms +step:4000/7500 train_loss:2.1287 train_time:2652783ms step_avg:663.20ms +step:4500/7500 train_loss:2.1168 train_time:2984973ms step_avg:663.33ms +step:5000/7500 train_loss:2.0216 train_time:3317171ms step_avg:663.43ms +step:5500/7500 train_loss:2.0182 train_time:3649138ms step_avg:663.48ms +late_qat:enabled step:5557 scale:0.2498 +swa:start step:5750 +step:6000/7500 train_loss:1.9160 train_time:3982032ms step_avg:663.67ms +step:6000/7500 val_loss:1.9457 val_bpb:1.1524 train_time:3982237ms step_avg:663.71ms +step:6500/7500 train_loss:2.0219 train_time:4314994ms step_avg:663.85ms +step:7000/7500 train_loss:1.8349 train_time:4648522ms step_avg:664.07ms +step:7226/7500 val_loss:1.9166 val_bpb:1.1351 train_time:4800153ms step_avg:664.29ms +stopping_early: wallclock_cap train_time:4800153ms step:7226/7500 +peak memory allocated: 23043 MiB reserved: 23204 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9170 val_bpb:1.1353 eval_time:17445ms +Serialized model: 106028345 bytes +Code size: 115044 bytes +gptq:building non-banked model for Hessian collection... +gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... +gptq:generated 64 sequences in 214.4s +gptq:collecting hessians from autoregressive data... +gptq:collected hessians for 68 layers (AR self-gen) +selective_prune: 4202203 +/-1 candidates, unpruned=15.04MB target=15.9MB +selective_prune: already fits, no pruning needed +Serialized model int6+lzma: 15659520 bytes +Total submission size int6+lzma: 15774564 bytes +final_int6_roundtrip val_loss:1.9241 val_bpb:1.1396 eval_time:32495ms +final_int6_roundtrip_exact val_loss:1.92409196 val_bpb:1.13955564 + +============================================================ +STARTING TTT (Test-Time Training) +============================================================ +ttt_sliding:start chunks=947 chunk_tokens=65536 total_windows=969088 stride=64 ttt_lr=0.004 ttt_epochs=4 freeze_blocks=2 +ttt_sliding:params unfrozen=26956879 frozen=4112 + ttt 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+legal_ttt_exact val_loss:1.88472279 val_bpb:1.11624195 diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/submission.json b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/submission.json new file mode 100644 index 0000000000..ff976fb23d --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/submission.json @@ -0,0 +1,43 @@ +{ + "author": "Sidhant Thole", + "github_id": "SPThole", + "name": "Meta-TTT Ablation: FOMAML Off (exp105a)", + "blurb": "Pure ablation of exp101: single change META_TTT_ENABLED=1→0. Every other hyperparameter is byte-identical (TRIGRAM=0, POS_CONDITIONAL_BIGRAM=1, XSA-all-11L, VE 7-10, same schedule). Result: legal_ttt 1.11624 vs exp101's 1.11588 — difference of +0.00036 bpb (noise level). Meta-TTT (FOMAML, same-batch inner/outer) adds no meaningful value at 3% extra compute cost; exp105a ran 206 more steps than exp101 in the same wallclock due to eliminated FOMAML overhead. TTT delta identical at ~0.0233 bpb regardless of meta-TTT. This run motivated the exp106 cross-chunk + delta-loss + MetaSGD redesign.", + "date": "2026-04-09", + "track": "10min_16mb", + "val_loss": 1.88472279, + "val_bpb": 1.11624195, + "pre_quant_val_loss": 1.9170, + "pre_quant_val_bpb": 1.1353, + "int6_roundtrip_val_loss": 1.92409196, + "int6_roundtrip_val_bpb": 1.13955564, + "seeds": [42], + "seed_results": { + "42": { + "val_loss": 1.88472279, + "val_bpb": 1.11624195, + "pre_quant_val_bpb": 1.1353, + "int6_roundtrip_val_bpb": 1.13955564, + "artifact_bytes": 15774564, + "model_bytes": 15659520, + "code_bytes": 115044, + "steps": 7226, + "step_avg_ms": 664.29, + "wallclock_s": 4800, + "late_qat_step": 5557, + "swa_start_step": 5750, + "adaptive_warmdown_step": 2200, + "peak_gpu_mib": 23043 + } + }, + "hardware": "1×H100 80GB SXM", + "gptq_calibration": "AR self-generated (64 seqs × 2048 tokens, temp=0.8)", + "gptq_layers": 68, + "selective_prune_candidates": 4202203, + "selective_prune_applied": false, + "non_record": true, + "experiment_type": "ablation", + "parent_arch": "11L XSA-all · BigramHash 4096×64 pos-conditional (ws/non-ws split) · trigram · VE7-10 · FOMAML every=4 · SGD+cosine TTT · int6 GPTQ+lzma · legal_ttt 1.11588", + "delta_vs_exp101_bpb": 0.00036, + "conclusion": "FOMAML meta-TTT (same-batch inner/outer) contributes +0.00036 bpb at 3% compute overhead — not worth it" +} diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/Inference.ipynb b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/Inference.ipynb new file mode 100644 index 0000000000..7780972f23 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/Inference.ipynb @@ -0,0 +1,406 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell-0", + "metadata": {}, + "source": [ + "# exp105a: Meta-TTT Ablation — Inference & Analysis\n", + "\n", + "**Experiment**: `exp105a_no-metattt_from_exp101` \n", + "**Parent**: `exp101_poscond-bigram-trigram_from_exp95` \n", + "**Single change**: `META_TTT_ENABLED=0` (FOMAML disabled) \n", + "**Results**: pre-quant 1.1353 | int6 1.1396 | legal_ttt **1.1162**\n", + "\n", + "Sections:\n", + "1. Setup & path detection\n", + "2. Load model (float `.pt` and int6 `.ptz`)\n", + "3. Compute val_bpb\n", + "4. Text generation\n", + "5. Per-token loss distribution\n", + "6. Per-position loss curve\n", + "7. Top worst-predicted tokens\n", + "8. Summary" + ] + }, + { + "cell_type": "markdown", + "id": "cell-1", + "metadata": {}, + "source": [ + "## 1. Setup & Path Detection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-2", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os, json, io, math, glob, importlib.util\n", + "import torch, torch.nn.functional as F\n", + "import numpy as np\n", + "\n", + "try:\n", + " _nb = globals().get('__vsc_ipynb_file__') or __file__\n", + " EXP_DIR = os.path.dirname(os.path.abspath(_nb))\n", + "except NameError:\n", + " EXP_DIR = os.getcwd()\n", + "\n", + "REPO_ROOT = os.path.abspath(os.path.join(EXP_DIR, '..', '..', '..'))\n", + "CHECKPOINT_DIR = os.path.join(EXP_DIR, 'checkpoint')\n", + "TOKENIZER_PATH = os.path.join(REPO_ROOT, 'data', 'tokenizers', 'fineweb_1024_bpe.model')\n", + "VAL_DATA_PATTERN = os.path.join(REPO_ROOT, 'data', 'datasets', 'fineweb10B_sp1024', 'fineweb_val_*.bin')\n", + "DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'\n", + "\n", + "# Paths to model files (adjust if running directly in pod)\n", + "MODEL_PT = os.environ.get('MODEL_PT', os.path.join(EXP_DIR, 'checkpoint', 'model.pt'))\n", + "MODEL_PTZ = os.environ.get('MODEL_PTZ', os.path.join(EXP_DIR, 'checkpoint', 'model.int6.ptz'))\n", + "\n", + "print(f'EXP_DIR : {EXP_DIR}')\n", + "print(f'REPO_ROOT : {REPO_ROOT}')\n", + "print(f'DEVICE : {DEVICE}')\n", + "print(f'model.pt : {MODEL_PT} — exists={os.path.exists(MODEL_PT)}')\n", + "print(f'int6.ptz : {MODEL_PTZ} — exists={os.path.exists(MODEL_PTZ)}')" + ] + }, + { + "cell_type": "markdown", + "id": "cell-3", + "metadata": {}, + "source": [ + "## 2. Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-4", + "metadata": {}, + "outputs": [], + "source": [ + "# Import train_gpt from this experiment\n", + "spec = importlib.util.spec_from_file_location('train_gpt', os.path.join(EXP_DIR, 'train_gpt.py'))\n", + "tg = importlib.util.module_from_spec(spec)\n", + "sys.path.insert(0, EXP_DIR)\n", + "spec.loader.exec_module(tg)\n", + "sys.path.pop(0)\n", + "\n", + "# Build model from hyperparameters\n", + "import inspect\n", + "hp = tg.Hyperparameters()\n", + "valid_keys = set(inspect.signature(tg.GPT.__init__).parameters) - {'self'}\n", + "hp_dict = {k: getattr(hp, k) for k in valid_keys if hasattr(hp, k)}\n", + "model = tg.GPT(**hp_dict).eval()\n", + "print(f'Parameters: {sum(p.numel() for p in model.parameters()):,}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-5", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Load float model ---\n", + "model_float = None\n", + "if os.path.exists(MODEL_PT):\n", + " sd = torch.load(MODEL_PT, map_location='cpu', weights_only=True)\n", + " if isinstance(sd, dict) and 'model' in sd:\n", + " sd = sd['model']\n", + " model_float = tg.GPT(**hp_dict).eval()\n", + " model_float.load_state_dict(sd, strict=True)\n", + " model_float = model_float.to(DEVICE)\n", + " print(f'Loaded float model from {MODEL_PT}')\n", + "else:\n", + " print('[skip] float model not found')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-6", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Load int6 dequantized model ---\n", + "model_int6 = None\n", + "if os.path.exists(MODEL_PTZ):\n", + " import lzma\n", + " with open(MODEL_PTZ, 'rb') as f:\n", + " blob = f.read()\n", + " # Try LZMA first (competition standard), fall back to zlib\n", + " try:\n", + " decompressed = lzma.decompress(blob)\n", + " except Exception:\n", + " import zlib\n", + " decompressed = zlib.decompress(blob)\n", + " qs = torch.load(io.BytesIO(decompressed), map_location='cpu', weights_only=True)\n", + " sd_cpu = {k: v.cpu() for k, v in (sd if model_float else {}).items()}\n", + " if not sd_cpu and model_float:\n", + " sd_cpu = {k: v.cpu() for k, v in model_float.state_dict().items()}\n", + " deq = tg.dequantize_mixed_int6(qs['w'], qs['m'], sd_cpu)\n", + " # re-inject meta_sgd params if present in a fresh model\n", + " fresh = tg.GPT(**hp_dict)\n", + " for k in ('meta_sgd_qo', 'meta_sgd_kv', 'meta_sgd_up', 'meta_sgd_down'):\n", + " if k not in deq and hasattr(fresh, k):\n", + " deq[k] = getattr(fresh, k).detach().cpu().clone()\n", + " model_int6 = tg.GPT(**hp_dict).eval()\n", + " tg.CastedLinear._qat_enabled = True\n", + " model_int6.load_state_dict(deq, strict=True)\n", + " model_int6 = model_int6.to(DEVICE)\n", + " print(f'Loaded int6 model from {MODEL_PTZ}')\n", + "else:\n", + " print('[skip] int6 model not found')" + ] + }, + { + "cell_type": "markdown", + "id": "cell-7", + "metadata": {}, + "source": [ + "## 3. Compute val_bpb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-8", + "metadata": {}, + "outputs": [], + "source": [ + "import sentencepiece as spm\n", + "sp = spm.SentencePieceProcessor()\n", + "sp.Load(TOKENIZER_PATH)\n", + "\n", + "VAL_SHARDS = sorted(glob.glob(VAL_DATA_PATTERN))\n", + "assert VAL_SHARDS, f'No val shards at {VAL_DATA_PATTERN}'\n", + "\n", + "def load_val_shard(path):\n", + " hdr = np.fromfile(path, dtype=' len(toks):\n", + " break\n", + " chunk = toks[i:i + SEQ_LEN*BATCH_SEQ + 1].astype(np.int64)\n", + " x = torch.from_numpy(chunk[:-1]).reshape(BATCH_SEQ, SEQ_LEN).to(DEVICE)\n", + " y = torch.from_numpy(chunk[1: ]).reshape(BATCH_SEQ, SEQ_LEN).to(DEVICE)\n", + " logits = m.forward_logits(x)\n", + " loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)),\n", + " y.reshape(-1), reduction='none')\n", + " all_losses.append(loss.cpu()); all_ids.append(y.reshape(-1).cpu())\n", + " total_loss += loss.sum().item(); total_toks += loss.numel()\n", + " i += SEQ_LEN * BATCH_SEQ\n", + " mean_loss = total_loss / total_toks\n", + " return mean_loss, mean_loss * LOG2E, torch.cat(all_losses), torch.cat(all_ids)\n", + "\n", + "if model_float:\n", + " fl_loss, fl_bpb, fl_losses, fl_ids = eval_bpb(model_float, val_tokens)\n", + " print(f'Float val_loss={fl_loss:.4f} val_bpb={fl_bpb:.4f} (expected ~1.1353)')\n", + "if model_int6:\n", + " q_loss, q_bpb, q_losses, q_ids = eval_bpb(model_int6, val_tokens)\n", + " print(f'Int6 val_loss={q_loss:.4f} val_bpb={q_bpb:.4f} (expected ~1.1396)')" + ] + }, + { + "cell_type": "markdown", + "id": "cell-9", + "metadata": {}, + "source": [ + "## 4. Text Generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-10", + "metadata": {}, + "outputs": [], + "source": [ + "def generate(m, prompt, max_new=150, temp=0.8, top_k=40):\n", + " ids = sp.EncodeAsIds(prompt)\n", + " x = torch.tensor(ids, dtype=torch.long, device=DEVICE).unsqueeze(0)\n", + " m.eval()\n", + " with torch.no_grad():\n", + " for _ in range(max_new):\n", + " logits = m.forward_logits(x)[:, -1, :] / temp\n", + " if top_k:\n", + " v, _ = torch.topk(logits, top_k)\n", + " logits[logits < v[:, -1:]] = -float('inf')\n", + " x = torch.cat([x, torch.multinomial(F.softmax(logits, -1), 1)], dim=1)\n", + " return sp.DecodeIds(x[0].tolist())\n", + "\n", + "active_model = model_float or model_int6\n", + "if active_model:\n", + " for p in ['The history of artificial intelligence began',\n", + " 'Scientists discovered that language models']:\n", + " print('='*60)\n", + " print(generate(active_model, p))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-11", + "metadata": {}, + "source": [ + "## 5. Per-Token Loss Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-12", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "if model_float:\n", + " fig, ax = plt.subplots(figsize=(10, 4))\n", + " ax.hist(fl_losses.numpy(), bins=100, log=True, color='steelblue', alpha=0.8)\n", + " ax.axvline(fl_losses.mean(), color='red', linestyle='--',\n", + " label=f'mean={fl_losses.mean():.3f}')\n", + " ax.set_title('exp105a (no meta-TTT) — Per-Token Loss Distribution (float)')\n", + " ax.set_xlabel('Cross-entropy'); ax.set_ylabel('Count (log)')\n", + " ax.legend(); plt.tight_layout(); plt.show()\n", + "\n", + " for p in [50, 75, 90, 95, 99]:\n", + " print(f' p{p:2d}: {np.percentile(fl_losses.numpy(), p):.3f}')" + ] + }, + { + "cell_type": "markdown", + "id": "cell-13", + "metadata": {}, + "source": [ + "## 6. Per-Position Loss Curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-14", + "metadata": {}, + "outputs": [], + "source": [ + "if model_float:\n", + " pos_acc = np.zeros(SEQ_LEN); pos_cnt = np.zeros(SEQ_LEN, dtype=np.int64)\n", + " i = 0\n", + " model_float.eval()\n", + " with torch.no_grad():\n", + " for _ in range(4):\n", + " if i + SEQ_LEN * BATCH_SEQ + 1 > len(val_tokens): break\n", + " chunk = val_tokens[i:i+SEQ_LEN*BATCH_SEQ+1].astype(np.int64)\n", + " x = torch.from_numpy(chunk[:-1]).reshape(BATCH_SEQ, SEQ_LEN).to(DEVICE)\n", + " y = torch.from_numpy(chunk[1: ]).reshape(BATCH_SEQ, SEQ_LEN).to(DEVICE)\n", + " loss = F.cross_entropy(model_float.forward_logits(x).reshape(-1, model_float.forward_logits(x).size(-1)),\n", + " y.reshape(-1), reduction='none').reshape(BATCH_SEQ, SEQ_LEN).cpu().numpy()\n", + " pos_acc += loss.sum(0); pos_cnt += BATCH_SEQ; i += SEQ_LEN * BATCH_SEQ\n", + " pos_mean = pos_acc / pos_cnt\n", + " fig, ax = plt.subplots(figsize=(12, 4))\n", + " ax.plot(pos_mean, lw=0.6, color='steelblue', label='raw')\n", + " w = max(1, SEQ_LEN//64)\n", + " ax.plot(np.convolve(pos_mean, np.ones(w)/w, 'same'), lw=2, color='red',\n", + " alpha=0.7, label=f'smoothed (w={w})')\n", + " ax.set_title('exp105a — Per-Position Mean Loss'); ax.set_xlabel('Position')\n", + " ax.set_ylabel('Mean CE'); ax.legend(); plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-15", + "metadata": {}, + "source": [ + "## 7. Top Worst-Predicted Tokens" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-16", + "metadata": {}, + "outputs": [], + "source": [ + "if model_float:\n", + " top_n = 20\n", + " idx = fl_losses.topk(top_n).indices.numpy()\n", + " token_ids_np = fl_ids.numpy()\n", + " print(f'Top {top_n} worst predicted tokens (float model):')\n", + " print(f'{\"Rank\":>4} {\"Loss\":>7} {\"TokenID\":>8} Piece')\n", + " print('-'*50)\n", + " for rank, i in enumerate(idx, 1):\n", + " tid = int(token_ids_np[i])\n", + " loss_val = fl_losses[i].item()\n", + " piece = repr(sp.IdToPiece(tid))\n", + " print(f'{rank:>4} {loss_val:>7.3f} {tid:>8} {piece}')" + ] + }, + { + "cell_type": "markdown", + "id": "cell-17", + "metadata": {}, + "source": [ + "## 8. Summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-18", + "metadata": {}, + "outputs": [], + "source": [ + "print('='*60)\n", + "print('EXPERIMENT: exp105a_no-metattt_from_exp101')\n", + "print('='*60)\n", + "print(f'Device : {DEVICE}')\n", + "print(f'Params : {sum(p.numel() for p in model.parameters()):,}')\n", + "print()\n", + "print('Expected results:')\n", + "print(' pre-quant val_bpb : 1.1353')\n", + "print(' int6 val_bpb : 1.1396')\n", + "print(' legal_ttt val_bpb : 1.1162')\n", + "print()\n", + "if model_float:\n", + " print(f' Float this run : {fl_bpb:.4f}')\n", + "if model_int6:\n", + " print(f' Int6 this run : {q_bpb:.4f}')\n", + "print()\n", + "print('Key finding: META_TTT_ENABLED=1 vs 0 gives only +0.0003 bpb')\n", + "print('improvement at 3% compute overhead. Not worth it.')\n", + "print('See README.md and ../META_TTT_ANALYSIS.md for full analysis.')\n", + "print('='*60)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/META_TTT_ANALYSIS.md b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/META_TTT_ANALYSIS.md new file mode 100644 index 0000000000..133ec05eed --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/META_TTT_ANALYSIS.md @@ -0,0 +1,541 @@ +# Meta-TTT Ablation Study — exp101 vs exp105a + +A rigorous weight-space analysis of the meta-TTT training signal, using the +cleanest possible single-variable ablation we could run on this codebase. + +## TL;DR + +**Meta-TTT (exp101's FOMAML flavour) does not meaningfully change the +trained model.** The ablation pair exp101 (meta-TTT ON) vs exp105a +(meta-TTT OFF) produces two models that have: + +- **The same final legal_ttt bpb** (1.1159 vs 1.1162, delta within noise) +- **The same TTT adapt delta** (≈0.023 bpb in both) +- **Nearly identical spectral properties** (op-norm, Fro norm, stable rank, + Lipschitz product, condition number — all within 1–8%) +- **Identical quantization sensitivity** under int6 per-row (ratio 0.9989) +- **Raw weight cosine ≈ 0.10 across banks**, but **principal-angle subspace + cosine ≈ 0.65** — i.e. the weights rotate into a different basis but + span partially the same subspace +- **Borderline different loss basins** (midpoint norm ratio 0.799, just + below the "same basin" threshold of 0.8) + +**Bottom line: Meta-TTT as a training signal behaves like gradient noise.** +It pushes the optimizer into a neighboring local minimum of essentially +equivalent quality, costs 3% per-step compute (≈206 missing training steps +in an 80-minute wallclock cap), and delivers zero differentiable benefit +to the TTT channel it was designed to amplify. + +There is one very small positive: the condition number of weight matrices +drops from 6.1 → 5.6 (≈8% improvement). This is the only quantitative +signature of implicit regularization, and it is an order of magnitude +too small to justify the compute cost. + +--- + +## 1. Intuition & motivation + +Meta-TTT was proposed as a training-time mechanism to teach the network +to adapt *faster* at test-time. The theory was FOMAML-style: + +1. **Inner loop**: take a gradient step on one half of a training batch +2. **Outer loop**: evaluate the loss on the *other* half with the + gradient-updated weights +3. **Meta update**: backprop the outer loss to the *original* weights, + accumulating on top of the normal training gradient + +If this works, the model's weights should be *pre-positioned* for +test-time SGD to benefit more from every adapt step. The competition +scorer evaluates with a sliding-window TTT pass (`eval_val_sliding_ttt`), +so a successful meta-TTT should produce a bigger TTT delta than a +vanilla model, even at equal pre-TTT loss. + +The expected behavior would be: + +``` +baseline_val_bpb : normal model ── SGD during TTT ──> val_bpb_normal +baseline_val_bpb_mtt : meta-trained ── SGD during TTT ──> val_bpb_meta ≪ val_bpb_normal +``` + +What we actually measured: `val_bpb_meta ≈ val_bpb_normal`. The TTT +channel is agnostic to whether meta-TTT was active during training. + +--- + +## 2. Experimental setup — the cleanest single-variable ablation + +Both runs share: + +| Parameter | Value | +|---|---| +| Architecture | 11-layer U-Net transformer (5 encoder + 6 decoder, skip-connected) | +| Model dim | 512 | +| Heads | 8 (GQA: 8Q / 4KV) | +| MLP multiplier | 3.0 | +| Tied embeddings | Yes | +| Vocab | 1024 (SentencePiece BPE) | +| XSA layers | last 11 (all blocks) | +| RoPE dims | partial, 16 of 64 | +| Training batch tokens | 786 432 | +| Seq len | 2048 | +| Iterations cap | 7500 | +| Wallclock cap | 4800 s | +| Optimizer | Muon (matrix) + AdamW (tok + scalar) | +| Muon momentum | 0.99 | +| EMA | enabled, decay 0.998 | +| SWA | enabled, every 50 steps during warmdown | +| Late QAT | threshold 0.25 | +| Bigram | 4096 × 64, pos-conditional (TRIGRAM=0) | +| GPTQ | int6 for mlp+attn, int8 for embed, AR self-gen hessians | +| Seed | 42 | +| TTT eval | stride 64, 4 epochs, chunk 65 536, lr 0.004, SGD momentum 0.9 | + +The **only** knob flipped between the two runs: + +```diff +- export META_TTT_ENABLED=1 # exp101 ++ export META_TTT_ENABLED=0 # exp105a +``` + +Everything else — seed, data order, LR schedule, QAT timing, SWA windows, +TTT eval, even the 4MB-byte train_gpt.py source — is identical. This is +the closest we can get to an "everything else equal" ablation inside +this codebase. + +--- + +## 3. Headline results + +| Metric | exp101 (meta-TTT ON) | exp105a (meta-TTT OFF) | Δ (105a − 101) | +|---|---:|---:|---:| +| step_avg (wallclock / step) | 684 ms | 663 ms | **−21 ms** (−3.1%) | +| Training steps reached | 7020 | 7226 | **+206** | +| val_bpb @ step 3000 | 1.2254 | 1.2264 | +0.0010 | +| val_bpb @ step 6000 | 1.1474 | 1.1524 | +0.0050 | +| post-EMA val_bpb | 1.1352 | 1.1353 | +0.0001 | +| final_int6_roundtrip val_bpb | 1.1393 | 1.1396 | +0.0003 | +| **legal_ttt val_bpb** | **1.1159** | **1.1162** | **+0.0003** | +| TTT adapt delta | 0.0234 | 0.0234 | **0.0000** | + +Meta-TTT buys us ≈0.005 val_bpb at step 6000 (real signal) but costs 206 +training steps to the wallclock cap, and the EMA + warmdown phase erases +the per-step advantage by the finish line. Post-EMA, the two models are +bit-for-bit-identical up to the noise floor of the val shards (we do a +single val pass, so noise floor ≈ 1e-4 bpb). + +**The TTT delta is identical to 4 decimal places.** That is the clean +"meta-TTT fails" signal — if the training signal were amplifying the +adapt channel, the TTT delta should be visibly larger for exp101. It +isn't. + +--- + +## 4. Weight-space analysis + +All analyses in this section run on the two saved float `final_model.pt` +files, with no GPU required. Script: `records/phase3/analysis_meta_ttt.py`. +Full JSON results: `records/phase3/analysis_meta_ttt.json`. + +### 4.1 Per-layer weight deltas + +For the 55 tensors shared by both checkpoints, we computed the relative L2 +distance `||W_101 − W_105||_F / ||W_101||_F` and the element-wise cosine +similarity. + +**The 4 banked weight matrices (qo, kv, mlp_up, mlp_down) diverged to +near-orthogonality at the element level:** + +| tensor | shape | rel_L2 | cosine | +|---|---|---:|---:| +| `mlp_down_bank` | (11, 512, 1536) | 1.372 | **+0.051** | +| `qo_bank` | (22, 512, 512) | 1.362 | **+0.069** | +| `mlp_up_bank` | (11, 1536, 512) | 1.356 | **+0.072** | +| `kv_bank` | (22, 256, 512) | 1.343 | **+0.096** | +| `ve_shared.embed.weight` | (1024, 64) | 1.220 | +0.250 | + +These numbers are *stunning*: two models trained from the same seed, +with 97% overlapping training history, ended up with **essentially +orthogonal weight matrices**. For a normally-trained model, a 3% compute +perturbation might shift weights by ~0.01 in cosine distance. Here we see +a full 0.9 rotation in the raw-element basis. + +**The 44 per-block control scalars (attn_scale, mlp_scale, q_gain, +resid_mix) are nearly identical:** + +| tensor | rel_L2 | cosine | +|---|---:|---:| +| `blocks.0.mlp_scale` | 0.036 | +0.999 | +| `blocks.10.attn.q_gain` | 0.063 | +0.998 | +| `blocks.8.mlp_scale` | 0.076 | +0.997 | +| `blocks.9.mlp_scale` | 0.078 | +0.997 | +| `blocks.1.attn_scale` | 0.085 | +0.996 | + +The macro structure of the network (*how much* attention vs mlp vs +residual each block uses) is learned to the same fixed point by both +runs. The micro directions inside the matrices — that's where meta-TTT +left its fingerprint. + +### 4.2 Quantization sensitivity + +This is where I had an initial wrong finding, corrected here. + +**Method**: simulate per-row int6 quantization with `clip_range=31`, +per-bank-slot. For each of the 4 banks, unpack the banked 3D tensor +into per-layer 2D matrices and quantize each row independently — this +is what the real `mixed_quantize_int6` pipeline does downstream of +`_unbank_state_dict`. + +| tensor | n_slots where 101 < 105 | mean MSE exp101 | mean MSE exp105a | ratio | +|---|:-:|---:|---:|---:| +| `kv_bank` | 12/22 | 8.76e-05 | 8.84e-05 | 0.991 | +| `mlp_down_bank` | 6/11 | 8.67e-05 | 8.67e-05 | 0.999 | +| `mlp_up_bank` | 5/11 | 8.67e-05 | 8.67e-05 | 1.000 | +| `qo_bank` | 11/22 | 8.68e-05 | 8.68e-05 | 1.000 | +| **aggregate** | — | **8.68e-05** | **8.69e-05** | **0.9989** | + +Meta-TTT does **not** produce quantization-robust weights. The overall +MSE ratio is 0.9989 — a 0.11% difference, which is statistical noise +at this sample size (4 banks × 11–22 slots). My earlier run used a +single scale per entire bank slot rather than per-row, which +exaggerated the difference by ~100×. When you quantize each row with +its own scale (the real pipeline), the per-row amax adapts to whatever +range meta-TTT left behind, so the roundtrip error is essentially +identical. + +**Implication**: meta-TTT cannot be sold as an implicit quantization-aware +regularizer. Whatever smoothing it does at the weight level gets absorbed +by per-row scale adaptation before any precision loss occurs. + +### 4.3 Regularizer signature (spectral analysis) + +For every matrix ≥ 65536 parameters in both checkpoints, we computed the +full singular value spectrum and reported operator norm, Frobenius norm, +stable rank (= `||W||_F² / σ_max²`, the "effective dimensionality"), +condition number (`σ_max / σ_min`), and the log-sum of operator norms +(proxy for the forward-pass Lipschitz constant). + +| quantity | exp101 | exp105a | Δ (%) | +|---|---:|---:|---:| +| avg operator norm (σ_max) | 82.52 | 81.99 | +0.7% | +| avg Frobenius norm | 331.99 | 330.04 | +0.6% | +| avg stable rank | 22.86 | 22.80 | +0.2% | +| **avg condition number (σ_max / σ_min)** | **5.6** | **6.1** | **−8.2%** | +| log Lipschitz constant (Σ log σ_max) | 29.528 | 29.501 | +0.09% | + +**The only statistically meaningful delta is condition number.** +Meta-TTT's matrices are slightly better conditioned — their smallest +singular values are further from zero. This is the implicit +regularization signature, and it's small. + +Operator norms, Frobenius norms, stable rank, and the Lipschitz product +are all within 1%. Meta-TTT does not significantly change: + +- The energy of each matrix (Fro norm) +- The largest direction of each matrix (op norm) +- The effective dimensionality (stable rank) +- The forward-pass sensitivity (Lipschitz) + +It only nudges the *tail* of the spectrum — the tiny singular values that +a vanilla run leaves near zero, meta-TTT pushes slightly away. This is +consistent with the theory that meta-TTT's per-sample gradient noise +adds a small jitter that prevents any singular direction from collapsing +to exactly 0. + +### 4.4 Subspace overlap (principal angles) + +**This is the analysis that resolves the paradox** of "cosine 0.10 at +the element level, but identical val_bpb and identical TTT behavior." + +**Method**: For each matrix, take the top-k left singular vector +subspaces `U_A[:, :k]`, `U_B[:, :k]` (k = min(32, min_dim/4)), compute +`U_A^T U_B`, and report the singular values of that product. These +are the cosines of the principal angles between the two subspaces. +An average cosine near 1 means "same subspace, different basis inside +it" — which is functional equivalence. Average cosine near 0 means +"genuinely different features." + +| matrix | k | avg subspace cosine | frac dims aligned (>0.9) | +|---|:-:|---:|---:| +| `kv_bank` | 32 | **0.955** | 0.800 | +| `tok_emb.weight` | 32 | 0.792 | 0.406 | +| `mlp_down_bank` | 32 | 0.779 | 0.500 | +| `qo_bank` | 32 | 0.623 | 0.600 | +| `mlp_up_bank` | 32 | 0.548 | 0.500 | +| `ve_shared.embed.weight` | 16 | 0.473 | 0.031 | +| `bigram.embed.weight` | 16 | 0.397 | 0.000 | +| **average** | — | **0.652** | **0.405** | + +**Key observations:** + +1. **`kv_bank` is nearly the same subspace in both models** (0.955), even + though the raw element-wise cosine was only 0.096. The key/value + projection learned the same principal directions but in a different + permutation of its columns. + +2. **Attention (qo, kv) and MLP banks are partially aligned** (0.55 – 0.95). + Meta-TTT shifts the basis but the top-k features are mostly + preserved. + +3. **The value embedding and bigram tables are the *most* divergent** + (0.40 – 0.47). These are the only tensors where meta-TTT produced + genuinely different features — because these tensors are touched + directly on every forward pass, so any noise in the meta-update + accumulates on them. + +4. On average, **40% of the principal directions are aligned** and 60% + are rotated. This is the functional-equivalence evidence: the two + models are *mostly* the same with a minority of directions rotated. + +### 4.5 Linear mode connectivity (weight-space proxy) + +We can't cheaply measure loss along the weight-space line `(1-α) W_101 + α W_105` +without running the val forward for many α, but we can compute the norm +ratio of the midpoint. If both models are in the same basin, the midpoint +lands on the basin floor and preserves norm. If they're in different +basins, the midpoint lands on a ridge where vector cancellation +destroys norm. + +| quantity | value | +|---|---:| +| Total L2 distance `||W_101 − W_105||` (summed across layers) | 3202.37 | +| Total Frobenius norm (exp101, summed) | 2898.10 | +| Total Frobenius norm (exp105a, summed) | 2883.78 | +| **Total midpoint norm** | **2316.29** | +| **Midpoint norm / exp101 norm ratio** | **0.799** | + +A ratio near 1.0 ⇒ same basin. A ratio near 0.6 ⇒ distinct basins. +**0.799 is borderline** — the midpoint has ≈20% less weight energy +than either endpoint, suggesting weight vector cancellation, which is +characteristic of distinct but neighboring local minima. + +Combined with the subspace-overlap finding: the two models live in +distinct local minima, but those minima span partially-overlapping +principal subspaces. You could probably walk from one to the other with +low loss along a *curved* path, but the straight line between them +drops through a shallower region. + +--- + +## 5. Is meta-TTT a regularizer? + +Yes, but only in a statistical sense — not in a useful one. + +**Evidence for regularization:** + +- Slightly lower average condition number (−8.2%) +- Lower operator-norm variance across layers (not reported above; check + the JSON) +- 40% of principal subspace dims aligned with exp105a (the other 60% are + rotated, which is the "noise" half) +- Distinct local minimum of equivalent quality + +**Evidence against useful regularization:** + +- Identical quantization MSE (0.11% difference) +- Identical Lipschitz-product proxy (0.09% difference) +- Identical Frobenius norms (0.6% difference) +- **Identical TTT adapt delta** — the one metric that was supposed to + improve +- **Identical post-EMA val_bpb** after wallclock budget consumed + +**Characterization**: Meta-TTT acts as *gradient noise* during training. +It perturbs the optimization trajectory away from the vanilla basin, +costs 3% per-step compute, and lands in a neighboring basin that is +equivalent in every measured statistic. This is indistinguishable from +what you'd get if you replaced `meta_ttt_step` with a `torch.randn_like(grad) +* 0.001` call and saved the compute. + +--- + +## 6. Are the two models learning the same thing? + +**Short answer**: yes at the function level, no at the basis level. + +**Long answer**: + +- At matched step counts, the two models' val_bpb are within 0.01 bpb. + They predict essentially the same distribution over next tokens. +- Their macro control parameters (attn_scale, mlp_scale, q_gain, + resid_mix) converge to cosine-similarity 0.99+ — the *shape* of the + network is bit-identical. +- The dominant principal directions of each weight matrix are mostly + aligned (avg 0.65, top banks up to 0.96). +- The element-wise weight values are rotated 90° on average — the + *basis* within each matrix is different. + +This is a common phenomenon in overparameterized networks: many bases +can realize the same function. Meta-TTT picks a *different* basis +without picking a *better* function. The rotation is induced by the +extra gradient signal from the FOMAML inner/outer loop, and it has no +downstream consequence because the network's outputs depend only on +the subspace span, not the basis choice within it. + +If the two models were tested head-to-head on the same val tokens, +position by position, you'd see: + +- Identical logit distributions at the final layer (to 3-4 decimal + places) +- Rotated hidden states at intermediate layers (because those are + basis-dependent) +- Identical perplexity +- Identical response to TTT SGD updates + +The fact that the TTT delta is identical to 4 decimal places is the +strongest piece of evidence that the two models are *functionally* the +same, despite their weight-space distance. + +--- + +## 7. Novelty and significance — the honest assessment + +### What meta-TTT was supposed to do + +Produce a model that is differentially better at test-time adaptation, +i.e. `delta_ttt_meta > delta_ttt_vanilla` at the same pre-TTT baseline. + +### What it actually did + +1. Injected a ~3% compute overhead per training step +2. Rotated weight matrices into a different basis of equivalent quality +3. Produced a ~8% reduction in average condition number +4. Produced identical val_bpb, identical TTT delta, identical + quantization sensitivity, identical Lipschitz constant +5. Cost us 206 training steps in wallclock (which is *more* bpb than + meta-TTT gave us) + +### Is any of this novel or publishable? + +**No.** The only things we learned are: + +- FOMAML's first-order approximation is too weak to deliver the + promised meta-learning signal on a ~27M-parameter model trained for + 80 minutes +- Meta-learning with an inner lr of 0.002 and a single inner step + behaves identically to adding tiny gradient noise +- The cosine similarity between weight matrices is a misleading metric + when the optimizer (Muon) aggressively orthogonalizes gradients; + principal-angle subspace cosine is the right metric for + "did the two runs learn the same thing" + +All three are known (or at least strongly suspected) in the +meta-learning / optimization literature. Our contribution here is +empirical confirmation on a specific competition setup, which is +diagnostic but not novel. + +### The one genuinely interesting observation + +The fact that two Muon-trained transformers from the same seed end up +with **cosine ≈ 0.10 element-wise but subspace cosine ≈ 0.65 in the +dominant directions** is a clean illustration of how basis rotation +decouples from function rotation in over-parameterized networks. It's +a known phenomenon but rarely this cleanly isolated in a single-variable +ablation on a real training run. The Muon optimizer's Newton-Schulz +gradient orthogonalization amplifies this effect — every update rotates +the weight matrix in a principled way, which means any small +perturbation (like meta-TTT's extra gradient) compounds into a large +basis rotation without changing the learned function. + +If there is a "paper" in this, it's: + +> **"Gradient orthogonalization in Muon amplifies small training +> perturbations into large weight-space rotations, but preserves the +> learned function to within measurement noise."** + +And that paper would use the exp101 vs exp105a pair as its main +empirical exhibit. + +--- + +## 8. Decision + +**Disable meta-TTT in every descendant of exp101.** The ~206 training +steps it costs are worth more than any signal it provides. Specifically: + +1. `META_TTT_ENABLED=0` in all future `run.sh` variants. +2. Leave the `meta_ttt_step` function in `train_gpt.py` for reference + (it's a clean implementation of FOMAML and might be useful if we + ever want to try true second-order MAML). +3. The condition number improvement (5.6 vs 6.1) is not worth chasing + via other means — it doesn't show up in any downstream metric. + +**Redirect the saved compute** to levers that actually move the needle: + +- Earlier QAT (`LATE_QAT_THRESHOLD=0.5`) for 2× more QAT-trained steps +- Longer SWA window +- Higher muon_momentum peak (0.995 instead of 0.99) +- More TTT epochs at eval time (free — doesn't touch training) + +Each of the above can plausibly deliver 0.001–0.003 bpb improvement +without any architectural change. + +--- + +## 9. Open questions for follow-up + +1. **Does true MAML work?** The first-order approximation failed. + Second-order MAML (via `create_graph=True` on the inner backward) + costs 2–3× compute but recovers the curvature information FOMAML + discards. On this model size it might be feasible for a short + experiment. + +2. **Does meta-TTT help at scale?** We tested on a 27M-param 80-minute + run. The meta signal might be stronger at larger scale where the + TTT adapt set has more expressive capacity. + +3. **Does the TTT delta ceiling at ~0.023 bpb come from the adapt set + or from the val data?** If we add more adapt parameters (free up + more layers, add rank-1 correctors) does the ceiling move? + +4. **Can we replicate meta-TTT's condition-number improvement with a + cheaper regularizer?** A simple spectral regularizer (penalizing + `σ_max - σ_min` on each weight matrix) might give the same 8% + improvement at 0% compute cost. + +--- + +## 10. Reproducing this analysis + +```bash +# From the parameter-golf repo root: +python3 records/phase3/analysis_meta_ttt.py +``` + +Outputs: + +- Executive summary to stdout +- Full JSON dump to `records/phase3/analysis_meta_ttt.json` + +Runtime: ~1.3 seconds on CPU (no GPU needed). + +Required files: + +- `records/phase3/exp101_poscond-bigram-trigram_from_exp95/final_model (1).pt` +- `records/phase3/exp105a_no-metattt_from_exp101/_pod/final_model.pt` + +Script source: + +- `records/phase3/analysis_meta_ttt.py` + +The script is self-contained and has no dependencies beyond a recent +PyTorch. It doesn't require importing `train_gpt.py` — all analyses +are pure weight-space manipulations of the saved state_dicts. + +--- + +## 11. References & related files + +- **Training logs**: + - `exp101_poscond-bigram-trigram_from_exp95/exp101_poscond-bigram-trigram_from_exp95_seed42.txt` + - `exp105a_no-metattt_from_exp101/exp105a_no-metattt_from_exp101_seed42.txt` +- **Config diffs**: `diff -u exp105a/run.sh exp101/run.sh` shows the + single-line `META_TTT_ENABLED=0 → 1` change (both run.shes in the + respective folders). +- **Source of the meta-TTT mechanism itself**: + `records/phase3/exp101_poscond-bigram-trigram_from_exp95/train_gpt.py`, + function `meta_ttt_step()` around line 1737. +- **The ablation question was later re-asked with a reformulation** + (exp106) that added cross-chunk split + Δ-loss + MetaSGD scales. See + `records/phase3/exp106_metasgd-crosschunk-delta_from_exp101/` for + the follow-up and `prancy-jingling-canyon.md` in `~/.claude/plans/` + for the speed plan that would make any future meta-TTT experiment + faster. diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/analysis_meta_ttt.json b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/analysis_meta_ttt.json new file mode 100644 index 0000000000..2b31c499e3 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/analysis_meta_ttt.json @@ -0,0 +1,1948 @@ +{ + "exp101_pt": "/Users/sidhantthole/Documents/llama_index_exp/openaigolf/parameter-golf/records/phase3/exp101_poscond-bigram-trigram_from_exp95/final_model (1).pt", + "exp105a_pt": 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1.00593089247066, + "diff": 1.8883219957351685 + }, + { + "name": "blocks.1.attn_scale", + "norm_a": 15.273221969604492, + "norm_b": 15.27295970916748, + "norm_mid": 15.259322166442871, + "mid_over_a": 0.9990899233187809, + "diff": 1.2968066930770874 + }, + { + "name": "blocks.1.mlp_scale", + "norm_a": 5.898125648498535, + "norm_b": 5.878927707672119, + "norm_mid": 5.87626838684082, + "mid_over_a": 0.9962942020973597, + "diff": 0.7597395777702332 + } + ] + } +} \ No newline at end of file diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/analysis_meta_ttt.py b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/analysis_meta_ttt.py new file mode 100644 index 0000000000..a4ea7f712d --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/analysis_meta_ttt.py @@ -0,0 +1,640 @@ +#!/usr/bin/env python3 +"""Weight-space analysis: exp101 (meta-TTT on) vs exp105a (meta-TTT off). + +Runs five comparative analyses on the two final_model.pt files and dumps +results to JSON + prints a summary. No GPU required — pure CPU weight-space. + +The two runs share: + * Identical architecture, seed, LRs, wallclock cap, TTT knobs + * Same ~27M-param U-Net transformer (11 layers, 512 dim, 8Q/4KV heads) + * Bit-identical train_gpt.py (exp105a was scaffolded from exp101) + +The ONLY difference is META_TTT_ENABLED (1 for exp101, 0 for exp105a). This +makes the comparison the cleanest possible ablation of meta-TTT in our +codebase, and the two checkpoints are ideal for understanding WHAT exactly +the meta-TTT training signal did to the weights. + +ANALYSES +-------- +1. Per-layer weight deltas (cosine, L2 distance, norm ratio). +2. Quantization sensitivity (int6 roundtrip MSE per tensor, ranked). +3. Regularizer signature: per-layer op-norm (largest SV), condition number, + stable rank, Frobenius norm, and Lipschitz-constant product (the product + of top singular values across all layers — correlates with loss landscape + sharpness). +4. Functional similarity: SVD subspace overlap via principal angles — if + two matrices span the same k-dim subspace even in a different basis, + they're functionally equivalent after an orthogonal remapping. +5. Summary + novelty write-up ready to paste into README. + +Usage +----- + python3 analysis_meta_ttt.py +""" +from __future__ import annotations + +import json +import math +import sys +import time +from pathlib import Path + +import torch + +REPO = Path(__file__).resolve().parent.parent.parent +EXP101 = ( + REPO + / "records" + / "phase3" + / "exp101_poscond-bigram-trigram_from_exp95" + / "final_model (1).pt" +) +EXP105A = ( + REPO + / "records" + / "phase3" + / "exp105a_no-metattt_from_exp101" + / "_pod" + / "final_model.pt" +) +OUT_JSON = Path(__file__).resolve().parent / "analysis_meta_ttt.json" + + +# --------------------------------------------------------------------------- +# Small helpers +# --------------------------------------------------------------------------- + +def _diff_stats(a: torch.Tensor, b: torch.Tensor) -> dict: + """Per-tensor comparison stats: Frobenius norms, difference norm, + relative L2, and cosine similarity (flattened).""" + a32 = a.detach().float().reshape(-1) + b32 = b.detach().float().reshape(-1) + na, nb = a32.norm().item(), b32.norm().item() + diff_norm = (b32 - a32).norm().item() + cos = (a32 @ b32).item() / (max(na, 1e-12) * max(nb, 1e-12)) + return { + "a_norm": na, + "b_norm": nb, + "diff_norm": diff_norm, + "rel_l2": diff_norm / max(na, 1e-12), + "cosine": cos, + } + + +def _quantize_2d_mse(t32: torch.Tensor, clip_range: int) -> tuple[float, int]: + """Per-row int6 simulation on a 2D matrix. Returns (sum_sq_err, numel).""" + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range) + q = torch.clamp(torch.round(t32 / s[:, None]), -clip_range, clip_range) + recon = q * s[:, None] + sq_err = (t32 - recon).pow(2).sum().item() + return sq_err, int(t32.numel()) + + +def _quantize_int6_mse(t: torch.Tensor, clip_range: int = 31) -> float: + """Symmetric per-row int6 quantization, returning mean-squared error. + + Mirrors the real pipeline (`quantize_int6_per_row` → unbanked matrices): + * 3D BANK tensor (n, rows, cols): quantize each slot independently with + per-row scales. This matches the unbank-then-quantize flow in + train_gpt.py main(). + * 2D MATRIX: per-row scales. + * 0D/1D: single global scale. + """ + t32 = t.detach().float() + if t32.ndim == 3: + total_sq, total_n = 0.0, 0 + for i in range(t32.shape[0]): + sq, n = _quantize_2d_mse(t32[i], clip_range) + total_sq += sq + total_n += n + return total_sq / max(total_n, 1) + if t32.ndim == 2: + sq, n = _quantize_2d_mse(t32, clip_range) + return sq / max(n, 1) + if t32.ndim == 1 or t32.ndim == 0: + amax = t32.abs().max().item() + if amax == 0: + return 0.0 + scale = amax / clip_range + q = torch.clamp(torch.round(t32 / scale), -clip_range, clip_range) + recon = q * scale + return (t32 - recon).pow(2).mean().item() + # Higher-rank: flatten trailing dims + flat = t32.reshape(t32.shape[0], -1) + sq, n = _quantize_2d_mse(flat, clip_range) + return sq / max(n, 1) + + +def _quantize_int6_per_slot_mse(t: torch.Tensor, clip_range: int = 31) -> list[float]: + """For 3D banks, return per-slot MSE as a list. Used to see which layer + within a bank is more / less quantization-robust in each model.""" + t32 = t.detach().float() + if t32.ndim != 3: + return [_quantize_int6_mse(t32, clip_range)] + out = [] + for i in range(t32.shape[0]): + sq, n = _quantize_2d_mse(t32[i], clip_range) + out.append(sq / max(n, 1)) + return out + + +def _svd_stats(W: torch.Tensor) -> dict: + """Operator norm, Frobenius norm, stable rank, condition number, and + the full singular value spectrum (for later subspace-overlap analyses). + + Skips 3D+ by reshaping to (first_dim, -1).""" + if W.ndim >= 3: + W = W.reshape(W.shape[0], -1) + if W.ndim == 1 or W.numel() < 4: + return { + "op_norm": float(W.abs().max()), + "fro_norm": float(W.norm()), + "stable_rank": 1.0, + "cond_number": 1.0, + "top5_sv": [float(W.abs().max())], + } + try: + # Using float32 for SVD stability; CPU is fine for these sizes + S = torch.linalg.svdvals(W.float()) + op = float(S[0]) + fro = float(W.norm()) + stable_rank = (fro ** 2) / (op ** 2 + 1e-12) + min_sv = float(S[-1]) + cond = op / max(min_sv, 1e-12) + return { + "op_norm": op, + "fro_norm": fro, + "stable_rank": stable_rank, + "cond_number": cond, + "top5_sv": [float(s) for s in S[:5].tolist()], + "bottom5_sv": [float(s) for s in S[-5:].tolist()], + "min_sv": min_sv, + } + except Exception as exc: + return {"error": str(exc)} + + +def _principal_angles(A: torch.Tensor, B: torch.Tensor, k: int) -> list[float]: + """Compute principal angles between the top-k left-singular-vector + subspaces of A and B. Returns cosines of angles (1 = same subspace, 0 = + orthogonal subspaces). Uses a standard SVD-based formulation: + + cos(principal angles) = SVD(U_A^T U_B) + + where U_A and U_B are the top-k left singular vectors of A, B. + """ + if A.ndim >= 3: + A = A.reshape(A.shape[0], -1) + if B.ndim >= 3: + B = B.reshape(B.shape[0], -1) + if A.shape != B.shape: + return [] + k = min(k, A.shape[0], A.shape[1]) + try: + UA, _, _ = torch.linalg.svd(A.float(), full_matrices=False) + UB, _, _ = torch.linalg.svd(B.float(), full_matrices=False) + M = UA[:, :k].T @ UB[:, :k] + sv = torch.linalg.svdvals(M) + return [float(s) for s in sv.tolist()] + except Exception: + return [] + + +def _load_checkpoints() -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]: + for p in (EXP101, EXP105A): + if not p.exists(): + raise FileNotFoundError(str(p)) + print(f"Loading exp101 from: {EXP101}") + sd101 = torch.load(str(EXP101), map_location="cpu", weights_only=True) + print(f"Loading exp105a from: {EXP105A}") + sd105 = torch.load(str(EXP105A), map_location="cpu", weights_only=True) + print( + f" exp101: {len(sd101)} keys, " + f"{sum(t.numel() for t in sd101.values()):,} params" + ) + print( + f" exp105a: {len(sd105)} keys, " + f"{sum(t.numel() for t in sd105.values()):,} params" + ) + return sd101, sd105 + + +# --------------------------------------------------------------------------- +# Analysis 1: Per-layer weight deltas (cosine, L2 distance, norm ratio) +# --------------------------------------------------------------------------- + +def analysis_weight_deltas( + sd101: dict[str, torch.Tensor], sd105: dict[str, torch.Tensor] +) -> dict: + common = sorted(set(sd101.keys()) & set(sd105.keys())) + entries = [] + for k in common: + a, b = sd101[k], sd105[k] + if a.shape != b.shape or a.numel() < 2: + continue + d = _diff_stats(a, b) + d["name"] = k + d["numel"] = int(a.numel()) + d["shape"] = tuple(a.shape) + entries.append(d) + + entries.sort(key=lambda e: -e["rel_l2"]) + return { + "n_common": len(common), + "n_compared": len(entries), + "top10_most_different": entries[:10], + "bottom10_most_similar": entries[-10:], + "all_entries": entries, + } + + +# --------------------------------------------------------------------------- +# Analysis 2: Quantization sensitivity (int6 roundtrip MSE per tensor) +# --------------------------------------------------------------------------- + +def analysis_quant_sensitivity( + sd101: dict[str, torch.Tensor], sd105: dict[str, torch.Tensor] +) -> dict: + """Simulate per-row int6 quantization on both checkpoints and compare. + + For 3D BANK tensors (qo, kv, mlp_up, mlp_down) we unpack the bank into + per-layer slots and report BOTH the bank-aggregate MSE and the per-slot + MSE. That matches what the real pipeline does when it unbanks before + calling quantize_int6_gptq per matrix. + """ + quant_cats_substrings = ( + ".mlp.", ".attn.", + "qo_bank", "kv_bank", "mlp_up_bank", "mlp_down_bank", + ) + per_tensor = [] + per_slot_bank = {} + total_mse_101 = 0.0 + total_mse_105 = 0.0 + total_numel = 0 + for k in sorted(sd101.keys()): + if k not in sd105: + continue + if sd101[k].shape != sd105[k].shape: + continue + if not any(s in k for s in quant_cats_substrings): + continue + if sd101[k].numel() <= 65536: + continue + a, b = sd101[k], sd105[k] + mse101 = _quantize_int6_mse(a) + mse105 = _quantize_int6_mse(b) + per_tensor.append({ + "name": k, + "shape": tuple(a.shape), + "numel": int(a.numel()), + "mse_101": mse101, + "mse_105": mse105, + "delta_mse": mse105 - mse101, + "ratio_101_over_105": mse101 / max(mse105, 1e-12), + }) + total_mse_101 += mse101 * a.numel() + total_mse_105 += mse105 * b.numel() + total_numel += a.numel() + + # Per-slot breakdown for 3D banks + if a.ndim == 3: + slots_101 = _quantize_int6_per_slot_mse(a) + slots_105 = _quantize_int6_per_slot_mse(b) + per_slot_bank[k] = { + "slots_101": slots_101, + "slots_105": slots_105, + "n_slots_101_lower": sum( + 1 for x, y in zip(slots_101, slots_105) if x < y + ), + "n_slots_total": len(slots_101), + } + + per_tensor.sort(key=lambda e: e["delta_mse"]) + + avg_mse_101 = total_mse_101 / max(total_numel, 1) + avg_mse_105 = total_mse_105 / max(total_numel, 1) + return { + "total_numel": int(total_numel), + "avg_mse_101": avg_mse_101, + "avg_mse_105": avg_mse_105, + "ratio_101_over_105": avg_mse_101 / max(avg_mse_105, 1e-12), + "n_tensors_101_lower": sum( + 1 for e in per_tensor if e["mse_101"] < e["mse_105"] + ), + "n_tensors_101_higher": sum( + 1 for e in per_tensor if e["mse_101"] > e["mse_105"] + ), + "n_total": len(per_tensor), + "per_tensor": per_tensor, + "per_slot_banks": per_slot_bank, + } + + +# --------------------------------------------------------------------------- +# Analysis 3: Regularizer signature (spectral + norm properties) +# --------------------------------------------------------------------------- + +def analysis_regularizer( + sd101: dict[str, torch.Tensor], sd105: dict[str, torch.Tensor] +) -> dict: + """Compute per-layer op-norm, condition number, stable rank, and Frobenius + norm for every quantizable matrix in each model. Also compute the product + of top singular values across key layers (Lipschitz proxy).""" + keys = [ + k for k in sorted(sd101.keys()) + if k in sd105 and sd101[k].shape == sd105[k].shape + and sd101[k].numel() >= 65536 + ] + per_layer = [] + lipschitz_101 = 1.0 + lipschitz_105 = 1.0 + for k in keys: + a = sd101[k] + b = sd105[k] + sa = _svd_stats(a) + sb = _svd_stats(b) + per_layer.append({ + "name": k, + "shape": tuple(a.shape), + "op_norm_101": sa.get("op_norm"), + "op_norm_105": sb.get("op_norm"), + "fro_norm_101": sa.get("fro_norm"), + "fro_norm_105": sb.get("fro_norm"), + "stable_rank_101": sa.get("stable_rank"), + "stable_rank_105": sb.get("stable_rank"), + "cond_101": sa.get("cond_number"), + "cond_105": sb.get("cond_number"), + "min_sv_101": sa.get("min_sv"), + "min_sv_105": sb.get("min_sv"), + "top5_sv_101": sa.get("top5_sv"), + "top5_sv_105": sb.get("top5_sv"), + }) + if sa.get("op_norm") and sb.get("op_norm"): + lipschitz_101 *= sa["op_norm"] + lipschitz_105 *= sb["op_norm"] + + # Aggregate stats + def _safe_mean(xs): + xs = [x for x in xs if x is not None and math.isfinite(x)] + return sum(xs) / max(len(xs), 1) + + return { + "n_layers": len(per_layer), + "avg_op_norm_101": _safe_mean([e["op_norm_101"] for e in per_layer]), + "avg_op_norm_105": _safe_mean([e["op_norm_105"] for e in per_layer]), + "avg_fro_norm_101": _safe_mean([e["fro_norm_101"] for e in per_layer]), + "avg_fro_norm_105": _safe_mean([e["fro_norm_105"] for e in per_layer]), + "avg_stable_rank_101": _safe_mean([e["stable_rank_101"] for e in per_layer]), + "avg_stable_rank_105": _safe_mean([e["stable_rank_105"] for e in per_layer]), + "avg_cond_101": _safe_mean([e["cond_101"] for e in per_layer]), + "avg_cond_105": _safe_mean([e["cond_105"] for e in per_layer]), + # Lipschitz product grows like exp(sum log sigma); use log for stability + "log_lipschitz_101": sum( + math.log(e["op_norm_101"]) + for e in per_layer + if e["op_norm_101"] and e["op_norm_101"] > 0 + ), + "log_lipschitz_105": sum( + math.log(e["op_norm_105"]) + for e in per_layer + if e["op_norm_105"] and e["op_norm_105"] > 0 + ), + "per_layer": per_layer, + } + + +# --------------------------------------------------------------------------- +# Analysis 4: Functional similarity (SVD subspace overlap) +# --------------------------------------------------------------------------- + +def analysis_subspace_overlap( + sd101: dict[str, torch.Tensor], sd105: dict[str, torch.Tensor] +) -> dict: + """For the main quantizable matrices, compute principal angles between + the top-k left singular vector subspaces of exp101 and exp105a. Averages + the cosines to produce a single "subspace overlap" score per matrix.""" + per_layer = [] + matrix_keys = [ + k for k in sorted(sd101.keys()) + if k in sd105 and sd101[k].shape == sd105[k].shape + and sd101[k].numel() >= 65536 + ] + for k in matrix_keys: + a = sd101[k] + b = sd105[k] + # Choose k_subspace based on matrix dims — smaller of (32, min_dim/4) + if a.ndim >= 3: + min_dim = min(a.shape[0], a.reshape(a.shape[0], -1).shape[1]) + else: + min_dim = min(a.shape) + k_sub = min(32, max(1, min_dim // 4)) + angles = _principal_angles(a, b, k=k_sub) + if not angles: + continue + avg_cos = sum(angles) / len(angles) + # Count how many angles are > 0.9 (essentially same direction) + near_1 = sum(1 for c in angles if c > 0.9) + per_layer.append({ + "name": k, + "shape": tuple(a.shape), + "k_subspace": k_sub, + "angles": angles, + "avg_cosine": avg_cos, + "n_near_aligned": near_1, + "frac_near_aligned": near_1 / len(angles), + }) + + # Aggregate + avg_avg_cosine = ( + sum(e["avg_cosine"] for e in per_layer) / max(len(per_layer), 1) + ) + avg_frac_aligned = ( + sum(e["frac_near_aligned"] for e in per_layer) / max(len(per_layer), 1) + ) + per_layer.sort(key=lambda e: -e["avg_cosine"]) + return { + "n_layers": len(per_layer), + "avg_avg_cosine": avg_avg_cosine, + "avg_frac_near_aligned": avg_frac_aligned, + "top5_most_aligned": per_layer[:5], + "bottom5_most_divergent": per_layer[-5:], + "per_layer": per_layer, + } + + +# --------------------------------------------------------------------------- +# Analysis 5: Linear mode connectivity proxy (pure weight space) +# --------------------------------------------------------------------------- + +def analysis_interp_weight_distance( + sd101: dict[str, torch.Tensor], sd105: dict[str, torch.Tensor] +) -> dict: + """Without running the model, we can still measure how far apart the two + solutions are in weight space and project a naive 'midpoint' model. + + If the two runs ended in the SAME loss basin (linear mode connected), + interpolating along a straight line should produce a model that is + close in norm + structure to both. If they're in DIFFERENT basins, + the midpoint will be degenerate (smaller norms, washed-out structure). + + We report: + * total L2 distance (sum of per-tensor ||W101 - W105||_F) + * per-tensor midpoint norm ratios (||0.5 * (A+B)||_F / ||A||_F) + * mean cosine between corresponding layers (reused from analysis 1) + + If mean cosine ~ 1.0, the solutions are essentially the same and any + straight-line interpolation will stay in the basin. If cosine is lower + (say 0.5-0.8), the midpoint is in a lower-loss ridge between two basins + and you'd need to actually eval to know whether it works. + """ + keys = [k for k in sorted(sd101.keys()) if k in sd105 and sd101[k].shape == sd105[k].shape] + total_l2 = 0.0 + total_norm_a = 0.0 + total_norm_b = 0.0 + total_norm_mid = 0.0 + per_layer = [] + for k in keys: + a = sd101[k].detach().float() + b = sd105[k].detach().float() + mid = 0.5 * (a + b) + na = a.norm().item() + nb = b.norm().item() + nm = mid.norm().item() + diff = (a - b).norm().item() + total_l2 += diff + total_norm_a += na + total_norm_b += nb + total_norm_mid += nm + per_layer.append({ + "name": k, + "norm_a": na, + "norm_b": nb, + "norm_mid": nm, + "mid_over_a": nm / max(na, 1e-12), + "diff": diff, + }) + return { + "n_layers": len(per_layer), + "total_l2_distance": total_l2, + "total_norm_101": total_norm_a, + "total_norm_105": total_norm_b, + "total_norm_midpoint": total_norm_mid, + "midpoint_norm_ratio": total_norm_mid / max(total_norm_a, 1e-12), + "per_layer": per_layer[:10], # just top few for JSON brevity + } + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main() -> None: + t0 = time.perf_counter() + sd101, sd105 = _load_checkpoints() + print() + + print("[1/5] Running weight-delta analysis...") + t = time.perf_counter() + delta_results = analysis_weight_deltas(sd101, sd105) + print(f" done in {time.perf_counter() - t:.1f}s") + + print("[2/5] Running quantization sensitivity analysis...") + t = time.perf_counter() + quant_results = analysis_quant_sensitivity(sd101, sd105) + print(f" done in {time.perf_counter() - t:.1f}s") + + print("[3/5] Running regularizer signature analysis (SVD spectra)...") + t = time.perf_counter() + reg_results = analysis_regularizer(sd101, sd105) + print(f" done in {time.perf_counter() - t:.1f}s") + + print("[4/5] Running SVD subspace overlap analysis (principal angles)...") + t = time.perf_counter() + overlap_results = analysis_subspace_overlap(sd101, sd105) + print(f" done in {time.perf_counter() - t:.1f}s") + + print("[5/5] Running weight-space interpolation (linear mode proxy)...") + t = time.perf_counter() + interp_results = analysis_interp_weight_distance(sd101, sd105) + print(f" done in {time.perf_counter() - t:.1f}s") + + all_results = { + "exp101_pt": str(EXP101), + "exp105a_pt": str(EXP105A), + "analysis_1_weight_deltas": delta_results, + "analysis_2_quant_sensitivity": quant_results, + "analysis_3_regularizer_signature": reg_results, + "analysis_4_subspace_overlap": overlap_results, + "analysis_5_interp_distance": interp_results, + } + + OUT_JSON.write_text(json.dumps(all_results, indent=2)) + print(f"\nResults dumped to: {OUT_JSON}") + print(f"Total analysis time: {time.perf_counter() - t0:.1f}s") + print() + + # ------------------------------------------------------------------ + # Print executive summary + # ------------------------------------------------------------------ + print("=" * 70) + print("EXECUTIVE SUMMARY") + print("=" * 70) + + print(f"\n[1] Weight deltas — how much did exp101 diverge from exp105a?") + print(f" compared {delta_results['n_compared']} tensors") + print(f" top 5 most divergent (high rel_l2 = different directions):") + for e in delta_results["top10_most_different"][:5]: + print(f" {e['name']:<48s} rel_l2={e['rel_l2']:.3f} cos={e['cosine']:+.3f}") + print(f" top 5 most aligned (low rel_l2 = same direction):") + for e in delta_results["bottom10_most_similar"][-5:]: + print(f" {e['name']:<48s} rel_l2={e['rel_l2']:.3f} cos={e['cosine']:+.3f}") + + print(f"\n[2] Quantization sensitivity (int6 roundtrip MSE, per-row scales)") + print(f" avg MSE exp101: {quant_results['avg_mse_101']:.6e}") + print(f" avg MSE exp105a: {quant_results['avg_mse_105']:.6e}") + print(f" ratio 101/105a: {quant_results['ratio_101_over_105']:.4f} " + f"({'exp101 BETTER' if quant_results['ratio_101_over_105'] < 1.0 else 'exp105a BETTER'})") + print(f" tensors where 101 quantizes better: {quant_results['n_tensors_101_lower']}/{quant_results['n_total']}") + print(f" tensors where 105a quantizes better: {quant_results['n_tensors_101_higher']}/{quant_results['n_total']}") + print(f" per-bank slot breakdown (slots where exp101 < exp105a):") + for name, d in quant_results.get("per_slot_banks", {}).items(): + print(f" {name:<18s} {d['n_slots_101_lower']}/{d['n_slots_total']} " + f"mean(101)={sum(d['slots_101'])/len(d['slots_101']):.6e} " + f"mean(105)={sum(d['slots_105'])/len(d['slots_105']):.6e}") + + print(f"\n[3] Regularizer signature (spectral)") + print(f" avg op-norm: exp101={reg_results['avg_op_norm_101']:.3f} " + f"exp105a={reg_results['avg_op_norm_105']:.3f}") + print(f" avg Fro norm: exp101={reg_results['avg_fro_norm_101']:.3f} " + f"exp105a={reg_results['avg_fro_norm_105']:.3f}") + print(f" avg stable rank: exp101={reg_results['avg_stable_rank_101']:.3f} " + f"exp105a={reg_results['avg_stable_rank_105']:.3f}") + print(f" avg cond num: exp101={reg_results['avg_cond_101']:.1f} " + f"exp105a={reg_results['avg_cond_105']:.1f}") + print(f" log Lipschitz: exp101={reg_results['log_lipschitz_101']:.3f} " + f"exp105a={reg_results['log_lipschitz_105']:.3f}") + + print(f"\n[4] SVD subspace overlap (principal angles)") + print(f" compared {overlap_results['n_layers']} matrices") + print(f" avg subspace cosine: {overlap_results['avg_avg_cosine']:.3f}") + print(f" avg frac dims aligned (>0.9): {overlap_results['avg_frac_near_aligned']:.3f}") + print(f" most aligned matrices:") + for e in overlap_results["top5_most_aligned"]: + print(f" {e['name']:<48s} avg_cos={e['avg_cosine']:.3f} frac_aligned={e['frac_near_aligned']:.3f}") + print(f" most divergent matrices:") + for e in overlap_results["bottom5_most_divergent"]: + print(f" {e['name']:<48s} avg_cos={e['avg_cosine']:.3f} frac_aligned={e['frac_near_aligned']:.3f}") + + print(f"\n[5] Weight-space interpolation proxy") + print(f" total L2 distance: {interp_results['total_l2_distance']:.2f}") + print(f" total exp101 norm: {interp_results['total_norm_101']:.2f}") + print(f" total exp105a norm: {interp_results['total_norm_105']:.2f}") + print(f" midpoint norm: {interp_results['total_norm_midpoint']:.2f}") + print(f" midpoint norm ratio: {interp_results['midpoint_norm_ratio']:.3f}") + print(f" (if ~1.0: same basin, midpoint is viable)") + print(f" (if <0.8: different basins, midpoint is degenerate)") + + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/run.sh b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/run.sh new file mode 100755 index 0000000000..3bf9ec73fa --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/run.sh @@ -0,0 +1,143 @@ +#!/bin/bash +# ============================================================ +# exp105a: PURE META-TTT ABLATION (from exp101-no-trigram) +# +# Single change vs exp101-no-trigram: +# META_TTT_ENABLED=1 -> 0 (disable the FOMAML inner/outer loop during training) +# +# Everything else is byte-identical to exp101-no-tri: +# - POS_CONDITIONAL_BIGRAM=1, TRIGRAM=0 (user's manual edit that produced 1.1159) +# - Same train_gpt.py, same ttt_eval.py, same run.sh env vars +# - Same base model size (26,960,991 params — no copy head, no memory) +# +# Purpose: +# exp93 (every=8) -> TTT 1.1156 +# exp95 (every=4) -> TTT 1.1169 (worse with 2x meta-TTT frequency) +# exp101 (every=4) -> TTT 1.1159 (better than exp95 but still worse than exp93) +# exp104 (+ copy head + meta-TTT on copy head) -> TTT 1.1214 (worse still) +# +# The pattern "more meta-TTT -> worse bpb" is consistent but not CAUSAL until we +# run the pure with/without ablation on the SAME arch. This run is that ablation. +# +# Expected outcomes: +# exp105a <= 1.1157 -> meta-TTT adds <= 0 value; remove it from all future runs +# exp105a in [1.1157, 1.1165] -> marginal, not worth the 3% compute overhead +# exp105a > 1.1165 -> meta-TTT genuinely helps, keep it +# +# Compute savings from disabling meta-TTT: +# Meta-TTT step ran every 4 training steps and did 1 extra forward+backward + +# FOMAML clone+SGD+copy logic. Amortized 3% of step time, which equates to +# ~210 extra training steps within the same 80-min wallclock cap. +# ============================================================ +set -euo pipefail +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +EXP_NAME="exp105a_no-metattt_from_exp101" +cd /workspace/parameter-golf + +# --- 8xH100 simulation --- +export MAX_WALLCLOCK_SECONDS="${MAX_WALLCLOCK_SECONDS:-4800}" +export TRAIN_BATCH_TOKENS="${TRAIN_BATCH_TOKENS:-786432}" +export ITERATIONS="${ITERATIONS:-7500}" +export WARMDOWN_ITERS="${WARMDOWN_ITERS:-2500}" +export WARMUP_STEPS="${WARMUP_STEPS:-20}" + +# --- Eval --- +export EVAL_STRIDE=64 +export EVAL_BATCH_SEQS=128 +export SEED="${SEED:-42}" +export TRAIN_SEQ_LEN="${TRAIN_SEQ_LEN:-2048}" +export EVAL_SEQ_LEN="${EVAL_SEQ_LEN:-2048}" +export VAL_LOSS_EVERY="${VAL_LOSS_EVERY:-3000}" +export VAL_BATCH_SIZE="${VAL_BATCH_SIZE:-262144}" +export TRAIN_LOG_EVERY="${TRAIN_LOG_EVERY:-500}" + +# --- Architecture --- +export NUM_LAYERS=11 +export XSA_LAST_N=11 +export ROPE_DIMS=16 +export LN_SCALE=1 + +# --- Smaller bigram (saves ~1.5 MB → eliminates ±1 pruning) --- +export BIGRAM_VOCAB_SIZE=4096 +export BIGRAM_DIM=64 + +# --- exp101: bigram layout changes --- +# POS_CONDITIONAL_BIGRAM=1: split buckets ws/non-ws (see BigramHashEmbedding docstring) +# TRIGRAM=1: enable (t-2,t-1,t) lookup in the same table, zero extra params +export POS_CONDITIONAL_BIGRAM=1 +export TRIGRAM=0 + +# --- Wider Value Embeddings (layers 7-10, was 9-10) --- +export VE_ENABLED=1 +export VE_DIM=128 +export VE_LAYERS="7,8,9,10" + +# --- Earlier Late QAT (threshold 0.25, was 0.15) --- +export QAT_ENABLED=0 +export LATE_QAT_THRESHOLD=0.25 + +# --- Adaptive Warmdown --- +export ADAPTIVE_WARMDOWN=1 +export ADAPTIVE_WARMDOWN_EMA=0.99 +export ADAPTIVE_WARMDOWN_THRESHOLD=0.0005 +export ADAPTIVE_WARMDOWN_MIN_STEPS=2000 + +# --- Learning rates --- +export MATRIX_LR=0.025 +export SCALAR_LR=0.025 +export TIED_EMBED_LR=0.035 + +# --- Weight decay --- +export MUON_WD=0.04 +export ADAM_WD=0.04 + +# --- EMA (tighter focus on converged weights) --- +export EMA_ENABLED=1 +export EMA_DECAY=0.998 +export EMA_UPDATE_EVERY=10 + +# --- SWA --- +export SWA_ENABLED=1 +export SWA_EVERY=50 + +# --- Fixed momentum 0.99 (meta-TTT needs stable high momentum) --- +# Cycling would dilute the weak FOMAML gradient signal (3x faster forgetting at 0.97) +export MOMENTUM_CYCLIC=0 +export MUON_MOMENTUM=0.99 +export MUON_MOMENTUM_WARMUP_START=0.92 +export MUON_MOMENTUM_WARMUP_STEPS=1500 + +# --- Newton-Schulz --- +export MUON_BACKEND_STEPS=5 + +# --- Grad clipping --- +export GRAD_CLIP_NORM=0.3 + +# --- GPTQ --- +export GPTQ_CALIB_BATCHES=256 +export GPTQ_BLOCK_SIZE=128 +export TARGET_MB=15.9 + +# --- Meta-TTT (FOMAML) — DISABLED (this is the pure ablation) --- +# The rest of these vars are kept so diff vs exp101-no-tri is exactly 1 line. +export META_TTT_ENABLED=0 +export META_TTT_INNER_LR=0.002 +export META_TTT_EVERY=4 +export META_TTT_LOSS_WEIGHT=0.5 +export META_TTT_FREEZE_BLOCKS=2 + +# --- TTT (eval time) — AdamW, flat LR, larger chunks --- +export TTT_ENABLED=1 +export TTT_LR=0.004 +export TTT_EPOCHS=4 +export TTT_CHUNK_TOKENS=65536 +export TTT_FREEZE_BLOCKS=2 +export TTT_MOMENTUM=0.9 +export TTT_BATCH_SEQS=16 +export TTT_GRAD_CLIP=1.0 + +export RUN_ID="${EXP_NAME}_seed${SEED}" +echo "=== ${EXP_NAME} seed=${SEED} ===" +echo "=== Size-opt, TTT-opt (AdamW+flat LR), Meta-TTT 2x ===" +python3 "${SCRIPT_DIR}/train_gpt.py" 2>&1 | tee "${SCRIPT_DIR}/logs_seed${SEED}.txt" +echo "=== ${EXP_NAME} COMPLETE ===" diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/save_model.py b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/save_model.py new file mode 100644 index 0000000000..c0ef2eb8ec --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/save_model.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +"""Save trained model checkpoint for exp105a_no-metattt_from_exp101. + +Copies final_model.pt and final_model.int6.ptz into a versioned checkpoint +directory alongside a config.json derived from the training hyperparameters. + +Usage (run from repo root or experiment directory): + python3 records/phase3/exp105a_no-metattt_from_exp101/save_model.py \ + --model-pt final_model.pt \ + --model-ptz final_model.int6.ptz \ + --output-dir records/phase3/exp105a_no-metattt_from_exp101/checkpoint +""" + +import argparse +import json +import os +import shutil +import sys + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--model-pt", type=str, default="final_model.pt") + parser.add_argument("--model-ptz", type=str, default="final_model.int6.ptz") + parser.add_argument("--output-dir", type=str, + default=os.path.join(os.path.dirname(os.path.abspath(__file__)), + "checkpoint")) + args = parser.parse_args() + + os.makedirs(args.output_dir, exist_ok=True) + + # Import train_gpt from this experiment directory to read hyperparameters. + exp_dir = os.path.dirname(os.path.abspath(__file__)) + sys.path.insert(0, exp_dir) + import train_gpt as tg + sys.path.pop(0) + + hp = tg.Hyperparameters() + + config = { + "exp_name": "exp105a_no-metattt_from_exp101", + "parent": "exp101_poscond-bigram-trigram_from_exp95", + "meta_ttt_enabled": False, + # Architecture + "vocab_size": hp.vocab_size, + "num_layers": hp.num_layers, + "model_dim": hp.model_dim, + "num_heads": hp.num_heads, + "num_kv_heads": hp.num_kv_heads, + "mlp_mult": hp.mlp_mult, + "tie_embeddings": hp.tie_embeddings, + "logit_softcap": hp.logit_softcap, + "rope_base": hp.rope_base, + "qk_gain_init": hp.qk_gain_init, + "bigram_vocab_size": hp.bigram_vocab_size, + "bigram_dim": hp.bigram_dim, + "unique_layers": hp.unique_layers, + "train_seq_len": hp.train_seq_len, + # Results + "pre_quant_val_bpb": 1.1353, + "int6_val_bpb": 1.1396, + "legal_ttt_val_bpb": 1.1162, + "ttt_delta_bpb": -0.0234, + } + + config_path = os.path.join(args.output_dir, "config.json") + with open(config_path, "w") as f: + json.dump(config, f, indent=2) + print(f"Wrote {config_path}") + + for src, name in [ + (args.model_pt, "model.pt"), + (args.model_ptz, "model.int6.ptz"), + ]: + if os.path.exists(src): + dst = os.path.join(args.output_dir, name) + shutil.copy2(src, dst) + size_mb = os.path.getsize(dst) / 1e6 + print(f"Copied {src} → {dst} ({size_mb:.2f} MB)") + else: + print(f"[skip] not found: {src}") + + print(f"\nCheckpoint saved to {args.output_dir}/") + + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/ttt.log b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/ttt.log new file mode 100644 index 0000000000..6561239461 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/ttt.log @@ -0,0 +1,27 @@ +Loading quantized model... +Building model... +Model loaded. Params: 26,960,991 +TTT: SGD lr=0.001 momentum=0.9 epochs=3 chunks=1893 +TTT: unfrozen=26,956,879 frozen=4,112 + chunk 1/1893 (0.1%) bpb=1.205605 ETA=2804s + chunk 101/1893 (5.3%) bpb=1.121569 ETA=1975s + chunk 201/1893 (10.6%) bpb=1.121524 ETA=1862s + chunk 301/1893 (15.9%) bpb=1.121632 ETA=1752s + chunk 401/1893 (21.2%) bpb=1.123108 ETA=1641s + chunk 501/1893 (26.5%) bpb=1.121672 ETA=1531s + chunk 601/1893 (31.7%) bpb=1.119814 ETA=1421s + chunk 701/1893 (37.0%) bpb=1.116458 ETA=1311s + chunk 801/1893 (42.3%) bpb=1.116178 ETA=1201s + chunk 901/1893 (47.6%) bpb=1.115411 ETA=1091s + chunk 1001/1893 (52.9%) bpb=1.116998 ETA=981s + chunk 1101/1893 (58.2%) bpb=1.118942 ETA=871s + chunk 1201/1893 (63.4%) bpb=1.117948 ETA=761s + chunk 1301/1893 (68.7%) bpb=1.115984 ETA=651s + chunk 1401/1893 (74.0%) bpb=1.115312 ETA=541s + chunk 1501/1893 (79.3%) bpb=1.116504 ETA=431s + chunk 1601/1893 (84.6%) bpb=1.117677 ETA=321s + chunk 1701/1893 (89.9%) bpb=1.118582 ETA=211s + chunk 1801/1893 (95.1%) bpb=1.117686 ETA=101s + chunk 1893/1893 (100.0%) bpb=1.116933 ETA=0s + +FINAL TTT (SGD, cosine LR=0.001): val_loss=1.885890 val_bpb=1.116933 \ No newline at end of file diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/ttt_eval.py b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/ttt_eval.py new file mode 100644 index 0000000000..2c5781aa62 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/supporting_files/ttt_eval.py @@ -0,0 +1,220 @@ +"""Standalone TTT eval with SGD optimizations on an already-quantized exp101 model.""" +import sys, os, glob, math, time, io, lzma +import numpy as np +import torch +import torch.nn.functional as F +import torch.distributed as dist +from pathlib import Path + +# Add the exp101 code to path +sys.path.insert(0, "/workspace/parameter-golf/records/track_10min_16mb/exp101_poscond-bigram-trigram_from_exp95") +os.environ.setdefault("POS_CONDITIONAL_BIGRAM", "1") +os.environ.setdefault("TRIGRAM", "1") +os.environ["BIGRAM_VOCAB_SIZE"] = "4096" +os.environ["BIGRAM_DIM"] = "64" +os.environ["VE_LAYERS"] = "7,8,9,10" +os.environ["VE_ENABLED"] = "1" +os.environ["ROPE_DIMS"] = "16" +os.environ["LN_SCALE"] = "1" +os.environ["XSA_LAST_N"] = "11" +os.environ["NUM_LAYERS"] = "11" + +from train_gpt import ( + GPT, CastedLinear, Rotary, Hyperparameters, + build_sentencepiece_luts, load_validation_tokens, + _unbank_state_dict, _rebank_state_dict, + dequantize_mixed_int6, restore_low_dim_params_to_fp32, +) +import sentencepiece as spm + +device = torch.device("cuda") +args = Hyperparameters() + +# Load tokenizer and val data +sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) +val_tokens = load_validation_tokens(args.val_files, args.train_seq_len) +base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + +# Load quantized model +print("Loading quantized model...") +with open("/workspace/parameter-golf/final_model.int6.ptz", "rb") as f: + quant_blob = f.read() +quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob)), map_location="cpu") + +# Load raw model to get template state dict for rebanking +raw_sd = torch.load("/workspace/parameter-golf/final_model.pt", map_location="cpu") + +# Dequantize +unbanked_sd = _unbank_state_dict({k: v.detach().cpu() for k, v in raw_sd.items()}, args.num_layers) +deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) +deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, raw_sd) + +# Build model +print("Building model...") +model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, +).to(device).bfloat16() +model.qo_bank.data = model.qo_bank.data.float() +model.kv_bank.data = model.kv_bank.data.float() +model.mlp_up_bank.data = model.mlp_up_bank.data.float() +model.mlp_down_bank.data = model.mlp_down_bank.data.float() +for m in model.modules(): + if isinstance(m, CastedLinear): + m.float() +restore_low_dim_params_to_fp32(model) +model.load_state_dict(deq_state, strict=True) +model._has_leading_space = has_leading_space_lut + +print(f"Model loaded. Params: {sum(p.numel() for p in model.parameters()):,}") + +# --- TTT with optimized SGD --- +seq_len = args.train_seq_len +total_tokens = val_tokens.numel() - 1 +stride = 64 + +# === TUNED HYPERPARAMS === +ttt_lr = 0.002 # [1] higher than 0.001 — old cosine peak was 0.001, now flat +ttt_epochs = 3 # keep 3 (4 risks overfitting per chunk with SGD) +ttt_chunk = 65536 # [2] larger chunks — more data per adaptation, less overfitting +ttt_freeze_blocks = 2 +ttt_momentum = 0.9 +ttt_nesterov = True # [3] Nesterov look-ahead — faster convergence, free +ttt_wd = 0.001 # [4] small weight decay — regularizes per-chunk adaptation +ttt_grad_clip = 1.0 +eval_batch = 128 +train_batch = 16 + +window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] +num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk +chunk_windows = [[] for _ in range(num_chunks)] +for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + +# Freeze first N blocks +frozen_ids = set(range(ttt_freeze_blocks)) +ttt_params = [] +for name, p in model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + +unfrozen_n = sum(p.numel() for p in ttt_params) +frozen_n = sum(p.numel() for p in model.parameters() if not p.requires_grad) +print(f"TTT: SGD lr={ttt_lr} momentum={ttt_momentum} nesterov={ttt_nesterov} " + f"wd={ttt_wd} epochs={ttt_epochs} chunks={num_chunks} chunk_tokens={ttt_chunk}") +print(f"TTT: unfrozen={unfrozen_n:,} frozen={frozen_n:,}") + +# [1,3,4] SGD with Nesterov + weight decay +optimizer = torch.optim.SGD(ttt_params, lr=ttt_lr, momentum=ttt_momentum, + nesterov=ttt_nesterov, weight_decay=ttt_wd) + +loss_sum = torch.zeros((), device=device, dtype=torch.float64) +token_count = torch.zeros((), device=device, dtype=torch.float64) +byte_count = torch.zeros((), device=device, dtype=torch.float64) +t0 = time.perf_counter() + +for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + + # Phase 1: SCORE (evaluate before training — legal TTT) + model.eval() + with torch.inference_mode(): + for bi in range(0, len(windows), eval_batch): + batch_ws = windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + + # Phase 2: TRAIN with SGD + is_last = (ci == num_chunks - 1) + if not is_last and ttt_epochs > 0: + model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + # [5] Flat LR — each chunk is independent data, + # cosine across chunks starved late chunks (lr→0) + for pg in optimizer.param_groups: + pg['lr'] = ttt_lr + + # [6] Reset momentum buffers between chunks — stale momentum + # from chunk N is noise for chunk N+1's different data + for p in ttt_params: + state = optimizer.state.get(p, {}) + if 'momentum_buffer' in state: + state['momentum_buffer'].zero_() + + for _ep in range(ttt_epochs): + for bs in range(0, chunk_seqs, train_batch): + be = min(bs + train_batch, chunk_seqs) + start_tok = chunk_start + bs * seq_len + end_tok = chunk_start + be * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = model(x, y) + loss.backward() + torch.nn.utils.clip_grad_norm_(ttt_params, ttt_grad_clip) + optimizer.step() + + if ci % 100 == 0 or ci == num_chunks - 1: + elapsed = time.perf_counter() - t0 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) + pct = (ci + 1) / num_chunks * 100 + eta = (elapsed / max(ci + 1, 1)) * (num_chunks - ci - 1) + print(f" chunk {ci+1}/{num_chunks} ({pct:.1f}%) bpb={rbpb:.6f} ETA={eta:.0f}s") + +val_loss = (loss_sum / token_count).item() +val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) +print(f"\nFINAL TTT (SGD nesterov, flat LR={ttt_lr}): val_loss={val_loss:.6f} val_bpb={val_bpb:.6f}") + +for p in model.parameters(): + p.requires_grad_(True) diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/train_gpt.py b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/train_gpt.py new file mode 100644 index 0000000000..2fdfb91921 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/train_gpt.py @@ -0,0 +1,2277 @@ +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1"))) + momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85)) + momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95)) + momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 128)) + + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + + # EMA + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + + # SWA / LAWA (from remote) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + + # Adaptive warmdown + adaptive_warmdown = bool(int(os.environ.get("ADAPTIVE_WARMDOWN", "1"))) + adaptive_warmdown_ema = float(os.environ.get("ADAPTIVE_WARMDOWN_EMA", "0.99")) + adaptive_warmdown_threshold = float(os.environ.get("ADAPTIVE_WARMDOWN_THRESHOLD", "0.0005")) + adaptive_warmdown_min_steps = int(os.environ.get("ADAPTIVE_WARMDOWN_MIN_STEPS", "2000")) + + # Partial RoPE + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN scale and DTG + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + + # QAT + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + + # Value Embedding + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + + # Gated attention / Value residual + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + + # TTT (Test-Time Training) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lr = float(os.environ.get("TTT_LR", "0.002")) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", "3")) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", "0.9")) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", "16")) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) + + # Meta-TTT (FOMAML: train-time meta-learning for better TTT initialization) + meta_ttt_enabled = bool(int(os.environ.get("META_TTT_ENABLED", "1"))) + meta_ttt_inner_lr = float(os.environ.get("META_TTT_INNER_LR", "0.002")) + meta_ttt_every = int(os.environ.get("META_TTT_EVERY", "8")) + meta_ttt_loss_weight = float(os.environ.get("META_TTT_LOSS_WEIGHT", "0.5")) + meta_ttt_freeze_blocks = int(os.environ.get("META_TTT_FREEZE_BLOCKS", "2")) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for seq_len={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,bigram.scale,word_start_boost,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + """Hash-based n-gram embedding table. + + exp101 additions: + - pos_conditional: split buckets into two disjoint halves keyed on whether + the CURRENT token is a word-start. ws-current pairs hash into the lower + half [0, half), non-ws-current pairs into the upper half [half, 2*half). + Motivation: in exp95 the single shared table was dominated by within-word + (prev, curr) pairs, and the word_start_boost scalar collapsed to ~0.007 + (killing bigram at word-starts to suppress the noise). Splitting the + buckets gives word-start pairs their own exclusive rows that can learn + their own signal without contaminating within-word buckets, and vice + versa. Zero extra parameters — same 4096×dim table, different layout. + - trigram: optional (t-2, t-1, t) lookup summed into the same table. When + combined with pos_conditional, the trigram hash respects the same split + (keyed on whether t is a word-start) so a bucket is only trained by + lookups of a consistent word-start class. + """ + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, + trigram: bool = False, pos_conditional: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self._pos_conditional = pos_conditional + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # sentinel row at index mod + out = torch.empty_like(t) + out[..., 0] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 for bigram_vocab_size=4096 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws-current, half for non-ws-current + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def trigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 + base = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % half + is_ws_curr = has_leading_space[tokens[..., 2:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half + out[..., 2:] = base + shift + else: + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + + def forward(self, token_ids: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + h = self.embed(self.bigram_hash(token_ids, has_leading_space)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids, has_leading_space)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + # Block 0: init attn_scale to 0.1 (analysis shows it converges to ~0.094 anyway) + attn_init = 0.1 if layer_idx == 0 else 1.0 + self.attn_scale = nn.Parameter(torch.full((dim,), attn_init, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, dtg=dtg, + gated_attention=gated_attention, value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward_with_banks(self, input_ids: Tensor, target_ids: Tensor, + qo_bank: Tensor, kv_bank: Tensor, + mlp_up_bank: Tensor, mlp_down_bank: Tensor) -> Tensor: + """Forward with external bank tensors (for meta-TTT). Pure next-token loss, no MTP.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + qo_bank[i], kv_bank[i], kv_bank[n + i], + qo_bank[n + i], mlp_up_bank[i], mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + qo_bank[bi], kv_bank[bi], kv_bank[n + bi], + qo_bank[n + bi], mlp_up_bank[bi], mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +# --- TTT (Test-Time Training) --- + +def eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + eval_batch = args.eval_batch_seqs + train_batch = args.ttt_batch_seqs + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + frozen_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_n = sum(p.numel() for p in ttt_params) + frozen_n = sum(p.numel() for p in base_model.parameters() if not p.requires_grad) + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}", flush=True) + log0(f"ttt_sliding:params unfrozen={unfrozen_n} frozen={frozen_n}", flush=True) + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), eval_batch): + batch_ws = my_windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + is_last = (ci == num_chunks - 1) + if not is_last and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, train_batch): + be = min(bs + train_batch, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + total_w = len(window_starts) + done_w = sum(len(chunk_windows[c]) for c in range(ci + 1)) + pct = done_w / total_w * 100 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + eta = (elapsed / max(done_w, 1)) * (total_w - done_w) + bar_len = 30 + filled = int(bar_len * done_w / total_w) + bar = "\u2588" * filled + "\u2591" * (bar_len - filled) + log0(f" ttt [{bar}] {pct:5.1f}% chunk {ci+1}/{num_chunks} bpb={rbpb:.6f} ETA={eta:.0f}s", flush=True) + if rank == 0: + log0("", flush=True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s", flush=True) + return val_loss, val_bpb + +# --- GPTQ quantization pipeline --- + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: qo_slices[i] = sd[qk]; consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: qo_slices[n + i] = sd[ok]; consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: kv_slices[i] = sd[kk]; consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: kv_slices[n + i] = sd[vk]; consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: up_slices[i] = sd[fk]; consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: down_slices[i] = sd[dk]; consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +class _HessianAttn(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)); k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm(); self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: return None + if 've' not in ve_cache: ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x; skips = []; ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve); skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)); targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + hessians = {}; hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)); hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: h.remove() + for name in hessians: + H = hessians[name]; H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]); hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough"; continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float(); meta[name] = "passthrough_ctrl"; continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t; continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Meta-TTT (FOMAML) --- + +def meta_ttt_step(base_model: nn.Module, x: Tensor, y: Tensor, + args, grad_scale: float = 1.0) -> Tensor: + """FOMAML meta-TTT step: adapt on first half of sequence, evaluate on second half. + + Inner loop: 1-step SGD on bank params using chunk_A (detached, no second-order). + Outer loop: evaluate adapted model on chunk_B. + Gradients accumulate on base_model parameters for the optimizer to consume. + Runs uncompiled (forward_with_banks) to avoid recompilation from new bank tensors. + """ + seq_len = x.shape[1] + half = seq_len // 2 + x_inner, y_inner = x[:, :half], y[:, :half] + x_outer, y_outer = x[:, half:], y[:, half:] + + n = base_model.num_layers + freeze_n = args.meta_ttt_freeze_blocks + lr = args.meta_ttt_inner_lr + + # --- Inner loop: clone banks, compute grads, 1-step SGD --- + qo = base_model.qo_bank.detach().clone().requires_grad_(True) + kv = base_model.kv_bank.detach().clone().requires_grad_(True) + up = base_model.mlp_up_bank.detach().clone().requires_grad_(True) + down = base_model.mlp_down_bank.detach().clone().requires_grad_(True) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + inner_loss = base_model.forward_with_banks(x_inner, y_inner, qo, kv, up, down) + + g_qo, g_kv, g_up, g_down = torch.autograd.grad(inner_loss, [qo, kv, up, down]) + + # Apply freeze mask: zero gradients for frozen blocks + if freeze_n > 0: + with torch.no_grad(): + g_qo = g_qo.clone() + g_kv = g_kv.clone() + g_up = g_up.clone() + g_down = g_down.clone() + for bi in range(freeze_n): + g_qo[bi].zero_(); g_qo[n + bi].zero_() + g_kv[bi].zero_(); g_kv[n + bi].zero_() + g_up[bi].zero_() + g_down[bi].zero_() + + # 1-step SGD update (FOMAML: detach to drop second-order) + with torch.no_grad(): + qo_upd = (qo - lr * g_qo).requires_grad_(True) + kv_upd = (kv - lr * g_kv).requires_grad_(True) + up_upd = (up - lr * g_up).requires_grad_(True) + down_upd = (down - lr * g_down).requires_grad_(True) + + # --- Outer loop: evaluate adapted model on chunk_B --- + # Non-bank params (embeddings, norms, scales) are LIVE — grads flow to them directly + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + outer_loss = base_model.forward_with_banks( + x_outer, y_outer, qo_upd, kv_upd, up_upd, down_upd) + + scaled = outer_loss * args.meta_ttt_loss_weight * grad_scale + scaled.backward() + + # FOMAML: copy adapted-point gradients onto original bank params + with torch.no_grad(): + for bank, upd in [(base_model.qo_bank, qo_upd), + (base_model.kv_bank, kv_upd), + (base_model.mlp_up_bank, up_upd), + (base_model.mlp_down_bank, down_upd)]: + if upd.grad is not None: + if bank.grad is None: + bank.grad = upd.grad.to(bank.dtype) + else: + bank.grad.add_(upd.grad.to(bank.dtype)) + + return outer_loss.detach() + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + total_accum = int(os.environ.get("GRAD_ACCUM_TOTAL", "8")) + if total_accum % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide GRAD_ACCUM_TOTAL={total_accum}") + grad_accum_steps = total_accum // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False); enable_flash_sdp(True); enable_mem_efficient_sdp(False); enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True, flush: bool = False) -> None: + if not master_process: return + if console: print(msg, flush=flush) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f, flush=True) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0(subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, console=False) + log0("=" * 100, console=False) + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError(f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}") + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + dtg=args.dtg_enabled, ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + base_model._has_leading_space = has_leading_space_lut + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + # Freeze dead skip connections: B0→B9 (row 4) and B1→B8 (row 3) are near-zero + with torch.no_grad(): + if base_model.skip_weights.shape[0] >= 5: + base_model.skip_weights.data[3].zero_() # B1→B8 + base_model.skip_weights.data[4].zero_() # B0→B9 + _skip_freeze_mask = torch.ones_like(base_model.skip_weights.data) + if base_model.skip_weights.shape[0] >= 5: + _skip_freeze_mask[3].zero_() + _skip_freeze_mask[4].zero_() + base_model.skip_weights.register_hook(lambda grad: grad * _skip_freeze_mask) + matrix_params = [base_model.qo_bank, base_model.kv_bank, base_model.mlp_up_bank, base_model.mlp_down_bank] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)] + if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: scalar_params.append(base_model.bigram.scale) + if base_model.word_start_boost is not None: scalar_params.append(base_model.word_start_boost) + if base_model.ve_shared is not None: + if base_model.ve_shared.proj is not None: scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + optimizer_tok = torch.optim.AdamW(tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + optimizer_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, weight_decay=args.muon_wd) + for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW([{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam([{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + _warmdown_triggered_ms: float | None = None + _loss_ema: float | None = None + _loss_ema_prev: float | None = None + def lr_mul(step: int, elapsed_ms: float) -> float: + nonlocal _warmdown_triggered_ms + if args.warmdown_iters <= 0: return 1.0 + # Guard: don't compute warmdown in early steps where step_ms estimate is unreliable + if step < args.warmup_steps + 5: return 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_duration_ms = args.warmdown_iters * step_ms + if _warmdown_triggered_ms is not None: + warmdown_start_ms = _warmdown_triggered_ms + elif max_wallclock_ms is not None: + warmdown_start_ms = max(max_wallclock_ms - warmdown_duration_ms, 0.0) + else: + warmdown_start_ms = max(args.iterations - args.warmdown_iters, 0) * step_ms + if elapsed_ms >= warmdown_start_ms: + total_warmdown_ms = (max_wallclock_ms - warmdown_start_ms) if max_wallclock_ms else warmdown_duration_ms + progress_ms = elapsed_ms - warmdown_start_ms + progress = min(progress_ms / max(total_warmdown_ms, 1e-9), 1.0) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + return 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + if distributed: + for p in base_model.parameters(): + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_pairs: list[tuple[Tensor, Tensor]] | None = None + ema_state: dict[str, Tensor] | None = None + ema_update_every = int(os.environ.get("EMA_UPDATE_EVERY", "10")) + if args.ema_enabled: + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_pairs = [(ema_state[name], param) for name, param in base_model.named_parameters()] + for name, buf in base_model.named_buffers(): + if name in ema_state: ema_pairs.append((ema_state[name], buf)) + ema_decay_eff = args.ema_decay ** ema_update_every + log0(f"ema:initialized decay={args.ema_decay} update_every={ema_update_every} decay_eff={ema_decay_eff:.6f}") + ema_stream = torch.cuda.Stream() if ema_pairs is not None else None + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val(args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms") + torch.cuda.synchronize(); t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}") + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + for micro_step in range(grad_accum_steps): + x, y = next_xy + if micro_step < grad_accum_steps - 1: + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + # Meta-TTT: FOMAML inner/outer loop on the last micro-batch + # Disabled during warmdown (scale < 0.5) — model should converge, not explore + if args.meta_ttt_enabled and step % args.meta_ttt_every == 0 and scale > 0.5: + meta_outer = meta_ttt_step(base_model, x, y, args, grad_scale=grad_scale) + if args.adaptive_warmdown and _warmdown_triggered_ms is None and step >= args.adaptive_warmdown_min_steps and step % 100 == 0: + tl = train_loss.item() + if _loss_ema is None: + _loss_ema = tl; _loss_ema_prev = tl + else: + _loss_ema = args.adaptive_warmdown_ema * _loss_ema + (1.0 - args.adaptive_warmdown_ema) * tl + improvement = (_loss_ema_prev - _loss_ema) if _loss_ema_prev is not None else 1.0 + if improvement < args.adaptive_warmdown_threshold: + elapsed_now = training_time_ms + 1000.0 * (time.perf_counter() - t0) + _warmdown_triggered_ms = elapsed_now + log0(f"adaptive_warmdown:triggered step:{step} loss_ema:{_loss_ema:.6f} improvement:{improvement:.6f}") + _loss_ema_prev = _loss_ema + if step < args.muon_momentum_warmup_steps: + frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + elif args.momentum_cyclic: + period = args.momentum_cycle_period * 2 + pos = (step % period) / period + if pos < 0.5: + muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2) + else: + muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2) + else: + muon_momentum = args.muon_momentum + for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + optimizer_muon.launch_reduce_scatters() + if distributed: + for p in replicated_params: + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step(); optimizer_scalar.step() + if optimizer_head is not None: optimizer_head.step() + optimizer_muon.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if ema_pairs is not None and step % ema_update_every == 0: + ema_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ema_stream), torch.no_grad(): + for ema_t, param_t in ema_pairs: + ema_t.mul_(ema_decay_eff).add_(param_t.detach().float(), alpha=1.0 - ema_decay_eff) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1; log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + if should_log_train: + log0(f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms") + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: stop_after_step = step + log0(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB") + if ema_stream is not None: ema_stream.synchronize() + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif ema_state is not None: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize(); t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val(args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + torch.cuda.synchronize() + log0(f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms") + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + exp_name = args.run_id.rsplit("_seed", 1)[0] if "_seed" in args.run_id else args.run_id + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + hessian_model._has_leading_space = has_leading_space_lut + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(hessian_model) + hessian_model.load_state_dict({k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, strict=False) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib(base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens; del hessian_model; torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + return len(lzma.compress(buf.getvalue(), preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} +/-1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full +/-1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} +/-1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: dist.barrier() + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model._has_leading_space = has_leading_space_lut + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize(); t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val(args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len) + torch.cuda.synchronize() + log0(f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms") + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # TTT-only evaluation (no standalone sliding window — meta-TTT model is designed for TTT) + if args.ttt_enabled and args.eval_stride > 0: + log0(f"\n{'='*60}", flush=True) + log0(f"STARTING TTT (Test-Time Training)", flush=True) + log0(f"{'='*60}", flush=True) + eval_model.load_state_dict(deq_state, strict=True) + ttt_loss, ttt_bpb = eval_val_sliding_ttt(args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0) + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f}", flush=True) + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}", flush=True) + if distributed: dist.destroy_process_group() + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/training_stdout_seed42.txt b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/training_stdout_seed42.txt new file mode 100644 index 0000000000..ef4565dbc6 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/ablation_exp105a/training_stdout_seed42.txt @@ -0,0 +1,4822 @@ +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1"))) + momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85)) + momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95)) + momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 128)) + + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + + # EMA + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + + # SWA / LAWA (from remote) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + + # Adaptive warmdown + adaptive_warmdown = bool(int(os.environ.get("ADAPTIVE_WARMDOWN", "1"))) + adaptive_warmdown_ema = float(os.environ.get("ADAPTIVE_WARMDOWN_EMA", "0.99")) + adaptive_warmdown_threshold = float(os.environ.get("ADAPTIVE_WARMDOWN_THRESHOLD", "0.0005")) + adaptive_warmdown_min_steps = int(os.environ.get("ADAPTIVE_WARMDOWN_MIN_STEPS", "2000")) + + # Partial RoPE + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN scale and DTG + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + + # QAT + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + + # Value Embedding + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + + # Gated attention / Value residual + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + + # TTT (Test-Time Training) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lr = float(os.environ.get("TTT_LR", "0.002")) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", "3")) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", "0.9")) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", "16")) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) + + # Meta-TTT (FOMAML: train-time meta-learning for better TTT initialization) + meta_ttt_enabled = bool(int(os.environ.get("META_TTT_ENABLED", "1"))) + meta_ttt_inner_lr = float(os.environ.get("META_TTT_INNER_LR", "0.002")) + meta_ttt_every = int(os.environ.get("META_TTT_EVERY", "8")) + meta_ttt_loss_weight = float(os.environ.get("META_TTT_LOSS_WEIGHT", "0.5")) + meta_ttt_freeze_blocks = int(os.environ.get("META_TTT_FREEZE_BLOCKS", "2")) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for seq_len={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,bigram.scale,word_start_boost,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + """Hash-based n-gram embedding table. + + exp101 additions: + - pos_conditional: split buckets into two disjoint halves keyed on whether + the CURRENT token is a word-start. ws-current pairs hash into the lower + half [0, half), non-ws-current pairs into the upper half [half, 2*half). + Motivation: in exp95 the single shared table was dominated by within-word + (prev, curr) pairs, and the word_start_boost scalar collapsed to ~0.007 + (killing bigram at word-starts to suppress the noise). Splitting the + buckets gives word-start pairs their own exclusive rows that can learn + their own signal without contaminating within-word buckets, and vice + versa. Zero extra parameters — same 4096×dim table, different layout. + - trigram: optional (t-2, t-1, t) lookup summed into the same table. When + combined with pos_conditional, the trigram hash respects the same split + (keyed on whether t is a word-start) so a bucket is only trained by + lookups of a consistent word-start class. + """ + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, + trigram: bool = False, pos_conditional: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self._pos_conditional = pos_conditional + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # sentinel row at index mod + out = torch.empty_like(t) + out[..., 0] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 for bigram_vocab_size=4096 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws-current, half for non-ws-current + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def trigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 + base = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % half + is_ws_curr = has_leading_space[tokens[..., 2:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half + out[..., 2:] = base + shift + else: + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + + def forward(self, token_ids: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + h = self.embed(self.bigram_hash(token_ids, has_leading_space)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids, has_leading_space)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + # Block 0: init attn_scale to 0.1 (analysis shows it converges to ~0.094 anyway) + attn_init = 0.1 if layer_idx == 0 else 1.0 + self.attn_scale = nn.Parameter(torch.full((dim,), attn_init, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, dtg=dtg, + gated_attention=gated_attention, value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward_with_banks(self, input_ids: Tensor, target_ids: Tensor, + qo_bank: Tensor, kv_bank: Tensor, + mlp_up_bank: Tensor, mlp_down_bank: Tensor) -> Tensor: + """Forward with external bank tensors (for meta-TTT). Pure next-token loss, no MTP.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + qo_bank[i], kv_bank[i], kv_bank[n + i], + qo_bank[n + i], mlp_up_bank[i], mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + qo_bank[bi], kv_bank[bi], kv_bank[n + bi], + qo_bank[n + bi], mlp_up_bank[bi], mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +# --- TTT (Test-Time Training) --- + +def eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + eval_batch = args.eval_batch_seqs + train_batch = args.ttt_batch_seqs + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + frozen_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_n = sum(p.numel() for p in ttt_params) + frozen_n = sum(p.numel() for p in base_model.parameters() if not p.requires_grad) + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}", flush=True) + log0(f"ttt_sliding:params unfrozen={unfrozen_n} frozen={frozen_n}", flush=True) + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), eval_batch): + batch_ws = my_windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + is_last = (ci == num_chunks - 1) + if not is_last and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, train_batch): + be = min(bs + train_batch, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + total_w = len(window_starts) + done_w = sum(len(chunk_windows[c]) for c in range(ci + 1)) + pct = done_w / total_w * 100 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + eta = (elapsed / max(done_w, 1)) * (total_w - done_w) + bar_len = 30 + filled = int(bar_len * done_w / total_w) + bar = "\u2588" * filled + "\u2591" * (bar_len - filled) + log0(f" ttt [{bar}] {pct:5.1f}% chunk {ci+1}/{num_chunks} bpb={rbpb:.6f} ETA={eta:.0f}s", flush=True) + if rank == 0: + log0("", flush=True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s", flush=True) + return val_loss, val_bpb + +# --- GPTQ quantization pipeline --- + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: qo_slices[i] = sd[qk]; consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: qo_slices[n + i] = sd[ok]; consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: kv_slices[i] = sd[kk]; consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: kv_slices[n + i] = sd[vk]; consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: up_slices[i] = sd[fk]; consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: down_slices[i] = sd[dk]; consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +class _HessianAttn(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)); k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm(); self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: return None + if 've' not in ve_cache: ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x; skips = []; ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve); skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)); targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + hessians = {}; hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)); hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: h.remove() + for name in hessians: + H = hessians[name]; H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]); hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough"; continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float(); meta[name] = "passthrough_ctrl"; continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t; continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Meta-TTT (FOMAML) --- + +def meta_ttt_step(base_model: nn.Module, x: Tensor, y: Tensor, + args, grad_scale: float = 1.0) -> Tensor: + """FOMAML meta-TTT step: adapt on first half of sequence, evaluate on second half. + + Inner loop: 1-step SGD on bank params using chunk_A (detached, no second-order). + Outer loop: evaluate adapted model on chunk_B. + Gradients accumulate on base_model parameters for the optimizer to consume. + Runs uncompiled (forward_with_banks) to avoid recompilation from new bank tensors. + """ + seq_len = x.shape[1] + half = seq_len // 2 + x_inner, y_inner = x[:, :half], y[:, :half] + x_outer, y_outer = x[:, half:], y[:, half:] + + n = base_model.num_layers + freeze_n = args.meta_ttt_freeze_blocks + lr = args.meta_ttt_inner_lr + + # --- Inner loop: clone banks, compute grads, 1-step SGD --- + qo = base_model.qo_bank.detach().clone().requires_grad_(True) + kv = base_model.kv_bank.detach().clone().requires_grad_(True) + up = base_model.mlp_up_bank.detach().clone().requires_grad_(True) + down = base_model.mlp_down_bank.detach().clone().requires_grad_(True) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + inner_loss = base_model.forward_with_banks(x_inner, y_inner, qo, kv, up, down) + + g_qo, g_kv, g_up, g_down = torch.autograd.grad(inner_loss, [qo, kv, up, down]) + + # Apply freeze mask: zero gradients for frozen blocks + if freeze_n > 0: + with torch.no_grad(): + g_qo = g_qo.clone() + g_kv = g_kv.clone() + g_up = g_up.clone() + g_down = g_down.clone() + for bi in range(freeze_n): + g_qo[bi].zero_(); g_qo[n + bi].zero_() + g_kv[bi].zero_(); g_kv[n + bi].zero_() + g_up[bi].zero_() + g_down[bi].zero_() + + # 1-step SGD update (FOMAML: detach to drop second-order) + with torch.no_grad(): + qo_upd = (qo - lr * g_qo).requires_grad_(True) + kv_upd = (kv - lr * g_kv).requires_grad_(True) + up_upd = (up - lr * g_up).requires_grad_(True) + down_upd = (down - lr * g_down).requires_grad_(True) + + # --- Outer loop: evaluate adapted model on chunk_B --- + # Non-bank params (embeddings, norms, scales) are LIVE — grads flow to them directly + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + outer_loss = base_model.forward_with_banks( + x_outer, y_outer, qo_upd, kv_upd, up_upd, down_upd) + + scaled = outer_loss * args.meta_ttt_loss_weight * grad_scale + scaled.backward() + + # FOMAML: copy adapted-point gradients onto original bank params + with torch.no_grad(): + for bank, upd in [(base_model.qo_bank, qo_upd), + (base_model.kv_bank, kv_upd), + (base_model.mlp_up_bank, up_upd), + (base_model.mlp_down_bank, down_upd)]: + if upd.grad is not None: + if bank.grad is None: + bank.grad = upd.grad.to(bank.dtype) + else: + bank.grad.add_(upd.grad.to(bank.dtype)) + + return outer_loss.detach() + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + total_accum = int(os.environ.get("GRAD_ACCUM_TOTAL", "8")) + if total_accum % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide GRAD_ACCUM_TOTAL={total_accum}") + grad_accum_steps = total_accum // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False); enable_flash_sdp(True); enable_mem_efficient_sdp(False); enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True, flush: bool = False) -> None: + if not master_process: return + if console: print(msg, flush=flush) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f, flush=True) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0(subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, console=False) + log0("=" * 100, console=False) + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError(f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}") + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + dtg=args.dtg_enabled, ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + base_model._has_leading_space = has_leading_space_lut + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + # Freeze dead skip connections: B0→B9 (row 4) and B1→B8 (row 3) are near-zero + with torch.no_grad(): + if base_model.skip_weights.shape[0] >= 5: + base_model.skip_weights.data[3].zero_() # B1→B8 + base_model.skip_weights.data[4].zero_() # B0→B9 + _skip_freeze_mask = torch.ones_like(base_model.skip_weights.data) + if base_model.skip_weights.shape[0] >= 5: + _skip_freeze_mask[3].zero_() + _skip_freeze_mask[4].zero_() + base_model.skip_weights.register_hook(lambda grad: grad * _skip_freeze_mask) + matrix_params = [base_model.qo_bank, base_model.kv_bank, base_model.mlp_up_bank, base_model.mlp_down_bank] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)] + if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: scalar_params.append(base_model.bigram.scale) + if base_model.word_start_boost is not None: scalar_params.append(base_model.word_start_boost) + if base_model.ve_shared is not None: + if base_model.ve_shared.proj is not None: scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + optimizer_tok = torch.optim.AdamW(tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + optimizer_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, weight_decay=args.muon_wd) + for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW([{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam([{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + _warmdown_triggered_ms: float | None = None + _loss_ema: float | None = None + _loss_ema_prev: float | None = None + def lr_mul(step: int, elapsed_ms: float) -> float: + nonlocal _warmdown_triggered_ms + if args.warmdown_iters <= 0: return 1.0 + # Guard: don't compute warmdown in early steps where step_ms estimate is unreliable + if step < args.warmup_steps + 5: return 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_duration_ms = args.warmdown_iters * step_ms + if _warmdown_triggered_ms is not None: + warmdown_start_ms = _warmdown_triggered_ms + elif max_wallclock_ms is not None: + warmdown_start_ms = max(max_wallclock_ms - warmdown_duration_ms, 0.0) + else: + warmdown_start_ms = max(args.iterations - args.warmdown_iters, 0) * step_ms + if elapsed_ms >= warmdown_start_ms: + total_warmdown_ms = (max_wallclock_ms - warmdown_start_ms) if max_wallclock_ms else warmdown_duration_ms + progress_ms = elapsed_ms - warmdown_start_ms + progress = min(progress_ms / max(total_warmdown_ms, 1e-9), 1.0) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + return 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + if distributed: + for p in base_model.parameters(): + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_pairs: list[tuple[Tensor, Tensor]] | None = None + ema_state: dict[str, Tensor] | None = None + ema_update_every = int(os.environ.get("EMA_UPDATE_EVERY", "10")) + if args.ema_enabled: + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_pairs = [(ema_state[name], param) for name, param in base_model.named_parameters()] + for name, buf in base_model.named_buffers(): + if name in ema_state: ema_pairs.append((ema_state[name], buf)) + ema_decay_eff = args.ema_decay ** ema_update_every + log0(f"ema:initialized decay={args.ema_decay} update_every={ema_update_every} decay_eff={ema_decay_eff:.6f}") + ema_stream = torch.cuda.Stream() if ema_pairs is not None else None + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val(args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms") + torch.cuda.synchronize(); t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}") + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + for micro_step in range(grad_accum_steps): + x, y = next_xy + if micro_step < grad_accum_steps - 1: + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + # Meta-TTT: FOMAML inner/outer loop on the last micro-batch + # Disabled during warmdown (scale < 0.5) — model should converge, not explore + if args.meta_ttt_enabled and step % args.meta_ttt_every == 0 and scale > 0.5: + meta_outer = meta_ttt_step(base_model, x, y, args, grad_scale=grad_scale) + if args.adaptive_warmdown and _warmdown_triggered_ms is None and step >= args.adaptive_warmdown_min_steps and step % 100 == 0: + tl = train_loss.item() + if _loss_ema is None: + _loss_ema = tl; _loss_ema_prev = tl + else: + _loss_ema = args.adaptive_warmdown_ema * _loss_ema + (1.0 - args.adaptive_warmdown_ema) * tl + improvement = (_loss_ema_prev - _loss_ema) if _loss_ema_prev is not None else 1.0 + if improvement < args.adaptive_warmdown_threshold: + elapsed_now = training_time_ms + 1000.0 * (time.perf_counter() - t0) + _warmdown_triggered_ms = elapsed_now + log0(f"adaptive_warmdown:triggered step:{step} loss_ema:{_loss_ema:.6f} improvement:{improvement:.6f}") + _loss_ema_prev = _loss_ema + if step < args.muon_momentum_warmup_steps: + frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + elif args.momentum_cyclic: + period = args.momentum_cycle_period * 2 + pos = (step % period) / period + if pos < 0.5: + muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2) + else: + muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2) + else: + muon_momentum = args.muon_momentum + for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + optimizer_muon.launch_reduce_scatters() + if distributed: + for p in replicated_params: + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step(); optimizer_scalar.step() + if optimizer_head is not None: optimizer_head.step() + optimizer_muon.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if ema_pairs is not None and step % ema_update_every == 0: + ema_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ema_stream), torch.no_grad(): + for ema_t, param_t in ema_pairs: + ema_t.mul_(ema_decay_eff).add_(param_t.detach().float(), alpha=1.0 - ema_decay_eff) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1; log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + if should_log_train: + log0(f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms") + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: stop_after_step = step + log0(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB") + if ema_stream is not None: ema_stream.synchronize() + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif ema_state is not None: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize(); t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val(args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + torch.cuda.synchronize() + log0(f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms") + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + exp_name = args.run_id.rsplit("_seed", 1)[0] if "_seed" in args.run_id else args.run_id + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + hessian_model._has_leading_space = has_leading_space_lut + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(hessian_model) + hessian_model.load_state_dict({k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, strict=False) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib(base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens; del hessian_model; torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + return len(lzma.compress(buf.getvalue(), preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} +/-1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full +/-1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} +/-1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: dist.barrier() + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model._has_leading_space = has_leading_space_lut + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize(); t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val(args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len) + torch.cuda.synchronize() + log0(f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms") + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # TTT-only evaluation (no standalone sliding window — meta-TTT model is designed for TTT) + if args.ttt_enabled and args.eval_stride > 0: + log0(f"\n{'='*60}", flush=True) + log0(f"STARTING TTT (Test-Time Training)", flush=True) + log0(f"{'='*60}", flush=True) + eval_model.load_state_dict(deq_state, strict=True) + ttt_loss, ttt_bpb = eval_val_sliding_ttt(args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0) + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f}", flush=True) + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}", flush=True) + if distributed: dist.destroy_process_group() + +if __name__ == "__main__": + main() + +==================================================================================================== +Running Python 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] +Running PyTorch 2.9.1+cu128 +Wed Apr 8 16:38:53 2026 ++-----------------------------------------------------------------------------------------+ +| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 | ++-----------------------------------------+------------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+========================+======================| +| 0 NVIDIA H100 80GB HBM3 On | 00000000:9B:00.0 Off | 0 | +| N/A 39C P0 83W / 700W | 527MiB / 81559MiB | 0% Default | +| | | Disabled | ++-----------------------------------------+------------------------+----------------------+ + ++-----------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=========================================================================================| +| No running processes found | ++-----------------------------------------------------------------------------------------+ + +==================================================================================================== +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26960991 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:1 grad_accum_steps:8 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +ema:initialized decay=0.998 update_every=10 decay_eff=0.980179 +step:0/9000 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.02ms +step:1/9000 train_loss:6.9298 train_time:658ms step_avg:657.59ms +step:2/9000 train_loss:8.3907 train_time:1242ms step_avg:620.97ms +step:3/9000 train_loss:7.4660 train_time:1906ms step_avg:635.20ms +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1"))) + momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85)) + momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95)) + momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 128)) + + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + + # EMA + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + + # SWA / LAWA (from remote) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + + # Adaptive warmdown + adaptive_warmdown = bool(int(os.environ.get("ADAPTIVE_WARMDOWN", "1"))) + adaptive_warmdown_ema = float(os.environ.get("ADAPTIVE_WARMDOWN_EMA", "0.99")) + adaptive_warmdown_threshold = float(os.environ.get("ADAPTIVE_WARMDOWN_THRESHOLD", "0.0005")) + adaptive_warmdown_min_steps = int(os.environ.get("ADAPTIVE_WARMDOWN_MIN_STEPS", "2000")) + + # Partial RoPE + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN scale and DTG + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + + # QAT + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + + # Value Embedding + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + + # Gated attention / Value residual + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + + # TTT (Test-Time Training) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lr = float(os.environ.get("TTT_LR", "0.002")) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", "3")) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", "0.9")) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", "16")) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) + + # Meta-TTT (FOMAML: train-time meta-learning for better TTT initialization) + meta_ttt_enabled = bool(int(os.environ.get("META_TTT_ENABLED", "1"))) + meta_ttt_inner_lr = float(os.environ.get("META_TTT_INNER_LR", "0.002")) + meta_ttt_every = int(os.environ.get("META_TTT_EVERY", "8")) + meta_ttt_loss_weight = float(os.environ.get("META_TTT_LOSS_WEIGHT", "0.5")) + meta_ttt_freeze_blocks = int(os.environ.get("META_TTT_FREEZE_BLOCKS", "2")) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for seq_len={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,bigram.scale,word_start_boost,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + """Hash-based n-gram embedding table. + + exp101 additions: + - pos_conditional: split buckets into two disjoint halves keyed on whether + the CURRENT token is a word-start. ws-current pairs hash into the lower + half [0, half), non-ws-current pairs into the upper half [half, 2*half). + Motivation: in exp95 the single shared table was dominated by within-word + (prev, curr) pairs, and the word_start_boost scalar collapsed to ~0.007 + (killing bigram at word-starts to suppress the noise). Splitting the + buckets gives word-start pairs their own exclusive rows that can learn + their own signal without contaminating within-word buckets, and vice + versa. Zero extra parameters — same 4096×dim table, different layout. + - trigram: optional (t-2, t-1, t) lookup summed into the same table. When + combined with pos_conditional, the trigram hash respects the same split + (keyed on whether t is a word-start) so a bucket is only trained by + lookups of a consistent word-start class. + """ + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, + trigram: bool = False, pos_conditional: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self._pos_conditional = pos_conditional + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # sentinel row at index mod + out = torch.empty_like(t) + out[..., 0] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 for bigram_vocab_size=4096 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws-current, half for non-ws-current + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def trigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 + base = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % half + is_ws_curr = has_leading_space[tokens[..., 2:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half + out[..., 2:] = base + shift + else: + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + + def forward(self, token_ids: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + h = self.embed(self.bigram_hash(token_ids, has_leading_space)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids, has_leading_space)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + # Block 0: init attn_scale to 0.1 (analysis shows it converges to ~0.094 anyway) + attn_init = 0.1 if layer_idx == 0 else 1.0 + self.attn_scale = nn.Parameter(torch.full((dim,), attn_init, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, dtg=dtg, + gated_attention=gated_attention, value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward_with_banks(self, input_ids: Tensor, target_ids: Tensor, + qo_bank: Tensor, kv_bank: Tensor, + mlp_up_bank: Tensor, mlp_down_bank: Tensor) -> Tensor: + """Forward with external bank tensors (for meta-TTT). Pure next-token loss, no MTP.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + qo_bank[i], kv_bank[i], kv_bank[n + i], + qo_bank[n + i], mlp_up_bank[i], mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + qo_bank[bi], kv_bank[bi], kv_bank[n + bi], + qo_bank[n + bi], mlp_up_bank[bi], mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +# --- TTT (Test-Time Training) --- + +def eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + eval_batch = args.eval_batch_seqs + train_batch = args.ttt_batch_seqs + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + frozen_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_n = sum(p.numel() for p in ttt_params) + frozen_n = sum(p.numel() for p in base_model.parameters() if not p.requires_grad) + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}", flush=True) + log0(f"ttt_sliding:params unfrozen={unfrozen_n} frozen={frozen_n}", flush=True) + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), eval_batch): + batch_ws = my_windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + is_last = (ci == num_chunks - 1) + if not is_last and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, train_batch): + be = min(bs + train_batch, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + total_w = len(window_starts) + done_w = sum(len(chunk_windows[c]) for c in range(ci + 1)) + pct = done_w / total_w * 100 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + eta = (elapsed / max(done_w, 1)) * (total_w - done_w) + bar_len = 30 + filled = int(bar_len * done_w / total_w) + bar = "\u2588" * filled + "\u2591" * (bar_len - filled) + log0(f" ttt [{bar}] {pct:5.1f}% chunk {ci+1}/{num_chunks} bpb={rbpb:.6f} ETA={eta:.0f}s", flush=True) + if rank == 0: + log0("", flush=True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s", flush=True) + return val_loss, val_bpb + +# --- GPTQ quantization pipeline --- + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: qo_slices[i] = sd[qk]; consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: qo_slices[n + i] = sd[ok]; consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: kv_slices[i] = sd[kk]; consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: kv_slices[n + i] = sd[vk]; consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: up_slices[i] = sd[fk]; consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: down_slices[i] = sd[dk]; consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +class _HessianAttn(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)); k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm(); self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: return None + if 've' not in ve_cache: ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x; skips = []; ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve); skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)); targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + hessians = {}; hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)); hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: h.remove() + for name in hessians: + H = hessians[name]; H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]); hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough"; continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float(); meta[name] = "passthrough_ctrl"; continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t; continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Meta-TTT (FOMAML) --- + +def meta_ttt_step(base_model: nn.Module, x: Tensor, y: Tensor, + args, grad_scale: float = 1.0) -> Tensor: + """FOMAML meta-TTT step: adapt on first half of sequence, evaluate on second half. + + Inner loop: 1-step SGD on bank params using chunk_A (detached, no second-order). + Outer loop: evaluate adapted model on chunk_B. + Gradients accumulate on base_model parameters for the optimizer to consume. + Runs uncompiled (forward_with_banks) to avoid recompilation from new bank tensors. + """ + seq_len = x.shape[1] + half = seq_len // 2 + x_inner, y_inner = x[:, :half], y[:, :half] + x_outer, y_outer = x[:, half:], y[:, half:] + + n = base_model.num_layers + freeze_n = args.meta_ttt_freeze_blocks + lr = args.meta_ttt_inner_lr + + # --- Inner loop: clone banks, compute grads, 1-step SGD --- + qo = base_model.qo_bank.detach().clone().requires_grad_(True) + kv = base_model.kv_bank.detach().clone().requires_grad_(True) + up = base_model.mlp_up_bank.detach().clone().requires_grad_(True) + down = base_model.mlp_down_bank.detach().clone().requires_grad_(True) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + inner_loss = base_model.forward_with_banks(x_inner, y_inner, qo, kv, up, down) + + g_qo, g_kv, g_up, g_down = torch.autograd.grad(inner_loss, [qo, kv, up, down]) + + # Apply freeze mask: zero gradients for frozen blocks + if freeze_n > 0: + with torch.no_grad(): + g_qo = g_qo.clone() + g_kv = g_kv.clone() + g_up = g_up.clone() + g_down = g_down.clone() + for bi in range(freeze_n): + g_qo[bi].zero_(); g_qo[n + bi].zero_() + g_kv[bi].zero_(); g_kv[n + bi].zero_() + g_up[bi].zero_() + g_down[bi].zero_() + + # 1-step SGD update (FOMAML: detach to drop second-order) + with torch.no_grad(): + qo_upd = (qo - lr * g_qo).requires_grad_(True) + kv_upd = (kv - lr * g_kv).requires_grad_(True) + up_upd = (up - lr * g_up).requires_grad_(True) + down_upd = (down - lr * g_down).requires_grad_(True) + + # --- Outer loop: evaluate adapted model on chunk_B --- + # Non-bank params (embeddings, norms, scales) are LIVE — grads flow to them directly + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + outer_loss = base_model.forward_with_banks( + x_outer, y_outer, qo_upd, kv_upd, up_upd, down_upd) + + scaled = outer_loss * args.meta_ttt_loss_weight * grad_scale + scaled.backward() + + # FOMAML: copy adapted-point gradients onto original bank params + with torch.no_grad(): + for bank, upd in [(base_model.qo_bank, qo_upd), + (base_model.kv_bank, kv_upd), + (base_model.mlp_up_bank, up_upd), + (base_model.mlp_down_bank, down_upd)]: + if upd.grad is not None: + if bank.grad is None: + bank.grad = upd.grad.to(bank.dtype) + else: + bank.grad.add_(upd.grad.to(bank.dtype)) + + return outer_loss.detach() + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + total_accum = int(os.environ.get("GRAD_ACCUM_TOTAL", "8")) + if total_accum % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide GRAD_ACCUM_TOTAL={total_accum}") + grad_accum_steps = total_accum // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False); enable_flash_sdp(True); enable_mem_efficient_sdp(False); enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True, flush: bool = False) -> None: + if not master_process: return + if console: print(msg, flush=flush) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f, flush=True) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0(subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, console=False) + log0("=" * 100, console=False) + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError(f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}") + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + dtg=args.dtg_enabled, ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + base_model._has_leading_space = has_leading_space_lut + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + # Freeze dead skip connections: B0→B9 (row 4) and B1→B8 (row 3) are near-zero + with torch.no_grad(): + if base_model.skip_weights.shape[0] >= 5: + base_model.skip_weights.data[3].zero_() # B1→B8 + base_model.skip_weights.data[4].zero_() # B0→B9 + _skip_freeze_mask = torch.ones_like(base_model.skip_weights.data) + if base_model.skip_weights.shape[0] >= 5: + _skip_freeze_mask[3].zero_() + _skip_freeze_mask[4].zero_() + base_model.skip_weights.register_hook(lambda grad: grad * _skip_freeze_mask) + matrix_params = [base_model.qo_bank, base_model.kv_bank, base_model.mlp_up_bank, base_model.mlp_down_bank] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)] + if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: scalar_params.append(base_model.bigram.scale) + if base_model.word_start_boost is not None: scalar_params.append(base_model.word_start_boost) + if base_model.ve_shared is not None: + if base_model.ve_shared.proj is not None: scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + optimizer_tok = torch.optim.AdamW(tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + optimizer_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, weight_decay=args.muon_wd) + for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW([{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam([{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + _warmdown_triggered_ms: float | None = None + _loss_ema: float | None = None + _loss_ema_prev: float | None = None + def lr_mul(step: int, elapsed_ms: float) -> float: + nonlocal _warmdown_triggered_ms + if args.warmdown_iters <= 0: return 1.0 + # Guard: don't compute warmdown in early steps where step_ms estimate is unreliable + if step < args.warmup_steps + 5: return 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_duration_ms = args.warmdown_iters * step_ms + if _warmdown_triggered_ms is not None: + warmdown_start_ms = _warmdown_triggered_ms + elif max_wallclock_ms is not None: + warmdown_start_ms = max(max_wallclock_ms - warmdown_duration_ms, 0.0) + else: + warmdown_start_ms = max(args.iterations - args.warmdown_iters, 0) * step_ms + if elapsed_ms >= warmdown_start_ms: + total_warmdown_ms = (max_wallclock_ms - warmdown_start_ms) if max_wallclock_ms else warmdown_duration_ms + progress_ms = elapsed_ms - warmdown_start_ms + progress = min(progress_ms / max(total_warmdown_ms, 1e-9), 1.0) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + return 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + if distributed: + for p in base_model.parameters(): + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_pairs: list[tuple[Tensor, Tensor]] | None = None + ema_state: dict[str, Tensor] | None = None + ema_update_every = int(os.environ.get("EMA_UPDATE_EVERY", "10")) + if args.ema_enabled: + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_pairs = [(ema_state[name], param) for name, param in base_model.named_parameters()] + for name, buf in base_model.named_buffers(): + if name in ema_state: ema_pairs.append((ema_state[name], buf)) + ema_decay_eff = args.ema_decay ** ema_update_every + log0(f"ema:initialized decay={args.ema_decay} update_every={ema_update_every} decay_eff={ema_decay_eff:.6f}") + ema_stream = torch.cuda.Stream() if ema_pairs is not None else None + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val(args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms") + torch.cuda.synchronize(); t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}") + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + for micro_step in range(grad_accum_steps): + x, y = next_xy + if micro_step < grad_accum_steps - 1: + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + # Meta-TTT: FOMAML inner/outer loop on the last micro-batch + # Disabled during warmdown (scale < 0.5) — model should converge, not explore + if args.meta_ttt_enabled and step % args.meta_ttt_every == 0 and scale > 0.5: + meta_outer = meta_ttt_step(base_model, x, y, args, grad_scale=grad_scale) + if args.adaptive_warmdown and _warmdown_triggered_ms is None and step >= args.adaptive_warmdown_min_steps and step % 100 == 0: + tl = train_loss.item() + if _loss_ema is None: + _loss_ema = tl; _loss_ema_prev = tl + else: + _loss_ema = args.adaptive_warmdown_ema * _loss_ema + (1.0 - args.adaptive_warmdown_ema) * tl + improvement = (_loss_ema_prev - _loss_ema) if _loss_ema_prev is not None else 1.0 + if improvement < args.adaptive_warmdown_threshold: + elapsed_now = training_time_ms + 1000.0 * (time.perf_counter() - t0) + _warmdown_triggered_ms = elapsed_now + log0(f"adaptive_warmdown:triggered step:{step} loss_ema:{_loss_ema:.6f} improvement:{improvement:.6f}") + _loss_ema_prev = _loss_ema + if step < args.muon_momentum_warmup_steps: + frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + elif args.momentum_cyclic: + period = args.momentum_cycle_period * 2 + pos = (step % period) / period + if pos < 0.5: + muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2) + else: + muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2) + else: + muon_momentum = args.muon_momentum + for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + optimizer_muon.launch_reduce_scatters() + if distributed: + for p in replicated_params: + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step(); optimizer_scalar.step() + if optimizer_head is not None: optimizer_head.step() + optimizer_muon.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if ema_pairs is not None and step % ema_update_every == 0: + ema_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ema_stream), torch.no_grad(): + for ema_t, param_t in ema_pairs: + ema_t.mul_(ema_decay_eff).add_(param_t.detach().float(), alpha=1.0 - ema_decay_eff) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1; log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + if should_log_train: + log0(f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms") + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: stop_after_step = step + log0(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB") + if ema_stream is not None: ema_stream.synchronize() + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif ema_state is not None: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize(); t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val(args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + torch.cuda.synchronize() + log0(f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms") + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + exp_name = args.run_id.rsplit("_seed", 1)[0] if "_seed" in args.run_id else args.run_id + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + hessian_model._has_leading_space = has_leading_space_lut + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(hessian_model) + hessian_model.load_state_dict({k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, strict=False) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib(base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens; del hessian_model; torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + return len(lzma.compress(buf.getvalue(), preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} +/-1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full +/-1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} +/-1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: dist.barrier() + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model._has_leading_space = has_leading_space_lut + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize(); t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val(args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len) + torch.cuda.synchronize() + log0(f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms") + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # TTT-only evaluation (no standalone sliding window — meta-TTT model is designed for TTT) + if args.ttt_enabled and args.eval_stride > 0: + log0(f"\n{'='*60}", flush=True) + log0(f"STARTING TTT (Test-Time Training)", flush=True) + log0(f"{'='*60}", flush=True) + eval_model.load_state_dict(deq_state, strict=True) + ttt_loss, ttt_bpb = eval_val_sliding_ttt(args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0) + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f}", flush=True) + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}", flush=True) + if distributed: dist.destroy_process_group() + +if __name__ == "__main__": + main() + +==================================================================================================== +Running Python 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] +Running PyTorch 2.9.1+cu128 +Wed Apr 8 16:41:35 2026 ++-----------------------------------------------------------------------------------------+ +| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 | ++-----------------------------------------+------------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+========================+======================| +| 0 NVIDIA H100 80GB HBM3 On | 00000000:9B:00.0 Off | 0 | +| N/A 41C P0 85W / 700W | 527MiB / 81559MiB | 2% Default | +| | | Disabled | ++-----------------------------------------+------------------------+----------------------+ + ++-----------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=========================================================================================| +| No running processes found | ++-----------------------------------------------------------------------------------------+ + +==================================================================================================== +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26960991 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:1 grad_accum_steps:8 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +ema:initialized decay=0.998 update_every=10 decay_eff=0.980179 +step:0/7500 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.01ms +step:1/7500 train_loss:6.9298 train_time:658ms step_avg:658.09ms +step:2/7500 train_loss:8.3907 train_time:1245ms step_avg:622.73ms +step:3/7500 train_loss:7.4660 train_time:1910ms step_avg:636.60ms +step:4/7500 train_loss:7.6125 train_time:2564ms step_avg:640.92ms +step:5/7500 train_loss:7.4386 train_time:3219ms step_avg:643.83ms +step:6/7500 train_loss:7.1132 train_time:3878ms step_avg:646.40ms +step:7/7500 train_loss:6.7981 train_time:4534ms step_avg:647.71ms +step:8/7500 train_loss:6.6367 train_time:5193ms step_avg:649.12ms +step:9/7500 train_loss:6.4074 train_time:5892ms step_avg:654.64ms +step:10/7500 train_loss:6.0814 train_time:6548ms step_avg:654.84ms +step:500/7500 train_loss:2.3127 train_time:331074ms step_avg:662.15ms +step:1000/7500 train_loss:2.2630 train_time:662462ms step_avg:662.46ms +step:1500/7500 train_loss:2.1337 train_time:993886ms step_avg:662.59ms +step:2000/7500 train_loss:2.0518 train_time:1325657ms step_avg:662.83ms +adaptive_warmdown:triggered step:2200 loss_ema:2.114333 improvement:-0.000150 +step:2500/7500 train_loss:2.0959 train_time:1657815ms step_avg:663.13ms +step:3000/7500 train_loss:2.0748 train_time:1989567ms step_avg:663.19ms +step:3000/7500 val_loss:2.0708 val_bpb:1.2264 train_time:1989632ms step_avg:663.21ms +step:3500/7500 train_loss:2.0620 train_time:2321048ms step_avg:663.16ms +step:4000/7500 train_loss:2.1287 train_time:2652783ms step_avg:663.20ms +step:4500/7500 train_loss:2.1168 train_time:2984973ms step_avg:663.33ms +step:5000/7500 train_loss:2.0216 train_time:3317171ms step_avg:663.43ms +step:5500/7500 train_loss:2.0182 train_time:3649138ms step_avg:663.48ms +late_qat:enabled step:5557 scale:0.2498 +swa:start step:5750 +step:6000/7500 train_loss:1.9160 train_time:3982032ms step_avg:663.67ms +step:6000/7500 val_loss:1.9457 val_bpb:1.1524 train_time:3982237ms step_avg:663.71ms +step:6500/7500 train_loss:2.0219 train_time:4314994ms step_avg:663.85ms +step:7000/7500 train_loss:1.8349 train_time:4648522ms step_avg:664.07ms +step:7226/7500 val_loss:1.9166 val_bpb:1.1351 train_time:4800153ms step_avg:664.29ms +stopping_early: wallclock_cap train_time:4800153ms step:7226/7500 +peak memory allocated: 23043 MiB reserved: 23204 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9170 val_bpb:1.1353 eval_time:17445ms +Serialized model: 106028345 bytes +Code size: 115044 bytes +gptq:building non-banked model for Hessian collection... +gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... +gptq:generated 64 sequences in 214.4s +gptq:collecting hessians from autoregressive data... +gptq:collected hessians for 68 layers (AR self-gen) +selective_prune: 4202203 +/-1 candidates, unpruned=15.04MB target=15.9MB +selective_prune: already fits, no pruning needed +Serialized model int6+lzma: 15659520 bytes +Total submission size int6+lzma: 15774564 bytes +final_int6_roundtrip val_loss:1.9241 val_bpb:1.1396 eval_time:32495ms +final_int6_roundtrip_exact val_loss:1.92409196 val_bpb:1.13955564 + +============================================================ +STARTING TTT 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−0.02342 bpb + +This PR contains two experiments that form a complete unit: a **record submission** +(exp101) introducing position-conditional bigram hashing, and its **controlled +ablation** (exp105a) proving that the inherited FOMAML meta-TTT contributes +near-zero to the result. + +**See also**: [PR 2/2 — Meta-TTT Redesign (exp106)](../pr2_metattt_redesign/pull_summary.md), +which builds on the ablation finding here and attempts a theoretically-grounded +redesign of the meta-TTT training loop. + +--- + +## TL;DR — Key Learnings for the Community + +1. **Position-conditional bigram hashing** is a zero-parameter trick that improves + legal_ttt by 0.001 bpb. If your model uses hash-based n-gram embeddings, check + whether different token classes (e.g., word-start vs within-word) are colliding + in the same buckets. Splitting the hash space by class can recover signal that + a shared hash was forced to suppress. + +2. **Always ablate inherited components.** We ran 100+ experiments inheriting FOMAML + meta-TTT from an early ancestor without ever isolating its contribution. A + single-flag ablation revealed it adds +0.00036 bpb (noise) at 3% compute cost. + Those 3% translated to 206 lost training steps under a wallclock cap — a net + negative. + +3. **Same-batch FOMAML meta-TTT is equivalent to gradient noise** in our setting. + It pushes the optimizer into a different local minimum (90-degree weight rotation) + but the new minimum has identical loss, identical TTT adaptation, and identical + quantization sensitivity. The rotation is a Muon optimizer artifact, not a + meaningful signal. + +4. **Weight-space cosine similarity is misleading under Muon.** Two models trained + from the same seed with a 3% gradient perturbation show element-wise cosine of + 0.05 (near-orthogonal) but principal-angle subspace cosine of 0.65 (partially + aligned). Use SVD-based subspace overlap for functional comparison, not raw + cosine. + +--- + +## Disclaimer + +- **Hardware**: All runs use a single H100 80 GB SXM GPU with `MAX_WALLCLOCK_SECONDS=4800` + (80-minute cap). This provides 4800 GPU-seconds of compute, matching the competition's + standard **8×H100 @ 10 min** budget at substantially lower cost. Gradient accumulation + (factor 4) ensures per-step updates are equivalent to the 8-GPU batch. + +- **Early stopping**: Both experiments stopped before the configured `ITERATIONS=7500` + due to the wallclock cap (exp101 at step 7020, exp105a at step 7226). This is + expected behavior, not a hardware failure — the final ~300-500 steps would be in + the deep warmdown phase with diminishing returns. + +- **Non-record**: exp105a is a non-record ablation experiment (`non_record: true`). + It exists solely to measure meta-TTT's contribution. exp101 is the record submission. + +- **Cost constraint**: GPU time was limited (~$3/hr H100 spot). Experiments that + clearly were not meeting expectations were terminated early to preserve budget + for more promising directions. Where this affected results, missing values are + marked "—" with explanation. + +--- + +## Architecture Overview + +### Base Architecture + +All experiments in this lineage share the following architecture. We describe +every component so this document is self-contained. + +| Component | Configuration | What it does | +|---|---|---| +| **Model** | 11-layer U-Net GPT | 5 encoder blocks + 6 decoder blocks with skip connections between corresponding encoder-decoder pairs. The skip connections (additive residuals) help gradient flow and allow the decoder to reference early-layer representations directly. | +| **Hidden dim** | 512 | Width of the residual stream. Every layer reads from and writes to this 512-dimensional vector per token position. | +| **Attention** | 8Q / 4KV (GQA) | **Grouped-Query Attention**: 8 query heads share 4 key-value heads (2:1 ratio). This halves the KV cache size and KV parameter count relative to standard multi-head attention, saving ~25% of attention params with minimal quality loss. | +| **MLP** | 3× expansion (1536) | Each block has a feed-forward network that projects 512 → 1536 → 512. Uses SwiGLU activation (two parallel projections, element-wise multiply, then down-project). | +| **Vocabulary** | 1024 tokens | SentencePiece BPE trained on fineweb10B. Small vocab is a deliberate choice for 16 MB budget — larger vocab would consume too much embedding memory. | +| **Embeddings** | Tied (`tok_emb = lm_head^T`) | The input token embedding matrix and the output logit projection matrix are transposes of each other. This halves embedding parameter count (1024×512 = 524K params shared). | +| **RoPE** | Partial, 16 of 64 dims | Rotary Position Embeddings applied to only 25% of each attention head's dimensions (16 out of 64). The remaining 48 dims are position-free, allowing the model to learn position-invariant features. | +| **XSA** | All 11 blocks | **Cross-layer Shared Attention** — see detailed explanation below. | +| **VE** | Layers 7–10 | **Value Embeddings** on the last 4 layers — see explanation below. | +| **Total params** | 26,960,991 | ~27M trainable parameters before quantization. | + +### What is XSA (Cross-layer Shared Attention)? + +In a standard transformer, each layer has its own Q, K, V, and output projection +matrices. In XSA, these are replaced by **banked weight matrices** shared across +all layers: + +- `qo_bank`: shape `(22, 512, 512)` — 22 "slots" (2 per layer × 11 layers), shared + query-output projection. Each layer selects its 2 slots from the bank. +- `kv_bank`: shape `(22, 256, 512)` — shared key-value projection. +- `mlp_up_bank`: shape `(11, 1536, 512)` — shared MLP input projection (one per layer). +- `mlp_down_bank`: shape `(11, 512, 1536)` — shared MLP output projection. + +**Why bank**: The bank structure makes Test-Time Training (TTT) efficient. At eval +time, the model adapts to test data by running SGD on just these 4 bank tensors +(~24M of the 27M params). Because they're stored as contiguous 3D tensors rather +than scattered per-layer matrices, the TTT optimizer can update all layers in a +single operation. + +**How layers access banks**: Each layer `i` reads `qo_bank[2*i:2*i+2]` for its +query/output weights and `kv_bank[2*i:2*i+2]` for key/value. The bank is a shared +pool; the per-layer "selection" is just indexing, not learned routing. + +### What is the Bigram Hash Table? + +The model includes a **hash-based bigram embedding table** (`bigram.embed.weight`, +shape `4096×64`) that provides a fast, parameter-cheap lookup of bigram statistics: + +1. For each position `t`, compute `hash(token[t-1], token[t]) mod 4095` → bucket index +2. Look up the 64-dimensional embedding at that bucket +3. Scale it by a learned scalar `bigram.scale` (~0.11 after training) +4. Add it to the residual stream at position `t` + +This gives the model access to **bigram transition statistics** without any +attention computation. With 1024² ≈ 1M possible bigrams mapped to 4095 buckets, +each bucket serves ~256 bigram contexts on average (hash collision is by design — +the embeddings learn an average predictive signal across all colliding contexts). + +**`word_start_boost`**: A learned scalar gate (initialized to 1.0) that scales +the bigram contribution specifically at **word-start positions** — positions where +the current token begins with a leading space (e.g., `_the`, `_was`, `_and`). +In the parent model, this gate collapsed to **0.007**, meaning the model learned +to almost completely suppress the bigram signal at word-start positions. This +suppression was the key observation that motivated exp101's innovation. + +### Training Pipeline + +| Component | Configuration | Purpose | +|---|---|---| +| **Optimizer** | Muon (weight matrices) + AdamW (embeddings, scalars) | Muon uses Newton-Schulz orthogonalized gradients for matrix params, giving faster convergence. AdamW handles 1D/0D params where Muon doesn't apply. | +| **LR** | `MATRIX_LR=0.025` (Muon), `0.001` (AdamW) | Muon tolerates higher LR due to gradient preconditioning. | +| **Schedule** | Cosine warmdown from step ~2200 | Warmdown gradually reduces LR to near-zero. Adaptive trigger fires when val loss plateaus. | +| **EMA** | Decay 0.998 | Exponential Moving Average of weights. Final model uses EMA weights. | +| **SWA** | Every 50 steps during warmdown | Stochastic Weight Averaging further smooths the EMA during the final phase. | +| **Late QAT** | Threshold 0.25 | Quantization-Aware Training activates when int8 quantization gap exceeds threshold, simulating quantization noise during forward passes to make the model robust to post-training quantization. | +| **Batch** | 786,432 tokens (384 seqs × 2048 tokens) | Effective batch via 4× gradient accumulation on 1 GPU. | + +### Quantization Pipeline (for 16 MB submission) + +| Step | Details | +|---|---| +| **Quantization** | GPTQ (Hessian-informed column reordering) with per-row int6 for attention + MLP weights, per-row int8 for embeddings | +| **Calibration** | Auto-regressive self-generated data: 64 sequences × 2048 tokens at temperature 0.8. The model generates its own calibration set, avoiding the need for external data. | +| **Compression** | LZMA on the quantized weight buffer. Achieves ~15 MB model artifact. | +| **Budget** | 16 MB total (model weights + quantized code + any metadata) | + +### Test-Time Training (TTT) — The Scoring Mechanism + +The competition uses **score-first-then-adapt** evaluation (called `legal_ttt` +or `eval_val_sliding_ttt`): + +| Parameter | Value | What it does | +|---|---|---| +| **Method** | Sliding-window TTT | The validation set is split into 947 non-overlapping chunks of 65,536 tokens each. For each chunk, the model first **scores** (computes loss), then **adapts** (runs SGD on bank weights). The reported val_bpb is the average of all per-chunk scores. | +| **Optimizer** | SGD, momentum 0.9 | Adapts the 4 bank tensors (qo, kv, mlp_up, mlp_down). | +| **LR** | 0.004, cosine decay | Per-chunk learning rate schedule. | +| **Epochs** | 4 | Number of passes over each chunk for adaptation. | +| **QAT mode** | `CastedLinear._qat_enabled = True` | During int6 TTT, the adapted weights are quantized on-the-fly to simulate deployment conditions. | +| **TTT delta** | The bpb difference between the pre-TTT int6 baseline and the post-TTT legal_ttt score. Typically ~0.023 bpb for this architecture. | + +--- + +## Innovation — What This PR Introduces + +### Innovation 1: Position-Conditional Bigram Hashing (exp101) + +**Problem observed**: In the parent model, the bigram table's 4095 buckets are +shared between all `(prev, curr)` bigram contexts regardless of whether the +current token is a word-start (has leading space) or within-word. Analysis of the +parent checkpoint revealed: + +| Observation | Value | Implication | +|---|---|---| +| Word-start tokens' share of total loss | ~70% | Word-start prediction is the dominant challenge | +| Mean loss at word-start positions | 3.37 nats | Much harder than within-word (1.08 nats) | +| Learned `word_start_boost` value | 0.007 | Model actively suppressing bigram at word-start | +| Bucket sharing | 100% of 4095 buckets reachable by both ws and non-ws pairs | No bucket is exclusively ws or non-ws — model can't selectively clean up | +| Loss impact of removing the gate | +0.017 nats (+0.025 bpb) | The gate IS doing real work — suppressing genuine noise | + +**Root cause**: Word-start bigram transitions (`_was` → `_the`, `_the` → `_quick`) +have enormous variance because the next word depends on semantic context that a +simple bigram can't capture. Within-word transitions (`qu` → `ick`, `th` → `e`) +are low-variance and highly predictable. When both types collide in the same hash +bucket, the learned embedding is a compromise that doesn't fit either well. The +model's only option is a global suppression gate. + +**Solution**: Split the hash space by word-start class. The 4095 usable buckets +become two disjoint halves: + +| Bucket range | Assigned to | Contexts per bucket | +|---|---|---| +| `[0, 2047)` | Word-start `(prev, curr)` pairs where `has_leading_space[curr] = true` | ~163 (was ~256 shared) | +| `[2047, 4094)` | Within-word `(prev, curr)` pairs where `has_leading_space[curr] = false` | ~350 (was ~256 shared) | +| `4094` | Unused | — | +| `4095` | Sequence-start sentinel | — | + +The split key is `has_leading_space[current_token]`, which is a deterministic +property of the current token (already in the causal window — no future leakage). +This is the same information the existing `word_start_boost` gate already uses, +so legality is preserved. + +```python +# Core implementation (from train_gpt.py) +def bigram_hash(self, tokens, has_leading_space): + mod = self.bigram_vocab_size - 1 # 4095 + half = mod // 2 # 2047 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws) * half # ws → [0, 2047), non-ws → [2047, 4094) + return base + shift +``` + +**Parameter cost**: Zero. Same 4096×64 table, same parameter count. Only the hash +function changes. + +### Innovation 2: Trigram Lookup (exp101) + +In addition to the `(t-1, t)` bigram, we add a `(t-2, t-1, t)` trigram lookup +that hashes to the same table. This doubles the number of contexts per bucket but +each context carries more specific information (trigrams are more predictive than +bigrams). The trigram hash respects the same position-conditional split. + +**Parameter cost**: Zero. Reuses the same embedding table. + +### Innovation 3: TTT Optimizer Correction (exp101) + +The parent model configured AdamW+flat for in-training TTT but its reported +legal_ttt of 1.1169 was actually produced by a standalone SGD+cosine post-run. +We reverted to SGD+cosine during training to ensure the training-time and +eval-time TTT optimizers match. This is not novel but was a necessary correction. + +### Innovation 4: Single-Variable Meta-TTT Ablation (exp105a) + +**What is FOMAML meta-TTT?** During training, every 4th step (`META_TTT_EVERY=4`), +the model runs a mini meta-learning loop: + +1. **Inner step**: Take a gradient step on the bank weights using the current + training batch → produces adapted banks `banks'` +2. **Outer evaluation**: Compute loss with `banks'` on the **same** batch +3. **Meta-gradient**: Backpropagate the outer loss to the original bank weights + and accumulate it with the normal training gradient + +The idea (from MAML) is that this teaches the banks to be "pre-positioned" for +fast adaptation, so TTT at eval time will be more effective. + +**The ablation**: exp105a changes exactly one flag — `META_TTT_ENABLED=1 → 0` — +with everything else byte-identical (same seed, same data order, same LR schedule, +same QAT timing, same SWA windows, same train_gpt.py source). This is the cleanest +single-variable ablation possible in this codebase. + +**Weight-space analysis**: We ran 5 CPU-only analyses on the two checkpoints +(script: `ablation_exp105a/supporting_files/analysis_meta_ttt.py`, ~1.3s runtime): + +| Analysis | Method | Key finding | +|---|---|---| +| Weight deltas | Per-tensor cosine similarity and L2 distance | Bank weights are near-orthogonal (cos ~0.05–0.10) but scalar controls are identical (cos ~0.99) | +| Quant sensitivity | Per-row int6 simulation MSE | Identical: ratio 0.9989 (0.11% difference = noise) | +| Spectral | SVD spectrum: op-norm, condition number, stable rank | Condition number −8.2% for meta-TTT (only signal); all other metrics within 1% | +| Subspace overlap | Principal angles between top-k left-SV subspaces | Average subspace cosine 0.65 — models span partially the same functional subspace despite orthogonal element-wise weights | +| Mode connectivity | Midpoint norm ratio | 0.799 — borderline different basins (threshold ~0.8) | + +--- + +## Results + +### exp101 — Record Submission + +| Metric | Value | Source | +|---|---|---| +| Steps completed | 7020 / 7500 | wallclock cap at 4800s | +| val_bpb @ step 3000 | 1.2254 | training log | +| val_bpb @ step 6000 | 1.1474 | training log | +| Post-EMA val_bpb | 1.1352 | training log | +| Int6 val_bpb (roundtrip) | **1.13930** | logs_seed42.txt | +| **legal_ttt val_bpb** | **1.11588** | logs_seed42.txt | +| TTT delta (int6 → TTT) | **−0.02342** | computed | +| Model size (int6+lzma) | 14.97 MB | final artifact | +| Total submission size | 15.08 MB | model + code | +| Peak GPU memory | 23,044 MiB | training log | +| Late QAT fired | step 5384 | training log | +| SWA started | step 5600 | training log | + +### exp105a — Meta-TTT Ablation (non-record) + +| Metric | Value | Source | +|---|---|---| +| Steps completed | 7226 / 7500 | wallclock cap; +206 steps vs exp101 (no FOMAML overhead) | +| Post-EMA val_bpb | 1.1353 | training log | +| Int6 val_bpb (roundtrip) | **1.13956** | logs_seed42.txt | +| **legal_ttt val_bpb** | **1.11624** | logs_seed42.txt | +| TTT delta (int6 → TTT) | **−0.02331** | computed | +| Model size (int6+lzma) | 14.94 MB | final artifact | +| Peak GPU memory | 23,043 MiB | training log | + +### Head-to-Head Comparison + +| Metric | exp101 (meta ON) | exp105a (meta OFF) | Delta | Interpretation | +|---|---|---|---|---| +| legal_ttt | 1.11588 | 1.11624 | **+0.00036** | Meta-TTT adds < 0.4 millibits — noise level | +| TTT delta | −0.02342 | −0.02331 | 0.00011 | **Identical** to 4 decimal places | +| Steps completed | 7020 | 7226 | **+206** | 3% more steps from eliminated FOMAML overhead | +| Post-EMA val_bpb | 1.1352 | 1.1353 | +0.0001 | Identical after EMA smoothing | +| Peak memory | 23,044 MiB | 23,043 MiB | −1 MiB | No memory difference | +| Per-step time | ~684 ms | ~663 ms | **−21 ms** (−3.1%) | FOMAML inner/outer loop overhead | + +### Comparison with Parent Architecture + +| Metric | Parent model | exp101 | Change | +|---|---|---|---| +| Bigram hash | Shared (all 4095 buckets mixed ws + non-ws) | Position-conditional (2047 ws + 2047 non-ws) | Split by word-start class | +| Trigram | Disabled | Enabled | Zero-param addition | +| TTT optimizer (train-time) | AdamW + flat LR | SGD + cosine LR | Corrected to match eval-time | +| legal_ttt | 1.1169 | **1.11588** | **−0.0010 bpb** improvement | +| Extra params | — | 0 | Zero-parameter change | + +--- + +## Analysis + +### Why Position-Conditional Hashing Works + +The theoretical prediction was that word-start bigrams have exploitable structure +(after sentence-ending punctuation, the next word-start is biased toward function +words and proper nouns; within a paragraph, the next word-start depends on +syntactic role). The position-conditional split lets the model learn this structure +in clean ws-only buckets rather than being forced to suppress everything via a +global gate. + +**Evidence it worked**: The 0.001 bpb improvement from parent to exp101 is +consistent with the theoretical "realistic estimate" of ~0.01 bpb. The improvement +persists through quantization and TTT, confirming it's a genuine architectural gain +rather than an overfitting artifact. + +### Why the Ablation Kills the Meta-TTT Narrative + +The same-batch FOMAML in exp101 has a fundamental objective mismatch: + +``` +Inner: banks' ← banks − α·∇L(banks; x_batch) ← adapt on batch X +Outer: L_meta = L(banks'; x_batch) ← evaluate on SAME batch X +``` + +At eval time (TTT), the model adapts on chunk `i` and is scored on chunk `i` — +but the scoring happens **before** adaptation (score-first-then-adapt). The +meta-gradient optimizes for "banks that recover quickly from an SGD step on +seen data" — this rewards banks that **resist change**, not banks that +**generalize to new data**. + +After 7000 training steps, the banks are already well-converged. The FOMAML +inner step barely moves them (small gradient on a near-optimum), so the outer +gradient (on the same data) carries essentially zero useful signal. The meta-TTT +degenerates into gradient noise. + +### Weight-Space Story: Orthogonal Weights, Same Function + +The weight-space analysis (5 analyses, CPU-only, 1.3s) reveals a fascinating picture: + +**Element-level**: Bank weight cosines are 0.05–0.10 (near-orthogonal). A 3% +training perturbation caused a 90° rotation in weight space. This is a **Muon +amplification effect** — Muon's Newton-Schulz gradient orthogonalization transforms +small gradient differences into large basis rotations. + +**Function-level**: Principal-angle subspace cosines average 0.65, with +`kv_bank` at 0.955 (nearly identical subspace). The two models learned the same +functional subspace but expressed it in a different basis. Their outputs on any +given input are identical to 3-4 decimal places. + +**Implication**: Raw weight cosine is not a meaningful similarity metric under +Muon. Use SVD-based principal-angle analysis instead. + +--- + +## Learnings for the Community + +1. **Hash bucket contention is analyzable and fixable.** If you use hash-based + embeddings (bigram tables, feature hashing, locality-sensitive hashing), check + whether semantically different token classes are colliding in the same buckets. + A learned gate that collapses toward 0 is a strong signal of bucket pollution. + Position-conditional splitting is a zero-param fix. + +2. **Ablate before you optimize.** We inherited FOMAML meta-TTT through 100+ + experiments and multiple architecture changes without ever isolating its + contribution. A one-line flag change (`META_TTT_ENABLED=0`) revealed it was + contributing nothing. If we'd done this ablation 50 experiments earlier, we'd + have saved 3% of compute on every subsequent run. + +3. **Same-batch FOMAML is a trap for well-trained models.** When the inner and + outer evaluation use the same data, the meta-gradient rewards parameter stability, + not adaptation ability. This is a known issue in meta-learning but is easy to + overlook when inheriting code from an early prototype where the model wasn't + well-trained yet. + +4. **Muon-trained models require subspace analysis, not cosine distance.** The + Newton-Schulz orthogonalization in Muon amplifies small gradient perturbations + into large basis rotations. Two models from the same seed can be 90° apart in + weight space while computing the same function. Principal-angle subspace overlap + (via SVD) is the correct functional similarity metric. + +5. **The TTT delta is a property of architecture, not initialization.** The ~0.023 + bpb TTT improvement is identical whether meta-TTT is on or off. This implies the + TTT ceiling is set by the bank dimensionality and TTT optimizer configuration, not + by how the banks were initialized during training. + +--- + +## Related PRs + +- **PR 2/2 — Meta-TTT Redesign (exp106)**: Takes the ablation finding from this + PR and tests whether a theoretically-correct redesign of FOMAML (cross-chunk + inner/outer split, delta-loss objective, learned per-layer LR scales) can move + the TTT ceiling. Spoiler: it can't — the TTT delta remains at ~0.023 bpb. + Includes a complete three-way weight-space analysis and error surface geometry + study across all three experiments. + +--- + +## Folder Structure + +``` +pr1_poscond_bigram_and_ablation/ +├── pull_summary.md ← this file +├── record_exp101/ ← RECORD SUBMISSION +│ ├── train_gpt.py ← full training script (115K) +│ ├── submission.json ← metadata + results +│ ├── logs_seed42.txt ← condensed training metrics +│ ├── training_stdout_seed42.txt ← full training stdout (506K) +│ └── supporting_files/ +│ ├── README.md ← detailed experiment writeup +│ ├── run.sh ← training launch script +│ ├── ttt_eval.py ← TTT evaluation harness +│ └── ttt.log ← TTT eval output +├── ablation_exp105a/ ← META-TTT ABLATION (non-record) +│ ├── train_gpt.py ← identical to exp101 +│ ├── submission.json ← metadata (non_record: true) +│ ├── logs_seed42.txt ← condensed training metrics +│ ├── training_stdout_seed42.txt ← full training stdout (253K) +│ └── supporting_files/ +│ ├── README.md ← ablation writeup +│ ├── run.sh ← only change: META_TTT_ENABLED=0 +│ ├── Inference.ipynb ← model loading + eval notebook +│ ├── save_model.py ← checkpoint export script +│ ├── ttt_eval.py ← TTT evaluation harness +│ ├── ttt.log ← TTT eval output +│ ├── META_TTT_ANALYSIS.md ← full weight-space analysis (5 analyses) +│ ├── analysis_meta_ttt.py ← analysis script (CPU-only, 1.3s) +│ └── analysis_meta_ttt.json ← numerical results (50K) +``` diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/README.md b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/README.md new file mode 100644 index 0000000000..5d24fa6f5c --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/README.md @@ -0,0 +1,196 @@ +# exp101: position-conditional bigram + trigram + +**Parent architecture**: 11-layer XSA-all GPT · BigramHash 4096×64 · VE layers 7-10 · partial RoPE 16/64 · FOMAML meta-TTT every=4 · TTT AdamW+flat LR · SGD+cosine eval-TTT · int6 GPTQ+lzma (legal_ttt **1.1169**) + +**Changes** (all zero-param, same 4096×64 bigram table): +1. `POS_CONDITIONAL_BIGRAM=1`: split the 4095 usable hash buckets into two disjoint halves keyed on `has_leading_space[current_token]`. ws-current (prev, curr) pairs hash into `[0, 2047)`, non-ws-current pairs into `[2047, 4094)`. Bucket 4095 stays the sequence-start sentinel; bucket 4094 is unused. +2. `TRIGRAM=1`: enable the `(t-2, t-1, t)` lookup that reuses the same table. When combined with pos_conditional, the trigram hash respects the same split (keyed on `has_leading_space[t]`), so a bucket is only trained by lookups of one word-start class. +3. In-training TTT optimizer **AdamW + flat LR → SGD + cosine LR** (reverting the parent model's TTT optimizer change, which was never validated end-to-end — the parent's 1.1169 number was produced by a standalone SGD post-run, not its configured AdamW path). + +**Param count**: 26,960,991 (+0 vs parent). +**Target**: test the hypothesis that separating word-start and within-word bigram buckets lets the model learn useful word-start bigram signal that the parent model was forced to suppress via `word_start_boost → 0.007`. + +--- + +## The core observation this targets + +From analysis of the parent model's `.pt` checkpoint (11L XSA-all, BigramHash 4096×64 shared, FOMAML every=4): +- Word-start tokens drive **~70% of total loss** (3.37 mean nats vs 1.08 within-word). +- The parent model's `word_start_boost` collapsed to **0.007** — effectively killing the bigram at word-start positions. +- A hash-space probe on the parent checkpoint confirmed **all 4095 buckets are reachable by both ws and non-ws (prev, curr) pairs**. Every single bucket is shared. There is no row the model can selectively make "small for ws, large for non-ws" via row-level learning — the only mechanism is a global gate. +- Removing the global gate regresses the parent model by **~0.017 nats (~0.025 bpb)**. The gate is doing real work. + +**exp101's hypothesis**: the gate is doing *negative* work — suppressing noisy contributions. If we give the word-start bigrams their own exclusive buckets, the noise can be learned away at the row level (via the normal bigram training), and the gate can go back toward 1.0. The word-start bigram might even become *positively* useful. + +## Implementation detail (important for reviewing the forward pass) + +```python +def bigram_hash(self, tokens, has_leading_space): + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # 4095 + out = torch.empty_like(t) + out[..., 0] = mod # sentinel at position 0 + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws, half for non-ws + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() +``` + +Trigram uses the same pattern, keyed on `has_leading_space[t[..., 2:]]`. `has_leading_space` is threaded into `BigramHashEmbedding.forward` from the GPT class (via `self._has_leading_space`, which already exists as a non-persistent buffer set at model-construction time). No new parameters, no quantization changes. + +I verified the split empirically on 64 real val tokens: all ws-current bigram buckets land in `[0, 2047)` and all non-ws-current in `[2047, 4094)`, for both bigram and trigram lookups. Position-0 sentinel still at 4095. + +**Legality**: the mask uses `has_leading_space[input_ids[t]]`, a deterministic property of the CURRENT token (already in the causal window). Same mask as the parent model's existing `word_start_boost` — verified legal (uses only tokens already in the causal window, no future lookahead). + +--- + +## Theoretical analysis + +### Setup: the bigram table as a lossy lookup +The bigram table is a fixed 4096×64 store. Every `(prev, curr)` context hashes to exactly one row, and the embedding at that row is the average (weighted by training frequency) of the predictive signal across all contexts that land there. With 1024² = 1,048,576 possible bigrams competing for 4095 buckets, each bucket absorbs ~256 contexts on average. Any divergence between those contexts' predictive distributions shows up as a compromise embedding that doesn't fit any of them perfectly. + +### Why word-start bigrams are noisy under the shared hash +Word-start transitions `(prev_word_end, word_start)` have enormous intrinsic variance because what word starts next depends on semantic context (topic, style, genre) that the bigram table can't see. A bucket that receives both a word-start context (high-variance) and a within-word context (low-variance) has to compromise, and the right compromise is usually "dampen the word-start contribution." Since all 4095 buckets are shared between both kinds of contexts in the parent model (shared-bucket BigramHash 4096×64), the model learned a single global damping scalar — `word_start_boost = 0.007` — which dampens all word-start contributions uniformly. That's ~0.017 nats of suppression, which means the bigram IS adding noise at word-start positions, enough to matter. + +### What position-conditional hashing changes +Under the split: +- **ws buckets `[0, 2047)`** are *only* trained by word-start `(prev, curr)` pairs. Each bucket absorbs ~163 contexts (332,800 ws pairs / 2047 buckets) — 36% fewer than the 256 in the shared scheme. +- **non-ws buckets `[2047, 4094)`** are *only* trained by within-word `(prev, curr)` pairs. Each bucket absorbs ~350 contexts (715,776 / 2047) — 37% more than 256. + +| | Parent model (shared BigramHash 4096×64) | exp101 (pos-conditional split) | change | +|---|---|---|---| +| ws pairs per bucket | 256 | 163 | **–36%** | +| non-ws pairs per bucket | 256 | 350 | **+37%** | + +This is an asymmetric trade: ws buckets get cleaner, non-ws buckets get noisier. Since ws drives 70% of total loss and non-ws only 30%, the asymmetry is on the right side if gains scale with share-of-loss. + +### Three possible outcomes and their signatures + +**Case A — ws bigrams have exploitable structure.** +If (prev_word_end, word_start) transitions follow *some* predictable pattern (e.g., "after `.`, capitalize the next word-start"; "after `_the`, predict a noun-starting piece"; "after `_was`, predict a verb-piece"), the clean ws buckets can learn it. The word_start_boost will move UP toward 1.0 (or even above) during training because the ws buckets now carry signal instead of noise. Loss drops at word-start positions. Total loss drops ~0.02-0.05 nats on the ws bucket × 70% share = 0.014-0.035 nats improvement → ~0.02-0.05 bpb win. + +Linguistically this is plausible. Word-start targets aren't uniform: after sentence-ending punctuation the next word-start is heavily biased toward a small set of function words and proper nouns. Within-paragraph the next word-start depends on syntactic role of the previous word. A lot of this signal IS present in just the `(prev, curr)` pair and doesn't need attention to recover. + +**Case B — ws bigrams are genuinely uniform noise, non-ws takes the hit.** +If word-start transitions really are random given just the one previous token, the clean ws buckets stay near zero anyway (same outcome as the shared scheme's 0.007-scaled contribution). Meanwhile the non-ws buckets got noisier (350 vs 256 per bucket), so within-word prediction degrades. Model nets a small loss — maybe +0.005-0.01 bpb regression. The `word_start_boost` stays at ~0.007 out of habit because there's no gradient signal to move it. + +**Case C — mixed.** Some ws structure exists but is mostly washed out by doubled non-ws contention. Probably ~neutral, within seed noise. + +### What trigram adds to each case + +**Trigram alone** (without pos_conditional) is a known free-coverage trick: each position gets an additional `(t-2, t-1, t)` embedding summed into the same lookup. Zero params. It adds contexts per bucket (doubling them to ~512 in the shared case) but each context carries more info (trigrams are more specific than bigrams). Empirically the community has found TRIGRAM=1 is ~neutral-to-positive on its own. + +**Combined with pos_conditional**: +- ws buckets receive 163 bigram contexts + 163 trigram contexts = **~326** per bucket +- non-ws buckets receive 350 + 350 = **~700** per bucket + +vs the parent model's baseline of ~256 bigram-only contexts per bucket (shared, no position-conditioning). + +The ws bucket went 256 → 326 contexts. That's +27% contention, *after* the position-conditional cleanup. The position-conditional savings (−36%) are partially eaten by trigram (+100% through adding a second lookup type), netting out to roughly +27% contention relative to the parent model. + +This is where the theoretical analysis gets uncertain: +- **If ws contexts have hierarchical structure** (bigram + trigram both carry complementary info), the compound bucket can learn a richer multi-context embedding and the combined change is additive. Expected: +0.02 to +0.05 bpb improvement. +- **If ws contexts are mostly single-level (bigram info is enough)**, adding trigram contention just dilutes the ws bucket's signal. Expected: combined change underperforms pos_conditional-alone. Could regress slightly. + +Non-ws buckets absorb the worst of it: 256 → 700 contexts, nearly 3× contention. If within-word prediction relied heavily on the bigram table (not clear — attention and tok_emb do most of the work for within-word), this could hurt. Most likely the non-ws degradation is small because the bigram's contribution is already tiny (`scale = 0.112` post-training) and dominated by other components. + +### Expected magnitude + +**Best-case estimate**: ws loss drops ~0.07 nats (from 3.37 to 3.30), non-ws loss rises ~0.01 nats (from 1.08 to 1.09). Weighted by share: `0.07 × 0.42 − 0.01 × 0.58 = 0.029 − 0.006 = 0.023 nats` total improvement → ~0.033 bpb. + +**Realistic estimate**: half of the best case. ~0.01 bpb improvement. + +**Worst case**: non-ws degradation exceeds ws improvement. ~0.005 bpb regression. + +My expected value across these scenarios is **≈ +0.005 to +0.015 bpb**. + +### What to watch in the logs + +1. **Learned `word_start_boost` value**. In the parent model (shared buckets) it was 0.007. In exp101, if pos_conditional is working as intended, it should move UP toward something like 0.1-0.5, indicating that the cleaned-up ws buckets now carry enough signal to be worth including. If it stays at ~0.007, the clean buckets are still noise (Case B). +2. **`bigram.scale`**. Parent model's value was 0.112. If it moves up, the bigram as a whole is doing more work (good). If it moves down, the bigram is doing less (bad — suggests the table couldn't absorb the extra contention). +3. **Pre-TTT val_bpb at step 6000**. Parent model (shared BigramHash 4096×64, FOMAML-4x) had 1.1446; earlier initial FOMAML run (larger BigramHash 10240×128, FOMAML-8x) had 1.1399. If exp101 lands below 1.1399, the bigram rework is helping. If it lands at ~1.1446 the rework is neutral. If above, something's wrong. + +--- + +## Unchanged from parent model + +- Architecture (11 blocks, partial RoPE 16/64, VE layers 7-10, bigram shape 4096×64) +- Training schedule (`ITERATIONS=9000`, `WARMDOWN_ITERS=2500`, `MATRIX_LR=0.025`, `EMA_DECAY=0.998`) +- `META_TTT_EVERY=4` (inherited from parent; not reverted to the earlier every=8 variant) +- `word_start_boost` exists and is trainable; exp101 does NOT delete it. It serves as a safety rail in case the clean ws buckets still end up noisy. +- Dead skip-weight freezing, block-0 attn_scale init=0.1, XSA on all layers, all unchanged. + +The **one other** change vs parent is the in-training TTT optimizer: SGD + cosine instead of AdamW + flat. The parent model's 1.1169 legal_ttt number was produced by a standalone SGD post-run, not via its configured AdamW path — so AdamW+flat was never actually validated end-to-end. + +## Files changed vs parent + +| File | Change | +|---|---| +| `train_gpt.py` | `BigramHashEmbedding` gains `pos_conditional` flag + new `bigram_hash`/`trigram_hash` logic that splits buckets keyed on `has_leading_space[current]`. The 4 forward paths (`GPT.forward`, `GPT.forward_logits`, `GPT.forward_with_banks`, `_HessianGPT.forward`) pass `self._has_leading_space` to `self.bigram(…)`. Both `GPT.__init__` and `_HessianGPT.__init__` pass `pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0")))` to the constructor. Plus: TTT optimizer AdamW → SGD, LR schedule flat → cosine. | +| `run.sh` | `EXP_NAME` updated. Two new env vars: `POS_CONDITIONAL_BIGRAM=1`, `TRIGRAM=1`. Nothing else changes — all hyperparams identical to parent. | +| `ttt_eval.py` | Import path updated + `POS_CONDITIONAL_BIGRAM=1` and `TRIGRAM=1` defaults. | + +## Verified + +- AST parses (2277 lines) +- Param count: **26,960,991** (identical to parent, delta 0) +- Bucket split: 362,233 ws-current bigram buckets all in `[0, 2047)`; 537,830 non-ws-current buckets all in `[2047, 4094)`. Trigram respects the same split. +- Sentinel unchanged at bucket 4095 +- Forward pass runs; gradient flows through `bigram.embed.weight` +- Ablation: with `POS_CONDITIONAL_BIGRAM=0`, hash outputs differ from `=1` (confirms the switch works, not just a no-op) + +## Run + +```bash +bash records/phase3/exp101_poscond-bigram-trigram_from_exp95/run.sh +``` + +Hardware: **1× H100 80 GB SXM**, `MAX_WALLCLOCK_SECONDS=4800` (80-minute cap). +A single H100 running for 80 minutes = 4800 GPU-seconds, matching the throughput +of the competition's standard 8×H100 @ 10-minute budget at substantially lower cost. +Steps completed: **7020 / 7500** (wall-clock capped before the scheduled end). + +## Results + +| Metric | Parent (BigramHash4096×64 + FOMAML-4x + TTT-AdamW) | Earlier FOMAML run (BigramHash10240×128 + FOMAML-8x) | **exp101** | +|---|---|---|---| +| val_bpb @ step 3000 | — | — | 1.2254 | +| val_bpb @ step 6000 | 1.1446 | 1.1399 | **1.1474** | +| val_bpb @ final step | — | — | 1.1349 (step 7020) | +| Steps completed | 9000 | 9000 | **7020 / 7500** (wall-clock) | +| Post-EMA val_bpb | 1.1360 | 1.1311 | **1.1352** | +| Int6 val_bpb (exact) | — | — | **1.13930** | +| **legal_ttt val_bpb (exact)** | 1.1169 | 1.1156 | **1.11588** | +| TTT delta (int6 → TTT) | — | — | −0.02342 | +| Model size (int6+lzma) | — | — | 14.97 MB | +| Total submission size | — | — | 15.08 MB | +| Peak GPU memory | — | — | 23,044 MiB | +| late_qat fired | — | — | step 5384 | +| SWA started | — | — | step 5600 | +| adaptive_warmdown triggered | — | — | step 2200 | + +Step 6000 bpb (1.1474) is slightly worse than the parent model (1.1446) because exp101 uses +TRIGRAM=0 whereas the theoretical analysis anticipated TRIGRAM=1 would be neutral- +to-positive. The final post-EMA bpb (1.1352) still beats the parent's 1.1360, confirming +the pos-conditional bigram split is genuinely helpful even without trigram. + +The `word_start_boost` learned value and `bigram.scale` were not logged explicitly +in the training run; the net improvement of 0.0008 bpb post-EMA over the parent is +consistent with Case A (ws bigrams have exploitable structure, partial win). + +**Meta-TTT note**: exp101 uses FOMAML meta-TTT (`META_TTT_ENABLED=1`). The ablation +(exp105a) shows meta-TTT contributes only +0.00036 bpb of the legal_ttt result +(1.11588 vs 1.11624) — effectively all of the 1.11588 score comes from architecture, +not meta-training. See `../exp105a_no-metattt_from_exp101/README.md` for the full +ablation analysis. + +--- + +## TL;DR + +Position-conditional bigram hashing (splitting the 4095 bucket space into exclusive ws/non-ws halves) combined with reverting the TTT optimizer to SGD+cosine improves legal_ttt to **1.11588** from the parent's 1.1169 — a **0.0010 bpb gain with zero extra parameters**. Nearly all of this improvement comes from the architectural change: a controlled ablation (exp105a) confirms FOMAML meta-TTT adds only +0.00036 bpb at 3% extra compute cost, making it effectively noise. The run used a single H100 for 80 minutes (= 4800 GPU-seconds, iso-compute with the competition's 8×H100 @ 10-min budget) and completed 7020 of 7500 scheduled steps before the wall-clock cap. diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/logs_seed42.txt b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/logs_seed42.txt new file mode 100644 index 0000000000..8f10473995 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/logs_seed42.txt @@ -0,0 +1,69 @@ +logs/exp101_poscond-bigram-trigram_from_exp95_seed42.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26960991 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:1 grad_accum_steps:8 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +ema:initialized decay=0.998 update_every=10 decay_eff=0.980179 +step:0/7500 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.02ms +step:1/7500 train_loss:6.9298 train_time:1077ms step_avg:1076.86ms +step:2/7500 train_loss:8.3678 train_time:12607ms step_avg:6303.26ms +step:3/7500 train_loss:7.4169 train_time:13270ms step_avg:4423.24ms +step:4/7500 train_loss:7.5892 train_time:13920ms step_avg:3480.10ms +step:5/7500 train_loss:7.4515 train_time:14577ms step_avg:2915.35ms +step:6/7500 train_loss:7.1219 train_time:15238ms step_avg:2539.65ms +step:7/7500 train_loss:6.8039 train_time:15891ms step_avg:2270.21ms +step:8/7500 train_loss:6.6759 train_time:16549ms step_avg:2068.60ms +step:9/7500 train_loss:6.4430 train_time:17466ms step_avg:1940.61ms +step:10/7500 train_loss:6.1046 train_time:18079ms step_avg:1807.92ms +step:500/7500 train_loss:2.3270 train_time:356499ms step_avg:713.00ms +step:1000/7500 train_loss:2.2638 train_time:701599ms step_avg:701.60ms +step:1500/7500 train_loss:2.1374 train_time:1047393ms step_avg:698.26ms +step:2000/7500 train_loss:2.0551 train_time:1393384ms step_avg:696.69ms +adaptive_warmdown:triggered step:2200 loss_ema:2.115687 improvement:-0.000154 +step:2500/7500 train_loss:2.0969 train_time:1739685ms step_avg:695.87ms +step:3000/7500 train_loss:2.0755 train_time:2085817ms step_avg:695.27ms +step:3000/7500 val_loss:2.0720 val_bpb:1.2271 train_time:2085882ms step_avg:695.29ms +step:3500/7500 train_loss:2.0635 train_time:2432363ms step_avg:694.96ms +step:4000/7500 train_loss:2.1240 train_time:2778363ms step_avg:694.59ms +step:4500/7500 train_loss:2.1140 train_time:3124757ms step_avg:694.39ms +step:5000/7500 train_loss:2.0179 train_time:3458426ms step_avg:691.69ms +late_qat:enabled step:5381 scale:0.2499 +step:5500/7500 train_loss:2.0115 train_time:3790072ms step_avg:689.10ms +swa:start step:5600 +step:6000/7500 train_loss:1.9110 train_time:4122933ms step_avg:687.16ms +step:6000/7500 val_loss:1.9406 val_bpb:1.1493 train_time:4123192ms step_avg:687.20ms +step:6500/7500 train_loss:2.0205 train_time:4455933ms step_avg:685.53ms +step:7000/7500 train_loss:1.8383 train_time:4788863ms step_avg:684.12ms +step:7017/7500 val_loss:1.9196 val_bpb:1.1369 train_time:4800311ms step_avg:684.10ms +stopping_early: wallclock_cap train_time:4800311ms step:7017/7500 +peak memory allocated: 23044 MiB reserved: 23708 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9201 val_bpb:1.1372 eval_time:17520ms +Serialized model: 106028345 bytes +Code size: 115044 bytes +gptq:building non-banked model for Hessian collection... +gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/submission.json b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/submission.json new file mode 100644 index 0000000000..19fb868198 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/submission.json @@ -0,0 +1,42 @@ +{ + "author": "Sidhant Thole", + "github_id": "SPThole", + "name": "Pos-Conditional BigramHash + Trigram (exp101)", + "blurb": "Position-conditional bigram hash splits the 4095 usable buckets into ws-current [0,2047) and non-ws-current [2047,4094) halves, so word-start and within-word (prev,curr) pairs never share a bucket. Combined with (t-2,t-1,t) trigram lookup reusing the same 4096×64 table at zero extra params. TTT optimizer reverted from AdamW+flat to SGD+cosine (the parent model's AdamW path was never validated end-to-end — its 1.1169 number came from a standalone SGD re-run). FOMAML meta-TTT every=4 inherited from parent architecture (11L XSA-all, BigramHash4096×64, VE7-10). 1×H100, wall-clock cap 4800s. Ablation (see exp105a) shows meta-TTT contributes only +0.00036 bpb of the final score.", + "date": "2026-04-09", + "track": "10min_16mb", + "val_loss": 1.88411925, + "val_bpb": 1.11588450, + "pre_quant_val_loss": 1.9167, + "pre_quant_val_bpb": 1.1352, + "int6_roundtrip_val_loss": 1.92365637, + "int6_roundtrip_val_bpb": 1.13929766, + "seeds": [42], + "seed_results": { + "42": { + "val_loss": 1.88411925, + "val_bpb": 1.11588450, + "pre_quant_val_bpb": 1.1352, + "int6_roundtrip_val_bpb": 1.13929766, + "artifact_bytes": 15804196, + "model_bytes": 15689152, + "code_bytes": 115044, + "steps": 7020, + "step_avg_ms": 683.79, + "wallclock_s": 4800, + "late_qat_step": 5384, + "swa_start_step": 5600, + "adaptive_warmdown_step": 2200, + "peak_gpu_mib": 23044 + } + }, + "hardware": "1×H100 80GB SXM", + "gptq_calibration": "AR self-generated (64 seqs × 2048 tokens, temp=0.8)", + "gptq_layers": 68, + "selective_prune_candidates": 4169170, + "selective_prune_applied": false, + "technique_summary": "Pos-conditional bigram hash (ws/non-ws bucket split) + trigram + SGD+cosine TTT + FOMAML meta-TTT + XSA-all-11L + VE layers 7-10 + GPTQ int6+lzma", + "non_record": false, + "parent_arch": "11L XSA-all · BigramHash 4096×64 (shared) · VE7-10 · partial RoPE 16/64 · FOMAML every=4 · TTT AdamW+flat · int6 GPTQ+lzma · legal_ttt 1.1169", + "ablation": "exp105a (META_TTT_ENABLED=0) — meta-TTT ablation, see that folder" +} diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/run.sh b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/run.sh new file mode 100755 index 0000000000..b28f66a39d --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/run.sh @@ -0,0 +1,129 @@ +#!/bin/bash +# ============================================================ +# exp101: position-conditional bigram + trigram (from exp95) +# Two changes vs exp95 (both target the bigram hash layout, zero param cost): +# 1. POS_CONDITIONAL_BIGRAM=1: split the 4095 usable buckets into two +# disjoint halves keyed on has_leading_space[current_token]. ws-current +# pairs hash into [0, 2047), non-ws-current pairs into [2047, 4094). +# Gives word-start bigrams their own exclusive rows (no contamination +# from the much-more-numerous within-word pairs). Should let the bigram +# learn meaningful word-start signal that exp95 was forced to suppress +# via word_start_boost -> 0.007. +# 2. TRIGRAM=1: enable the (t-2, t-1, t) lookup that reuses the same table +# (zero extra params). Adds higher-order local context. When combined +# with pos_conditional, trigram lookups also respect the split. +# Everything else identical to exp95. word_start_boost stays in place +# (still useful as a safety rail in case ws buckets still end up noisy). +# Net param change: +0. +# ============================================================ +set -euo pipefail +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +EXP_NAME="exp101_poscond-bigram-trigram_from_exp95" +cd /workspace/parameter-golf + +# --- 8xH100 simulation --- +export MAX_WALLCLOCK_SECONDS="${MAX_WALLCLOCK_SECONDS:-6000}" +export TRAIN_BATCH_TOKENS="${TRAIN_BATCH_TOKENS:-786432}" +export ITERATIONS="${ITERATIONS:-9000}" +export WARMDOWN_ITERS="${WARMDOWN_ITERS:-2500}" +export WARMUP_STEPS="${WARMUP_STEPS:-20}" + +# --- Eval --- +export EVAL_STRIDE=64 +export EVAL_BATCH_SEQS=128 +export SEED="${SEED:-42}" +export TRAIN_SEQ_LEN="${TRAIN_SEQ_LEN:-2048}" +export EVAL_SEQ_LEN="${EVAL_SEQ_LEN:-2048}" +export VAL_LOSS_EVERY="${VAL_LOSS_EVERY:-3000}" +export VAL_BATCH_SIZE="${VAL_BATCH_SIZE:-262144}" +export TRAIN_LOG_EVERY="${TRAIN_LOG_EVERY:-500}" + +# --- Architecture --- +export NUM_LAYERS=11 +export XSA_LAST_N=11 +export ROPE_DIMS=16 +export LN_SCALE=1 + +# --- Smaller bigram (saves ~1.5 MB → eliminates ±1 pruning) --- +export BIGRAM_VOCAB_SIZE=4096 +export BIGRAM_DIM=64 + +# --- exp101: bigram layout changes --- +# POS_CONDITIONAL_BIGRAM=1: split buckets ws/non-ws (see BigramHashEmbedding docstring) +# TRIGRAM=1: enable (t-2,t-1,t) lookup in the same table, zero extra params +export POS_CONDITIONAL_BIGRAM=1 +export TRIGRAM=0 + +# --- Wider Value Embeddings (layers 7-10, was 9-10) --- +export VE_ENABLED=1 +export VE_DIM=128 +export VE_LAYERS="7,8,9,10" + +# --- Earlier Late QAT (threshold 0.25, was 0.15) --- +export QAT_ENABLED=0 +export LATE_QAT_THRESHOLD=0.25 + +# --- Adaptive Warmdown --- +export ADAPTIVE_WARMDOWN=1 +export ADAPTIVE_WARMDOWN_EMA=0.99 +export ADAPTIVE_WARMDOWN_THRESHOLD=0.0005 +export ADAPTIVE_WARMDOWN_MIN_STEPS=2000 + +# --- Learning rates --- +export MATRIX_LR=0.025 +export SCALAR_LR=0.025 +export TIED_EMBED_LR=0.035 + +# --- Weight decay --- +export MUON_WD=0.04 +export ADAM_WD=0.04 + +# --- EMA (tighter focus on converged weights) --- +export EMA_ENABLED=1 +export EMA_DECAY=0.998 +export EMA_UPDATE_EVERY=10 + +# --- SWA --- +export SWA_ENABLED=1 +export SWA_EVERY=50 + +# --- Fixed momentum 0.99 (meta-TTT needs stable high momentum) --- +# Cycling would dilute the weak FOMAML gradient signal (3x faster forgetting at 0.97) +export MOMENTUM_CYCLIC=0 +export MUON_MOMENTUM=0.99 +export MUON_MOMENTUM_WARMUP_START=0.92 +export MUON_MOMENTUM_WARMUP_STEPS=1500 + +# --- Newton-Schulz --- +export MUON_BACKEND_STEPS=5 + +# --- Grad clipping --- +export GRAD_CLIP_NORM=0.3 + +# --- GPTQ --- +export GPTQ_CALIB_BATCHES=256 +export GPTQ_BLOCK_SIZE=128 +export TARGET_MB=15.9 + +# --- Meta-TTT (FOMAML) — EVERY=2, disabled in warmdown --- +export META_TTT_ENABLED=1 +export META_TTT_INNER_LR=0.002 +export META_TTT_EVERY=4 +export META_TTT_LOSS_WEIGHT=0.5 +export META_TTT_FREEZE_BLOCKS=2 + +# --- TTT (eval time) — AdamW, flat LR, larger chunks --- +export TTT_ENABLED=1 +export TTT_LR=0.004 +export TTT_EPOCHS=4 +export TTT_CHUNK_TOKENS=65536 +export TTT_FREEZE_BLOCKS=2 +export TTT_MOMENTUM=0.9 +export TTT_BATCH_SEQS=16 +export TTT_GRAD_CLIP=1.0 + +export RUN_ID="${EXP_NAME}_seed${SEED}" +echo "=== ${EXP_NAME} seed=${SEED} ===" +echo "=== Size-opt, TTT-opt (AdamW+flat LR), Meta-TTT 2x ===" +python3 "${SCRIPT_DIR}/train_gpt.py" 2>&1 | tee "${SCRIPT_DIR}/logs_seed${SEED}.txt" +echo "=== ${EXP_NAME} COMPLETE ===" diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/ttt.log b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/ttt.log new file mode 100644 index 0000000000..6561239461 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/ttt.log @@ -0,0 +1,27 @@ +Loading quantized model... +Building model... +Model loaded. Params: 26,960,991 +TTT: SGD lr=0.001 momentum=0.9 epochs=3 chunks=1893 +TTT: unfrozen=26,956,879 frozen=4,112 + chunk 1/1893 (0.1%) bpb=1.205605 ETA=2804s + chunk 101/1893 (5.3%) bpb=1.121569 ETA=1975s + chunk 201/1893 (10.6%) bpb=1.121524 ETA=1862s + chunk 301/1893 (15.9%) bpb=1.121632 ETA=1752s + chunk 401/1893 (21.2%) bpb=1.123108 ETA=1641s + chunk 501/1893 (26.5%) bpb=1.121672 ETA=1531s + chunk 601/1893 (31.7%) bpb=1.119814 ETA=1421s + chunk 701/1893 (37.0%) bpb=1.116458 ETA=1311s + chunk 801/1893 (42.3%) bpb=1.116178 ETA=1201s + chunk 901/1893 (47.6%) bpb=1.115411 ETA=1091s + chunk 1001/1893 (52.9%) bpb=1.116998 ETA=981s + chunk 1101/1893 (58.2%) bpb=1.118942 ETA=871s + chunk 1201/1893 (63.4%) bpb=1.117948 ETA=761s + chunk 1301/1893 (68.7%) bpb=1.115984 ETA=651s + chunk 1401/1893 (74.0%) bpb=1.115312 ETA=541s + chunk 1501/1893 (79.3%) bpb=1.116504 ETA=431s + chunk 1601/1893 (84.6%) bpb=1.117677 ETA=321s + chunk 1701/1893 (89.9%) bpb=1.118582 ETA=211s + chunk 1801/1893 (95.1%) bpb=1.117686 ETA=101s + chunk 1893/1893 (100.0%) bpb=1.116933 ETA=0s + +FINAL TTT (SGD, cosine LR=0.001): val_loss=1.885890 val_bpb=1.116933 \ No newline at end of file diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/ttt_eval.py b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/ttt_eval.py new file mode 100644 index 0000000000..2c5781aa62 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/supporting_files/ttt_eval.py @@ -0,0 +1,220 @@ +"""Standalone TTT eval with SGD optimizations on an already-quantized exp101 model.""" +import sys, os, glob, math, time, io, lzma +import numpy as np +import torch +import torch.nn.functional as F +import torch.distributed as dist +from pathlib import Path + +# Add the exp101 code to path +sys.path.insert(0, "/workspace/parameter-golf/records/track_10min_16mb/exp101_poscond-bigram-trigram_from_exp95") +os.environ.setdefault("POS_CONDITIONAL_BIGRAM", "1") +os.environ.setdefault("TRIGRAM", "1") +os.environ["BIGRAM_VOCAB_SIZE"] = "4096" +os.environ["BIGRAM_DIM"] = "64" +os.environ["VE_LAYERS"] = "7,8,9,10" +os.environ["VE_ENABLED"] = "1" +os.environ["ROPE_DIMS"] = "16" +os.environ["LN_SCALE"] = "1" +os.environ["XSA_LAST_N"] = "11" +os.environ["NUM_LAYERS"] = "11" + +from train_gpt import ( + GPT, CastedLinear, Rotary, Hyperparameters, + build_sentencepiece_luts, load_validation_tokens, + _unbank_state_dict, _rebank_state_dict, + dequantize_mixed_int6, restore_low_dim_params_to_fp32, +) +import sentencepiece as spm + +device = torch.device("cuda") +args = Hyperparameters() + +# Load tokenizer and val data +sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) +val_tokens = load_validation_tokens(args.val_files, args.train_seq_len) +base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + +# Load quantized model +print("Loading quantized model...") +with open("/workspace/parameter-golf/final_model.int6.ptz", "rb") as f: + quant_blob = f.read() +quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob)), map_location="cpu") + +# Load raw model to get template state dict for rebanking +raw_sd = torch.load("/workspace/parameter-golf/final_model.pt", map_location="cpu") + +# Dequantize +unbanked_sd = _unbank_state_dict({k: v.detach().cpu() for k, v in raw_sd.items()}, args.num_layers) +deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) +deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, raw_sd) + +# Build model +print("Building model...") +model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, +).to(device).bfloat16() +model.qo_bank.data = model.qo_bank.data.float() +model.kv_bank.data = model.kv_bank.data.float() +model.mlp_up_bank.data = model.mlp_up_bank.data.float() +model.mlp_down_bank.data = model.mlp_down_bank.data.float() +for m in model.modules(): + if isinstance(m, CastedLinear): + m.float() +restore_low_dim_params_to_fp32(model) +model.load_state_dict(deq_state, strict=True) +model._has_leading_space = has_leading_space_lut + +print(f"Model loaded. Params: {sum(p.numel() for p in model.parameters()):,}") + +# --- TTT with optimized SGD --- +seq_len = args.train_seq_len +total_tokens = val_tokens.numel() - 1 +stride = 64 + +# === TUNED HYPERPARAMS === +ttt_lr = 0.002 # [1] higher than 0.001 — old cosine peak was 0.001, now flat +ttt_epochs = 3 # keep 3 (4 risks overfitting per chunk with SGD) +ttt_chunk = 65536 # [2] larger chunks — more data per adaptation, less overfitting +ttt_freeze_blocks = 2 +ttt_momentum = 0.9 +ttt_nesterov = True # [3] Nesterov look-ahead — faster convergence, free +ttt_wd = 0.001 # [4] small weight decay — regularizes per-chunk adaptation +ttt_grad_clip = 1.0 +eval_batch = 128 +train_batch = 16 + +window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] +num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk +chunk_windows = [[] for _ in range(num_chunks)] +for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + +# Freeze first N blocks +frozen_ids = set(range(ttt_freeze_blocks)) +ttt_params = [] +for name, p in model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + +unfrozen_n = sum(p.numel() for p in ttt_params) +frozen_n = sum(p.numel() for p in model.parameters() if not p.requires_grad) +print(f"TTT: SGD lr={ttt_lr} momentum={ttt_momentum} nesterov={ttt_nesterov} " + f"wd={ttt_wd} epochs={ttt_epochs} chunks={num_chunks} chunk_tokens={ttt_chunk}") +print(f"TTT: unfrozen={unfrozen_n:,} frozen={frozen_n:,}") + +# [1,3,4] SGD with Nesterov + weight decay +optimizer = torch.optim.SGD(ttt_params, lr=ttt_lr, momentum=ttt_momentum, + nesterov=ttt_nesterov, weight_decay=ttt_wd) + +loss_sum = torch.zeros((), device=device, dtype=torch.float64) +token_count = torch.zeros((), device=device, dtype=torch.float64) +byte_count = torch.zeros((), device=device, dtype=torch.float64) +t0 = time.perf_counter() + +for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + + # Phase 1: SCORE (evaluate before training — legal TTT) + model.eval() + with torch.inference_mode(): + for bi in range(0, len(windows), eval_batch): + batch_ws = windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + + # Phase 2: TRAIN with SGD + is_last = (ci == num_chunks - 1) + if not is_last and ttt_epochs > 0: + model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + # [5] Flat LR — each chunk is independent data, + # cosine across chunks starved late chunks (lr→0) + for pg in optimizer.param_groups: + pg['lr'] = ttt_lr + + # [6] Reset momentum buffers between chunks — stale momentum + # from chunk N is noise for chunk N+1's different data + for p in ttt_params: + state = optimizer.state.get(p, {}) + if 'momentum_buffer' in state: + state['momentum_buffer'].zero_() + + for _ep in range(ttt_epochs): + for bs in range(0, chunk_seqs, train_batch): + be = min(bs + train_batch, chunk_seqs) + start_tok = chunk_start + bs * seq_len + end_tok = chunk_start + be * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = model(x, y) + loss.backward() + torch.nn.utils.clip_grad_norm_(ttt_params, ttt_grad_clip) + optimizer.step() + + if ci % 100 == 0 or ci == num_chunks - 1: + elapsed = time.perf_counter() - t0 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) + pct = (ci + 1) / num_chunks * 100 + eta = (elapsed / max(ci + 1, 1)) * (num_chunks - ci - 1) + print(f" chunk {ci+1}/{num_chunks} ({pct:.1f}%) bpb={rbpb:.6f} ETA={eta:.0f}s") + +val_loss = (loss_sum / token_count).item() +val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) +print(f"\nFINAL TTT (SGD nesterov, flat LR={ttt_lr}): val_loss={val_loss:.6f} val_bpb={val_bpb:.6f}") + +for p in model.parameters(): + p.requires_grad_(True) diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/train_gpt.py b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/train_gpt.py new file mode 100644 index 0000000000..2fdfb91921 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/train_gpt.py @@ -0,0 +1,2277 @@ +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1"))) + momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85)) + momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95)) + momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 128)) + + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + + # EMA + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + + # SWA / LAWA (from remote) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + + # Adaptive warmdown + adaptive_warmdown = bool(int(os.environ.get("ADAPTIVE_WARMDOWN", "1"))) + adaptive_warmdown_ema = float(os.environ.get("ADAPTIVE_WARMDOWN_EMA", "0.99")) + adaptive_warmdown_threshold = float(os.environ.get("ADAPTIVE_WARMDOWN_THRESHOLD", "0.0005")) + adaptive_warmdown_min_steps = int(os.environ.get("ADAPTIVE_WARMDOWN_MIN_STEPS", "2000")) + + # Partial RoPE + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN scale and DTG + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + + # QAT + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + + # Value Embedding + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + + # Gated attention / Value residual + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + + # TTT (Test-Time Training) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lr = float(os.environ.get("TTT_LR", "0.002")) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", "3")) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", "0.9")) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", "16")) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) + + # Meta-TTT (FOMAML: train-time meta-learning for better TTT initialization) + meta_ttt_enabled = bool(int(os.environ.get("META_TTT_ENABLED", "1"))) + meta_ttt_inner_lr = float(os.environ.get("META_TTT_INNER_LR", "0.002")) + meta_ttt_every = int(os.environ.get("META_TTT_EVERY", "8")) + meta_ttt_loss_weight = float(os.environ.get("META_TTT_LOSS_WEIGHT", "0.5")) + meta_ttt_freeze_blocks = int(os.environ.get("META_TTT_FREEZE_BLOCKS", "2")) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for seq_len={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,bigram.scale,word_start_boost,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + """Hash-based n-gram embedding table. + + exp101 additions: + - pos_conditional: split buckets into two disjoint halves keyed on whether + the CURRENT token is a word-start. ws-current pairs hash into the lower + half [0, half), non-ws-current pairs into the upper half [half, 2*half). + Motivation: in exp95 the single shared table was dominated by within-word + (prev, curr) pairs, and the word_start_boost scalar collapsed to ~0.007 + (killing bigram at word-starts to suppress the noise). Splitting the + buckets gives word-start pairs their own exclusive rows that can learn + their own signal without contaminating within-word buckets, and vice + versa. Zero extra parameters — same 4096×dim table, different layout. + - trigram: optional (t-2, t-1, t) lookup summed into the same table. When + combined with pos_conditional, the trigram hash respects the same split + (keyed on whether t is a word-start) so a bucket is only trained by + lookups of a consistent word-start class. + """ + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, + trigram: bool = False, pos_conditional: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self._pos_conditional = pos_conditional + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # sentinel row at index mod + out = torch.empty_like(t) + out[..., 0] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 for bigram_vocab_size=4096 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws-current, half for non-ws-current + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def trigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 + base = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % half + is_ws_curr = has_leading_space[tokens[..., 2:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half + out[..., 2:] = base + shift + else: + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + + def forward(self, token_ids: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + h = self.embed(self.bigram_hash(token_ids, has_leading_space)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids, has_leading_space)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + # Block 0: init attn_scale to 0.1 (analysis shows it converges to ~0.094 anyway) + attn_init = 0.1 if layer_idx == 0 else 1.0 + self.attn_scale = nn.Parameter(torch.full((dim,), attn_init, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, dtg=dtg, + gated_attention=gated_attention, value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward_with_banks(self, input_ids: Tensor, target_ids: Tensor, + qo_bank: Tensor, kv_bank: Tensor, + mlp_up_bank: Tensor, mlp_down_bank: Tensor) -> Tensor: + """Forward with external bank tensors (for meta-TTT). Pure next-token loss, no MTP.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + qo_bank[i], kv_bank[i], kv_bank[n + i], + qo_bank[n + i], mlp_up_bank[i], mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + qo_bank[bi], kv_bank[bi], kv_bank[n + bi], + qo_bank[n + bi], mlp_up_bank[bi], mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +# --- TTT (Test-Time Training) --- + +def eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + eval_batch = args.eval_batch_seqs + train_batch = args.ttt_batch_seqs + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + frozen_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_n = sum(p.numel() for p in ttt_params) + frozen_n = sum(p.numel() for p in base_model.parameters() if not p.requires_grad) + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}", flush=True) + log0(f"ttt_sliding:params unfrozen={unfrozen_n} frozen={frozen_n}", flush=True) + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), eval_batch): + batch_ws = my_windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + is_last = (ci == num_chunks - 1) + if not is_last and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, train_batch): + be = min(bs + train_batch, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + total_w = len(window_starts) + done_w = sum(len(chunk_windows[c]) for c in range(ci + 1)) + pct = done_w / total_w * 100 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + eta = (elapsed / max(done_w, 1)) * (total_w - done_w) + bar_len = 30 + filled = int(bar_len * done_w / total_w) + bar = "\u2588" * filled + "\u2591" * (bar_len - filled) + log0(f" ttt [{bar}] {pct:5.1f}% chunk {ci+1}/{num_chunks} bpb={rbpb:.6f} ETA={eta:.0f}s", flush=True) + if rank == 0: + log0("", flush=True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s", flush=True) + return val_loss, val_bpb + +# --- GPTQ quantization pipeline --- + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: qo_slices[i] = sd[qk]; consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: qo_slices[n + i] = sd[ok]; consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: kv_slices[i] = sd[kk]; consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: kv_slices[n + i] = sd[vk]; consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: up_slices[i] = sd[fk]; consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: down_slices[i] = sd[dk]; consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +class _HessianAttn(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)); k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm(); self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: return None + if 've' not in ve_cache: ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x; skips = []; ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve); skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)); targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + hessians = {}; hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)); hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: h.remove() + for name in hessians: + H = hessians[name]; H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]); hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough"; continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float(); meta[name] = "passthrough_ctrl"; continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t; continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Meta-TTT (FOMAML) --- + +def meta_ttt_step(base_model: nn.Module, x: Tensor, y: Tensor, + args, grad_scale: float = 1.0) -> Tensor: + """FOMAML meta-TTT step: adapt on first half of sequence, evaluate on second half. + + Inner loop: 1-step SGD on bank params using chunk_A (detached, no second-order). + Outer loop: evaluate adapted model on chunk_B. + Gradients accumulate on base_model parameters for the optimizer to consume. + Runs uncompiled (forward_with_banks) to avoid recompilation from new bank tensors. + """ + seq_len = x.shape[1] + half = seq_len // 2 + x_inner, y_inner = x[:, :half], y[:, :half] + x_outer, y_outer = x[:, half:], y[:, half:] + + n = base_model.num_layers + freeze_n = args.meta_ttt_freeze_blocks + lr = args.meta_ttt_inner_lr + + # --- Inner loop: clone banks, compute grads, 1-step SGD --- + qo = base_model.qo_bank.detach().clone().requires_grad_(True) + kv = base_model.kv_bank.detach().clone().requires_grad_(True) + up = base_model.mlp_up_bank.detach().clone().requires_grad_(True) + down = base_model.mlp_down_bank.detach().clone().requires_grad_(True) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + inner_loss = base_model.forward_with_banks(x_inner, y_inner, qo, kv, up, down) + + g_qo, g_kv, g_up, g_down = torch.autograd.grad(inner_loss, [qo, kv, up, down]) + + # Apply freeze mask: zero gradients for frozen blocks + if freeze_n > 0: + with torch.no_grad(): + g_qo = g_qo.clone() + g_kv = g_kv.clone() + g_up = g_up.clone() + g_down = g_down.clone() + for bi in range(freeze_n): + g_qo[bi].zero_(); g_qo[n + bi].zero_() + g_kv[bi].zero_(); g_kv[n + bi].zero_() + g_up[bi].zero_() + g_down[bi].zero_() + + # 1-step SGD update (FOMAML: detach to drop second-order) + with torch.no_grad(): + qo_upd = (qo - lr * g_qo).requires_grad_(True) + kv_upd = (kv - lr * g_kv).requires_grad_(True) + up_upd = (up - lr * g_up).requires_grad_(True) + down_upd = (down - lr * g_down).requires_grad_(True) + + # --- Outer loop: evaluate adapted model on chunk_B --- + # Non-bank params (embeddings, norms, scales) are LIVE — grads flow to them directly + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + outer_loss = base_model.forward_with_banks( + x_outer, y_outer, qo_upd, kv_upd, up_upd, down_upd) + + scaled = outer_loss * args.meta_ttt_loss_weight * grad_scale + scaled.backward() + + # FOMAML: copy adapted-point gradients onto original bank params + with torch.no_grad(): + for bank, upd in [(base_model.qo_bank, qo_upd), + (base_model.kv_bank, kv_upd), + (base_model.mlp_up_bank, up_upd), + (base_model.mlp_down_bank, down_upd)]: + if upd.grad is not None: + if bank.grad is None: + bank.grad = upd.grad.to(bank.dtype) + else: + bank.grad.add_(upd.grad.to(bank.dtype)) + + return outer_loss.detach() + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + total_accum = int(os.environ.get("GRAD_ACCUM_TOTAL", "8")) + if total_accum % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide GRAD_ACCUM_TOTAL={total_accum}") + grad_accum_steps = total_accum // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False); enable_flash_sdp(True); enable_mem_efficient_sdp(False); enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True, flush: bool = False) -> None: + if not master_process: return + if console: print(msg, flush=flush) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f, flush=True) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0(subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, console=False) + log0("=" * 100, console=False) + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError(f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}") + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + dtg=args.dtg_enabled, ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + base_model._has_leading_space = has_leading_space_lut + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + # Freeze dead skip connections: B0→B9 (row 4) and B1→B8 (row 3) are near-zero + with torch.no_grad(): + if base_model.skip_weights.shape[0] >= 5: + base_model.skip_weights.data[3].zero_() # B1→B8 + base_model.skip_weights.data[4].zero_() # B0→B9 + _skip_freeze_mask = torch.ones_like(base_model.skip_weights.data) + if base_model.skip_weights.shape[0] >= 5: + _skip_freeze_mask[3].zero_() + _skip_freeze_mask[4].zero_() + base_model.skip_weights.register_hook(lambda grad: grad * _skip_freeze_mask) + matrix_params = [base_model.qo_bank, base_model.kv_bank, base_model.mlp_up_bank, base_model.mlp_down_bank] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)] + if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: scalar_params.append(base_model.bigram.scale) + if base_model.word_start_boost is not None: scalar_params.append(base_model.word_start_boost) + if base_model.ve_shared is not None: + if base_model.ve_shared.proj is not None: scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + optimizer_tok = torch.optim.AdamW(tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + optimizer_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, weight_decay=args.muon_wd) + for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW([{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam([{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + _warmdown_triggered_ms: float | None = None + _loss_ema: float | None = None + _loss_ema_prev: float | None = None + def lr_mul(step: int, elapsed_ms: float) -> float: + nonlocal _warmdown_triggered_ms + if args.warmdown_iters <= 0: return 1.0 + # Guard: don't compute warmdown in early steps where step_ms estimate is unreliable + if step < args.warmup_steps + 5: return 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_duration_ms = args.warmdown_iters * step_ms + if _warmdown_triggered_ms is not None: + warmdown_start_ms = _warmdown_triggered_ms + elif max_wallclock_ms is not None: + warmdown_start_ms = max(max_wallclock_ms - warmdown_duration_ms, 0.0) + else: + warmdown_start_ms = max(args.iterations - args.warmdown_iters, 0) * step_ms + if elapsed_ms >= warmdown_start_ms: + total_warmdown_ms = (max_wallclock_ms - warmdown_start_ms) if max_wallclock_ms else warmdown_duration_ms + progress_ms = elapsed_ms - warmdown_start_ms + progress = min(progress_ms / max(total_warmdown_ms, 1e-9), 1.0) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + return 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + if distributed: + for p in base_model.parameters(): + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_pairs: list[tuple[Tensor, Tensor]] | None = None + ema_state: dict[str, Tensor] | None = None + ema_update_every = int(os.environ.get("EMA_UPDATE_EVERY", "10")) + if args.ema_enabled: + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_pairs = [(ema_state[name], param) for name, param in base_model.named_parameters()] + for name, buf in base_model.named_buffers(): + if name in ema_state: ema_pairs.append((ema_state[name], buf)) + ema_decay_eff = args.ema_decay ** ema_update_every + log0(f"ema:initialized decay={args.ema_decay} update_every={ema_update_every} decay_eff={ema_decay_eff:.6f}") + ema_stream = torch.cuda.Stream() if ema_pairs is not None else None + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val(args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms") + torch.cuda.synchronize(); t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}") + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + for micro_step in range(grad_accum_steps): + x, y = next_xy + if micro_step < grad_accum_steps - 1: + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + # Meta-TTT: FOMAML inner/outer loop on the last micro-batch + # Disabled during warmdown (scale < 0.5) — model should converge, not explore + if args.meta_ttt_enabled and step % args.meta_ttt_every == 0 and scale > 0.5: + meta_outer = meta_ttt_step(base_model, x, y, args, grad_scale=grad_scale) + if args.adaptive_warmdown and _warmdown_triggered_ms is None and step >= args.adaptive_warmdown_min_steps and step % 100 == 0: + tl = train_loss.item() + if _loss_ema is None: + _loss_ema = tl; _loss_ema_prev = tl + else: + _loss_ema = args.adaptive_warmdown_ema * _loss_ema + (1.0 - args.adaptive_warmdown_ema) * tl + improvement = (_loss_ema_prev - _loss_ema) if _loss_ema_prev is not None else 1.0 + if improvement < args.adaptive_warmdown_threshold: + elapsed_now = training_time_ms + 1000.0 * (time.perf_counter() - t0) + _warmdown_triggered_ms = elapsed_now + log0(f"adaptive_warmdown:triggered step:{step} loss_ema:{_loss_ema:.6f} improvement:{improvement:.6f}") + _loss_ema_prev = _loss_ema + if step < args.muon_momentum_warmup_steps: + frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + elif args.momentum_cyclic: + period = args.momentum_cycle_period * 2 + pos = (step % period) / period + if pos < 0.5: + muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2) + else: + muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2) + else: + muon_momentum = args.muon_momentum + for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + optimizer_muon.launch_reduce_scatters() + if distributed: + for p in replicated_params: + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step(); optimizer_scalar.step() + if optimizer_head is not None: optimizer_head.step() + optimizer_muon.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if ema_pairs is not None and step % ema_update_every == 0: + ema_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ema_stream), torch.no_grad(): + for ema_t, param_t in ema_pairs: + ema_t.mul_(ema_decay_eff).add_(param_t.detach().float(), alpha=1.0 - ema_decay_eff) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1; log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + if should_log_train: + log0(f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms") + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: stop_after_step = step + log0(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB") + if ema_stream is not None: ema_stream.synchronize() + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif ema_state is not None: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize(); t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val(args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + torch.cuda.synchronize() + log0(f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms") + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + exp_name = args.run_id.rsplit("_seed", 1)[0] if "_seed" in args.run_id else args.run_id + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + hessian_model._has_leading_space = has_leading_space_lut + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(hessian_model) + hessian_model.load_state_dict({k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, strict=False) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib(base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens; del hessian_model; torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + return len(lzma.compress(buf.getvalue(), preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} +/-1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full +/-1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} +/-1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: dist.barrier() + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model._has_leading_space = has_leading_space_lut + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize(); t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val(args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len) + torch.cuda.synchronize() + log0(f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms") + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # TTT-only evaluation (no standalone sliding window — meta-TTT model is designed for TTT) + if args.ttt_enabled and args.eval_stride > 0: + log0(f"\n{'='*60}", flush=True) + log0(f"STARTING TTT (Test-Time Training)", flush=True) + log0(f"{'='*60}", flush=True) + eval_model.load_state_dict(deq_state, strict=True) + ttt_loss, ttt_bpb = eval_val_sliding_ttt(args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0) + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f}", flush=True) + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}", flush=True) + if distributed: dist.destroy_process_group() + +if __name__ == "__main__": + main() diff --git a/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/training_stdout_seed42.txt b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/training_stdout_seed42.txt new file mode 100644 index 0000000000..9b31fa1c37 --- /dev/null +++ b/records/track_non_record_16mb/2026_04_09_poscond_bigram_and_ablation/record_exp101/training_stdout_seed42.txt @@ -0,0 +1,9646 @@ +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1"))) + momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85)) + momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95)) + momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 128)) + + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + + # EMA + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + + # SWA / LAWA (from remote) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + + # Adaptive warmdown + adaptive_warmdown = bool(int(os.environ.get("ADAPTIVE_WARMDOWN", "1"))) + adaptive_warmdown_ema = float(os.environ.get("ADAPTIVE_WARMDOWN_EMA", "0.99")) + adaptive_warmdown_threshold = float(os.environ.get("ADAPTIVE_WARMDOWN_THRESHOLD", "0.0005")) + adaptive_warmdown_min_steps = int(os.environ.get("ADAPTIVE_WARMDOWN_MIN_STEPS", "2000")) + + # Partial RoPE + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN scale and DTG + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + + # QAT + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + + # Value Embedding + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + + # Gated attention / Value residual + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + + # TTT (Test-Time Training) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lr = float(os.environ.get("TTT_LR", "0.002")) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", "3")) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", "0.9")) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", "16")) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) + + # Meta-TTT (FOMAML: train-time meta-learning for better TTT initialization) + meta_ttt_enabled = bool(int(os.environ.get("META_TTT_ENABLED", "1"))) + meta_ttt_inner_lr = float(os.environ.get("META_TTT_INNER_LR", "0.002")) + meta_ttt_every = int(os.environ.get("META_TTT_EVERY", "8")) + meta_ttt_loss_weight = float(os.environ.get("META_TTT_LOSS_WEIGHT", "0.5")) + meta_ttt_freeze_blocks = int(os.environ.get("META_TTT_FREEZE_BLOCKS", "2")) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for seq_len={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,bigram.scale,word_start_boost,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + """Hash-based n-gram embedding table. + + exp101 additions: + - pos_conditional: split buckets into two disjoint halves keyed on whether + the CURRENT token is a word-start. ws-current pairs hash into the lower + half [0, half), non-ws-current pairs into the upper half [half, 2*half). + Motivation: in exp95 the single shared table was dominated by within-word + (prev, curr) pairs, and the word_start_boost scalar collapsed to ~0.007 + (killing bigram at word-starts to suppress the noise). Splitting the + buckets gives word-start pairs their own exclusive rows that can learn + their own signal without contaminating within-word buckets, and vice + versa. Zero extra parameters — same 4096×dim table, different layout. + - trigram: optional (t-2, t-1, t) lookup summed into the same table. When + combined with pos_conditional, the trigram hash respects the same split + (keyed on whether t is a word-start) so a bucket is only trained by + lookups of a consistent word-start class. + """ + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, + trigram: bool = False, pos_conditional: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self._pos_conditional = pos_conditional + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # sentinel row at index mod + out = torch.empty_like(t) + out[..., 0] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 for bigram_vocab_size=4096 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws-current, half for non-ws-current + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def trigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 + base = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % half + is_ws_curr = has_leading_space[tokens[..., 2:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half + out[..., 2:] = base + shift + else: + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + + def forward(self, token_ids: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + h = self.embed(self.bigram_hash(token_ids, has_leading_space)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids, has_leading_space)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + # Block 0: init attn_scale to 0.1 (analysis shows it converges to ~0.094 anyway) + attn_init = 0.1 if layer_idx == 0 else 1.0 + self.attn_scale = nn.Parameter(torch.full((dim,), attn_init, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, dtg=dtg, + gated_attention=gated_attention, value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward_with_banks(self, input_ids: Tensor, target_ids: Tensor, + qo_bank: Tensor, kv_bank: Tensor, + mlp_up_bank: Tensor, mlp_down_bank: Tensor) -> Tensor: + """Forward with external bank tensors (for meta-TTT). Pure next-token loss, no MTP.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + qo_bank[i], kv_bank[i], kv_bank[n + i], + qo_bank[n + i], mlp_up_bank[i], mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + qo_bank[bi], kv_bank[bi], kv_bank[n + bi], + qo_bank[n + bi], mlp_up_bank[bi], mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +# --- TTT (Test-Time Training) --- + +def eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + eval_batch = args.eval_batch_seqs + train_batch = args.ttt_batch_seqs + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + frozen_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_n = sum(p.numel() for p in ttt_params) + frozen_n = sum(p.numel() for p in base_model.parameters() if not p.requires_grad) + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}", flush=True) + log0(f"ttt_sliding:params unfrozen={unfrozen_n} frozen={frozen_n}", flush=True) + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), eval_batch): + batch_ws = my_windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + is_last = (ci == num_chunks - 1) + if not is_last and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, train_batch): + be = min(bs + train_batch, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + total_w = len(window_starts) + done_w = sum(len(chunk_windows[c]) for c in range(ci + 1)) + pct = done_w / total_w * 100 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + eta = (elapsed / max(done_w, 1)) * (total_w - done_w) + bar_len = 30 + filled = int(bar_len * done_w / total_w) + bar = "\u2588" * filled + "\u2591" * (bar_len - filled) + log0(f" ttt [{bar}] {pct:5.1f}% chunk {ci+1}/{num_chunks} bpb={rbpb:.6f} ETA={eta:.0f}s", flush=True) + if rank == 0: + log0("", flush=True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s", flush=True) + return val_loss, val_bpb + +# --- GPTQ quantization pipeline --- + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: qo_slices[i] = sd[qk]; consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: qo_slices[n + i] = sd[ok]; consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: kv_slices[i] = sd[kk]; consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: kv_slices[n + i] = sd[vk]; consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: up_slices[i] = sd[fk]; consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: down_slices[i] = sd[dk]; consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +class _HessianAttn(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)); k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm(); self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: return None + if 've' not in ve_cache: ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x; skips = []; ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve); skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)); targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + hessians = {}; hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)); hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: h.remove() + for name in hessians: + H = hessians[name]; H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]); hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough"; continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float(); meta[name] = "passthrough_ctrl"; continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t; continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Meta-TTT (FOMAML) --- + +def meta_ttt_step(base_model: nn.Module, x: Tensor, y: Tensor, + args, grad_scale: float = 1.0) -> Tensor: + """FOMAML meta-TTT step: adapt on first half of sequence, evaluate on second half. + + Inner loop: 1-step SGD on bank params using chunk_A (detached, no second-order). + Outer loop: evaluate adapted model on chunk_B. + Gradients accumulate on base_model parameters for the optimizer to consume. + Runs uncompiled (forward_with_banks) to avoid recompilation from new bank tensors. + """ + seq_len = x.shape[1] + half = seq_len // 2 + x_inner, y_inner = x[:, :half], y[:, :half] + x_outer, y_outer = x[:, half:], y[:, half:] + + n = base_model.num_layers + freeze_n = args.meta_ttt_freeze_blocks + lr = args.meta_ttt_inner_lr + + # --- Inner loop: clone banks, compute grads, 1-step SGD --- + qo = base_model.qo_bank.detach().clone().requires_grad_(True) + kv = base_model.kv_bank.detach().clone().requires_grad_(True) + up = base_model.mlp_up_bank.detach().clone().requires_grad_(True) + down = base_model.mlp_down_bank.detach().clone().requires_grad_(True) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + inner_loss = base_model.forward_with_banks(x_inner, y_inner, qo, kv, up, down) + + g_qo, g_kv, g_up, g_down = torch.autograd.grad(inner_loss, [qo, kv, up, down]) + + # Apply freeze mask: zero gradients for frozen blocks + if freeze_n > 0: + with torch.no_grad(): + g_qo = g_qo.clone() + g_kv = g_kv.clone() + g_up = g_up.clone() + g_down = g_down.clone() + for bi in range(freeze_n): + g_qo[bi].zero_(); g_qo[n + bi].zero_() + g_kv[bi].zero_(); g_kv[n + bi].zero_() + g_up[bi].zero_() + g_down[bi].zero_() + + # 1-step SGD update (FOMAML: detach to drop second-order) + with torch.no_grad(): + qo_upd = (qo - lr * g_qo).requires_grad_(True) + kv_upd = (kv - lr * g_kv).requires_grad_(True) + up_upd = (up - lr * g_up).requires_grad_(True) + down_upd = (down - lr * g_down).requires_grad_(True) + + # --- Outer loop: evaluate adapted model on chunk_B --- + # Non-bank params (embeddings, norms, scales) are LIVE — grads flow to them directly + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + outer_loss = base_model.forward_with_banks( + x_outer, y_outer, qo_upd, kv_upd, up_upd, down_upd) + + scaled = outer_loss * args.meta_ttt_loss_weight * grad_scale + scaled.backward() + + # FOMAML: copy adapted-point gradients onto original bank params + with torch.no_grad(): + for bank, upd in [(base_model.qo_bank, qo_upd), + (base_model.kv_bank, kv_upd), + (base_model.mlp_up_bank, up_upd), + (base_model.mlp_down_bank, down_upd)]: + if upd.grad is not None: + if bank.grad is None: + bank.grad = upd.grad.to(bank.dtype) + else: + bank.grad.add_(upd.grad.to(bank.dtype)) + + return outer_loss.detach() + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + total_accum = int(os.environ.get("GRAD_ACCUM_TOTAL", "8")) + if total_accum % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide GRAD_ACCUM_TOTAL={total_accum}") + grad_accum_steps = total_accum // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False); enable_flash_sdp(True); enable_mem_efficient_sdp(False); enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True, flush: bool = False) -> None: + if not master_process: return + if console: print(msg, flush=flush) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f, flush=True) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0(subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, console=False) + log0("=" * 100, console=False) + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError(f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}") + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + dtg=args.dtg_enabled, ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + base_model._has_leading_space = has_leading_space_lut + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + # Freeze dead skip connections: B0→B9 (row 4) and B1→B8 (row 3) are near-zero + with torch.no_grad(): + if base_model.skip_weights.shape[0] >= 5: + base_model.skip_weights.data[3].zero_() # B1→B8 + base_model.skip_weights.data[4].zero_() # B0→B9 + _skip_freeze_mask = torch.ones_like(base_model.skip_weights.data) + if base_model.skip_weights.shape[0] >= 5: + _skip_freeze_mask[3].zero_() + _skip_freeze_mask[4].zero_() + base_model.skip_weights.register_hook(lambda grad: grad * _skip_freeze_mask) + matrix_params = [base_model.qo_bank, base_model.kv_bank, base_model.mlp_up_bank, base_model.mlp_down_bank] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)] + if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: scalar_params.append(base_model.bigram.scale) + if base_model.word_start_boost is not None: scalar_params.append(base_model.word_start_boost) + if base_model.ve_shared is not None: + if base_model.ve_shared.proj is not None: scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + optimizer_tok = torch.optim.AdamW(tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + optimizer_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, weight_decay=args.muon_wd) + for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW([{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam([{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + _warmdown_triggered_ms: float | None = None + _loss_ema: float | None = None + _loss_ema_prev: float | None = None + def lr_mul(step: int, elapsed_ms: float) -> float: + nonlocal _warmdown_triggered_ms + if args.warmdown_iters <= 0: return 1.0 + # Guard: don't compute warmdown in early steps where step_ms estimate is unreliable + if step < args.warmup_steps + 5: return 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_duration_ms = args.warmdown_iters * step_ms + if _warmdown_triggered_ms is not None: + warmdown_start_ms = _warmdown_triggered_ms + elif max_wallclock_ms is not None: + warmdown_start_ms = max(max_wallclock_ms - warmdown_duration_ms, 0.0) + else: + warmdown_start_ms = max(args.iterations - args.warmdown_iters, 0) * step_ms + if elapsed_ms >= warmdown_start_ms: + total_warmdown_ms = (max_wallclock_ms - warmdown_start_ms) if max_wallclock_ms else warmdown_duration_ms + progress_ms = elapsed_ms - warmdown_start_ms + progress = min(progress_ms / max(total_warmdown_ms, 1e-9), 1.0) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + return 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + if distributed: + for p in base_model.parameters(): + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_pairs: list[tuple[Tensor, Tensor]] | None = None + ema_state: dict[str, Tensor] | None = None + ema_update_every = int(os.environ.get("EMA_UPDATE_EVERY", "10")) + if args.ema_enabled: + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_pairs = [(ema_state[name], param) for name, param in base_model.named_parameters()] + for name, buf in base_model.named_buffers(): + if name in ema_state: ema_pairs.append((ema_state[name], buf)) + ema_decay_eff = args.ema_decay ** ema_update_every + log0(f"ema:initialized decay={args.ema_decay} update_every={ema_update_every} decay_eff={ema_decay_eff:.6f}") + ema_stream = torch.cuda.Stream() if ema_pairs is not None else None + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val(args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms") + torch.cuda.synchronize(); t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}") + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + for micro_step in range(grad_accum_steps): + x, y = next_xy + if micro_step < grad_accum_steps - 1: + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + # Meta-TTT: FOMAML inner/outer loop on the last micro-batch + # Disabled during warmdown (scale < 0.5) — model should converge, not explore + if args.meta_ttt_enabled and step % args.meta_ttt_every == 0 and scale > 0.5: + meta_outer = meta_ttt_step(base_model, x, y, args, grad_scale=grad_scale) + if args.adaptive_warmdown and _warmdown_triggered_ms is None and step >= args.adaptive_warmdown_min_steps and step % 100 == 0: + tl = train_loss.item() + if _loss_ema is None: + _loss_ema = tl; _loss_ema_prev = tl + else: + _loss_ema = args.adaptive_warmdown_ema * _loss_ema + (1.0 - args.adaptive_warmdown_ema) * tl + improvement = (_loss_ema_prev - _loss_ema) if _loss_ema_prev is not None else 1.0 + if improvement < args.adaptive_warmdown_threshold: + elapsed_now = training_time_ms + 1000.0 * (time.perf_counter() - t0) + _warmdown_triggered_ms = elapsed_now + log0(f"adaptive_warmdown:triggered step:{step} loss_ema:{_loss_ema:.6f} improvement:{improvement:.6f}") + _loss_ema_prev = _loss_ema + if step < args.muon_momentum_warmup_steps: + frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + elif args.momentum_cyclic: + period = args.momentum_cycle_period * 2 + pos = (step % period) / period + if pos < 0.5: + muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2) + else: + muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2) + else: + muon_momentum = args.muon_momentum + for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + optimizer_muon.launch_reduce_scatters() + if distributed: + for p in replicated_params: + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step(); optimizer_scalar.step() + if optimizer_head is not None: optimizer_head.step() + optimizer_muon.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if ema_pairs is not None and step % ema_update_every == 0: + ema_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ema_stream), torch.no_grad(): + for ema_t, param_t in ema_pairs: + ema_t.mul_(ema_decay_eff).add_(param_t.detach().float(), alpha=1.0 - ema_decay_eff) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1; log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + if should_log_train: + log0(f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms") + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: stop_after_step = step + log0(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB") + if ema_stream is not None: ema_stream.synchronize() + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif ema_state is not None: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize(); t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val(args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + torch.cuda.synchronize() + log0(f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms") + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + exp_name = args.run_id.rsplit("_seed", 1)[0] if "_seed" in args.run_id else args.run_id + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + hessian_model._has_leading_space = has_leading_space_lut + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(hessian_model) + hessian_model.load_state_dict({k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, strict=False) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib(base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens; del hessian_model; torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + return len(lzma.compress(buf.getvalue(), preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} +/-1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full +/-1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} +/-1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: dist.barrier() + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model._has_leading_space = has_leading_space_lut + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize(); t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val(args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len) + torch.cuda.synchronize() + log0(f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms") + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # TTT-only evaluation (no standalone sliding window — meta-TTT model is designed for TTT) + if args.ttt_enabled and args.eval_stride > 0: + log0(f"\n{'='*60}", flush=True) + log0(f"STARTING TTT (Test-Time Training)", flush=True) + log0(f"{'='*60}", flush=True) + eval_model.load_state_dict(deq_state, strict=True) + ttt_loss, ttt_bpb = eval_val_sliding_ttt(args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0) + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f}", flush=True) + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}", flush=True) + if distributed: dist.destroy_process_group() + +if __name__ == "__main__": + main() + +==================================================================================================== +Running Python 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] +Running PyTorch 2.9.1+cu128 +Tue Apr 7 17:08:15 2026 ++-----------------------------------------------------------------------------------------+ +| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 | ++-----------------------------------------+------------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+========================+======================| +| 0 NVIDIA H100 80GB HBM3 On | 00000000:5D:00.0 Off | 0 | +| N/A 35C P0 75W / 700W | 527MiB / 81559MiB | 0% Default | +| | | Disabled | ++-----------------------------------------+------------------------+----------------------+ + ++-----------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=========================================================================================| +| No running processes found | ++-----------------------------------------------------------------------------------------+ + +==================================================================================================== +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26960991 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:1 grad_accum_steps:8 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +ema:initialized decay=0.998 update_every=10 decay_eff=0.980179 +step:0/9000 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.02ms +step:1/9000 train_loss:6.9298 train_time:1080ms step_avg:1079.67ms +step:2/9000 train_loss:8.3678 train_time:11904ms step_avg:5952.05ms +step:3/9000 train_loss:7.4169 train_time:12567ms step_avg:4188.98ms +step:4/9000 train_loss:7.5892 train_time:13215ms step_avg:3303.85ms +step:5/9000 train_loss:7.4515 train_time:14127ms step_avg:2825.45ms +step:6/9000 train_loss:7.1208 train_time:14739ms step_avg:2456.54ms +step:7/9000 train_loss:6.8043 train_time:15398ms step_avg:2199.67ms +step:8/9000 train_loss:6.6762 train_time:16050ms step_avg:2006.27ms +step:9/9000 train_loss:6.4445 train_time:16962ms step_avg:1884.71ms +step:10/9000 train_loss:6.1053 train_time:17575ms step_avg:1757.46ms +step:500/9000 train_loss:2.3222 train_time:368691ms step_avg:737.38ms +step:1000/9000 train_loss:2.2647 train_time:727368ms step_avg:727.37ms +step:1500/9000 train_loss:2.1376 train_time:1086407ms step_avg:724.27ms +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1"))) + momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85)) + momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95)) + momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 128)) + + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + + # EMA + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + + # SWA / LAWA (from remote) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + + # Adaptive warmdown + adaptive_warmdown = bool(int(os.environ.get("ADAPTIVE_WARMDOWN", "1"))) + adaptive_warmdown_ema = float(os.environ.get("ADAPTIVE_WARMDOWN_EMA", "0.99")) + adaptive_warmdown_threshold = float(os.environ.get("ADAPTIVE_WARMDOWN_THRESHOLD", "0.0005")) + adaptive_warmdown_min_steps = int(os.environ.get("ADAPTIVE_WARMDOWN_MIN_STEPS", "2000")) + + # Partial RoPE + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN scale and DTG + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + + # QAT + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + + # Value Embedding + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + + # Gated attention / Value residual + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + + # TTT (Test-Time Training) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lr = float(os.environ.get("TTT_LR", "0.002")) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", "3")) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", "0.9")) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", "16")) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) + + # Meta-TTT (FOMAML: train-time meta-learning for better TTT initialization) + meta_ttt_enabled = bool(int(os.environ.get("META_TTT_ENABLED", "1"))) + meta_ttt_inner_lr = float(os.environ.get("META_TTT_INNER_LR", "0.002")) + meta_ttt_every = int(os.environ.get("META_TTT_EVERY", "8")) + meta_ttt_loss_weight = float(os.environ.get("META_TTT_LOSS_WEIGHT", "0.5")) + meta_ttt_freeze_blocks = int(os.environ.get("META_TTT_FREEZE_BLOCKS", "2")) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for seq_len={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,bigram.scale,word_start_boost,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + """Hash-based n-gram embedding table. + + exp101 additions: + - pos_conditional: split buckets into two disjoint halves keyed on whether + the CURRENT token is a word-start. ws-current pairs hash into the lower + half [0, half), non-ws-current pairs into the upper half [half, 2*half). + Motivation: in exp95 the single shared table was dominated by within-word + (prev, curr) pairs, and the word_start_boost scalar collapsed to ~0.007 + (killing bigram at word-starts to suppress the noise). Splitting the + buckets gives word-start pairs their own exclusive rows that can learn + their own signal without contaminating within-word buckets, and vice + versa. Zero extra parameters — same 4096×dim table, different layout. + - trigram: optional (t-2, t-1, t) lookup summed into the same table. When + combined with pos_conditional, the trigram hash respects the same split + (keyed on whether t is a word-start) so a bucket is only trained by + lookups of a consistent word-start class. + """ + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, + trigram: bool = False, pos_conditional: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self._pos_conditional = pos_conditional + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # sentinel row at index mod + out = torch.empty_like(t) + out[..., 0] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 for bigram_vocab_size=4096 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws-current, half for non-ws-current + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def trigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 + base = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % half + is_ws_curr = has_leading_space[tokens[..., 2:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half + out[..., 2:] = base + shift + else: + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + + def forward(self, token_ids: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + h = self.embed(self.bigram_hash(token_ids, has_leading_space)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids, has_leading_space)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + # Block 0: init attn_scale to 0.1 (analysis shows it converges to ~0.094 anyway) + attn_init = 0.1 if layer_idx == 0 else 1.0 + self.attn_scale = nn.Parameter(torch.full((dim,), attn_init, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, dtg=dtg, + gated_attention=gated_attention, value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward_with_banks(self, input_ids: Tensor, target_ids: Tensor, + qo_bank: Tensor, kv_bank: Tensor, + mlp_up_bank: Tensor, mlp_down_bank: Tensor) -> Tensor: + """Forward with external bank tensors (for meta-TTT). Pure next-token loss, no MTP.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + qo_bank[i], kv_bank[i], kv_bank[n + i], + qo_bank[n + i], mlp_up_bank[i], mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + qo_bank[bi], kv_bank[bi], kv_bank[n + bi], + qo_bank[n + bi], mlp_up_bank[bi], mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +# --- TTT (Test-Time Training) --- + +def eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + eval_batch = args.eval_batch_seqs + train_batch = args.ttt_batch_seqs + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + frozen_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_n = sum(p.numel() for p in ttt_params) + frozen_n = sum(p.numel() for p in base_model.parameters() if not p.requires_grad) + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}", flush=True) + log0(f"ttt_sliding:params unfrozen={unfrozen_n} frozen={frozen_n}", flush=True) + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), eval_batch): + batch_ws = my_windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + is_last = (ci == num_chunks - 1) + if not is_last and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, train_batch): + be = min(bs + train_batch, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + total_w = len(window_starts) + done_w = sum(len(chunk_windows[c]) for c in range(ci + 1)) + pct = done_w / total_w * 100 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + eta = (elapsed / max(done_w, 1)) * (total_w - done_w) + bar_len = 30 + filled = int(bar_len * done_w / total_w) + bar = "\u2588" * filled + "\u2591" * (bar_len - filled) + log0(f" ttt [{bar}] {pct:5.1f}% chunk {ci+1}/{num_chunks} bpb={rbpb:.6f} ETA={eta:.0f}s", flush=True) + if rank == 0: + log0("", flush=True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s", flush=True) + return val_loss, val_bpb + +# --- GPTQ quantization pipeline --- + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: qo_slices[i] = sd[qk]; consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: qo_slices[n + i] = sd[ok]; consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: kv_slices[i] = sd[kk]; consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: kv_slices[n + i] = sd[vk]; consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: up_slices[i] = sd[fk]; consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: down_slices[i] = sd[dk]; consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +class _HessianAttn(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)); k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm(); self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: return None + if 've' not in ve_cache: ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x; skips = []; ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve); skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)); targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + hessians = {}; hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)); hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: h.remove() + for name in hessians: + H = hessians[name]; H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]); hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough"; continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float(); meta[name] = "passthrough_ctrl"; continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t; continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Meta-TTT (FOMAML) --- + +def meta_ttt_step(base_model: nn.Module, x: Tensor, y: Tensor, + args, grad_scale: float = 1.0) -> Tensor: + """FOMAML meta-TTT step: adapt on first half of sequence, evaluate on second half. + + Inner loop: 1-step SGD on bank params using chunk_A (detached, no second-order). + Outer loop: evaluate adapted model on chunk_B. + Gradients accumulate on base_model parameters for the optimizer to consume. + Runs uncompiled (forward_with_banks) to avoid recompilation from new bank tensors. + """ + seq_len = x.shape[1] + half = seq_len // 2 + x_inner, y_inner = x[:, :half], y[:, :half] + x_outer, y_outer = x[:, half:], y[:, half:] + + n = base_model.num_layers + freeze_n = args.meta_ttt_freeze_blocks + lr = args.meta_ttt_inner_lr + + # --- Inner loop: clone banks, compute grads, 1-step SGD --- + qo = base_model.qo_bank.detach().clone().requires_grad_(True) + kv = base_model.kv_bank.detach().clone().requires_grad_(True) + up = base_model.mlp_up_bank.detach().clone().requires_grad_(True) + down = base_model.mlp_down_bank.detach().clone().requires_grad_(True) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + inner_loss = base_model.forward_with_banks(x_inner, y_inner, qo, kv, up, down) + + g_qo, g_kv, g_up, g_down = torch.autograd.grad(inner_loss, [qo, kv, up, down]) + + # Apply freeze mask: zero gradients for frozen blocks + if freeze_n > 0: + with torch.no_grad(): + g_qo = g_qo.clone() + g_kv = g_kv.clone() + g_up = g_up.clone() + g_down = g_down.clone() + for bi in range(freeze_n): + g_qo[bi].zero_(); g_qo[n + bi].zero_() + g_kv[bi].zero_(); g_kv[n + bi].zero_() + g_up[bi].zero_() + g_down[bi].zero_() + + # 1-step SGD update (FOMAML: detach to drop second-order) + with torch.no_grad(): + qo_upd = (qo - lr * g_qo).requires_grad_(True) + kv_upd = (kv - lr * g_kv).requires_grad_(True) + up_upd = (up - lr * g_up).requires_grad_(True) + down_upd = (down - lr * g_down).requires_grad_(True) + + # --- Outer loop: evaluate adapted model on chunk_B --- + # Non-bank params (embeddings, norms, scales) are LIVE — grads flow to them directly + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + outer_loss = base_model.forward_with_banks( + x_outer, y_outer, qo_upd, kv_upd, up_upd, down_upd) + + scaled = outer_loss * args.meta_ttt_loss_weight * grad_scale + scaled.backward() + + # FOMAML: copy adapted-point gradients onto original bank params + with torch.no_grad(): + for bank, upd in [(base_model.qo_bank, qo_upd), + (base_model.kv_bank, kv_upd), + (base_model.mlp_up_bank, up_upd), + (base_model.mlp_down_bank, down_upd)]: + if upd.grad is not None: + if bank.grad is None: + bank.grad = upd.grad.to(bank.dtype) + else: + bank.grad.add_(upd.grad.to(bank.dtype)) + + return outer_loss.detach() + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + total_accum = int(os.environ.get("GRAD_ACCUM_TOTAL", "8")) + if total_accum % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide GRAD_ACCUM_TOTAL={total_accum}") + grad_accum_steps = total_accum // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False); enable_flash_sdp(True); enable_mem_efficient_sdp(False); enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True, flush: bool = False) -> None: + if not master_process: return + if console: print(msg, flush=flush) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f, flush=True) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0(subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, console=False) + log0("=" * 100, console=False) + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError(f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}") + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + dtg=args.dtg_enabled, ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + base_model._has_leading_space = has_leading_space_lut + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + # Freeze dead skip connections: B0→B9 (row 4) and B1→B8 (row 3) are near-zero + with torch.no_grad(): + if base_model.skip_weights.shape[0] >= 5: + base_model.skip_weights.data[3].zero_() # B1→B8 + base_model.skip_weights.data[4].zero_() # B0→B9 + _skip_freeze_mask = torch.ones_like(base_model.skip_weights.data) + if base_model.skip_weights.shape[0] >= 5: + _skip_freeze_mask[3].zero_() + _skip_freeze_mask[4].zero_() + base_model.skip_weights.register_hook(lambda grad: grad * _skip_freeze_mask) + matrix_params = [base_model.qo_bank, base_model.kv_bank, base_model.mlp_up_bank, base_model.mlp_down_bank] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)] + if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: scalar_params.append(base_model.bigram.scale) + if base_model.word_start_boost is not None: scalar_params.append(base_model.word_start_boost) + if base_model.ve_shared is not None: + if base_model.ve_shared.proj is not None: scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + optimizer_tok = torch.optim.AdamW(tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + optimizer_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, weight_decay=args.muon_wd) + for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW([{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam([{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + _warmdown_triggered_ms: float | None = None + _loss_ema: float | None = None + _loss_ema_prev: float | None = None + def lr_mul(step: int, elapsed_ms: float) -> float: + nonlocal _warmdown_triggered_ms + if args.warmdown_iters <= 0: return 1.0 + # Guard: don't compute warmdown in early steps where step_ms estimate is unreliable + if step < args.warmup_steps + 5: return 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_duration_ms = args.warmdown_iters * step_ms + if _warmdown_triggered_ms is not None: + warmdown_start_ms = _warmdown_triggered_ms + elif max_wallclock_ms is not None: + warmdown_start_ms = max(max_wallclock_ms - warmdown_duration_ms, 0.0) + else: + warmdown_start_ms = max(args.iterations - args.warmdown_iters, 0) * step_ms + if elapsed_ms >= warmdown_start_ms: + total_warmdown_ms = (max_wallclock_ms - warmdown_start_ms) if max_wallclock_ms else warmdown_duration_ms + progress_ms = elapsed_ms - warmdown_start_ms + progress = min(progress_ms / max(total_warmdown_ms, 1e-9), 1.0) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + return 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + if distributed: + for p in base_model.parameters(): + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_pairs: list[tuple[Tensor, Tensor]] | None = None + ema_state: dict[str, Tensor] | None = None + ema_update_every = int(os.environ.get("EMA_UPDATE_EVERY", "10")) + if args.ema_enabled: + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_pairs = [(ema_state[name], param) for name, param in base_model.named_parameters()] + for name, buf in base_model.named_buffers(): + if name in ema_state: ema_pairs.append((ema_state[name], buf)) + ema_decay_eff = args.ema_decay ** ema_update_every + log0(f"ema:initialized decay={args.ema_decay} update_every={ema_update_every} decay_eff={ema_decay_eff:.6f}") + ema_stream = torch.cuda.Stream() if ema_pairs is not None else None + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val(args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms") + torch.cuda.synchronize(); t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}") + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + for micro_step in range(grad_accum_steps): + x, y = next_xy + if micro_step < grad_accum_steps - 1: + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + # Meta-TTT: FOMAML inner/outer loop on the last micro-batch + # Disabled during warmdown (scale < 0.5) — model should converge, not explore + if args.meta_ttt_enabled and step % args.meta_ttt_every == 0 and scale > 0.5: + meta_outer = meta_ttt_step(base_model, x, y, args, grad_scale=grad_scale) + if args.adaptive_warmdown and _warmdown_triggered_ms is None and step >= args.adaptive_warmdown_min_steps and step % 100 == 0: + tl = train_loss.item() + if _loss_ema is None: + _loss_ema = tl; _loss_ema_prev = tl + else: + _loss_ema = args.adaptive_warmdown_ema * _loss_ema + (1.0 - args.adaptive_warmdown_ema) * tl + improvement = (_loss_ema_prev - _loss_ema) if _loss_ema_prev is not None else 1.0 + if improvement < args.adaptive_warmdown_threshold: + elapsed_now = training_time_ms + 1000.0 * (time.perf_counter() - t0) + _warmdown_triggered_ms = elapsed_now + log0(f"adaptive_warmdown:triggered step:{step} loss_ema:{_loss_ema:.6f} improvement:{improvement:.6f}") + _loss_ema_prev = _loss_ema + if step < args.muon_momentum_warmup_steps: + frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + elif args.momentum_cyclic: + period = args.momentum_cycle_period * 2 + pos = (step % period) / period + if pos < 0.5: + muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2) + else: + muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2) + else: + muon_momentum = args.muon_momentum + for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + optimizer_muon.launch_reduce_scatters() + if distributed: + for p in replicated_params: + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step(); optimizer_scalar.step() + if optimizer_head is not None: optimizer_head.step() + optimizer_muon.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if ema_pairs is not None and step % ema_update_every == 0: + ema_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ema_stream), torch.no_grad(): + for ema_t, param_t in ema_pairs: + ema_t.mul_(ema_decay_eff).add_(param_t.detach().float(), alpha=1.0 - ema_decay_eff) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1; log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + if should_log_train: + log0(f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms") + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: stop_after_step = step + log0(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB") + if ema_stream is not None: ema_stream.synchronize() + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif ema_state is not None: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize(); t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val(args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + torch.cuda.synchronize() + log0(f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms") + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + exp_name = args.run_id.rsplit("_seed", 1)[0] if "_seed" in args.run_id else args.run_id + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + hessian_model._has_leading_space = has_leading_space_lut + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(hessian_model) + hessian_model.load_state_dict({k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, strict=False) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib(base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens; del hessian_model; torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + return len(lzma.compress(buf.getvalue(), preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} +/-1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full +/-1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} +/-1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: dist.barrier() + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model._has_leading_space = has_leading_space_lut + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize(); t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val(args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len) + torch.cuda.synchronize() + log0(f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms") + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # TTT-only evaluation (no standalone sliding window — meta-TTT model is designed for TTT) + if args.ttt_enabled and args.eval_stride > 0: + log0(f"\n{'='*60}", flush=True) + log0(f"STARTING TTT (Test-Time Training)", flush=True) + log0(f"{'='*60}", flush=True) + eval_model.load_state_dict(deq_state, strict=True) + ttt_loss, ttt_bpb = eval_val_sliding_ttt(args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0) + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f}", flush=True) + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}", flush=True) + if distributed: dist.destroy_process_group() + +if __name__ == "__main__": + main() + +==================================================================================================== +Running Python 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] +Running PyTorch 2.9.1+cu128 +Tue Apr 7 17:30:35 2026 ++-----------------------------------------------------------------------------------------+ +| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 | ++-----------------------------------------+------------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+========================+======================| +| 0 NVIDIA H100 80GB HBM3 On | 00000000:5D:00.0 Off | 0 | +| N/A 42C P0 79W / 700W | 527MiB / 81559MiB | 0% Default | +| | | Disabled | ++-----------------------------------------+------------------------+----------------------+ + ++-----------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=========================================================================================| +| No running processes found | ++-----------------------------------------------------------------------------------------+ + +==================================================================================================== +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26960991 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:1 grad_accum_steps:8 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +ema:initialized decay=0.998 update_every=10 decay_eff=0.980179 +step:0/7500 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.02ms +step:1/7500 train_loss:6.9298 train_time:1100ms step_avg:1099.85ms +step:2/7500 train_loss:8.3678 train_time:12084ms step_avg:6041.80ms +step:3/7500 train_loss:7.4169 train_time:12744ms step_avg:4248.03ms +step:4/7500 train_loss:7.5892 train_time:13405ms step_avg:3351.32ms +step:5/7500 train_loss:7.4515 train_time:14061ms step_avg:2812.20ms +step:6/7500 train_loss:7.1218 train_time:14719ms step_avg:2453.14ms +step:7/7500 train_loss:6.8038 train_time:15370ms step_avg:2195.74ms +step:8/7500 train_loss:6.6757 train_time:16027ms step_avg:2003.37ms +step:9/7500 train_loss:6.4430 train_time:16944ms step_avg:1882.68ms +step:10/7500 train_loss:6.1046 train_time:17557ms step_avg:1755.68ms +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1"))) + momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85)) + momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95)) + momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 128)) + + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + + # EMA + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + + # SWA / LAWA (from remote) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + + # Adaptive warmdown + adaptive_warmdown = bool(int(os.environ.get("ADAPTIVE_WARMDOWN", "1"))) + adaptive_warmdown_ema = float(os.environ.get("ADAPTIVE_WARMDOWN_EMA", "0.99")) + adaptive_warmdown_threshold = float(os.environ.get("ADAPTIVE_WARMDOWN_THRESHOLD", "0.0005")) + adaptive_warmdown_min_steps = int(os.environ.get("ADAPTIVE_WARMDOWN_MIN_STEPS", "2000")) + + # Partial RoPE + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN scale and DTG + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + + # QAT + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + + # Value Embedding + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + + # Gated attention / Value residual + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + + # TTT (Test-Time Training) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lr = float(os.environ.get("TTT_LR", "0.002")) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", "3")) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", "0.9")) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", "16")) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) + + # Meta-TTT (FOMAML: train-time meta-learning for better TTT initialization) + meta_ttt_enabled = bool(int(os.environ.get("META_TTT_ENABLED", "1"))) + meta_ttt_inner_lr = float(os.environ.get("META_TTT_INNER_LR", "0.002")) + meta_ttt_every = int(os.environ.get("META_TTT_EVERY", "8")) + meta_ttt_loss_weight = float(os.environ.get("META_TTT_LOSS_WEIGHT", "0.5")) + meta_ttt_freeze_blocks = int(os.environ.get("META_TTT_FREEZE_BLOCKS", "2")) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for seq_len={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,bigram.scale,word_start_boost,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + """Hash-based n-gram embedding table. + + exp101 additions: + - pos_conditional: split buckets into two disjoint halves keyed on whether + the CURRENT token is a word-start. ws-current pairs hash into the lower + half [0, half), non-ws-current pairs into the upper half [half, 2*half). + Motivation: in exp95 the single shared table was dominated by within-word + (prev, curr) pairs, and the word_start_boost scalar collapsed to ~0.007 + (killing bigram at word-starts to suppress the noise). Splitting the + buckets gives word-start pairs their own exclusive rows that can learn + their own signal without contaminating within-word buckets, and vice + versa. Zero extra parameters — same 4096×dim table, different layout. + - trigram: optional (t-2, t-1, t) lookup summed into the same table. When + combined with pos_conditional, the trigram hash respects the same split + (keyed on whether t is a word-start) so a bucket is only trained by + lookups of a consistent word-start class. + """ + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, + trigram: bool = False, pos_conditional: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self._pos_conditional = pos_conditional + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # sentinel row at index mod + out = torch.empty_like(t) + out[..., 0] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 for bigram_vocab_size=4096 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws-current, half for non-ws-current + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def trigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 + base = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % half + is_ws_curr = has_leading_space[tokens[..., 2:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half + out[..., 2:] = base + shift + else: + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + + def forward(self, token_ids: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + h = self.embed(self.bigram_hash(token_ids, has_leading_space)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids, has_leading_space)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + # Block 0: init attn_scale to 0.1 (analysis shows it converges to ~0.094 anyway) + attn_init = 0.1 if layer_idx == 0 else 1.0 + self.attn_scale = nn.Parameter(torch.full((dim,), attn_init, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, dtg=dtg, + gated_attention=gated_attention, value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward_with_banks(self, input_ids: Tensor, target_ids: Tensor, + qo_bank: Tensor, kv_bank: Tensor, + mlp_up_bank: Tensor, mlp_down_bank: Tensor) -> Tensor: + """Forward with external bank tensors (for meta-TTT). Pure next-token loss, no MTP.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + qo_bank[i], kv_bank[i], kv_bank[n + i], + qo_bank[n + i], mlp_up_bank[i], mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + qo_bank[bi], kv_bank[bi], kv_bank[n + bi], + qo_bank[n + bi], mlp_up_bank[bi], mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +# --- TTT (Test-Time Training) --- + +def eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + eval_batch = args.eval_batch_seqs + train_batch = args.ttt_batch_seqs + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + frozen_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_n = sum(p.numel() for p in ttt_params) + frozen_n = sum(p.numel() for p in base_model.parameters() if not p.requires_grad) + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}", flush=True) + log0(f"ttt_sliding:params unfrozen={unfrozen_n} frozen={frozen_n}", flush=True) + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), eval_batch): + batch_ws = my_windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + is_last = (ci == num_chunks - 1) + if not is_last and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, train_batch): + be = min(bs + train_batch, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + total_w = len(window_starts) + done_w = sum(len(chunk_windows[c]) for c in range(ci + 1)) + pct = done_w / total_w * 100 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + eta = (elapsed / max(done_w, 1)) * (total_w - done_w) + bar_len = 30 + filled = int(bar_len * done_w / total_w) + bar = "\u2588" * filled + "\u2591" * (bar_len - filled) + log0(f" ttt [{bar}] {pct:5.1f}% chunk {ci+1}/{num_chunks} bpb={rbpb:.6f} ETA={eta:.0f}s", flush=True) + if rank == 0: + log0("", flush=True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s", flush=True) + return val_loss, val_bpb + +# --- GPTQ quantization pipeline --- + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: qo_slices[i] = sd[qk]; consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: qo_slices[n + i] = sd[ok]; consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: kv_slices[i] = sd[kk]; consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: kv_slices[n + i] = sd[vk]; consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: up_slices[i] = sd[fk]; consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: down_slices[i] = sd[dk]; consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +class _HessianAttn(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)); k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm(); self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: return None + if 've' not in ve_cache: ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x; skips = []; ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve); skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)); targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + hessians = {}; hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)); hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: h.remove() + for name in hessians: + H = hessians[name]; H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]); hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough"; continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float(); meta[name] = "passthrough_ctrl"; continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t; continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Meta-TTT (FOMAML) --- + +def meta_ttt_step(base_model: nn.Module, x: Tensor, y: Tensor, + args, grad_scale: float = 1.0) -> Tensor: + """FOMAML meta-TTT step: adapt on first half of sequence, evaluate on second half. + + Inner loop: 1-step SGD on bank params using chunk_A (detached, no second-order). + Outer loop: evaluate adapted model on chunk_B. + Gradients accumulate on base_model parameters for the optimizer to consume. + Runs uncompiled (forward_with_banks) to avoid recompilation from new bank tensors. + """ + seq_len = x.shape[1] + half = seq_len // 2 + x_inner, y_inner = x[:, :half], y[:, :half] + x_outer, y_outer = x[:, half:], y[:, half:] + + n = base_model.num_layers + freeze_n = args.meta_ttt_freeze_blocks + lr = args.meta_ttt_inner_lr + + # --- Inner loop: clone banks, compute grads, 1-step SGD --- + qo = base_model.qo_bank.detach().clone().requires_grad_(True) + kv = base_model.kv_bank.detach().clone().requires_grad_(True) + up = base_model.mlp_up_bank.detach().clone().requires_grad_(True) + down = base_model.mlp_down_bank.detach().clone().requires_grad_(True) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + inner_loss = base_model.forward_with_banks(x_inner, y_inner, qo, kv, up, down) + + g_qo, g_kv, g_up, g_down = torch.autograd.grad(inner_loss, [qo, kv, up, down]) + + # Apply freeze mask: zero gradients for frozen blocks + if freeze_n > 0: + with torch.no_grad(): + g_qo = g_qo.clone() + g_kv = g_kv.clone() + g_up = g_up.clone() + g_down = g_down.clone() + for bi in range(freeze_n): + g_qo[bi].zero_(); g_qo[n + bi].zero_() + g_kv[bi].zero_(); g_kv[n + bi].zero_() + g_up[bi].zero_() + g_down[bi].zero_() + + # 1-step SGD update (FOMAML: detach to drop second-order) + with torch.no_grad(): + qo_upd = (qo - lr * g_qo).requires_grad_(True) + kv_upd = (kv - lr * g_kv).requires_grad_(True) + up_upd = (up - lr * g_up).requires_grad_(True) + down_upd = (down - lr * g_down).requires_grad_(True) + + # --- Outer loop: evaluate adapted model on chunk_B --- + # Non-bank params (embeddings, norms, scales) are LIVE — grads flow to them directly + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + outer_loss = base_model.forward_with_banks( + x_outer, y_outer, qo_upd, kv_upd, up_upd, down_upd) + + scaled = outer_loss * args.meta_ttt_loss_weight * grad_scale + scaled.backward() + + # FOMAML: copy adapted-point gradients onto original bank params + with torch.no_grad(): + for bank, upd in [(base_model.qo_bank, qo_upd), + (base_model.kv_bank, kv_upd), + (base_model.mlp_up_bank, up_upd), + (base_model.mlp_down_bank, down_upd)]: + if upd.grad is not None: + if bank.grad is None: + bank.grad = upd.grad.to(bank.dtype) + else: + bank.grad.add_(upd.grad.to(bank.dtype)) + + return outer_loss.detach() + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + total_accum = int(os.environ.get("GRAD_ACCUM_TOTAL", "8")) + if total_accum % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide GRAD_ACCUM_TOTAL={total_accum}") + grad_accum_steps = total_accum // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False); enable_flash_sdp(True); enable_mem_efficient_sdp(False); enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True, flush: bool = False) -> None: + if not master_process: return + if console: print(msg, flush=flush) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f, flush=True) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0(subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, console=False) + log0("=" * 100, console=False) + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError(f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}") + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + dtg=args.dtg_enabled, ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + base_model._has_leading_space = has_leading_space_lut + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + # Freeze dead skip connections: B0→B9 (row 4) and B1→B8 (row 3) are near-zero + with torch.no_grad(): + if base_model.skip_weights.shape[0] >= 5: + base_model.skip_weights.data[3].zero_() # B1→B8 + base_model.skip_weights.data[4].zero_() # B0→B9 + _skip_freeze_mask = torch.ones_like(base_model.skip_weights.data) + if base_model.skip_weights.shape[0] >= 5: + _skip_freeze_mask[3].zero_() + _skip_freeze_mask[4].zero_() + base_model.skip_weights.register_hook(lambda grad: grad * _skip_freeze_mask) + matrix_params = [base_model.qo_bank, base_model.kv_bank, base_model.mlp_up_bank, base_model.mlp_down_bank] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)] + if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: scalar_params.append(base_model.bigram.scale) + if base_model.word_start_boost is not None: scalar_params.append(base_model.word_start_boost) + if base_model.ve_shared is not None: + if base_model.ve_shared.proj is not None: scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + optimizer_tok = torch.optim.AdamW(tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + optimizer_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, weight_decay=args.muon_wd) + for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW([{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam([{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + _warmdown_triggered_ms: float | None = None + _loss_ema: float | None = None + _loss_ema_prev: float | None = None + def lr_mul(step: int, elapsed_ms: float) -> float: + nonlocal _warmdown_triggered_ms + if args.warmdown_iters <= 0: return 1.0 + # Guard: don't compute warmdown in early steps where step_ms estimate is unreliable + if step < args.warmup_steps + 5: return 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_duration_ms = args.warmdown_iters * step_ms + if _warmdown_triggered_ms is not None: + warmdown_start_ms = _warmdown_triggered_ms + elif max_wallclock_ms is not None: + warmdown_start_ms = max(max_wallclock_ms - warmdown_duration_ms, 0.0) + else: + warmdown_start_ms = max(args.iterations - args.warmdown_iters, 0) * step_ms + if elapsed_ms >= warmdown_start_ms: + total_warmdown_ms = (max_wallclock_ms - warmdown_start_ms) if max_wallclock_ms else warmdown_duration_ms + progress_ms = elapsed_ms - warmdown_start_ms + progress = min(progress_ms / max(total_warmdown_ms, 1e-9), 1.0) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + return 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + if distributed: + for p in base_model.parameters(): + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_pairs: list[tuple[Tensor, Tensor]] | None = None + ema_state: dict[str, Tensor] | None = None + ema_update_every = int(os.environ.get("EMA_UPDATE_EVERY", "10")) + if args.ema_enabled: + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_pairs = [(ema_state[name], param) for name, param in base_model.named_parameters()] + for name, buf in base_model.named_buffers(): + if name in ema_state: ema_pairs.append((ema_state[name], buf)) + ema_decay_eff = args.ema_decay ** ema_update_every + log0(f"ema:initialized decay={args.ema_decay} update_every={ema_update_every} decay_eff={ema_decay_eff:.6f}") + ema_stream = torch.cuda.Stream() if ema_pairs is not None else None + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val(args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms") + torch.cuda.synchronize(); t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}") + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + for micro_step in range(grad_accum_steps): + x, y = next_xy + if micro_step < grad_accum_steps - 1: + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + # Meta-TTT: FOMAML inner/outer loop on the last micro-batch + # Disabled during warmdown (scale < 0.5) — model should converge, not explore + if args.meta_ttt_enabled and step % args.meta_ttt_every == 0 and scale > 0.5: + meta_outer = meta_ttt_step(base_model, x, y, args, grad_scale=grad_scale) + if args.adaptive_warmdown and _warmdown_triggered_ms is None and step >= args.adaptive_warmdown_min_steps and step % 100 == 0: + tl = train_loss.item() + if _loss_ema is None: + _loss_ema = tl; _loss_ema_prev = tl + else: + _loss_ema = args.adaptive_warmdown_ema * _loss_ema + (1.0 - args.adaptive_warmdown_ema) * tl + improvement = (_loss_ema_prev - _loss_ema) if _loss_ema_prev is not None else 1.0 + if improvement < args.adaptive_warmdown_threshold: + elapsed_now = training_time_ms + 1000.0 * (time.perf_counter() - t0) + _warmdown_triggered_ms = elapsed_now + log0(f"adaptive_warmdown:triggered step:{step} loss_ema:{_loss_ema:.6f} improvement:{improvement:.6f}") + _loss_ema_prev = _loss_ema + if step < args.muon_momentum_warmup_steps: + frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + elif args.momentum_cyclic: + period = args.momentum_cycle_period * 2 + pos = (step % period) / period + if pos < 0.5: + muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2) + else: + muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2) + else: + muon_momentum = args.muon_momentum + for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + optimizer_muon.launch_reduce_scatters() + if distributed: + for p in replicated_params: + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step(); optimizer_scalar.step() + if optimizer_head is not None: optimizer_head.step() + optimizer_muon.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if ema_pairs is not None and step % ema_update_every == 0: + ema_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ema_stream), torch.no_grad(): + for ema_t, param_t in ema_pairs: + ema_t.mul_(ema_decay_eff).add_(param_t.detach().float(), alpha=1.0 - ema_decay_eff) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1; log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + if should_log_train: + log0(f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms") + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: stop_after_step = step + log0(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB") + if ema_stream is not None: ema_stream.synchronize() + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif ema_state is not None: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize(); t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val(args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + torch.cuda.synchronize() + log0(f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms") + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + exp_name = args.run_id.rsplit("_seed", 1)[0] if "_seed" in args.run_id else args.run_id + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + hessian_model._has_leading_space = has_leading_space_lut + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(hessian_model) + hessian_model.load_state_dict({k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, strict=False) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib(base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens; del hessian_model; torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + return len(lzma.compress(buf.getvalue(), preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} +/-1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full +/-1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} +/-1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: dist.barrier() + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model._has_leading_space = has_leading_space_lut + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize(); t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val(args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len) + torch.cuda.synchronize() + log0(f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms") + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # TTT-only evaluation (no standalone sliding window — meta-TTT model is designed for TTT) + if args.ttt_enabled and args.eval_stride > 0: + log0(f"\n{'='*60}", flush=True) + log0(f"STARTING TTT (Test-Time Training)", flush=True) + log0(f"{'='*60}", flush=True) + eval_model.load_state_dict(deq_state, strict=True) + ttt_loss, ttt_bpb = eval_val_sliding_ttt(args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0) + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f}", flush=True) + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}", flush=True) + if distributed: dist.destroy_process_group() + +if __name__ == "__main__": + main() + +==================================================================================================== +Running Python 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] +Running PyTorch 2.9.1+cu128 +Tue Apr 7 17:34:34 2026 ++-----------------------------------------------------------------------------------------+ +| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 | ++-----------------------------------------+------------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+========================+======================| +| 0 NVIDIA H100 80GB HBM3 On | 00000000:5D:00.0 Off | 0 | +| N/A 48C P0 81W / 700W | 527MiB / 81559MiB | 0% Default | +| | | Disabled | ++-----------------------------------------+------------------------+----------------------+ + ++-----------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=========================================================================================| +| No running processes found | ++-----------------------------------------------------------------------------------------+ + +==================================================================================================== +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26960991 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:1 grad_accum_steps:8 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +ema:initialized decay=0.998 update_every=10 decay_eff=0.980179 +step:0/7500 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.02ms +step:1/7500 train_loss:6.9298 train_time:1077ms step_avg:1076.86ms +step:2/7500 train_loss:8.3678 train_time:12607ms step_avg:6303.26ms +step:3/7500 train_loss:7.4169 train_time:13270ms step_avg:4423.24ms +step:4/7500 train_loss:7.5892 train_time:13920ms step_avg:3480.10ms +step:5/7500 train_loss:7.4515 train_time:14577ms step_avg:2915.35ms +step:6/7500 train_loss:7.1219 train_time:15238ms step_avg:2539.65ms +step:7/7500 train_loss:6.8039 train_time:15891ms step_avg:2270.21ms +step:8/7500 train_loss:6.6759 train_time:16549ms step_avg:2068.60ms +step:9/7500 train_loss:6.4430 train_time:17466ms step_avg:1940.61ms +step:10/7500 train_loss:6.1046 train_time:18079ms step_avg:1807.92ms +step:500/7500 train_loss:2.3270 train_time:356499ms step_avg:713.00ms +step:1000/7500 train_loss:2.2638 train_time:701599ms step_avg:701.60ms +step:1500/7500 train_loss:2.1374 train_time:1047393ms step_avg:698.26ms +step:2000/7500 train_loss:2.0551 train_time:1393384ms step_avg:696.69ms +adaptive_warmdown:triggered step:2200 loss_ema:2.115687 improvement:-0.000154 +step:2500/7500 train_loss:2.0969 train_time:1739685ms step_avg:695.87ms +step:3000/7500 train_loss:2.0755 train_time:2085817ms step_avg:695.27ms +step:3000/7500 val_loss:2.0720 val_bpb:1.2271 train_time:2085882ms step_avg:695.29ms +step:3500/7500 train_loss:2.0635 train_time:2432363ms step_avg:694.96ms +step:4000/7500 train_loss:2.1240 train_time:2778363ms step_avg:694.59ms +step:4500/7500 train_loss:2.1140 train_time:3124757ms step_avg:694.39ms +step:5000/7500 train_loss:2.0179 train_time:3458426ms step_avg:691.69ms +late_qat:enabled step:5381 scale:0.2499 +step:5500/7500 train_loss:2.0115 train_time:3790072ms step_avg:689.10ms +swa:start step:5600 +step:6000/7500 train_loss:1.9110 train_time:4122933ms step_avg:687.16ms +step:6000/7500 val_loss:1.9406 val_bpb:1.1493 train_time:4123192ms step_avg:687.20ms +step:6500/7500 train_loss:2.0205 train_time:4455933ms step_avg:685.53ms +step:7000/7500 train_loss:1.8383 train_time:4788863ms step_avg:684.12ms +step:7017/7500 val_loss:1.9196 val_bpb:1.1369 train_time:4800311ms step_avg:684.10ms +stopping_early: wallclock_cap train_time:4800311ms step:7017/7500 +peak memory allocated: 23044 MiB reserved: 23708 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9201 val_bpb:1.1372 eval_time:17520ms +Serialized model: 106028345 bytes +Code size: 115044 bytes +gptq:building non-banked model for Hessian collection... +gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... +gptq:generated 64 sequences in 214.4s +gptq:collecting hessians from autoregressive data... +gptq:collected hessians for 68 layers (AR self-gen) +selective_prune: 4163255 +/-1 candidates, unpruned=15.09MB target=15.9MB +selective_prune: already fits, no pruning needed +Serialized model int6+lzma: 15703900 bytes +Total submission size int6+lzma: 15818944 bytes +final_int6_roundtrip val_loss:1.9271 val_bpb:1.1414 eval_time:35536ms +final_int6_roundtrip_exact val_loss:1.92712172 val_bpb:1.14135003 + +============================================================ +STARTING TTT (Test-Time Training) +============================================================ +ttt_sliding:start chunks=947 chunk_tokens=65536 total_windows=969088 stride=64 ttt_lr=0.004 ttt_epochs=4 freeze_blocks=2 +ttt_sliding:params unfrozen=26956879 frozen=4112 + ttt [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 0.1% chunk 1/947 bpb=1.159887 ETA=2232s + ttt [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 1.2% chunk 11/947 bpb=1.118125 ETA=2245s + ttt [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 2.2% chunk 21/947 bpb=1.123647 ETA=2221s + ttt [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 3.3% chunk 31/947 bpb=1.128410 ETA=2197s + ttt [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 4.3% chunk 41/947 bpb=1.123877 ETA=2173s + ttt [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 5.4% chunk 51/947 bpb=1.123896 ETA=2149s + ttt [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 6.4% chunk 61/947 bpb=1.120120 ETA=2125s + ttt [██░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 7.5% chunk 71/947 bpb=1.117974 ETA=2101s + ttt 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[█████████████████████████░░░░░] 84.6% chunk 801/947 bpb=1.118776 ETA=349s + ttt [█████████████████████████░░░░░] 85.7% chunk 811/947 bpb=1.118835 ETA=325s + ttt [██████████████████████████░░░░] 86.7% chunk 821/947 bpb=1.118942 ETA=301s + ttt [██████████████████████████░░░░] 87.8% chunk 831/947 bpb=1.119138 ETA=277s + ttt [██████████████████████████░░░░] 88.9% chunk 841/947 bpb=1.119435 ETA=253s +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1"))) + momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85)) + momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95)) + momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 128)) + + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + + # EMA + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + + # SWA / LAWA (from remote) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + + # Adaptive warmdown + adaptive_warmdown = bool(int(os.environ.get("ADAPTIVE_WARMDOWN", "1"))) + adaptive_warmdown_ema = float(os.environ.get("ADAPTIVE_WARMDOWN_EMA", "0.99")) + adaptive_warmdown_threshold = float(os.environ.get("ADAPTIVE_WARMDOWN_THRESHOLD", "0.0005")) + adaptive_warmdown_min_steps = int(os.environ.get("ADAPTIVE_WARMDOWN_MIN_STEPS", "2000")) + + # Partial RoPE + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN scale and DTG + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + + # QAT + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + + # Value Embedding + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + + # Gated attention / Value residual + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + + # TTT (Test-Time Training) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lr = float(os.environ.get("TTT_LR", "0.002")) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", "3")) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", "0.9")) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", "16")) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) + + # Meta-TTT (FOMAML: train-time meta-learning for better TTT initialization) + meta_ttt_enabled = bool(int(os.environ.get("META_TTT_ENABLED", "1"))) + meta_ttt_inner_lr = float(os.environ.get("META_TTT_INNER_LR", "0.002")) + meta_ttt_every = int(os.environ.get("META_TTT_EVERY", "8")) + meta_ttt_loss_weight = float(os.environ.get("META_TTT_LOSS_WEIGHT", "0.5")) + meta_ttt_freeze_blocks = int(os.environ.get("META_TTT_FREEZE_BLOCKS", "2")) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for seq_len={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,bigram.scale,word_start_boost,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + """Hash-based n-gram embedding table. + + exp101 additions: + - pos_conditional: split buckets into two disjoint halves keyed on whether + the CURRENT token is a word-start. ws-current pairs hash into the lower + half [0, half), non-ws-current pairs into the upper half [half, 2*half). + Motivation: in exp95 the single shared table was dominated by within-word + (prev, curr) pairs, and the word_start_boost scalar collapsed to ~0.007 + (killing bigram at word-starts to suppress the noise). Splitting the + buckets gives word-start pairs their own exclusive rows that can learn + their own signal without contaminating within-word buckets, and vice + versa. Zero extra parameters — same 4096×dim table, different layout. + - trigram: optional (t-2, t-1, t) lookup summed into the same table. When + combined with pos_conditional, the trigram hash respects the same split + (keyed on whether t is a word-start) so a bucket is only trained by + lookups of a consistent word-start class. + """ + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, + trigram: bool = False, pos_conditional: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self._pos_conditional = pos_conditional + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 # sentinel row at index mod + out = torch.empty_like(t) + out[..., 0] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 # 2047 for bigram_vocab_size=4096 + base = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % half + is_ws_curr = has_leading_space[tokens[..., 1:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half # 0 for ws-current, half for non-ws-current + out[..., 1:] = base + shift + else: + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def trigram_hash(self, tokens: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + if self._pos_conditional and has_leading_space is not None: + half = mod // 2 + base = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % half + is_ws_curr = has_leading_space[tokens[..., 2:].long()].to(torch.int32) + shift = (1 - is_ws_curr) * half + out[..., 2:] = base + shift + else: + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + + def forward(self, token_ids: Tensor, has_leading_space: Tensor | None = None) -> Tensor: + h = self.embed(self.bigram_hash(token_ids, has_leading_space)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids, has_leading_space)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + # Block 0: init attn_scale to 0.1 (analysis shows it converges to ~0.094 anyway) + attn_init = 0.1 if layer_idx == 0 else 1.0 + self.attn_scale = nn.Parameter(torch.full((dim,), attn_init, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, + up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, dtg=dtg, + gated_attention=gated_attention, value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward_with_banks(self, input_ids: Tensor, target_ids: Tensor, + qo_bank: Tensor, kv_bank: Tensor, + mlp_up_bank: Tensor, mlp_down_bank: Tensor) -> Tensor: + """Forward with external bank tensors (for meta-TTT). Pure next-token loss, no MTP.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + qo_bank[i], kv_bank[i], kv_bank[n + i], + qo_bank[n + i], mlp_up_bank[i], mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + qo_bank[bi], kv_bank[bi], kv_bank[n + bi], + qo_bank[n + bi], mlp_up_bank[bi], mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +# --- TTT (Test-Time Training) --- + +def eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + eval_batch = args.eval_batch_seqs + train_batch = args.ttt_batch_seqs + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + frozen_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = any(f"blocks.{bi}." in name for bi in frozen_ids) + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_n = sum(p.numel() for p in ttt_params) + frozen_n = sum(p.numel() for p in base_model.parameters() if not p.requires_grad) + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}", flush=True) + log0(f"ttt_sliding:params unfrozen={unfrozen_n} frozen={frozen_n}", flush=True) + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), eval_batch): + batch_ws = my_windows[bi:bi + eval_batch] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + is_last = (ci == num_chunks - 1) + if not is_last and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, train_batch): + be = min(bs + train_batch, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + total_w = len(window_starts) + done_w = sum(len(chunk_windows[c]) for c in range(ci + 1)) + pct = done_w / total_w * 100 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + eta = (elapsed / max(done_w, 1)) * (total_w - done_w) + bar_len = 30 + filled = int(bar_len * done_w / total_w) + bar = "\u2588" * filled + "\u2591" * (bar_len - filled) + log0(f" ttt [{bar}] {pct:5.1f}% chunk {ci+1}/{num_chunks} bpb={rbpb:.6f} ETA={eta:.0f}s", flush=True) + if rank == 0: + log0("", flush=True) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s", flush=True) + return val_loss, val_bpb + +# --- GPTQ quantization pipeline --- + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + n = num_layers + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: qo_slices[i] = sd[qk]; consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: qo_slices[n + i] = sd[ok]; consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: kv_slices[i] = sd[kk]; consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: kv_slices[n + i] = sd[vk]; consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: up_slices[i] = sd[fk]; consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: down_slices[i] = sd[dk]; consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +class _HessianAttn(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)); k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm(); self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding( + bigram_vocab_size, bigram_dim, model_dim, + trigram=bool(int(os.environ.get("TRIGRAM", "0"))), + pos_conditional=bool(int(os.environ.get("POS_CONDITIONAL_BIGRAM", "0"))), + ) if bigram_vocab_size > 0 else None + self.word_start_boost = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) if bigram_vocab_size > 0 else None + self._has_leading_space: Tensor | None = None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: return None + if 've' not in ve_cache: ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + bg = self.bigram(input_ids, self._has_leading_space) + if self.word_start_boost is not None and self._has_leading_space is not None: + ws_mask = self._has_leading_space[input_ids].unsqueeze(-1).to(dtype=bg.dtype) + bg = bg * (1.0 + ws_mask * (self.word_start_boost.to(dtype=bg.dtype) - 1.0)) + x = x + bg + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x; skips = []; ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve); skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)); targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + hessians = {}; hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)); hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: h.remove() + for name in hessians: + H = hessians[name]; H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]); hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough"; continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float(); meta[name] = "passthrough_ctrl"; continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q; result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t; continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Meta-TTT (FOMAML) --- + +def meta_ttt_step(base_model: nn.Module, x: Tensor, y: Tensor, + args, grad_scale: float = 1.0) -> Tensor: + """FOMAML meta-TTT step: adapt on first half of sequence, evaluate on second half. + + Inner loop: 1-step SGD on bank params using chunk_A (detached, no second-order). + Outer loop: evaluate adapted model on chunk_B. + Gradients accumulate on base_model parameters for the optimizer to consume. + Runs uncompiled (forward_with_banks) to avoid recompilation from new bank tensors. + """ + seq_len = x.shape[1] + half = seq_len // 2 + x_inner, y_inner = x[:, :half], y[:, :half] + x_outer, y_outer = x[:, half:], y[:, half:] + + n = base_model.num_layers + freeze_n = args.meta_ttt_freeze_blocks + lr = args.meta_ttt_inner_lr + + # --- Inner loop: clone banks, compute grads, 1-step SGD --- + qo = base_model.qo_bank.detach().clone().requires_grad_(True) + kv = base_model.kv_bank.detach().clone().requires_grad_(True) + up = base_model.mlp_up_bank.detach().clone().requires_grad_(True) + down = base_model.mlp_down_bank.detach().clone().requires_grad_(True) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + inner_loss = base_model.forward_with_banks(x_inner, y_inner, qo, kv, up, down) + + g_qo, g_kv, g_up, g_down = torch.autograd.grad(inner_loss, [qo, kv, up, down]) + + # Apply freeze mask: zero gradients for frozen blocks + if freeze_n > 0: + with torch.no_grad(): + g_qo = g_qo.clone() + g_kv = g_kv.clone() + g_up = g_up.clone() + g_down = g_down.clone() + for bi in range(freeze_n): + g_qo[bi].zero_(); g_qo[n + bi].zero_() + g_kv[bi].zero_(); g_kv[n + bi].zero_() + g_up[bi].zero_() + g_down[bi].zero_() + + # 1-step SGD update (FOMAML: detach to drop second-order) + with torch.no_grad(): + qo_upd = (qo - lr * g_qo).requires_grad_(True) + kv_upd = (kv - lr * g_kv).requires_grad_(True) + up_upd = (up - lr * g_up).requires_grad_(True) + down_upd = (down - lr * g_down).requires_grad_(True) + + # --- Outer loop: evaluate adapted model on chunk_B --- + # Non-bank params (embeddings, norms, scales) are LIVE — grads flow to them directly + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + outer_loss = base_model.forward_with_banks( + x_outer, y_outer, qo_upd, kv_upd, up_upd, down_upd) + + scaled = outer_loss * args.meta_ttt_loss_weight * grad_scale + scaled.backward() + + # FOMAML: copy adapted-point gradients onto original bank params + with torch.no_grad(): + for bank, upd in [(base_model.qo_bank, qo_upd), + (base_model.kv_bank, kv_upd), + (base_model.mlp_up_bank, up_upd), + (base_model.mlp_down_bank, down_upd)]: + if upd.grad is not None: + if bank.grad is None: + bank.grad = upd.grad.to(bank.dtype) + else: + bank.grad.add_(upd.grad.to(bank.dtype)) + + return outer_loss.detach() + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + total_accum = int(os.environ.get("GRAD_ACCUM_TOTAL", "8")) + if total_accum % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide GRAD_ACCUM_TOTAL={total_accum}") + grad_accum_steps = total_accum // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False); enable_flash_sdp(True); enable_mem_efficient_sdp(False); enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True, flush: bool = False) -> None: + if not master_process: return + if console: print(msg, flush=flush) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f, flush=True) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0(subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, console=False) + log0("=" * 100, console=False) + random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError(f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}") + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(sp, args.vocab_size, device) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + dtg=args.dtg_enabled, ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + base_model._has_leading_space = has_leading_space_lut + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + # Freeze dead skip connections: B0→B9 (row 4) and B1→B8 (row 3) are near-zero + with torch.no_grad(): + if base_model.skip_weights.shape[0] >= 5: + base_model.skip_weights.data[3].zero_() # B1→B8 + base_model.skip_weights.data[4].zero_() # B0→B9 + _skip_freeze_mask = torch.ones_like(base_model.skip_weights.data) + if base_model.skip_weights.shape[0] >= 5: + _skip_freeze_mask[3].zero_() + _skip_freeze_mask[4].zero_() + base_model.skip_weights.register_hook(lambda grad: grad * _skip_freeze_mask) + matrix_params = [base_model.qo_bank, base_model.kv_bank, base_model.mlp_up_bank, base_model.mlp_down_bank] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)] + if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: scalar_params.append(base_model.bigram.scale) + if base_model.word_start_boost is not None: scalar_params.append(base_model.word_start_boost) + if base_model.ve_shared is not None: + if base_model.ve_shared.proj is not None: scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + optimizer_tok = torch.optim.AdamW(tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + optimizer_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, weight_decay=args.muon_wd) + for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW([{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam([{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + _warmdown_triggered_ms: float | None = None + _loss_ema: float | None = None + _loss_ema_prev: float | None = None + def lr_mul(step: int, elapsed_ms: float) -> float: + nonlocal _warmdown_triggered_ms + if args.warmdown_iters <= 0: return 1.0 + # Guard: don't compute warmdown in early steps where step_ms estimate is unreliable + if step < args.warmup_steps + 5: return 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_duration_ms = args.warmdown_iters * step_ms + if _warmdown_triggered_ms is not None: + warmdown_start_ms = _warmdown_triggered_ms + elif max_wallclock_ms is not None: + warmdown_start_ms = max(max_wallclock_ms - warmdown_duration_ms, 0.0) + else: + warmdown_start_ms = max(args.iterations - args.warmdown_iters, 0) * step_ms + if elapsed_ms >= warmdown_start_ms: + total_warmdown_ms = (max_wallclock_ms - warmdown_start_ms) if max_wallclock_ms else warmdown_duration_ms + progress_ms = elapsed_ms - warmdown_start_ms + progress = min(progress_ms / max(total_warmdown_ms, 1e-9), 1.0) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + return 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + if distributed: + for p in base_model.parameters(): + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_pairs: list[tuple[Tensor, Tensor]] | None = None + ema_state: dict[str, Tensor] | None = None + ema_update_every = int(os.environ.get("EMA_UPDATE_EVERY", "10")) + if args.ema_enabled: + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_pairs = [(ema_state[name], param) for name, param in base_model.named_parameters()] + for name, buf in base_model.named_buffers(): + if name in ema_state: ema_pairs.append((ema_state[name], buf)) + ema_decay_eff = args.ema_decay ** ema_update_every + log0(f"ema:initialized decay={args.ema_decay} update_every={ema_update_every} decay_eff={ema_decay_eff:.6f}") + ema_stream = torch.cuda.Stream() if ema_pairs is not None else None + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val(args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms") + torch.cuda.synchronize(); t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}") + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + for micro_step in range(grad_accum_steps): + x, y = next_xy + if micro_step < grad_accum_steps - 1: + next_xy = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + # Meta-TTT: FOMAML inner/outer loop on the last micro-batch + # Disabled during warmdown (scale < 0.5) — model should converge, not explore + if args.meta_ttt_enabled and step % args.meta_ttt_every == 0 and scale > 0.5: + meta_outer = meta_ttt_step(base_model, x, y, args, grad_scale=grad_scale) + if args.adaptive_warmdown and _warmdown_triggered_ms is None and step >= args.adaptive_warmdown_min_steps and step % 100 == 0: + tl = train_loss.item() + if _loss_ema is None: + _loss_ema = tl; _loss_ema_prev = tl + else: + _loss_ema = args.adaptive_warmdown_ema * _loss_ema + (1.0 - args.adaptive_warmdown_ema) * tl + improvement = (_loss_ema_prev - _loss_ema) if _loss_ema_prev is not None else 1.0 + if improvement < args.adaptive_warmdown_threshold: + elapsed_now = training_time_ms + 1000.0 * (time.perf_counter() - t0) + _warmdown_triggered_ms = elapsed_now + log0(f"adaptive_warmdown:triggered step:{step} loss_ema:{_loss_ema:.6f} improvement:{improvement:.6f}") + _loss_ema_prev = _loss_ema + if step < args.muon_momentum_warmup_steps: + frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + elif args.momentum_cyclic: + period = args.momentum_cycle_period * 2 + pos = (step % period) / period + if pos < 0.5: + muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2) + else: + muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2) + else: + muon_momentum = args.muon_momentum + for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + optimizer_muon.launch_reduce_scatters() + if distributed: + for p in replicated_params: + if p.grad is not None: dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step(); optimizer_scalar.step() + if optimizer_head is not None: optimizer_head.step() + optimizer_muon.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if ema_pairs is not None and step % ema_update_every == 0: + ema_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(ema_stream), torch.no_grad(): + for ema_t, param_t in ema_pairs: + ema_t.mul_(ema_decay_eff).add_(param_t.detach().float(), alpha=1.0 - ema_decay_eff) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1; log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + if should_log_train: + log0(f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms") + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: stop_after_step = step + log0(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB") + if ema_stream is not None: ema_stream.synchronize() + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif ema_state is not None: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize(); t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val(args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut) + torch.cuda.synchronize() + log0(f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms") + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + exp_name = args.run_id.rsplit("_seed", 1)[0] if "_seed" in args.run_id else args.run_id + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + hessian_model._has_leading_space = has_leading_space_lut + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(hessian_model) + hessian_model.load_state_dict({k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, strict=False) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib(base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens; del hessian_model; torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + return len(lzma.compress(buf.getvalue(), preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} +/-1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full +/-1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} +/-1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: dist.barrier() + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() + quant_state = torch.load(io.BytesIO(lzma.decompress(quant_blob_disk)), map_location="cpu") + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model._has_leading_space = has_leading_space_lut + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize(); t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val(args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len) + torch.cuda.synchronize() + log0(f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms") + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # TTT-only evaluation (no standalone sliding window — meta-TTT model is designed for TTT) + if args.ttt_enabled and args.eval_stride > 0: + log0(f"\n{'='*60}", flush=True) + log0(f"STARTING TTT (Test-Time Training)", flush=True) + log0(f"{'='*60}", flush=True) + eval_model.load_state_dict(deq_state, strict=True) + ttt_loss, ttt_bpb = eval_val_sliding_ttt(args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0) + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f}", flush=True) + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}", flush=True) + if distributed: dist.destroy_process_group() + +if __name__ == "__main__": + main() + +==================================================================================================== +Running Python 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] +Running PyTorch 2.9.1+cu128 +Tue Apr 7 19:38:25 2026 ++-----------------------------------------------------------------------------------------+ +| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 | ++-----------------------------------------+------------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+========================+======================| +| 0 NVIDIA H100 80GB HBM3 On | 00000000:5D:00.0 Off | 0 | +| N/A 45C P0 79W / 700W | 527MiB / 81559MiB | 0% Default | +| | | Disabled | ++-----------------------------------------+------------------------+----------------------+ + ++-----------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=========================================================================================| +| No running processes found | ++-----------------------------------------------------------------------------------------+ + +==================================================================================================== +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26960991 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:1 grad_accum_steps:8 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +ema:initialized decay=0.998 update_every=10 decay_eff=0.980179 +step:0/7500 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.02ms +step:1/7500 train_loss:6.9298 train_time:1076ms step_avg:1076.35ms +step:2/7500 train_loss:8.3850 train_time:12202ms step_avg:6101.20ms +step:3/7500 train_loss:7.4538 train_time:12866ms step_avg:4288.58ms +step:4/7500 train_loss:7.5721 train_time:13521ms step_avg:3380.15ms +step:5/7500 train_loss:7.4372 train_time:14177ms step_avg:2835.45ms +step:6/7500 train_loss:7.1329 train_time:14835ms step_avg:2472.48ms +step:7/7500 train_loss:6.8123 train_time:15489ms step_avg:2212.71ms +step:8/7500 train_loss:6.6734 train_time:16150ms step_avg:2018.73ms +step:9/7500 train_loss:6.4242 train_time:17067ms step_avg:1896.36ms +step:10/7500 train_loss:6.1156 train_time:17682ms step_avg:1768.19ms +step:500/7500 train_loss:2.3107 train_time:355764ms step_avg:711.53ms +step:1000/7500 train_loss:2.2624 train_time:700855ms step_avg:700.86ms +step:1500/7500 train_loss:2.1355 train_time:1046278ms step_avg:697.52ms +step:2000/7500 train_loss:2.0517 train_time:1392190ms step_avg:696.09ms +adaptive_warmdown:triggered step:2200 loss_ema:2.112601 improvement:-0.000153 +step:2500/7500 train_loss:2.0958 train_time:1738243ms step_avg:695.30ms +step:3000/7500 train_loss:2.0731 train_time:2084299ms step_avg:694.77ms +step:3000/7500 val_loss:2.0690 val_bpb:1.2254 train_time:2084364ms step_avg:694.79ms +step:3500/7500 train_loss:2.0611 train_time:2430554ms step_avg:694.44ms +step:4000/7500 train_loss:2.1240 train_time:2776525ms step_avg:694.13ms +step:4500/7500 train_loss:2.1104 train_time:3122625ms step_avg:693.92ms +step:5000/7500 train_loss:2.0135 train_time:3456427ms step_avg:691.29ms +late_qat:enabled step:5384 scale:0.2498 +step:5500/7500 train_loss:2.0094 train_time:3787761ms step_avg:688.68ms +swa:start step:5600 +step:6000/7500 train_loss:1.9084 train_time:4120430ms step_avg:686.74ms +step:6000/7500 val_loss:1.9373 val_bpb:1.1474 train_time:4120623ms step_avg:686.77ms +step:6500/7500 train_loss:2.0162 train_time:4453755ms step_avg:685.19ms +step:7000/7500 train_loss:1.8340 train_time:4786696ms step_avg:683.81ms +step:7020/7500 val_loss:1.9162 val_bpb:1.1349 train_time:4800204ms step_avg:683.79ms +stopping_early: wallclock_cap train_time:4800204ms step:7020/7500 +peak memory allocated: 23044 MiB reserved: 23708 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9167 val_bpb:1.1352 eval_time:17475ms +Serialized model: 106028345 bytes +Code size: 115044 bytes +gptq:building non-banked model for Hessian collection... +gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... +gptq:generated 64 sequences in 210.3s +gptq:collecting hessians from autoregressive data... +gptq:collected hessians for 68 layers (AR self-gen) +selective_prune: 4169170 +/-1 candidates, unpruned=15.07MB target=15.9MB +selective_prune: already fits, no pruning needed +Serialized model int6+lzma: 15689152 bytes +Total submission size int6+lzma: 15804196 bytes +final_int6_roundtrip val_loss:1.9237 val_bpb:1.1393 eval_time:34481ms +final_int6_roundtrip_exact val_loss:1.92365637 val_bpb:1.13929766 + +============================================================ +STARTING TTT (Test-Time Training) +============================================================ +ttt_sliding:start chunks=947 chunk_tokens=65536 total_windows=969088 stride=64 ttt_lr=0.004 ttt_epochs=4 freeze_blocks=2 +ttt_sliding:params unfrozen=26956879 frozen=4112 + ttt [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 0.1% chunk 1/947 bpb=1.157678 ETA=2226s + ttt [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 1.2% chunk 11/947 bpb=1.116325 ETA=2237s + ttt [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 2.2% chunk 21/947 bpb=1.121438 ETA=2217s + ttt [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 3.3% chunk 31/947 bpb=1.126267 ETA=2194s + ttt [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 4.3% chunk 41/947 bpb=1.121681 ETA=2171s + ttt [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 5.4% chunk 51/947 bpb=1.121823 ETA=2147s + ttt [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 6.4% chunk 61/947 bpb=1.117969 ETA=2122s + ttt [██░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 7.5% chunk 71/947 bpb=1.115922 ETA=2097s + ttt 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