diff --git a/records/track_10min_16mb/2026-03-24_GPTQ_TTT/README.md b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/README.md new file mode 100644 index 0000000000..36694af1a3 --- /dev/null +++ b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/README.md @@ -0,0 +1,169 @@ +# GPTQ Int6 + SGD Test-Time Training + +## Summary + +An 11-layer 512-dim GPT model trained with PR#414's 10-technique stack plus LeakyReLU(0.5)² activation, then improved at eval time with two post-training techniques: + +1. **GPTQ int6 quantization** — Hessian-guided column-wise quantization that replaces naive per-row int6 rounding, reducing quantization error by 33.6% (Hessian-weighted MSE) and saving 0.0029 bpb. +2. **SGD test-time training (TTT)** — Continues training the model on validation data in a causal (score-first) manner, adapting the last 9 of 11 layers via SGD with cosine LR decay. + +Combined A800 bpb: **1.1190** (estimated H100: ~1.122). + +## Architecture + +| Component | Value | +|-----------|-------| +| Layers | 11 | +| Model dim | 512 | +| Attention heads | 8 (4 KV, GQA) | +| MLP multiplier | 3.0× | +| Activation | LeakyReLU(0.5)² | +| Vocab size | 1024 (SentencePiece BPE) | +| Embeddings | Tied input/output | +| RoPE | Partial (16 dims), base 10000 | +| Logit softcap | 30.0 | + +### Techniques from PR#414 + +- **XSA (Cross-Sequence Attention)**: Last 4 layers attend across batch sequences +- **EMA (Exponential Moving Average)**: Weight averaging for smoother convergence +- **U-Net skip connections**: Residual connections between early and late layers +- **SmearGate**: Learned gating for token mixing +- **BigramHash**: 2048-vocab bigram hash embeddings (dim=128) for local context +- **LNScale**: Learnable LayerNorm scaling +- **Value Embeddings (VE128)**: 128-dim value embeddings on layers 9-10 +- **Late QAT**: Quantization-aware training enabled after loss reaches 0.15 threshold +- **SWA (Stochastic Weight Averaging)**: Checkpoint averaging every 50 steps + +### Techniques we added + +- **LeakyReLU(0.5)²**: Replaces ReLU² in MLP. Negative-slope 0.5 preserves gradient flow through the squaring operation. Saves 0.0026 bpb over ReLU² at zero compute cost. +- **GPTQ int6**: Post-training Hessian-guided quantization (Frantar et al., 2022; 256 calibration samples, block-128, percdamp=0.01). Saves 0.0029 bpb over naive int6 rounding. +- **SGD TTT**: Test-time training (Sun et al., 2024) with SGD (lr=0.002, momentum=0.9), cosine LR schedule (T_max=1893 chunks), freeze first 2 embedding/layer blocks, 3 epochs per 32K-token chunk, score-first causal evaluation. Saves ~0.0024 bpb (on GPTQ int6 baseline 1.1214). + +### References + +- Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D. (2022). GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers. arXiv:2210.17323. +- Sun, Y., Li, X., Dalal, K., Xu, J., Vikram, A., Zhang, G., Dubois, Y., Chen, X., Wang, X., Koyejo, S., Hashimoto, T., & Guestrin, C. (2024). Learning to (Learn at Test Time): RNNs with Expressive Hidden States. arXiv:2407.04620. + +## Results + +### Training (8× A800-SXM4-80GB, 1200s) + +| Metric | Value | +|--------|-------| +| Training steps | 6202 / 20000 (wallclock capped) | +| Training time | 1200.0s (193.49 ms/step) | +| EMA val_bpb (pre-quant) | 1.1399 | +| Int6 roundtrip bpb | 1.1480 | +| Sliding-window bpb (stride=64) | 1.1243 | +| Artifact size (int6+zstd) | 15,871,987 bytes | + +### Eval-Time Improvements (8× A800) + +| Stage | bpb | Δ vs baseline | Time | +|-------|-----|---------------|------| +| Sliding window (stride=64) | 1.1243 | — | 162s | +| + GPTQ int6 | 1.1214 | −0.0029 | +19s | +| + SGD TTT (900 chunks) | **1.1190** | **−0.0053** | +546s | + +### Artifact Compression + +| Method | Size | Under 16MB? | +|--------|------|-------------| +| Int6 + zstd (default) | 15,871,987 | ✓ | +| GPTQ int6 + zstd-21 LDM | 15,750,888 | ✓ (249KB margin) | + +The GPTQ model has higher entropy weights (Hessian-compensated), causing a slightly larger raw file. Long Distance Matching (LDM) in zstd exploits cross-layer weight pattern similarity, recovering the size difference. + +### H100 Estimate + +| Scenario | Estimated bpb | +|----------|---------------| +| Expected (eval-only delta +0.0005) | 1.1195 | +| Conservative (full A800→H100 delta +0.0036) | 1.1226 | +| Best case (more TTT chunks from faster H100) | 1.1175 | + +H100 processes ~1490 chunks in 600s eval window (vs 900 on A800), which should further reduce bpb. + +## TTT Configuration + +``` +SGD lr=0.002, momentum=0.9 +Cosine LR schedule, T_max=1893 (total possible chunks) +freeze_blocks=2 (first 2 layers + embeddings frozen) +3 epochs per chunk +Chunk size: 32768 tokens +Score-first: score tokens before adapting on them (causal) +Eval stride: 64 (sliding window) +``` + +Temperature calibration was tested (T=0.94–1.05) but T=1.0 is optimal — the model is already well-calibrated after TTT. + +## Technique Discovery Log + +We ran 30+ experiments on 8×A800 to reach this configuration: + +1. **Baseline reproduction** (1.2259 bpb) → confirmed A800/H100 correlation (+0.0015 delta) +2. **Core 5 stack** (1.1530) → int6 + MLP3x + sliding window + FP16 embed + zstd +3. **LeakyReLU²** (1.1509) → −0.0021 on Core 5 +4. **PR#414 port** (1.1269) → 10-technique stack, best training-only result +5. **PR#414 + LeakyReLU²** (1.1243) → −0.0026 additive +6. **TTT: AdamW** (1.1305–1.1335) → worse than SGD, adaptive LR causes drift +7. **TTT: SGD cosine** (1.1238) → −0.0031, cosine > constant LR +8. **TTT: 10 epochs** (1.1310) → worse, catastrophic forgetting erases gains +9. **TTT: Sidecar** (1.1282–1.1290) → requires co-training, random init fails +10. **TTT: LoRA** (1.1243–1.1576) → model too small for rank-16 (3% subspace) +11. **GPTQ** (1.1214) → −0.0029 vs naive int6 +12. **GPTQ + TTT** (**1.1190**) → best result, gains additive +13. **Stride tuning** (inconclusive) → Swept stride {32, 64, 128, 256, 512} but wrong model checkpoint loaded during sweep (T045). Relative differences <0.002 bpb suggest stride is a marginal lever. Default stride=64 retained. + +Dead ends: AdamW TTT, 10-epoch TTT, sidecar TTT from random init, LoRA TTT on 512-dim model, temperature calibration, stride tuning. + +## Reproduction + +### Training +```bash +# On 8xH100 or 8xA800 +torchrun --nproc_per_node=8 train_gpt.py +``` + +### GPTQ Quantization (post-training) +```bash +# Requires final_model.pt from training +python eval_gptq.py +``` + +### TTT Evaluation +```bash +# Uses GPTQ-quantized model +# TTT config is controlled by env vars: +TTT_LR=0.002 TTT_EPOCHS=3 TTT_FREEZE_BLOCKS=2 \ +TTT_CHUNK_TOKENS=32768 TTT_MAX_CHUNKS=900 \ +TTT_SKIP_BASELINE=1 TTT_LR_SCHEDULE=cosine \ +torchrun --nproc_per_node=8 eval_ttt.py +``` + +## Hardware & Environment + +- Training: 8× A800-SXM4-80GB (1200s wallclock) +- Eval: 8× A800-SXM4-80GB (GPTQ: 19s, TTT: 546s) +- PyTorch 2.8.0+cu129 +- FlashAttention 2.8.3 +- CUDA 12.9 (A800), target CUDA 12.8 (H100 competition env) + +## Artifact Contents + +- `train_gpt.py` — training script (LeakyReLU² modification of PR#414) +- `train.log` — full training output (6202 steps, 1200s) +- `submission.json` — structured metadata +- `gptq_results.json` — GPTQ quantization metrics +- `ttt_gptq_results.json` — TTT evaluation metrics +- `README.md` — this file + +## Limitations + +- **Single seed**: Only 1 A800 training run. Competition requires 3-seed H100 validation. +- **No H100 run yet**: bpb estimate is projected from A800 results. +- **TTT coverage**: 900/1893 chunks processed on A800 (47.5%). H100 should reach ~79% (1490 chunks). +- **A800 vs H100 gap**: Training produces fewer steps on A800 (6202) vs H100 (~13000+), so the A800-trained model is weaker. Final submission must train on H100. diff --git a/records/track_10min_16mb/2026-03-24_GPTQ_TTT/eval_gptq.py b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/eval_gptq.py new file mode 100644 index 0000000000..a2d48aa3a3 --- /dev/null +++ b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/eval_gptq.py @@ -0,0 +1,666 @@ +"""GPTQ post-training quantization for PR414+LeakyReLU² base model. + +Implements Hessian-guided column-wise int6 quantization (GPTQ algorithm): +1. Load FP32 model from final_model.pt +2. Run calibration data through model to collect per-layer Hessians (H = X^T X) +3. Apply GPTQ: column-wise int6 quantization with block-128 error compensation +4. Save GPTQ-quantized model and eval with sliding window + +Reference: PR#578 (256-sample calibration, block-128, Cholesky-factored error propagation) +""" +from __future__ import annotations + +import copy +import glob +import io +import json +import math +import os +import sys +import time +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 + +try: + from flash_attn_interface import flash_attn_func as flash_attn_3_func +except ImportError: + from flash_attn import flash_attn_func as flash_attn_3_func + +sys.path.insert(0, str(Path(__file__).parent)) +from train_leakyrelu2_pr414 import ( + Hyperparameters, + GPT, + CastedLinear, + build_sentencepiece_luts, + load_validation_tokens, + load_data_shard, + eval_val, + eval_val_sliding, + restore_low_dim_params_to_fp32, + mixed_quantize_int6, + dequantize_mixed_int6, + quantize_int6_per_row, + _classify_param, + CONTROL_TENSOR_NAME_PATTERNS, +) + +# ── GPTQ Hyperparameters ───────────────────────────────────────────────────── +GPTQ_NSAMPLES = int(os.environ.get("GPTQ_NSAMPLES", "256")) +GPTQ_BLOCK_SIZE = int(os.environ.get("GPTQ_BLOCK_SIZE", "128")) +GPTQ_DAMP_PCT = float(os.environ.get("GPTQ_DAMP_PCT", "0.01")) +GPTQ_CLIP_RANGE = int(os.environ.get("GPTQ_CLIP_RANGE", "31")) # int6: ±31 +GPTQ_PERCDAMP = float(os.environ.get("GPTQ_PERCDAMP", "0.01")) + + +# ── GPTQ Core Algorithm ────────────────────────────────────────────────────── + +def gptq_quantize_weight( + W: Tensor, # [out_features, in_features], float32 + H: Tensor, # [in_features, in_features], Hessian = X^T X, float32 + block_size: int = 128, + clip_range: int = 31, + percdamp: float = 0.01, +) -> tuple[Tensor, Tensor, float, float]: + """Apply GPTQ algorithm to quantize weight matrix to int6. + + Returns: (quantized_int8, scale_fp16, elem_mse, hessian_weighted_mse) + + NOTE: GPTQ minimizes Hessian-weighted error, NOT element-wise MSE. + Element-wise MSE will typically be ~1.1-1.3x worse than naive quantization. + This is EXPECTED and correct — the Hessian-weighted error (which correlates + with bpb) should be ~5-10% better. + """ + W = W.clone().float() + nrow, ncol = W.shape + + H = H.float() + + # Dampening + damp = percdamp * torch.diag(H).mean() + diag_idx = torch.arange(ncol, device=H.device) + H[diag_idx, diag_idx] += damp + + # Find optimal per-row scale using same approach as naive quantizer + # (search over percentiles to find best scale) + W_orig = W.clone() + best_scale = None + best_scale_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(W_orig.abs(), pct, dim=1) + else: + row_clip = W_orig.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + # Quick estimate: naive quantize and check error + q = torch.clamp(torch.round(W_orig / s.float()[:, None]), -clip_range, clip_range) + recon = q * s.float()[:, None] + err = (W_orig - recon).pow(2).mean().item() + if err < best_scale_err: + best_scale = s + best_scale_err = err + + scale = best_scale.float() # [nrow] + + # Cholesky factorization of H + try: + L = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(L) + except RuntimeError: + # Fallback: add more dampening + H[diag_idx, diag_idx] += damp * 10 + try: + L = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(L) + except RuntimeError: + # Last resort: pseudo-inverse + Hinv = torch.linalg.pinv(H) + + Q = torch.zeros_like(W) + Err = torch.zeros_like(W) + + # Process columns in blocks + for col_start in range(0, ncol, block_size): + col_end = min(col_start + block_size, ncol) + block_cols = col_end - col_start + + # Get the block's Hessian inverse + Hinv_block = Hinv[col_start:col_end, col_start:col_end] + + for j in range(col_start, col_end): + w_col = W[:, j] # [nrow] + d = Hinv[j, j] + + # Quantize this column + q_col = torch.clamp( + torch.round(w_col / scale), + -clip_range, clip_range + ) + Q[:, j] = q_col + + # Quantization error + err = (w_col - q_col * scale) / d + Err[:, j] = err + + # Compensate remaining columns in this block + if j + 1 < col_end: + W[:, j + 1:col_end] -= err[:, None] * Hinv[j, j + 1:col_end][None, :] + + # Compensate remaining columns outside this block + if col_end < ncol: + W[:, col_end:] -= Err[:, col_start:col_end] @ Hinv[col_start:col_end, col_end:] + + Q_int8 = Q.to(torch.int8) + + # ── Post-GPTQ: Recompute per-row scale via least-squares ─────────── + # Q values are fixed (GPTQ-optimized integer assignments). + # Find the scale that minimizes ||W_orig - scale * Q||² per row: + # scale_i = dot(W_orig[i], Q[i]) / dot(Q[i], Q[i]) + Q_float = Q_int8.float() + dot_wq = (W_orig * Q_float).sum(dim=1) # [nrow] + dot_qq = (Q_float * Q_float).sum(dim=1) # [nrow] + ls_scale = torch.where(dot_qq > 0, dot_wq / dot_qq, scale) + ls_scale = ls_scale.clamp_min(1.0 / clip_range) + + # Per-row: pick whichever scale gives lower element-wise MSE + recon_orig = Q_float * scale[:, None] + recon_ls = Q_float * ls_scale[:, None] + mse_orig_per_row = (W_orig - recon_orig).pow(2).mean(dim=1) + mse_ls_per_row = (W_orig - recon_ls).pow(2).mean(dim=1) + use_ls = mse_ls_per_row < mse_orig_per_row + final_scale = torch.where(use_ls, ls_scale, scale) + scale_fp16 = final_scale.to(torch.float16) + + # Compute element-wise MSE + recon = Q_float * scale_fp16.float()[:, None] + elem_mse = (W_orig - recon).pow(2).mean().item() + + # Compute Hessian-weighted error (the metric GPTQ actually optimizes) + # hw_err = tr((W-Q*s)^T H (W-Q*s)) / nrow + H_raw = H.clone() # H already has dampening added + diff = W_orig - recon # [nrow, ncol] + hw_mse = (diff @ H_raw * diff).sum().item() / nrow + + return Q_int8, scale_fp16, elem_mse, hw_mse + + +# ── Hessian Collection ──────────────────────────────────────────────────────── + +class HessianCollector: + """Hook-based collector for per-layer input Hessians H = X^T X.""" + + def __init__(self): + self.hessians: dict[str, Tensor] = {} + self.nsamples: dict[str, int] = {} + self.hooks = [] + + def _make_hook(self, name: str): + def hook_fn(module, input, output): + inp = input[0].detach().clone() + if inp.ndim == 3: + inp = inp.reshape(-1, inp.shape[-1]) + inp = inp.float() + H = inp.T @ inp # [dim, dim] + if name not in self.hessians: + self.hessians[name] = H.cpu() + self.nsamples[name] = inp.shape[0] + else: + self.hessians[name] = self.hessians[name] + H.cpu() + self.nsamples[name] += inp.shape[0] + return hook_fn + + def register(self, model: nn.Module): + """Register hooks on all CastedLinear modules.""" + for name, module in model.named_modules(): + if isinstance(module, CastedLinear): + hook = module.register_forward_hook(self._make_hook(name)) + self.hooks.append(hook) + + def remove_hooks(self): + for hook in self.hooks: + hook.remove() + self.hooks.clear() + + def normalize(self): + """Average Hessians by number of samples.""" + for name in list(self.hessians.keys()): + self.hessians[name] = self.hessians[name].clone() / self.nsamples[name] + + +def make_model(args, device): + """Create a fresh GPT model for evaluation.""" + m = 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, + ).to(device).bfloat16() + for mod in m.modules(): + if isinstance(mod, CastedLinear): + mod.float() + restore_low_dim_params_to_fp32(m) + return m + + +def collect_calibration_data(args, device, nsamples: int = 256, seq_len: int = 2048): + """Load calibration sequences from training data.""" + train_files = sorted(glob.glob(args.train_files)) + if not train_files: + raise FileNotFoundError(f"No training files found: {args.train_files}") + + all_tokens = [] + for f in train_files: + tokens = load_data_shard(Path(f)) + all_tokens.append(tokens) + total = sum(t.numel() for t in all_tokens) + if total >= nsamples * seq_len + 1: + break + + all_tokens = torch.cat(all_tokens) + sequences = [] + for i in range(nsamples): + start = i * seq_len + end = start + seq_len + 1 + if end > all_tokens.numel(): + break + sequences.append(all_tokens[start:end]) + + return sequences + + +def collect_hessians(model, sequences, device, batch_size=8): + """Run calibration sequences through model and collect per-layer Hessians.""" + collector = HessianCollector() + collector.register(model) + + model.eval() + with torch.inference_mode(): + for i in range(0, len(sequences), batch_size): + batch_seqs = sequences[i:i+batch_size] + # Stack into batch + x_list = [s[:-1] for s in batch_seqs] + y_list = [s[1:] for s in batch_seqs] + x = torch.stack(x_list).to(device=device, dtype=torch.int64) + y = torch.stack(y_list).to(device=device, dtype=torch.int64) + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + _ = model(x, y) + + if (i // batch_size + 1) % 4 == 0: + print(f" Calibration: {min(i + batch_size, len(sequences))}/{len(sequences)} sequences") + + collector.remove_hooks() + collector.normalize() + + return collector.hessians + + +def gptq_quantize_state_dict( + state_dict: dict[str, Tensor], + hessians: dict[str, Tensor], + int6_cats: set[str], + block_size: int = 128, + clip_range: int = 31, + percdamp: float = 0.01, +) -> tuple[dict[str, Tensor], dict[str, object]]: + """Apply GPTQ quantization to state dict, replacing naive int6 for layers with Hessians. + + For layers without Hessians (embeddings, small tensors), falls back to naive quantization. + """ + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + + gptq_stats = {"gptq_layers": 0, "naive_layers": 0, "passthrough_layers": 0} + gptq_errors = {} + gptq_hw_errors = {} + naive_errors = {} + naive_hw_errors = {} + + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + + # Passthrough: non-float or small tensors + 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" + gptq_stats["passthrough_layers"] += 1 + continue + + # Passthrough: control tensors + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + gptq_stats["passthrough_layers"] += 1 + continue + + if cat in int6_cats and t.ndim >= 1: + # Find matching Hessian + # Module name for the layer containing this weight + # e.g., "blocks.0.attn.c_q.weight" -> module name "blocks.0.attn.c_q" + layer_module_name = name.rsplit(".weight", 1)[0] if name.endswith(".weight") else None + + hessian = hessians.get(layer_module_name) if layer_module_name else None + + if hessian is not None and t.ndim == 2: + # GPTQ quantization + # First compute naive error for comparison + q_naive, s_naive = quantize_int6_per_row(t) + naive_recon = q_naive.float() * s_naive.float()[:, None] + naive_mse = (t.float() - naive_recon).pow(2).mean().item() + # Naive Hessian-weighted error + H_raw = hessian.cpu().float() + diff_naive = t.float() - naive_recon + naive_hw = (diff_naive @ H_raw * diff_naive).sum().item() / t.shape[0] + + q_gptq, s_gptq, gptq_mse, gptq_hw = gptq_quantize_weight( + t, hessian.cpu(), block_size, clip_range, percdamp + ) + + result[name + ".q"] = q_gptq + result[name + ".scale"] = s_gptq + meta[name] = {"type": "int6"} + gptq_stats["gptq_layers"] += 1 + gptq_errors[name] = gptq_mse + gptq_hw_errors[name] = gptq_hw + naive_errors[name] = naive_mse + naive_hw_errors[name] = naive_hw + + ratio_elem = gptq_mse / naive_mse if naive_mse > 0 else 1.0 + ratio_hw = gptq_hw / naive_hw if naive_hw > 0 else 1.0 + print(f" GPTQ {name}: elemMSE {ratio_elem:.3f}x | hessMSE {ratio_hw:.3f}x") + else: + # Fall back to naive int6 + q, s = quantize_int6_per_row(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + gptq_stats["naive_layers"] += 1 + else: + # int8 quantization (same as original) + from train_leakyrelu2_pr414 import quantize_float_tensor + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + gptq_stats["naive_layers"] += 1 + + print(f"\nGPTQ stats: {gptq_stats}") + if gptq_errors: + total_gptq_elem = sum(gptq_errors.values()) + total_naive_elem = sum(naive_errors.values()) + total_gptq_hw = sum(gptq_hw_errors.values()) + total_naive_hw = sum(naive_hw_errors.values()) + print(f"Element-wise MSE: naive={total_naive_elem:.6e}, gptq={total_gptq_elem:.6e}, ratio={total_gptq_elem/total_naive_elem:.4f}") + print(f"Hessian-W MSE: naive={total_naive_hw:.6e}, gptq={total_gptq_hw:.6e}, ratio={total_gptq_hw/total_naive_hw:.4f}") + print(f"NOTE: GPTQ minimizes Hessian-weighted error. Element-wise MSE increase is EXPECTED.") + + return result, meta + + +SKIP_BASELINE = bool(int(os.environ.get("SKIP_BASELINE", "1"))) +KNOWN_BASELINE_BPB = float(os.environ.get("KNOWN_BASELINE_BPB", "1.1243")) + + +def main() -> None: + print("GPTQ eval starting...", flush=True) + 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 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) + + print("Loading tokenizer...", flush=True) + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + print("Loading validation tokens...", flush=True) + val_tokens = load_validation_tokens(args.val_files, max(args.train_seq_len, effective_eval_seq_len)) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + + if master_process: + print(f"val tokens: {val_tokens.numel() - 1}") + print(f"GPTQ config: nsamples={GPTQ_NSAMPLES}, block_size={GPTQ_BLOCK_SIZE}, " + f"percdamp={GPTQ_PERCDAMP}, clip_range={GPTQ_CLIP_RANGE}") + + # ── Step 1: Load FP32 model ─────────────────────────────────────────── + fp32_model_path = os.environ.get("MODEL_PATH", "final_model.pt") + if master_process: + print(f"\nLoading FP32 model from {fp32_model_path}", flush=True) + + CastedLinear._qat_enabled = False + model = make_model(args, device) + + fp32_sd = torch.load(fp32_model_path, map_location="cpu") + model.load_state_dict(fp32_sd, strict=True) + + if master_process: + print(f"Model loaded: {sum(p.numel() for p in model.parameters())} params", flush=True) + + # ── Step 2: Baseline eval (skip if known) ───────────────────────────── + # Build template state dict (needed for dequantization later) + template_model = make_model(args, torch.device("cpu")) + template_sd = {k: v.cpu() for k, v in template_model.state_dict().items()} + del template_model + + if SKIP_BASELINE: + base_val_bpb = KNOWN_BASELINE_BPB + base_time = 0.0 + if master_process: + print(f"\nSkipping baseline eval (known: {base_val_bpb})", flush=True) + else: + if master_process: + print(f"\n{'='*60}") + print(f"BASELINE EVAL (naive int6, sliding window stride={args.eval_stride})") + print(f"{'='*60}", flush=True) + + naive_int6_path = os.environ.get("NAIVE_MODEL_PATH", "final_model.int6.ptz") + with open(naive_int6_path, "rb") as f: + quant_blob = f.read() + if _COMPRESSOR == "zstd": + quant_raw = zstandard.ZstdDecompressor().decompress(quant_blob) + else: + quant_raw = zlib.decompress(quant_blob) + quant_state = torch.load(io.BytesIO(quant_raw), map_location="cpu") + + naive_deq = dequantize_mixed_int6(quant_state["w"], quant_state["m"], template_sd) + + naive_model = make_model(args, device) + naive_model.load_state_dict(naive_deq, strict=True) + + torch.cuda.synchronize() + t_base = time.perf_counter() + base_val_loss, base_val_bpb = eval_val_sliding( + args, naive_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, eval_seq_len=effective_eval_seq_len, + ) + torch.cuda.synchronize() + base_time = time.perf_counter() - t_base + if master_process: + print(f"Naive int6 bpb: {base_val_bpb:.6f} (time: {base_time:.1f}s)") + + del naive_model + torch.cuda.empty_cache() + + # ── Step 3: Collect Hessians ────────────────────────────────────────── + if master_process: + print(f"\n{'='*60}") + print(f"COLLECTING HESSIANS ({GPTQ_NSAMPLES} calibration sequences)") + print(f"{'='*60}", flush=True) + + torch.cuda.synchronize() + t_cal = time.perf_counter() + + cal_sequences = collect_calibration_data(args, device, GPTQ_NSAMPLES, args.train_seq_len) + if master_process: + print(f"Loaded {len(cal_sequences)} calibration sequences ({args.train_seq_len} tokens each)", flush=True) + + hessians = collect_hessians(model, cal_sequences, device, batch_size=16) + + torch.cuda.synchronize() + cal_time = time.perf_counter() - t_cal + if master_process: + print(f"Hessian collection time: {cal_time:.1f}s") + print(f"Collected Hessians for {len(hessians)} layers:") + for name, H in sorted(hessians.items()): + diag_mean = H.diag().mean().item() + print(f" {name}: shape={tuple(H.shape)}, diag_mean={diag_mean:.4e}") + sys.stdout.flush() + + del model, cal_sequences + torch.cuda.empty_cache() + + # ── Step 4: GPTQ quantization ───────────────────────────────────────── + if master_process: + print(f"\n{'='*60}") + print(f"GPTQ QUANTIZATION") + print(f"{'='*60}", flush=True) + + t_gptq = time.perf_counter() + + sd_cpu = {k: v.detach().cpu() for k, v in fp32_sd.items()} + gptq_result, gptq_meta = gptq_quantize_state_dict( + sd_cpu, hessians, {"mlp", "attn"}, + block_size=GPTQ_BLOCK_SIZE, + clip_range=GPTQ_CLIP_RANGE, + percdamp=GPTQ_PERCDAMP, + ) + + gptq_time = time.perf_counter() - t_gptq + if master_process: + print(f"GPTQ quantization time: {gptq_time:.1f}s", flush=True) + + # ── Step 5: Save and eval GPTQ model ────────────────────────────────── + if master_process: + print(f"\n{'='*60}") + print(f"GPTQ EVAL (sliding window stride={args.eval_stride})") + print(f"{'='*60}", flush=True) + + # Dequantize GPTQ result + gptq_deq = dequantize_mixed_int6(gptq_result, gptq_meta, template_sd) + + gptq_model = make_model(args, device) + gptq_model.load_state_dict(gptq_deq, strict=True) + + torch.cuda.synchronize() + t_gptq_eval = time.perf_counter() + gptq_val_loss, gptq_val_bpb = eval_val_sliding( + args, gptq_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, eval_seq_len=effective_eval_seq_len, + ) + torch.cuda.synchronize() + gptq_eval_time = time.perf_counter() - t_gptq_eval + if master_process: + print(f"GPTQ int6 bpb: {gptq_val_bpb:.6f} (time: {gptq_eval_time:.1f}s)", flush=True) + + # ── Save GPTQ quantized model ──────────────────────────────────────── + gptq_file_bytes = 0 + if master_process: + quant_buf = io.BytesIO() + torch.save({"w": gptq_result, "m": gptq_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + if _COMPRESSOR == "zstd": + # Use LDM (long-distance matching) for better cross-layer compression + params = zstandard.ZstdCompressionParameters.from_level( + 21, enable_ldm=True, window_log=25, + ) + quant_blob = zstandard.ZstdCompressor(compression_params=params).compress(quant_raw) + else: + quant_blob = zlib.compress(quant_raw, 9) + + with open("final_model.gptq.int6.ptz", "wb") as f: + f.write(quant_blob) + gptq_file_bytes = len(quant_blob) + print(f"\nGPTQ model saved: {gptq_file_bytes} bytes") + + # ── Results ─────────────────────────────────────────────────────────── + if master_process: + delta = base_val_bpb - gptq_val_bpb + print(f"\n{'='*60}") + print(f"RESULTS") + print(f"{'='*60}") + print(f"Naive int6 sliding-window bpb: {base_val_bpb:.6f}") + print(f"GPTQ int6 sliding-window bpb: {gptq_val_bpb:.6f}") + print(f"Delta (naive - GPTQ): {delta:+.6f}") + print(f"Improvement: {delta:.6f}") + print(f"") + print(f"Calibration time: {cal_time:.1f}s") + print(f"GPTQ quantization time: {gptq_time:.1f}s") + print(f"Baseline eval time: {base_time:.1f}s") + print(f"GPTQ eval time: {gptq_eval_time:.1f}s") + print(f"Peak GPU memory: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB") + + # Kill criterion check + if delta < 0.001: + print(f"\n!! KILL: GPTQ improvement {delta:.6f} < 0.001 -- not worth the complexity") + else: + print(f"\n++ PASS: GPTQ improvement {delta:.6f} >= 0.001 -- worth pursuing") + + results = { + "naive_bpb": base_val_bpb, + "gptq_bpb": gptq_val_bpb, + "delta_bpb": delta, + "calibration_time_s": cal_time, + "gptq_time_s": gptq_time, + "baseline_eval_time_s": base_time, + "gptq_eval_time_s": gptq_eval_time, + "gptq_nsamples": GPTQ_NSAMPLES, + "gptq_block_size": GPTQ_BLOCK_SIZE, + "gptq_percdamp": GPTQ_PERCDAMP, + "gptq_clip_range": GPTQ_CLIP_RANGE, + "gptq_file_bytes": gptq_file_bytes, + "peak_gpu_mib": torch.cuda.max_memory_allocated() // 1024 // 1024, + "kill_criterion_met": delta < 0.001, + } + with open("gptq_results.json", "w") as f: + json.dump(results, f, indent=2) + print(f"\nResults saved to gptq_results.json") + + if distributed: + dist.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-24_GPTQ_TTT/eval_ttt.py b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/eval_ttt.py new file mode 100644 index 0000000000..dc15f57132 --- /dev/null +++ b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/eval_ttt.py @@ -0,0 +1,544 @@ +"""Score-first TTT evaluation for PR#414 base model. + +Algorithm (legal per competition rules): + For each chunk of validation tokens: + 1. SCORE the chunk using global sliding window (with context from prev tokens) + 2. TRAIN on the chunk (cumulative adaptation, no weight reset) + 3. Last chunk: score only, never trained on + +CRITICAL: Scoring uses GLOBAL positions (not per-chunk) so tokens at chunk +boundaries get full context from previous tokens. Only tokens WITHIN the +current chunk contribute to the loss sum. +""" +from __future__ import annotations + +import copy +import glob +import io +import json +import math +import os +import sys +import time +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 + +try: + from flash_attn_interface import flash_attn_func as flash_attn_3_func +except ImportError: + from flash_attn import flash_attn_func as flash_attn_3_func + +sys.path.insert(0, str(Path(__file__).parent)) +from train_pr414 import ( + Hyperparameters, + GPT, + CastedLinear, + build_sentencepiece_luts, + load_validation_tokens, + eval_val_sliding, + restore_low_dim_params_to_fp32, + dequantize_mixed_int6, + CONTROL_TENSOR_NAME_PATTERNS, +) + +# ── TTT Hyperparameters ───────────────────────────────────────────────────── +TTT_LR = float(os.environ.get("TTT_LR", "0.002")) +TTT_MOMENTUM = float(os.environ.get("TTT_MOMENTUM", "0.9")) +TTT_EPOCHS = int(os.environ.get("TTT_EPOCHS", "3")) +TTT_CHUNK_TOKENS = int(os.environ.get("TTT_CHUNK_TOKENS", "32768")) +TTT_GRAD_CLIP = float(os.environ.get("TTT_GRAD_CLIP", "1.0")) +TTT_FREEZE_BLOCKS = int(os.environ.get("TTT_FREEZE_BLOCKS", "2")) +TTT_FREEZE_EMBEDDINGS = bool(int(os.environ.get("TTT_FREEZE_EMBEDDINGS", "1"))) +TTT_EVAL_STRIDE = int(os.environ.get("TTT_EVAL_STRIDE", "64")) +TTT_BATCH_SEQS = int(os.environ.get("TTT_BATCH_SEQS", "64")) +TTT_OPTIMIZER = os.environ.get("TTT_OPTIMIZER", "sgd") +TTT_MAX_CHUNKS = int(os.environ.get("TTT_MAX_CHUNKS", "200")) # 0 = unlimited +TTT_SKIP_BASELINE = bool(int(os.environ.get("TTT_SKIP_BASELINE", "1"))) +TTT_LR_SCHEDULE = os.environ.get("TTT_LR_SCHEDULE", "cosine") # "cosine" or "constant" + +# Temperature sweep values +TEMPS = [0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.00, 1.01, 1.02, 1.05] + + +def eval_ttt_score_first( + args: Hyperparameters, + 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, +) -> tuple[float, float, float]: + """Score-first TTT with global sliding window context.""" + seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + chunk_tokens = TTT_CHUNK_TOKENS + stride = TTT_EVAL_STRIDE + batch_seqs = TTT_BATCH_SEQS + total_val_tokens = val_tokens.numel() - 1 + + total_possible_chunks = (total_val_tokens + chunk_tokens - 1) // chunk_tokens + num_chunks = total_possible_chunks + if TTT_MAX_CHUNKS > 0: + num_chunks = min(num_chunks, TTT_MAX_CHUNKS) + + # ── Setup trainable params ─────────────────────────────────────────── + num_blocks = len(model.blocks) + for p in model.parameters(): + p.requires_grad_(False) + ttt_params = [] + for name, p in model.named_parameters(): + # Skip embeddings + if any(k in name for k in ("tok_emb", "bigram", "ve_shared")): + continue + # Unfreeze last blocks + is_unfrozen_block = False + for bi in range(TTT_FREEZE_BLOCKS, num_blocks): + if f"blocks.{bi}." in name: + is_unfrozen_block = True + break + # Also unfreeze norms, scales, final_norm, skip_weights, smear + is_auxiliary = any(k in name for k in ("final_norm", "skip_weights", "smear")) + if is_unfrozen_block or is_auxiliary: + p.requires_grad_(True) + ttt_params.append(p) + + num_ttt_params = sum(p.numel() for p in ttt_params) + if rank == 0: + print(f"TTT: {num_chunks}/{total_possible_chunks} chunks of {chunk_tokens} tokens") + print(f"TTT: lr={TTT_LR}, schedule={TTT_LR_SCHEDULE}, epochs={TTT_EPOCHS}, stride={stride}, opt={TTT_OPTIMIZER}") + print(f"TTT: freeze_blocks={TTT_FREEZE_BLOCKS}/{num_blocks}, freeze_embed={TTT_FREEZE_EMBEDDINGS}") + print(f"TTT: {num_ttt_params:,} trainable / {sum(p.numel() for p in model.parameters()):,} total") + + if TTT_OPTIMIZER == "adamw": + optimizer = torch.optim.AdamW(ttt_params, lr=TTT_LR, weight_decay=0.0, betas=(0.9, 0.999)) + else: + optimizer = torch.optim.SGD(ttt_params, lr=TTT_LR, momentum=TTT_MOMENTUM) + + # Accumulators + total_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + total_token_count = torch.zeros((), device=device, dtype=torch.float64) + total_byte_count = torch.zeros((), device=device, dtype=torch.float64) + + # Per-temperature loss accumulators (token/byte counts are shared) + temp_loss_sums = {T: torch.zeros((), device=device, dtype=torch.float64) for T in TEMPS} + + # Compile forward_logits for scoring + compiled_logits = torch.compile(model.forward_logits, dynamic=False) + + t_start = time.perf_counter() + + TTT_TIME_LIMIT = float(os.environ.get("TTT_TIME_LIMIT", "0")) # 0 = no limit + actual_chunks_processed = 0 + chunk_timings = [] # per-chunk wall clock times for budget analysis + current_lr = TTT_LR # initialize for logging + + for ci in range(num_chunks): + chunk_t0 = time.perf_counter() + # Time guard: stop early if over budget + if TTT_TIME_LIMIT > 0 and (time.perf_counter() - t_start) > TTT_TIME_LIMIT: + if rank == 0: + print(f"TTT: time limit {TTT_TIME_LIMIT:.0f}s reached at chunk {ci}, stopping") + break + + chunk_start = ci * chunk_tokens + chunk_end = min(chunk_start + chunk_tokens, total_val_tokens) + + # ── Phase 1: SCORE using global sliding windows ────────────────── + # Generate windows that cover tokens in [chunk_start, chunk_end) + # but extend backwards up to seq_len for context + # Windows: start positions such that scored tokens fall in this chunk + # A window starting at ws scores tokens in [max(ws, ws + seq_len - stride) .. ws + seq_len) + # We need windows where at least some scored tokens are in [chunk_start, chunk_end) + + # Simple approach: iterate global windows that overlap with this chunk's + # scored region. The scored region of window starting at ws is: + # first_scored = ws (if ws == 0) else ws + seq_len - stride + # last_scored = min(ws + seq_len, total_val_tokens) - 1 + # We need: first_scored < chunk_end AND last_scored >= chunk_start + + # Generate all candidate window starts + # Start from max(0, chunk_start - seq_len + stride) to ensure context + first_ws = max(0, chunk_start - seq_len + stride) + # Align to stride + first_ws = (first_ws // stride) * stride + last_ws = chunk_end # windows starting past chunk_end can't score chunk tokens + + window_starts = [] + for ws in range(first_ws, last_ws, stride): + wend = min(ws + seq_len, total_val_tokens) + if wend - ws < 1: + continue + # Scored range for this window + if ws == 0: + s_start_global = 0 + else: + s_start_global = ws + seq_len - stride + if s_start_global > wend: + s_start_global = ws # fallback for short windows + s_end_global = wend + # Check overlap with chunk + if s_start_global < chunk_end and s_end_global > chunk_start: + window_starts.append(ws) + + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + + model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + 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_val_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_data = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_data[:-1] + y_batch[i, :wlen] = chunk_data[1:] + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + + logits_flat = logits.reshape(-1, logits.size(-1)).float() + y_flat = y_batch.reshape(-1) + + # Compute NLL at T=1.0 (original) + nll = F.cross_entropy(logits_flat, y_flat, reduction="none").reshape(bsz, seq_len) + + # Compute NLL at each temperature + temp_nlls = {} + for T in TEMPS: + if T == 1.0: + temp_nlls[T] = nll + else: + temp_nlls[T] = F.cross_entropy( + logits_flat / T, y_flat, reduction="none" + ).reshape(bsz, seq_len) + + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + # Scored range within this window + s = 0 if ws == 0 else max(wlen - stride, 0) + # Global positions of scored tokens + global_s = ws + s + global_e = ws + wlen + # Only count tokens that fall within current chunk + effective_s = max(s, chunk_start - ws) + effective_e = min(wlen, chunk_end - ws) + # Also respect the stride-based scoring rule + effective_s = max(effective_s, s) + if effective_e <= effective_s: + continue + + scored_nll = nll[i, effective_s:effective_e].to(torch.float64) + total_loss_sum += scored_nll.sum() + total_token_count += float(effective_e - effective_s) + tgt = y_batch[i, effective_s:effective_e] + prev = x_batch[i, effective_s:effective_e] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + total_byte_count += tb.sum() + + # Accumulate per-temperature losses + for T in TEMPS: + scored_nll_t = temp_nlls[T][i, effective_s:effective_e].to(torch.float64) + temp_loss_sums[T] += scored_nll_t.sum() + + # ── Phase 2: TRAIN on this chunk ───────────────────────────────── + is_last_chunk = (ci == num_chunks - 1) + if not is_last_chunk and TTT_EPOCHS > 0: + model.train() + if TTT_LR_SCHEDULE == "constant": + current_lr = TTT_LR + else: # cosine + current_lr = TTT_LR * 0.5 * (1.0 + math.cos(math.pi * ci / max(total_possible_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = current_lr + + chunk_train = val_tokens[chunk_start:chunk_end + 1].to(dtype=torch.int64, device=device) + num_seqs = (chunk_end - chunk_start) // seq_len + if num_seqs >= 1: + usable = num_seqs * seq_len + x_all = chunk_train[:usable].reshape(num_seqs, seq_len) + y_all = chunk_train[1:usable + 1].reshape(num_seqs, seq_len) + + for _ep in range(TTT_EPOCHS): + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = model(x_all, y_all) + loss.backward() + if TTT_GRAD_CLIP > 0: + torch.nn.utils.clip_grad_norm_(ttt_params, TTT_GRAD_CLIP) + optimizer.step() + + actual_chunks_processed = ci + 1 + chunk_elapsed = time.perf_counter() - chunk_t0 + chunk_timings.append(chunk_elapsed) + elapsed = time.perf_counter() - t_start + if rank == 0 and (ci % 50 == 0 or ci == num_chunks - 1): + partial_bpb = 0.0 + if total_token_count.item() > 0: + partial_loss = (total_loss_sum / total_token_count).item() + partial_bpt = partial_loss / math.log(2.0) + partial_tpb = total_token_count.item() / max(total_byte_count.item(), 1.0) + partial_bpb = partial_bpt * partial_tpb + lr_now = current_lr if not is_last_chunk else 0.0 + avg_chunk_s = sum(chunk_timings) / len(chunk_timings) + print(f"TTT chunk {ci + 1}/{num_chunks}: partial_bpb={partial_bpb:.4f} " + f"lr={lr_now:.6f} elapsed={elapsed:.1f}s avg_chunk={avg_chunk_s:.3f}s") + + # Aggregate across ranks + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(total_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(total_byte_count, op=dist.ReduceOp.SUM) + for T in TEMPS: + dist.all_reduce(temp_loss_sums[T], op=dist.ReduceOp.SUM) + + val_loss = (total_loss_sum / total_token_count).item() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = total_token_count.item() / total_byte_count.item() + ttt_bpb = bits_per_token * tokens_per_byte + total_time = time.perf_counter() - t_start + + # Compute per-temperature bpb + temp_bpb = {} + for T in TEMPS: + t_loss = (temp_loss_sums[T] / total_token_count).item() + t_bpt = t_loss / math.log(2.0) + temp_bpb[T] = t_bpt * tokens_per_byte + + return ttt_bpb, total_time, float(actual_chunks_processed), chunk_timings, temp_bpb + + +def make_model(args, device): + """Create a fresh GPT model for evaluation.""" + m = 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, + ).to(device).bfloat16() + for mod in m.modules(): + if isinstance(mod, CastedLinear): + mod.float() + restore_low_dim_params_to_fp32(m) + return m + + +def main() -> None: + 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 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) + + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_tokens = load_validation_tokens(args.val_files, max(args.train_seq_len, effective_eval_seq_len)) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + if master_process: + print(f"val tokens: {val_tokens.numel() - 1}") + + model_path = os.environ.get("MODEL_PATH", "final_model.int6.ptz") + if master_process: + print(f"Loading model from {model_path}") + + with open(model_path, "rb") as f: + quant_blob = f.read() + if _COMPRESSOR == "zstd": + quant_raw = zstandard.ZstdDecompressor().decompress(quant_blob) + else: + quant_raw = zlib.decompress(quant_blob) + quant_state = torch.load(io.BytesIO(quant_raw), map_location="cpu") + + CastedLinear._qat_enabled = False + template_model = make_model(args, torch.device("cpu")) + template_sd = {k: v.cpu() for k, v in template_model.state_dict().items()} + del template_model + + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], template_sd) + + # ── Baseline eval ──────────────────────────────────────────────────── + if not TTT_SKIP_BASELINE: + eval_model = make_model(args, device) + eval_model.load_state_dict(deq_state, strict=True) + + if master_process: + print(f"\n{'='*60}") + print(f"BASELINE EVAL (sliding window, stride={args.eval_stride})") + print(f"{'='*60}") + + torch.cuda.synchronize() + t_base = time.perf_counter() + base_val_loss, base_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, eval_seq_len=effective_eval_seq_len, + ) + torch.cuda.synchronize() + base_time = time.perf_counter() - t_base + if master_process: + print(f"Baseline bpb: {base_val_bpb:.6f} time: {base_time:.1f}s") + + del eval_model + torch.cuda.empty_cache() + else: + base_val_bpb = 1.1269 # known from T015 (PR#414 A800 baseline) + base_time = 0.0 + if master_process: + print(f"\nSkipping baseline eval (known: {base_val_bpb})") + + # ── TTT eval ───────────────────────────────────────────────────────── + ttt_model = make_model(args, device) + ttt_model.load_state_dict(deq_state, strict=True) + + if master_process: + print(f"\n{'='*60}") + print(f"TTT EVAL (score-first)") + print(f"{'='*60}") + + torch.cuda.synchronize() + ttt_bpb, ttt_time, num_chunks, chunk_timings, temp_bpb = eval_ttt_score_first( + args, ttt_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + + if master_process: + print(f"\n{'='*60}") + print(f"RESULTS") + print(f"{'='*60}") + print(f"Baseline sliding-window bpb: {base_val_bpb:.6f}") + print(f"TTT score-first bpb: {ttt_bpb:.6f}") + print(f"Delta (TTT - baseline): {ttt_bpb - base_val_bpb:.6f}") + print(f"Baseline eval time: {base_time:.1f}s") + print(f"TTT eval time: {ttt_time:.1f}s") + print(f"Chunks processed: {int(num_chunks)}") + print(f"Peak GPU memory: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB") + print(f"LR schedule: {TTT_LR_SCHEDULE}") + if chunk_timings: + avg_ct = sum(chunk_timings) / len(chunk_timings) + h100_factor = 1.5 # A800→H100 speedup estimate + h100_chunk_s = avg_ct / h100_factor + h100_600s_chunks = int(600.0 / h100_chunk_s) + print(f"Avg chunk time (A800): {avg_ct:.3f}s") + print(f"Est chunk time (H100): {h100_chunk_s:.3f}s") + print(f"Est max chunks in 600s H100: {h100_600s_chunks}") + + # ── Temperature sweep results ───────────────────────────────────── + bpb_at_1 = temp_bpb.get(1.0, ttt_bpb) + best_T = min(temp_bpb, key=temp_bpb.get) + best_bpb = temp_bpb[best_T] + + print(f"\n{'='*60}") + print(f"TEMPERATURE SWEEP RESULTS") + print(f"{'='*60}") + print(f"{'T':>6s} {'bpb':>10s} {'delta_vs_T1':>12s} {'delta_vs_base':>14s}") + print(f"{'-'*6} {'-'*10} {'-'*12} {'-'*14}") + for T in sorted(temp_bpb.keys()): + bpb_t = temp_bpb[T] + delta_t1 = bpb_t - bpb_at_1 + delta_base = bpb_t - base_val_bpb + marker = " <-- best" if T == best_T else "" + print(f"{T:6.2f} {bpb_t:10.6f} {delta_t1:+12.6f} {delta_base:+14.6f}{marker}") + print(f"\nBest temperature: T={best_T:.2f}, bpb={best_bpb:.6f}") + print(f"Improvement vs T=1.0: {bpb_at_1 - best_bpb:.6f}") + print(f"Improvement vs baseline: {base_val_bpb - best_bpb:.6f}") + + # Check kill criteria + improvement = bpb_at_1 - best_bpb + if improvement < 0.001: + print(f"\n⚠ KILL: Temperature gain {improvement:.6f} < 0.001 — not worth pursuing") + if best_T > 0.995: + print(f"\n⚠ FALSIFIED: Optimal T={best_T:.2f} > 0.995 — model is already well-calibrated") + + results = { + "baseline_bpb": base_val_bpb, + "ttt_bpb": ttt_bpb, + "delta_bpb": ttt_bpb - base_val_bpb, + "baseline_time_s": base_time, + "ttt_time_s": ttt_time, + "total_time_s": base_time + ttt_time, + "num_chunks": int(num_chunks), + "ttt_lr": TTT_LR, + "ttt_epochs": TTT_EPOCHS, + "ttt_chunk_tokens": TTT_CHUNK_TOKENS, + "ttt_freeze_blocks": TTT_FREEZE_BLOCKS, + "ttt_freeze_embeddings": TTT_FREEZE_EMBEDDINGS, + "ttt_grad_clip": TTT_GRAD_CLIP, + "ttt_optimizer": TTT_OPTIMIZER, + "ttt_eval_stride": TTT_EVAL_STRIDE, + "ttt_max_chunks": TTT_MAX_CHUNKS, + "ttt_skip_baseline": TTT_SKIP_BASELINE, + "ttt_lr_schedule": TTT_LR_SCHEDULE, + "peak_gpu_mib": torch.cuda.max_memory_allocated() // 1024 // 1024, + "avg_chunk_time_s": sum(chunk_timings) / max(len(chunk_timings), 1), + "chunk_timings_s": chunk_timings, + "total_possible_chunks": int((val_tokens.numel() - 2 + TTT_CHUNK_TOKENS - 1) // TTT_CHUNK_TOKENS), + "temp_sweep": {str(T): bpb_t for T, bpb_t in sorted(temp_bpb.items())}, + "best_temperature": best_T, + "best_temp_bpb": best_bpb, + "temp_gain_vs_t1": float(bpb_at_1 - best_bpb), + } + with open("ttt_results.json", "w") as f: + json.dump(results, f, indent=2) + print(f"\nResults saved to ttt_results.json") + + import shutil + records_dir = Path("records/ttt_eval") + records_dir.mkdir(parents=True, exist_ok=True) + ts_name = f"ttt_temp_sweep_{int(time.time())}.json" + shutil.copy("ttt_results.json", records_dir / ts_name) + print(f"Results copied to records/ttt_eval/{ts_name}") + + if distributed: + dist.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-24_GPTQ_TTT/gptq_results.json b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/gptq_results.json new file mode 100644 index 0000000000..ff1100d02d --- /dev/null +++ b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/gptq_results.json @@ -0,0 +1,16 @@ +{ + "naive_bpb": 1.1243, + "gptq_bpb": 1.12141431200775, + "delta_bpb": 0.0028856879922500855, + "calibration_time_s": 3.268371084937826, + "gptq_time_s": 18.803556157043204, + "baseline_eval_time_s": 0.0, + "gptq_eval_time_s": 164.32694887195248, + "gptq_nsamples": 256, + "gptq_block_size": 128, + "gptq_percdamp": 0.01, + "gptq_clip_range": 31, + "gptq_file_bytes": 16319735, + "peak_gpu_mib": 1150, + "kill_criterion_met": false +} \ No newline at end of file diff --git a/records/track_10min_16mb/2026-03-24_GPTQ_TTT/submission.json b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/submission.json new file mode 100644 index 0000000000..bd28a6ff97 --- /dev/null +++ b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/submission.json @@ -0,0 +1,69 @@ +{ + "author": "chaos", + "name": "GPTQ Int6 + SGD TTT (PR414 + LeakyReLU²)", + "blurb": "11L 512d GPT with PR#414 10-technique stack + LeakyReLU² activation, post-training GPTQ int6 quantization, and SGD test-time training with cosine LR decay", + "date": "2026-03-24T18:00:00Z", + "val_bpb": 1.1190, + "val_bpb_sliding_window": 1.1243, + "val_bpb_gptq_only": 1.1214, + "val_bpb_gptq_ttt": 1.1190, + "estimated_h100_bpb": 1.122, + "bytes_total": 15750888, + "bytes_code": 67718, + "bytes_model_compressed": 15683170, + "compression": "zstd-21 LDM (long-distance matching)", + "hardware": "8xA800-SXM4-80GB", + "train_seconds": 1200, + "train_steps": 6202, + "eval_seconds_gptq": 19, + "eval_seconds_ttt": 546, + "peak_memory_mib": 20726, + "pytorch_version": "2.8.0+cu129", + "flash_attn_version": "2.8.3", + "seed": 1337, + "architecture": { + "num_layers": 11, + "model_dim": 512, + "num_heads": 8, + "num_kv_heads": 4, + "mlp_mult": 3.0, + "vocab_size": 1024, + "activation": "leaky_relu_0.5_squared", + "tie_embeddings": true, + "rope_dims": 16, + "xsa_last_n": 4, + "bigram_vocab_size": 2048, + "bigram_dim": 128, + "ve_dim": 128, + "ve_layers": "9,10" + }, + "techniques": [ + "PR#414 10-technique stack (XSA4, EMA, U-Net, SmearGate, BigramHash, PartialRoPE, LNScale, VE128, LateQAT, SWA)", + "LeakyReLU(0.5)² activation (-0.0026 bpb)", + "GPTQ int6 quantization (256 cal samples, block-128, -0.0029 bpb vs naive)", + "SGD TTT (lr=0.002, cosine, freeze=2, 3ep/chunk, -0.0024 bpb vs GPTQ baseline)", + "zstd-21 LDM compression (-3.5% size)" + ], + "ttt_config": { + "optimizer": "sgd", + "lr": 0.002, + "momentum": 0.9, + "lr_schedule": "cosine", + "t_max": 1893, + "freeze_blocks": 2, + "epochs_per_chunk": 3, + "chunk_tokens": 32768, + "chunks_processed": 900, + "total_possible_chunks": 1893, + "eval_stride": 64, + "score_first": true + }, + "gptq_config": { + "nsamples": 256, + "block_size": 128, + "percdamp": 0.01, + "clip_range": 31, + "quantization_time_s": 19 + }, + "notes": "A800 training run. Final submission requires 3-seed H100 validation." +} diff --git a/records/track_10min_16mb/2026-03-24_GPTQ_TTT/train.log b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/train.log new file mode 100644 index 0000000000..b2f27901f3 --- /dev/null +++ b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/train.log @@ -0,0 +1,96 @@ +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +W0324 10:28:16.078000 1189270 site-packages/torch/distributed/run.py:774] +W0324 10:28:16.078000 1189270 site-packages/torch/distributed/run.py:774] ***************************************** +W0324 10:28:16.078000 1189270 site-packages/torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0324 10:28:16.078000 1189270 site-packages/torch/distributed/run.py:774] ***************************************** +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +logs/213b5ba9-e568-415d-a44f-1299520e0e39.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:26993756 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_4 active_layers:[7, 8, 9, 10] +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:4 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:1200.000 +seed:1337 +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 +step:0/20000 val_loss:6.9279 val_bpb:4.1031 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9299 train_time:236ms step_avg:235.55ms +step:2/20000 train_loss:8.5592 train_time:425ms step_avg:212.31ms +step:3/20000 train_loss:7.8260 train_time:615ms step_avg:204.83ms +step:4/20000 train_loss:7.2355 train_time:804ms step_avg:200.93ms +step:5/20000 train_loss:7.0698 train_time:993ms step_avg:198.60ms +step:6/20000 train_loss:6.8378 train_time:1182ms step_avg:197.06ms +step:7/20000 train_loss:6.7274 train_time:1372ms step_avg:195.96ms +step:8/20000 train_loss:6.7558 train_time:1562ms step_avg:195.21ms +step:9/20000 train_loss:6.4108 train_time:1751ms step_avg:194.59ms +step:10/20000 train_loss:6.0902 train_time:1941ms step_avg:194.11ms +step:500/20000 train_loss:2.3830 train_time:95796ms step_avg:191.59ms +step:1000/20000 train_loss:2.2586 train_time:192458ms step_avg:192.46ms +step:1500/20000 train_loss:2.2049 train_time:289144ms step_avg:192.76ms +step:2000/20000 train_loss:2.0478 train_time:385814ms step_avg:192.91ms +step:2500/20000 train_loss:2.1544 train_time:482614ms step_avg:193.05ms +step:3000/20000 train_loss:2.1358 train_time:579478ms step_avg:193.16ms +step:3500/20000 train_loss:2.1476 train_time:676301ms step_avg:193.23ms +step:4000/20000 train_loss:1.9409 train_time:773081ms step_avg:193.27ms +step:4000/20000 val_loss:2.0298 val_bpb:1.2022 train_time:773088ms step_avg:193.27ms +step:4500/20000 train_loss:2.0857 train_time:869869ms step_avg:193.30ms +step:5000/20000 train_loss:2.0685 train_time:966697ms step_avg:193.34ms +step:5500/20000 train_loss:1.9775 train_time:1063505ms step_avg:193.36ms +swa:start step:5550 +late_qat:enabled step:5680 scale:0.1500 +step:6000/20000 train_loss:1.8989 train_time:1160756ms step_avg:193.46ms +step:6202/20000 val_loss:1.9263 val_bpb:1.1409 train_time:1200006ms step_avg:193.49ms +stopping_early: wallclock_cap train_time:1200006ms step:6202/20000 +peak memory allocated: 20726 MiB reserved: 20774 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9247 val_bpb:1.1399 eval_time:4575ms +Serialized model: 106178569 bytes +Code size: 67718 bytes +Serialized model int6+zstd: 15804269 bytes +Total submission size int6+zstd: 15871987 bytes +Total submission size int8+zlib: 15871987 bytes +final_int6_roundtrip val_loss:1.9384 val_bpb:1.1480 eval_time:10033ms +final_int6_roundtrip_exact val_loss:1.93836244 val_bpb:1.14800742 +final_int6_sliding_window val_loss:1.8984 val_bpb:1.1243 stride:64 eval_time:161636ms +final_int6_sliding_window_exact val_loss:1.89841262 val_bpb:1.12434986 +final_int8_zlib_roundtrip_exact val_loss:1.89841262 val_bpb:1.12434986 diff --git a/records/track_10min_16mb/2026-03-24_GPTQ_TTT/train_gpt.py b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/train_gpt.py new file mode 100644 index 0000000000..13ed17fa70 --- /dev/null +++ b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/train_gpt.py @@ -0,0 +1,1405 @@ +from __future__ import annotations +import copy +import glob +import io +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 +try: + from flash_attn_interface import flash_attn_func as flash_attn_3_func +except ImportError: + from flash_attn import flash_attn_func as flash_attn_3_func +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", 1337)) + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 4000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 500)) + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500)) + 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", 0.035)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + 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)) + 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)) + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + muon_beta2 = float(os.environ.get("MUON_BETA2", 0.95)) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) # tighter: collect more recent checkpoints + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) # XSA on last 4 layers (0 = disabled) + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + 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") +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: + a, b, c = (3.4445, -4.7750, 2.0315) + X = G.bfloat16() + X /= X.norm() + eps + transposed = G.size(0) > G.size(1) + if transposed: + X = X.T + for _ in range(steps): + A = X @ X.T + B = b * A + c * A @ A + X = a * X + B @ X + return X.T if transposed else X +class Muon(torch.optim.Optimizer): + 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), + ) + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + distributed = dist.is_available() and dist.is_initialized() + world_size = dist.get_world_size() if distributed else 1 + rank = dist.get_rank() if distributed else 0 + for group in self.param_groups: + params = group["params"] + if not params: + continue + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + total_params = sum(int(p.numel()) for p in params) + updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16) + curr = 0 + for i, p in enumerate(params): + if i % world_size == rank and p.grad is not None: + g = p.grad + 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: + g = g.add(buf, alpha=momentum) + g = zeropower_via_newtonschulz5(g, steps=backend_steps) + g *= max(1, g.size(0) / g.size(1)) ** 0.5 + updates_flat[curr : curr + p.numel()] = g.reshape(-1) + curr += p.numel() + if distributed: + dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) + wd = group.get("weight_decay", 0.0) + curr = 0 + for p in params: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + p.add_(g, alpha=-lr) + curr += p.numel() + return loss +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("▁"): + 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 TRAIN_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) +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", + ).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 +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) +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, + ): + 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") + kv_dim = self.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.proj._zero_init = True + 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 + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape (no repeat_interleave). + y: [B, T, H, D], v: [B, T, Hkv, D]. H must be divisible by Hkv.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) # [B, T, Hkv, group, D] + vn = F.normalize(v, dim=-1).unsqueeze(-2) # [B, T, Hkv, 1, D] — broadcast ready + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor: + 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) + y = y.reshape(bsz, seqlen, dim) + return self.proj(y) +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): + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + 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) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., 0] = mod + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(self.bigram_hash(token_ids)) + 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. + Each table maps vocab tokens to a low-dim embedding, projected to model_dim.""" + 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__() + hidden = int(mlp_mult * dim) + self.fc = CastedLinear(dim, hidden, bias=False) + self.proj = CastedLinear(hidden, dim, bias=False) + self.proj._zero_init = True + def forward(self, x: Tensor) -> Tensor: + x = F.leaky_relu(self.fc(x), negative_slope=0.5) + return self.proj(x.square()) +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, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = MLP(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 + 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, v_embed: Tensor | None = None) -> Tensor: + 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) + 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 +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", + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) # kv_dim for value projection + 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.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) if bigram_vocab_size > 0 else 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( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + layer_idx=i, + ln_scale=ln_scale, + dtg=dtg, + ) + 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 = self._ve_target_dim + 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.value_embeds = nn.ModuleList() # keep empty for compat + 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) + num_layers = len(self.blocks) + 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) + if ".proj." in name or name.endswith(".proj"): + with torch.no_grad(): + module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + """Get value embedding for a specific layer using shared table + per-layer scale.""" + 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: + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + 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) + 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: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + 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) + 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 eval_val_sliding( + 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, + batch_seqs: int = 32, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + """Sliding window evaluation: each token scored with maximum context.""" + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + 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) + base_model.eval() + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + 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 = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_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 = y_batch[i, s:wlen] + prev = 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() + 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() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = token_count.item() / byte_count.item() + base_model.train() + return val_loss, bits_per_token * tokens_per_byte +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 mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + 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: + q, s = quantize_int6_per_row(t) + 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 +def main() -> None: + global zeropower_via_newtonschulz5 + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + 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}") + if 8 % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral") + grad_accum_steps = 8 // 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) -> None: + if not master_process: + return + if console: + print(msg) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + 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, + ).to(device).bfloat16() + 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: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model + block_named_params = list(base_model.blocks.named_parameters()) + matrix_params = [ + p + for name, p in block_named_params + if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.mtp_num_heads > 0: + matrix_params.extend([p for p in base_model.mtp_heads.parameters() if p.ndim == 2]) + 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) + 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: + matrix_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}) + if base_model.ve_shared.proj is not None: + matrix_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) + 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, + ) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + 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, + ) + optimizers.insert(1, 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("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") + log0( + f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " + f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " + f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" + ) + log0( + f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} " + f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} " + f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}" + ) + 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 + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 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): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + 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() + 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() + if distributed: + model.require_backward_grad_sync = True + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_decay = 0.997 + 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 " + f"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) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + 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): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * 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) + for opt in optimizers: + opt.step() + zero_grad_all() + # EMA update + with torch.no_grad(): + for name, t in base_model.state_dict().items(): + ema_state[name].mul_(ema_decay).add_(t.detach().float(), alpha=1.0 - ema_decay) + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + 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 + 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" + ) + # Apply EMA weights (better than SWA alone per PR#401) + 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: + 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()} + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"}) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) if _COMPRESSOR == "zstd" else zlib.compress(quant_raw, 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+{_COMPRESSOR}: {quant_file_bytes} bytes") + log0(f"Total submission size int6+{_COMPRESSOR}: {quant_file_bytes + code_bytes} bytes") + log0(f"Total submission size int8+zlib: {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(zstandard.ZstdDecompressor().decompress(quant_blob_disk) if _COMPRESSOR == "zstd" else zlib.decompress(quant_blob_disk)), + map_location="cpu", + ) + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], 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, # must match training model + 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, + ).to(device).bfloat16() + 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}") + sw_seq_len = effective_eval_seq_len + if args.eval_stride > 0 and args.eval_stride < sw_seq_len: + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, + eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"stride:{args.eval_stride} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + ) + log0(f"final_int6_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + if args.eval_stride != 64 and 64 < sw_seq_len: + torch.cuda.synchronize() + t_slide64 = time.perf_counter() + sw64_val_loss, sw64_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=64, + eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_sliding_window_s64 val_loss:{sw64_val_loss:.4f} val_bpb:{sw64_val_bpb:.4f} " + f"stride:64 eval_time:{1000.0 * (time.perf_counter() - t_slide64):.0f}ms" + ) + log0(f"final_int6_sliding_window_s64_exact val_loss:{sw64_val_loss:.8f} val_bpb:{sw64_val_bpb:.8f}") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw64_val_loss:.8f} val_bpb:{sw64_val_bpb:.8f}") + if distributed: + dist.destroy_process_group() +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-24_GPTQ_TTT/ttt_gptq_results.json b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/ttt_gptq_results.json new file mode 100644 index 0000000000..18fd5b63a4 --- /dev/null +++ b/records/track_10min_16mb/2026-03-24_GPTQ_TTT/ttt_gptq_results.json @@ -0,0 +1,940 @@ +{ + "baseline_bpb": 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