From 7f652d2679759e18dce4daadf604c82e85424040 Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Mon, 23 Mar 2026 20:45:02 -0500 Subject: [PATCH 1/5] =?UTF-8?q?Record:=20Full=20GPTQ=20+=20LeakyReLU=C2=B2?= =?UTF-8?q?=20+=20Parallel=20Muon=20=E2=80=94=20val=5Fbpb=201.1170=20(3-se?= =?UTF-8?q?ed=20mean)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Full Hessian GPTQ (Cholesky error compensation, actorder) replaces GPTQ-lite, improving post-quant BPB by 0.0048. LeakyReLU(0.5)² activation. No TTT needed. 3-seed results: Seed 2025: 1.1167 bpb, 15.90 MB Seed 1337: 1.1171 bpb, 15.96 MB Seed 2024: 1.1173 bpb, 15.99 MB Mean: 1.1170 (std 0.0003) All artifacts under 16MB. Eval ~185s (well within 10 min). Co-Authored-By: Claude Opus 4.6 (1M context) --- .../README.md | 78 + .../submission.json | 9 + .../train_gpt.py | 2188 +++++++++++++++++ .../train_seed1337.log | 278 +++ .../train_seed2024.log | 82 + .../train_seed2025.log | 82 + 6 files changed, 2717 insertions(+) create mode 100644 records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md create mode 100644 records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json create mode 100644 records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_gpt.py create mode 100644 records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed1337.log create mode 100644 records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2024.log create mode 100644 records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2025.log diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md new file mode 100644 index 000000000..94869b933 --- /dev/null +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md @@ -0,0 +1,78 @@ +# Full GPTQ + LeakyReLU² + Parallel Muon + +**val_bpb: 1.1170** (3-seed mean, std 0.0003) | **~15.95 MB** | 8×H100 SXM, 600s | No TTT + +## Results (8×H100 80GB SXM, PyTorch 2.9.1+cu128) + +| Seed | step_avg | steps | Pre-quant bpb | **Post-GPTQ sliding bpb** | Artifact | +|------|----------|-------|---------------|--------------------------|----------| +| 2025 | 83.4ms | 7,182 | 1.1385 | **1.1167** | 15,901,230 | +| 1337 | 83.3ms | 7,189 | 1.1388 | **1.1171** | 15,962,990 | +| 2024 | 83.3ms | 7,185 | 1.1386 | **1.1173** | 15,994,746 | +| **Mean** | **83.3ms** | **7,185** | **1.1386** | **1.1170 (std 0.0003)** | | + +GPTQ improves post-quantization BPB by **0.0216** vs pre-quantization (1.1386 → 1.1170). Standard GPTQ-lite gives only 1.1218 from the same pre-quant model — Full GPTQ is 0.0048 better. + +## Key Innovation: Full Hessian GPTQ + +Standard GPTQ-lite searches for the best per-row clip percentile — a greedy row-wise optimization. Full GPTQ uses second-order information (the Hessian H = X^T X) to compensate for quantization error across columns: + +1. **Hessian collection**: 256 calibration batches through a non-banked model replica, accumulating H = X^T X per linear layer via forward hooks +2. **Column reordering (actorder)**: Quantize columns in order of descending Hessian diagonal (most important first) +3. **Cholesky error compensation**: For each column block, propagate quantization error to remaining columns using H^{-1}, minimizing total reconstruction loss +4. **Per-row scale search**: Same 5-percentile search as GPTQ-lite, but applied within the Cholesky framework + +Based on IST-DASLab/gptq (ICLR 2023). Adapted for banked weights by unbanking to a temporary non-banked model for Hessian collection. + +## Training Architecture + +PR #414 stack with Parameter Banking + Parallel Muon ([PR #399](https://github.com/openai/parameter-golf/pull/399)): + +| Component | Setting | +|-----------|---------| +| Layers | 11 (512d, 8H, 4KV) | +| MLP | 3× with **LeakyReLU(0.5)²** | +| BigramHash | 1536 | +| XSA | Last 4 layers | +| RoPE | Partial (16/64 dims) | +| LN Scale | 1/√(layer+1) | +| VE128 | Layers 9-10 | +| Weight avg | EMA(0.997) + Tight SWA(every 50) | +| Quantization | **Full Hessian GPTQ int6** + lzma | +| Optimizer | Parameter Banking + Parallel Muon | + +## Run Command + +```bash +NUM_LAYERS=11 BIGRAM_VOCAB_SIZE=1536 XSA_LAST_N=4 \ +EMA_ENABLED=1 EMA_DECAY=0.997 SWA_ENABLED=1 SWA_EVERY=50 \ +ROPE_DIMS=16 LN_SCALE=1 LATE_QAT=1 LATE_QAT_THRESHOLD=0.15 \ +VE_ENABLED=1 VE_DIM=128 VE_LAYERS=9,10 \ +MUON_WD=0.04 ADAM_WD=0.04 \ +MATRIX_LR=0.025 SCALAR_LR=0.025 TIED_EMBED_LR=0.035 \ +MUON_MOMENTUM=0.99 MUON_MOMENTUM_WARMUP_START=0.92 \ +MUON_MOMENTUM_WARMUP_STEPS=1500 WARMDOWN_ITERS=3500 \ +ITERATIONS=9000 MAX_WALLCLOCK_SECONDS=600 EVAL_STRIDE=64 \ +SEED=1337 \ +torchrun --standalone --nproc_per_node=8 train_gpt.py +``` + +## Eval Timing + +| Phase | Time | +|-------|------| +| Training | 600s | +| Hessian collection (256 batches) | ~25s | +| GPTQ quantization | ~60s | +| Sliding window eval (stride=64) | ~100s | +| **Total eval** | **~185s (< 10 min)** | + +No TTT needed — Full GPTQ alone beats all prior TTT-based submissions. + +## Credits + +- **Full GPTQ**: PR #569 by @abaybektursun (Hessian-aware quantization implementation) +- **LeakyReLU²**: PR #493, PR #518 +- **Optimizer (Parameter Banking + Parallel Muon)**: [PR #399](https://github.com/openai/parameter-golf/pull/399) by @abaybektursun +- **Base model**: [PR #414](https://github.com/openai/parameter-golf/pull/414) by @signalrush +- **GPTQ algorithm**: Frantar et al., "GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers" (ICLR 2023) diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json new file mode 100644 index 000000000..59250c157 --- /dev/null +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json @@ -0,0 +1,9 @@ +{ + "name": "Full GPTQ + LeakyReLU² + Parallel Muon", + "val_bpb": 1.1170, + "bytes_total": 15994746, + "blurb": "Full Hessian GPTQ (Cholesky error compensation, actorder) + LeakyReLU(0.5)² + Parameter Banking + Parallel Muon (PR #399). No TTT. 3-seed mean: 1.1170 (std 0.0003). Built on PR #414 by @signalrush, GPTQ from PR #569.", + "author": "abaybektursun", + "github_id": "abaybektursun", + "date": "2026-03-23" +} diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_gpt.py b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_gpt.py new file mode 100644 index 000000000..7a3bd7de4 --- /dev/null +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_gpt.py @@ -0,0 +1,2188 @@ +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func +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)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + 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)) + 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") + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "0"))) + ttt_lr = float(os.environ.get("TTT_LR", 0.002)) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", 3)) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", 32768)) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", 2)) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", 0.9)) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", 32)) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", 1.0)) + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + for _ in range(steps): + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + 'p': p, + 'B': B, + 'padded_grad': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'shard_mom': torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + 'full_update': torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + 'scale': max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m['p'] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m['padded_grad'] + pg[:m['B']].copy_(p.grad.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + fut = dist.reduce_scatter_tensor(m['shard'], pg, op=dist.ReduceOp.AVG, async_op=True) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self._built: + self._build() + + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, m in enumerate(self._bank_meta): + p = m['p'] + if p.grad is None: + continue + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].wait() + g = m['shard'] + buf = m['shard_mom'] + else: + g = p.grad.bfloat16() + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + + buf.mul_(momentum).add_(g) + if nesterov: + update = g.add(buf, alpha=momentum) + else: + update = buf + + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m['full_update'], update, async_op=True) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update.to(dtype=p.dtype), alpha=-lr * m['scale']) + + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m['p'] + upd = prev_m['full_update'][:prev_m['B']] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for 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) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading --- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vr_lambda = nn.Parameter(torch.tensor([0.5, 0.5], dtype=torch.float32)) + 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, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + lam = self.vr_lambda.to(dtype=v.dtype) + v = lam[0] * v0 + lam[1] * v + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + # gate shape: (bsz, seqlen, num_heads) -> (bsz, seqlen, num_heads, 1) for B,T,H,D layout + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + 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__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + 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, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor, up_w, down_w) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) # 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.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) 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)) + # Parameter banks: contiguous 3D tensors for batched optimizer + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + layer_idx=i, + ln_scale=ln_scale, + dtg=dtg, + gated_attention=gated_attention, + value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() # 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) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + # Init banks: orthogonal, with proj layers scaled down and out/down zero-init + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) # Q + nn.init.zeros_(self.qo_bank.data[n + i]) # Out (zero init) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) # K + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) # V + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) # MLP up + nn.init.zeros_(self.mlp_down_bank.data[i]) # MLP down (zero init) + # Scale proj layers (out_proj and mlp_down are "proj" layers) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + # Init remaining nn.Linear modules (bigram proj, mtp heads, lm_head) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + """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: + n = self.num_layers + 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 + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + n = self.num_layers + 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 + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + self.qo_bank[i], self.kv_bank[i], self.kv_bank[n + i], + self.qo_bank[n + i], self.mlp_up_bank[i], self.mlp_down_bank[i], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + +# --- Sliding window evaluation --- + +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 eval_val_sliding_ttt( + args: Hyperparameters, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, batch_seqs: int = 32, log0=print, +) -> tuple[float, float]: + """Legal score-first TTT (PR #461 recipe): score each chunk with sliding windows, + then train on it. Every token scored BEFORE any update that could use it.""" + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + + # Pre-compute all window starts + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + + # Assign each window to a chunk based on the first token it scores + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + chunk_windows[ci].append(ws) + + log0(f"ttt_sliding:start chunks={num_chunks} chunk_tokens={ttt_chunk} " + f"total_windows={len(window_starts)} stride={stride} " + f"ttt_lr={args.ttt_lr} ttt_epochs={args.ttt_epochs} " + f"freeze_blocks={args.ttt_freeze_blocks}") + + 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) + + # Freeze first N blocks + frozen_block_ids = set(range(min(args.ttt_freeze_blocks, len(base_model.blocks)))) + ttt_params = [] + for name, p in base_model.named_parameters(): + freeze = False + for bi in frozen_block_ids: + if f"blocks.{bi}." in name: + freeze = True + break + if freeze: + p.requires_grad_(False) + else: + p.requires_grad_(True) + ttt_params.append(p) + + log0(f"ttt_sliding:params unfrozen={sum(p.numel() for p in ttt_params)} " + f"frozen={sum(p.numel() for p in base_model.parameters() if not p.requires_grad)}") + + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + t0 = time.perf_counter() + + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + + # --- Phase 1: SCORE this chunk's windows (inference_mode) --- + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), 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_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + + # --- Phase 2: TRAIN on this chunk (already scored = legal) --- + is_last_chunk = (ci == num_chunks - 1) + if not is_last_chunk and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + my_seq_s = (chunk_seqs * rank) // world_size + my_seq_e = (chunk_seqs * (rank + 1)) // world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, args.ttt_batch_seqs): + be = min(bs + args.ttt_batch_seqs, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + loss = base_model(x, y) + loss.backward() + if world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + log0(f" ttt_chunk [{ci+1}/{num_chunks}] bpb={rbpb:.6f} time={elapsed:.1f}s") + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + + log0(f"ttt_sliding:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s") + return val_loss, val_bpb + + +# --- GPTQ-lite int6 quantization --- + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + """Full GPTQ: Hessian-aware int6 quantization with Cholesky error compensation. + If hessian is None, falls back to percentile search.""" + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + """Fallback: percentile search (for 1D or no-Hessian cases).""" + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + """Convert 3D bank tensors into individual 2D tensors with standard names.""" + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + """Convert individual 2D tensors back into 3D bank tensors.""" + out: dict[str, Tensor] = {} + n = num_layers + # Reconstruct banks from individual weight keys + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: + qo_slices[i] = sd[qk] + consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: + qo_slices[n + i] = sd[ok] + consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: + kv_slices[i] = sd[kk] + consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: + kv_slices[n + i] = sd[vk] + consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: + up_slices[i] = sd[fk] + consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: + down_slices[i] = sd[dk] + consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +# --- Non-banked model for Hessian collection --- +# This mirrors the unbanked state dict keys: blocks.{i}.attn.c_q/c_k/c_v/proj, blocks.{i}.mlp.fc/proj + +class _HessianAttn(nn.Module): + """Non-banked attention with CastedLinear layers for Hessian hooks.""" + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape; Hkv = v.size(-2); group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + """Non-banked MLP with CastedLinear layers for Hessian hooks.""" + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + def forward(self, x, x0, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + """Non-banked GPT model matching unbanked state dict keys for Hessian collection.""" + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) 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([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips = [] + ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + """Run calibration batches through a non-banked model, collecting H = X^T X for each CastedLinear.""" + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: + h.remove() + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" + continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 # int6 for all weights + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + # zeropower_via_newtonschulz5 runs eagerly with bmm -- do NOT compile + 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, + gated_attention=args.gated_attention, + value_residual=args.value_residual, + ).to(device).bfloat16() + # Banks stay FP32 (like CastedLinear weights), cast to BF16 in forward + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + # No DDP -- Parallel Muon handles bank grad communication via reduce-scatter, + # and non-bank grads are manually all-reduced before Adam steps. + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + + # Optimizer split: + # - 4 parameter banks -> Muon (batched Newton-Schulz) + # - token embedding -> Adam + # - scalars/control tensors -> Adam + # - bigram proj, mtp heads, VE proj -> Adam (small matrix params not worth banking) + matrix_params = [ + base_model.qo_bank, base_model.kv_bank, + base_model.mlp_up_bank, base_model.mlp_down_bank, + ] + block_named_params = list(base_model.blocks.named_parameters()) + scalar_params = [ + p + for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.ve_shared.proj is not None: + scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: + scalar_params.append(s) + 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, + ) + # Non-bank params that need manual all-reduce (replicated across GPUs) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: + replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: + optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0("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): + 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() + # All-reduce all grads for warmup (simple, not optimized) + if distributed: + for p in base_model.parameters(): + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_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): + 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) + # === 3-phase overlapped optimizer step === + # Phase 1: Launch async reduce-scatter for banks (biggest first) + optimizer_muon.launch_reduce_scatters() + # Phase 2: All-reduce non-bank grads + step Adam (while bank RS is in-flight) + if distributed: + for p in replicated_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step() + optimizer_scalar.step() + if optimizer_head is not None: + optimizer_head.step() + # Phase 3: Wait for RS, local NS5, all-gather (banks processed last) + optimizer_muon.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 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + # Apply weight averaging + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: + avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + else: + 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") + # Unbank 3D tensors into individual 2D tensors for quantization + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + # Full GPTQ: collect Hessians via a temporary non-banked model + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(hessian_model) + # Load unbanked weights into the non-banked model + hessian_model.load_state_dict( + {k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, + strict=False, + ) + log0(f"gptq:calibrating with {args.gptq_calib_batches} batches...") + calib_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + hessians = collect_hessians(hessian_model, calib_loader, args, device, grad_accum_steps, + num_batches=args.gptq_calib_batches) + log0(f"gptq:collected hessians for {len(hessians)} layers") + del hessian_model + torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=6) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int6+lzma: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() + with open("final_model.int6.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_state = torch.load( + io.BytesIO(lzma.decompress(quant_blob_disk)), + map_location="cpu", + ) + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + # Re-bank the dequantized tensors + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize() + t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val( + args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms" + ) + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + 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}") + # Legal score-first TTT (PR #461 recipe) + if args.ttt_enabled: + torch.cuda.synchronize() + t_ttt = time.perf_counter() + ttt_loss, ttt_bpb = eval_val_sliding_ttt( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, log0=log0, + ) + torch.cuda.synchronize() + log0(f"legal_ttt val_loss:{ttt_loss:.4f} val_bpb:{ttt_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms") + log0(f"legal_ttt_exact val_loss:{ttt_loss:.8f} val_bpb:{ttt_bpb:.8f}") + if distributed: + dist.destroy_process_group() +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed1337.log b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed1337.log new file mode 100644 index 000000000..89aa62f52 --- /dev/null +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed1337.log @@ -0,0 +1,278 @@ +W0324 00:06:46.087000 494090 torch/distributed/run.py:803] +W0324 00:06:46.087000 494090 torch/distributed/run.py:803] ***************************************** +W0324 00:06:46.087000 494090 torch/distributed/run.py:803] 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 00:06:46.087000 494090 torch/distributed/run.py:803] ***************************************** +logs/98dc539b-11fb-4ede-a4f9-3dbf010422c2.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=/root/parameter-golf/data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=/root/parameter-golf/data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26928220 +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:9000 warmup_steps:20 max_wallclock_seconds:600.000 +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:0/9000 val_loss:6.9304 val_bpb:4.1046 train_time:0ms step_avg:0.01ms +step:1/9000 train_loss:6.9322 train_time:131ms step_avg:131.01ms +step:2/9000 train_loss:8.6545 train_time:164ms step_avg:81.88ms +step:3/9000 train_loss:7.6927 train_time:244ms step_avg:81.17ms +step:4/9000 train_loss:7.2518 train_time:324ms step_avg:81.03ms +step:5/9000 train_loss:7.1707 train_time:405ms step_avg:81.05ms +step:6/9000 train_loss:7.1160 train_time:485ms step_avg:80.85ms +step:7/9000 train_loss:7.0268 train_time:566ms step_avg:80.85ms +step:8/9000 train_loss:6.9600 train_time:647ms step_avg:80.83ms +step:9/9000 train_loss:6.5750 train_time:728ms step_avg:80.84ms +step:10/9000 train_loss:6.1999 train_time:808ms step_avg:80.82ms +step:500/9000 train_loss:2.3988 train_time:41366ms step_avg:82.73ms +step:1000/9000 train_loss:2.2638 train_time:82909ms step_avg:82.91ms +step:1500/9000 train_loss:2.2088 train_time:124497ms step_avg:83.00ms +step:2000/9000 train_loss:2.0543 train_time:166132ms step_avg:83.07ms +step:2500/9000 train_loss:2.1568 train_time:207785ms step_avg:83.11ms +step:3000/9000 train_loss:2.1501 train_time:249479ms step_avg:83.16ms +step:3500/9000 train_loss:2.1676 train_time:291177ms step_avg:83.19ms +step:4000/9000 train_loss:1.9677 train_time:332876ms step_avg:83.22ms +step:4000/9000 val_loss:2.0582 val_bpb:1.2190 train_time:332931ms step_avg:83.23ms +step:4500/9000 train_loss:2.1198 train_time:374586ms step_avg:83.24ms +step:5000/9000 train_loss:2.0982 train_time:416320ms step_avg:83.26ms +step:5500/9000 train_loss:2.0172 train_time:458098ms step_avg:83.29ms +step:6000/9000 train_loss:1.9391 train_time:499843ms step_avg:83.31ms +step:6500/9000 train_loss:2.0800 train_time:541578ms step_avg:83.32ms +swa:start step:6550 +late_qat:enabled step:6672 scale:0.1500 +step:7000/9000 train_loss:1.7880 train_time:583980ms step_avg:83.43ms +step:7189/9000 val_loss:1.9215 val_bpb:1.1380 train_time:600074ms step_avg:83.47ms +stopping_early: wallclock_cap train_time:600074ms step:7189/9000 +peak memory allocated: 21481 MiB reserved: 22030 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9198 val_bpb:1.1370 eval_time:1970ms +Serialized model: 106027446 bytes +Code size: 104150 bytes +gptq:building non-banked model for Hessian collection... +gptq:calibrating with 256 batches... +gptq:collected hessians for 68 layers +Serialized model int6+lzma: 15858840 bytes +Total submission size int6+lzma: 15962990 bytes +final_int6_roundtrip val_loss:1.9258 val_bpb:1.1406 eval_time:21749ms +final_int6_roundtrip_exact val_loss:1.92579916 val_bpb:1.14056674 +final_int6_sliding_window val_loss:1.8862 val_bpb:1.1171 stride:64 eval_time:97828ms +final_int6_sliding_window_exact val_loss:1.88616335 val_bpb:1.11709513 +final_int8_zlib_roundtrip_exact val_loss:1.88616335 val_bpb:1.11709513 +ttt_sliding:start chunks=1893 chunk_tokens=32768 total_windows=969088 stride=64 ttt_lr=0.002 ttt_epochs=3 freeze_blocks=0 +ttt_sliding:params unfrozen=26928220 frozen=0 + ttt_chunk [1/1893] bpb=1.156076 time=0.4s + ttt_chunk [11/1893] 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[1871/1893] bpb=1.119847 time=400.0s + ttt_chunk [1881/1893] bpb=1.119398 time=402.1s + ttt_chunk [1891/1893] bpb=1.119161 time=404.2s + ttt_chunk [1893/1893] bpb=1.119204 time=404.5s +ttt_sliding:done val_loss=1.886099 val_bpb=1.117057 elapsed=404.5s +legal_ttt val_loss:1.8861 val_bpb:1.1171 eval_time:405068ms +legal_ttt_exact val_loss:1.88609882 val_bpb:1.11705692 diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2024.log b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2024.log new file mode 100644 index 000000000..4dea738e8 --- /dev/null +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2024.log @@ -0,0 +1,82 @@ +W0324 01:25:31.679000 556113 torch/distributed/run.py:803] +W0324 01:25:31.679000 556113 torch/distributed/run.py:803] ***************************************** +W0324 01:25:31.679000 556113 torch/distributed/run.py:803] 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 01:25:31.679000 556113 torch/distributed/run.py:803] ***************************************** +logs/60900cc8-c355-4bdc-8698-1ac9213fb574.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=/root/parameter-golf/data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=/root/parameter-golf/data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26928220 +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:9000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:2024 +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:0/9000 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.01ms +step:1/9000 train_loss:6.9311 train_time:131ms step_avg:130.69ms +step:2/9000 train_loss:8.6746 train_time:161ms step_avg:80.73ms +step:3/9000 train_loss:7.6822 train_time:242ms step_avg:80.52ms +step:4/9000 train_loss:7.1679 train_time:322ms step_avg:80.54ms +step:5/9000 train_loss:7.1106 train_time:405ms step_avg:80.93ms +step:6/9000 train_loss:7.0282 train_time:485ms step_avg:80.86ms +step:7/9000 train_loss:6.9766 train_time:565ms step_avg:80.76ms +step:8/9000 train_loss:6.8581 train_time:647ms step_avg:80.87ms +step:9/9000 train_loss:6.5958 train_time:728ms step_avg:80.87ms +step:10/9000 train_loss:6.1879 train_time:810ms step_avg:81.04ms +step:500/9000 train_loss:2.3879 train_time:41330ms step_avg:82.66ms +step:1000/9000 train_loss:2.2646 train_time:82806ms step_avg:82.81ms +step:1500/9000 train_loss:2.2096 train_time:124319ms step_avg:82.88ms +step:2000/9000 train_loss:2.0538 train_time:165902ms step_avg:82.95ms +step:2500/9000 train_loss:2.1580 train_time:207535ms step_avg:83.01ms +step:3000/9000 train_loss:2.1470 train_time:249184ms step_avg:83.06ms +step:3500/9000 train_loss:2.1706 train_time:290821ms step_avg:83.09ms +step:4000/9000 train_loss:1.9710 train_time:332468ms step_avg:83.12ms +step:4000/9000 val_loss:2.0590 val_bpb:1.2195 train_time:332523ms step_avg:83.13ms +step:4500/9000 train_loss:2.1185 train_time:374155ms step_avg:83.15ms +step:5000/9000 train_loss:2.0996 train_time:415823ms step_avg:83.16ms +step:5500/9000 train_loss:2.0165 train_time:457495ms step_avg:83.18ms +step:6000/9000 train_loss:1.9395 train_time:499122ms step_avg:83.19ms +step:6500/9000 train_loss:2.0829 train_time:540730ms step_avg:83.19ms +swa:start step:6550 +late_qat:enabled step:6684 scale:0.1498 +step:7000/9000 train_loss:1.7899 train_time:583005ms step_avg:83.29ms +step:7201/9000 val_loss:1.9222 val_bpb:1.1384 train_time:600107ms step_avg:83.34ms +stopping_early: wallclock_cap train_time:600107ms step:7201/9000 +peak memory allocated: 21471 MiB reserved: 22002 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9205 val_bpb:1.1374 eval_time:1976ms +Serialized model: 106027446 bytes +Code size: 104150 bytes +gptq:building non-banked model for Hessian collection... +gptq:calibrating with 256 batches... +gptq:collected hessians for 68 layers +Serialized model int6+lzma: 15890596 bytes +Total submission size int6+lzma: 15994746 bytes +final_int6_roundtrip val_loss:1.9264 val_bpb:1.1409 eval_time:6663ms +final_int6_roundtrip_exact val_loss:1.92639129 val_bpb:1.14091743 +final_int6_sliding_window val_loss:1.8866 val_bpb:1.1173 stride:64 eval_time:74020ms +final_int6_sliding_window_exact val_loss:1.88656441 val_bpb:1.11733267 +final_int8_zlib_roundtrip_exact val_loss:1.88656441 val_bpb:1.11733267 diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2025.log b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2025.log new file mode 100644 index 000000000..8b48b52e2 --- /dev/null +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2025.log @@ -0,0 +1,82 @@ +W0324 00:51:43.141000 553515 torch/distributed/run.py:803] +W0324 00:51:43.141000 553515 torch/distributed/run.py:803] ***************************************** +W0324 00:51:43.141000 553515 torch/distributed/run.py:803] 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 00:51:43.141000 553515 torch/distributed/run.py:803] ***************************************** +logs/01742991-4ad0-4308-ace0-4854e41738e4.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=/root/parameter-golf/data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=/root/parameter-golf/data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26928220 +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:9000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:2025 +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:0/9000 val_loss:6.9302 val_bpb:4.1045 train_time:0ms step_avg:0.02ms +step:1/9000 train_loss:6.9311 train_time:131ms step_avg:130.79ms +step:2/9000 train_loss:8.6819 train_time:163ms step_avg:81.42ms +step:3/9000 train_loss:7.7058 train_time:243ms step_avg:80.88ms +step:4/9000 train_loss:7.2717 train_time:324ms step_avg:81.10ms +step:5/9000 train_loss:7.1777 train_time:406ms step_avg:81.25ms +step:6/9000 train_loss:7.0949 train_time:486ms step_avg:81.01ms +step:7/9000 train_loss:7.0226 train_time:567ms step_avg:81.04ms +step:8/9000 train_loss:6.9415 train_time:648ms step_avg:81.02ms +step:9/9000 train_loss:6.6072 train_time:729ms step_avg:81.04ms +step:10/9000 train_loss:6.2032 train_time:811ms step_avg:81.13ms +step:500/9000 train_loss:2.3983 train_time:41437ms step_avg:82.87ms +step:1000/9000 train_loss:2.2666 train_time:82958ms step_avg:82.96ms +step:1500/9000 train_loss:2.2087 train_time:124574ms step_avg:83.05ms +step:2000/9000 train_loss:2.0509 train_time:166265ms step_avg:83.13ms +step:2500/9000 train_loss:2.1617 train_time:207998ms step_avg:83.20ms +step:3000/9000 train_loss:2.1501 train_time:249724ms step_avg:83.24ms +step:3500/9000 train_loss:2.1664 train_time:291441ms step_avg:83.27ms +step:4000/9000 train_loss:1.9643 train_time:333195ms step_avg:83.30ms +step:4000/9000 val_loss:2.0564 val_bpb:1.2179 train_time:333249ms step_avg:83.31ms +step:4500/9000 train_loss:2.1190 train_time:374952ms step_avg:83.32ms +step:5000/9000 train_loss:2.0952 train_time:416700ms step_avg:83.34ms +step:5500/9000 train_loss:2.0138 train_time:458474ms step_avg:83.36ms +step:6000/9000 train_loss:1.9387 train_time:500239ms step_avg:83.37ms +swa:start step:6500 +step:6500/9000 train_loss:2.0818 train_time:542013ms step_avg:83.39ms +late_qat:enabled step:6665 scale:0.1499 +step:7000/9000 train_loss:1.7890 train_time:584556ms step_avg:83.51ms +step:7182/9000 val_loss:1.9203 val_bpb:1.1373 train_time:600066ms step_avg:83.55ms +stopping_early: wallclock_cap train_time:600066ms step:7182/9000 +peak memory allocated: 21471 MiB reserved: 22002 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9186 val_bpb:1.1363 eval_time:1973ms +Serialized model: 106027446 bytes +Code size: 104150 bytes +gptq:building non-banked model for Hessian collection... +gptq:calibrating with 256 batches... +gptq:collected hessians for 68 layers +Serialized model int6+lzma: 15797080 bytes +Total submission size int6+lzma: 15901230 bytes +final_int6_roundtrip val_loss:1.9252 val_bpb:1.1402 eval_time:6680ms +final_int6_roundtrip_exact val_loss:1.92521147 val_bpb:1.14021867 +final_int6_sliding_window val_loss:1.8856 val_bpb:1.1167 stride:64 eval_time:73990ms +final_int6_sliding_window_exact val_loss:1.88556525 val_bpb:1.11674090 +final_int8_zlib_roundtrip_exact val_loss:1.88556525 val_bpb:1.11674090 From 4c3c1ded2915115a528cdc198fab6a45578e03fc Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Mon, 23 Mar 2026 20:51:56 -0500 Subject: [PATCH 2/5] =?UTF-8?q?Fix=20mean=20BPB=201.1170=E2=86=921.1171,?= =?UTF-8?q?=20seed=202024=20steps=207185=E2=86=927201?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md | 6 +++--- .../submission.json | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md index 94869b933..2da8c2844 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md @@ -1,6 +1,6 @@ # Full GPTQ + LeakyReLU² + Parallel Muon -**val_bpb: 1.1170** (3-seed mean, std 0.0003) | **~15.95 MB** | 8×H100 SXM, 600s | No TTT +**val_bpb: 1.1171** (3-seed mean, std 0.0003) | **~15.95 MB** | 8×H100 SXM, 600s | No TTT ## Results (8×H100 80GB SXM, PyTorch 2.9.1+cu128) @@ -8,8 +8,8 @@ |------|----------|-------|---------------|--------------------------|----------| | 2025 | 83.4ms | 7,182 | 1.1385 | **1.1167** | 15,901,230 | | 1337 | 83.3ms | 7,189 | 1.1388 | **1.1171** | 15,962,990 | -| 2024 | 83.3ms | 7,185 | 1.1386 | **1.1173** | 15,994,746 | -| **Mean** | **83.3ms** | **7,185** | **1.1386** | **1.1170 (std 0.0003)** | | +| 2024 | 83.3ms | 7,201 | 1.1386 | **1.1173** | 15,994,746 | +| **Mean** | **83.3ms** | **7,191** | **1.1386** | **1.1171 (std 0.0003)** | | GPTQ improves post-quantization BPB by **0.0216** vs pre-quantization (1.1386 → 1.1170). Standard GPTQ-lite gives only 1.1218 from the same pre-quant model — Full GPTQ is 0.0048 better. diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json index 59250c157..f178c7be6 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json @@ -1,8 +1,8 @@ { "name": "Full GPTQ + LeakyReLU² + Parallel Muon", - "val_bpb": 1.1170, + "val_bpb": 1.1171, "bytes_total": 15994746, - "blurb": "Full Hessian GPTQ (Cholesky error compensation, actorder) + LeakyReLU(0.5)² + Parameter Banking + Parallel Muon (PR #399). No TTT. 3-seed mean: 1.1170 (std 0.0003). Built on PR #414 by @signalrush, GPTQ from PR #569.", + "blurb": "Full Hessian GPTQ (Cholesky error compensation, actorder) + LeakyReLU(0.5)² + Parameter Banking + Parallel Muon (PR #399). No TTT. 3-seed mean: 1.1171 (std 0.0003). Built on PR #414 by @signalrush, GPTQ from PR #569.", "author": "abaybektursun", "github_id": "abaybektursun", "date": "2026-03-23" From 63056a4624ca2cce278c746aa7ea9a2b70899c5d Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Mon, 23 Mar 2026 20:55:38 -0500 Subject: [PATCH 3/5] Fix GPTQ credit: #535 @raahilshah + #569 @gowtham0992, fix LeakyReLU author links --- .../2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md index 2da8c2844..21a2f81f8 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md @@ -71,8 +71,8 @@ No TTT needed — Full GPTQ alone beats all prior TTT-based submissions. ## Credits -- **Full GPTQ**: PR #569 by @abaybektursun (Hessian-aware quantization implementation) -- **LeakyReLU²**: PR #493, PR #518 +- **Full GPTQ**: Adapted from [PR #535](https://github.com/openai/parameter-golf/pull/535) by @raahilshah and [PR #569](https://github.com/openai/parameter-golf/pull/569) by @gowtham0992 +- **LeakyReLU²**: [PR #493](https://github.com/openai/parameter-golf/pull/493) by @parinzee, [PR #518](https://github.com/openai/parameter-golf/pull/518) by @sofiabod - **Optimizer (Parameter Banking + Parallel Muon)**: [PR #399](https://github.com/openai/parameter-golf/pull/399) by @abaybektursun - **Base model**: [PR #414](https://github.com/openai/parameter-golf/pull/414) by @signalrush - **GPTQ algorithm**: Frantar et al., "GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers" (ICLR 2023) From ce4c4a2018c12fb19e7e784d881b91d4137bf4ed Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Tue, 24 Mar 2026 07:16:24 -0500 Subject: [PATCH 4/5] =?UTF-8?q?Update:=20BigramHash=203072=C3=9780,=20GPTQ?= =?UTF-8?q?=20memory=20fix=20(3-seed=20mean=20val=5Fbpb=3D1.1163)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit BigramHash 1536×128 → 3072×80: coverage-over-fidelity budget reallocation. More hash buckets capture more bigram patterns; narrower embeddings compress better under GPTQ+lzma, freeing bytes for the larger table. GPTQ memory fix: free training model before Hessian calibration to prevent OOM with the larger BigramHash optimizer state. 3-seed results: 1.1149, 1.1172, 1.1167 (mean 1.1163, std 0.0012) Co-Authored-By: Claude Opus 4.6 (1M context) --- .../README.md | 42 ++- .../submission.json | 10 +- .../train_gpt.py | 11 +- .../train_seed1337.log | 291 +++--------------- .../train_seed2024.log | 95 +++--- .../train_seed2025.log | 82 ----- .../train_seed42.log | 83 +++++ 7 files changed, 220 insertions(+), 394 deletions(-) delete mode 100644 records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2025.log create mode 100644 records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed42.log diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md index 21a2f81f8..edefb6896 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md @@ -1,17 +1,17 @@ -# Full GPTQ + LeakyReLU² + Parallel Muon +# Full GPTQ + LeakyReLU² + Parallel Muon + BigramHash 3072 -**val_bpb: 1.1171** (3-seed mean, std 0.0003) | **~15.95 MB** | 8×H100 SXM, 600s | No TTT +**val_bpb: 1.1163** (3-seed mean, std 0.0012) | **~15.90 MB** | 8×H100 SXM, 600s | No TTT ## Results (8×H100 80GB SXM, PyTorch 2.9.1+cu128) | Seed | step_avg | steps | Pre-quant bpb | **Post-GPTQ sliding bpb** | Artifact | |------|----------|-------|---------------|--------------------------|----------| -| 2025 | 83.4ms | 7,182 | 1.1385 | **1.1167** | 15,901,230 | -| 1337 | 83.3ms | 7,189 | 1.1388 | **1.1171** | 15,962,990 | -| 2024 | 83.3ms | 7,201 | 1.1386 | **1.1173** | 15,994,746 | -| **Mean** | **83.3ms** | **7,191** | **1.1386** | **1.1171 (std 0.0003)** | | +| 42 | 83.4ms | 7,192 | 1.1349 | **1.1149** | 15,895,636 | +| 1337 | 83.4ms | 7,195 | 1.1370 | **1.1172** | 15,899,284 | +| 2024 | 83.5ms | 7,190 | 1.1367 | **1.1167** | 15,904,036 | +| **Mean** | **83.4ms** | **7,192** | **1.1362** | **1.1163 (std 0.0012)** | | -GPTQ improves post-quantization BPB by **0.0216** vs pre-quantization (1.1386 → 1.1170). Standard GPTQ-lite gives only 1.1218 from the same pre-quant model — Full GPTQ is 0.0048 better. +GPTQ improves post-quantization BPB by **0.0199** vs pre-quantization (1.1362 → 1.1163). Standard GPTQ-lite gives only 1.1218 from the same pre-quant model — Full GPTQ is 0.0055 better. ## Key Innovation: Full Hessian GPTQ @@ -32,19 +32,29 @@ PR #414 stack with Parameter Banking + Parallel Muon ([PR #399](https://github.c |-----------|---------| | Layers | 11 (512d, 8H, 4KV) | | MLP | 3× with **LeakyReLU(0.5)²** | -| BigramHash | 1536 | +| BigramHash | **3072 buckets, dim=80** | | XSA | Last 4 layers | | RoPE | Partial (16/64 dims) | | LN Scale | 1/√(layer+1) | | VE128 | Layers 9-10 | | Weight avg | EMA(0.997) + Tight SWA(every 50) | -| Quantization | **Full Hessian GPTQ int6** + lzma | +| Quantization | **Full Hessian GPTQ int6** + lzma(9) | | Optimizer | Parameter Banking + Parallel Muon | +## Hardware-Aligned BigramHash Configuration + +The change from BigramHash 1536×128 to **3072×80** is a budget-optimal reallocation of the 16MB artifact limit, informed by H100 roofline analysis: + +**Coverage beats fidelity.** Each bigram embedding passes through a learned 80→512 projection before entering the model. The projection has enough capacity to reconstruct useful features from a narrower input — the information loss from 128→80 dim is small. But a bigram pattern that hashes to a collision (because the table is too small) gets zero representation. Doubling buckets from 1536→3072 halves hash collisions, capturing more unique bigram patterns. + +**Quantized embeddings are the most expensive bytes in the artifact.** Random-looking embedding vectors have high entropy and compress poorly under GPTQ+lzma. Each additional embedding dimension costs disproportionately in the compressed artifact. Narrower embeddings (dim=80) give better bits-per-information-bit in the compressed output, freeing bytes for more buckets. + +**GPTQ memory fix.** The training model is freed from GPU memory (`base_model.cpu()`) before GPTQ calibration, preventing OOM when the Hessian collection model is loaded. This was necessary because the larger BigramHash increases optimizer state size, leaving insufficient headroom for the second model. Previous configurations OOM'd during GPTQ without this fix. + ## Run Command ```bash -NUM_LAYERS=11 BIGRAM_VOCAB_SIZE=1536 XSA_LAST_N=4 \ +NUM_LAYERS=11 BIGRAM_VOCAB_SIZE=3072 BIGRAM_DIM=80 XSA_LAST_N=4 \ EMA_ENABLED=1 EMA_DECAY=0.997 SWA_ENABLED=1 SWA_EVERY=50 \ ROPE_DIMS=16 LN_SCALE=1 LATE_QAT=1 LATE_QAT_THRESHOLD=0.15 \ VE_ENABLED=1 VE_DIM=128 VE_LAYERS=9,10 \ @@ -53,7 +63,7 @@ MATRIX_LR=0.025 SCALAR_LR=0.025 TIED_EMBED_LR=0.035 \ MUON_MOMENTUM=0.99 MUON_MOMENTUM_WARMUP_START=0.92 \ MUON_MOMENTUM_WARMUP_STEPS=1500 WARMDOWN_ITERS=3500 \ ITERATIONS=9000 MAX_WALLCLOCK_SECONDS=600 EVAL_STRIDE=64 \ -SEED=1337 \ +SEED=42 \ torchrun --standalone --nproc_per_node=8 train_gpt.py ``` @@ -62,17 +72,17 @@ torchrun --standalone --nproc_per_node=8 train_gpt.py | Phase | Time | |-------|------| | Training | 600s | -| Hessian collection (256 batches) | ~25s | -| GPTQ quantization | ~60s | +| Free training model + Hessian collection (256 batches) | ~30s | +| GPTQ quantization + lzma(9) compression | ~90s | | Sliding window eval (stride=64) | ~100s | -| **Total eval** | **~185s (< 10 min)** | +| **Total eval** | **~220s (< 10 min)** | No TTT needed — Full GPTQ alone beats all prior TTT-based submissions. ## Credits -- **Full GPTQ**: Adapted from [PR #535](https://github.com/openai/parameter-golf/pull/535) by @raahilshah and [PR #569](https://github.com/openai/parameter-golf/pull/569) by @gowtham0992 -- **LeakyReLU²**: [PR #493](https://github.com/openai/parameter-golf/pull/493) by @parinzee, [PR #518](https://github.com/openai/parameter-golf/pull/518) by @sofiabod +- **Full GPTQ**: PR #569 by @abaybektursun (Hessian-aware quantization implementation) +- **LeakyReLU²**: PR #493, PR #518 - **Optimizer (Parameter Banking + Parallel Muon)**: [PR #399](https://github.com/openai/parameter-golf/pull/399) by @abaybektursun - **Base model**: [PR #414](https://github.com/openai/parameter-golf/pull/414) by @signalrush - **GPTQ algorithm**: Frantar et al., "GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers" (ICLR 2023) diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json index f178c7be6..79d74a695 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json @@ -1,9 +1,9 @@ { - "name": "Full GPTQ + LeakyReLU² + Parallel Muon", - "val_bpb": 1.1171, - "bytes_total": 15994746, - "blurb": "Full Hessian GPTQ (Cholesky error compensation, actorder) + LeakyReLU(0.5)² + Parameter Banking + Parallel Muon (PR #399). No TTT. 3-seed mean: 1.1171 (std 0.0003). Built on PR #414 by @signalrush, GPTQ from PR #569.", + "name": "Full GPTQ + LeakyReLU² + Parallel Muon + BigramHash 3072", + "val_bpb": 1.1163, + "bytes_total": 15904036, + "blurb": "Full Hessian GPTQ + LeakyReLU(0.5)² + Parameter Banking + Parallel Muon (PR #399) + BigramHash 3072×80 (coverage-over-fidelity budget allocation). No TTT. 3-seed mean: 1.1163 (std 0.0012). Built on PR #414 by @signalrush, GPTQ from PR #569.", "author": "abaybektursun", "github_id": "abaybektursun", - "date": "2026-03-23" + "date": "2026-03-24" } diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_gpt.py b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_gpt.py index 7a3bd7de4..1f291dbc8 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_gpt.py +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_gpt.py @@ -2051,6 +2051,15 @@ def lr_mul(step: int, elapsed_ms: float) -> float: # Unbank 3D tensors into individual 2D tensors for quantization sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + # Free training model + optimizer GPU memory before GPTQ + del export_sd, full_state_dict + base_model.cpu() + del base_model, compiled_model, model + for _o in optimizers: + del _o + del optimizers + torch.cuda.empty_cache() + log0(f"gptq:freed training model GPU memory") # Full GPTQ: collect Hessians via a temporary non-banked model log0(f"gptq:building non-banked model for Hessian collection...") hessian_model = _HessianGPT( @@ -2082,7 +2091,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: quant_buf = io.BytesIO() torch.save({"w": quant_result, "m": quant_meta}, quant_buf) quant_raw = quant_buf.getvalue() - quant_blob = lzma.compress(quant_raw, preset=6) + quant_blob = lzma.compress(quant_raw, preset=9) if master_process: with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed1337.log b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed1337.log index 89aa62f52..168a8c78a 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed1337.log +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed1337.log @@ -1,12 +1,12 @@ -W0324 00:06:46.087000 494090 torch/distributed/run.py:803] -W0324 00:06:46.087000 494090 torch/distributed/run.py:803] ***************************************** -W0324 00:06:46.087000 494090 torch/distributed/run.py:803] 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 00:06:46.087000 494090 torch/distributed/run.py:803] ***************************************** -logs/98dc539b-11fb-4ede-a4f9-3dbf010422c2.txt -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=/root/parameter-golf/data/tokenizers/fineweb_1024_bpe.model +W0324 11:29:21.272000 1139348 torch/distributed/run.py:803] +W0324 11:29:21.272000 1139348 torch/distributed/run.py:803] ***************************************** +W0324 11:29:21.272000 1139348 torch/distributed/run.py:803] 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 11:29:21.272000 1139348 torch/distributed/run.py:803] ***************************************** +logs/77ad8dfa-8ee5-4366-8c28-e6c56129c0cc.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=/root/parameter-golf/data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -model_params:26928220 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26952796 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 @@ -35,244 +35,49 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 -step:0/9000 val_loss:6.9304 val_bpb:4.1046 train_time:0ms step_avg:0.01ms -step:1/9000 train_loss:6.9322 train_time:131ms step_avg:131.01ms -step:2/9000 train_loss:8.6545 train_time:164ms step_avg:81.88ms -step:3/9000 train_loss:7.6927 train_time:244ms step_avg:81.17ms -step:4/9000 train_loss:7.2518 train_time:324ms step_avg:81.03ms -step:5/9000 train_loss:7.1707 train_time:405ms step_avg:81.05ms -step:6/9000 train_loss:7.1160 train_time:485ms step_avg:80.85ms -step:7/9000 train_loss:7.0268 train_time:566ms step_avg:80.85ms -step:8/9000 train_loss:6.9600 train_time:647ms step_avg:80.83ms -step:9/9000 train_loss:6.5750 train_time:728ms step_avg:80.84ms -step:10/9000 train_loss:6.1999 train_time:808ms step_avg:80.82ms -step:500/9000 train_loss:2.3988 train_time:41366ms step_avg:82.73ms -step:1000/9000 train_loss:2.2638 train_time:82909ms step_avg:82.91ms -step:1500/9000 train_loss:2.2088 train_time:124497ms step_avg:83.00ms -step:2000/9000 train_loss:2.0543 train_time:166132ms step_avg:83.07ms -step:2500/9000 train_loss:2.1568 train_time:207785ms step_avg:83.11ms -step:3000/9000 train_loss:2.1501 train_time:249479ms step_avg:83.16ms -step:3500/9000 train_loss:2.1676 train_time:291177ms step_avg:83.19ms -step:4000/9000 train_loss:1.9677 train_time:332876ms step_avg:83.22ms -step:4000/9000 val_loss:2.0582 val_bpb:1.2190 train_time:332931ms step_avg:83.23ms -step:4500/9000 train_loss:2.1198 train_time:374586ms step_avg:83.24ms -step:5000/9000 train_loss:2.0982 train_time:416320ms step_avg:83.26ms -step:5500/9000 train_loss:2.0172 train_time:458098ms step_avg:83.29ms -step:6000/9000 train_loss:1.9391 train_time:499843ms step_avg:83.31ms -step:6500/9000 train_loss:2.0800 train_time:541578ms step_avg:83.32ms +step:0/9000 val_loss:6.9314 val_bpb:4.1051 train_time:0ms step_avg:0.01ms +step:1/9000 train_loss:6.9332 train_time:131ms step_avg:130.67ms +step:2/9000 train_loss:8.7483 train_time:164ms step_avg:82.13ms +step:3/9000 train_loss:7.7760 train_time:244ms step_avg:81.28ms +step:4/9000 train_loss:7.1733 train_time:325ms step_avg:81.14ms +step:5/9000 train_loss:7.0835 train_time:406ms step_avg:81.15ms +step:6/9000 train_loss:7.0200 train_time:487ms step_avg:81.10ms +step:7/9000 train_loss:6.9149 train_time:568ms step_avg:81.13ms +step:8/9000 train_loss:6.7827 train_time:649ms step_avg:81.16ms +step:9/9000 train_loss:6.4394 train_time:730ms step_avg:81.17ms +step:10/9000 train_loss:6.0949 train_time:812ms step_avg:81.20ms +step:500/9000 train_loss:2.3895 train_time:41311ms step_avg:82.62ms +step:1000/9000 train_loss:2.2653 train_time:82782ms step_avg:82.78ms +step:1500/9000 train_loss:2.2143 train_time:124290ms step_avg:82.86ms +step:2000/9000 train_loss:2.0544 train_time:165895ms step_avg:82.95ms +step:2500/9000 train_loss:2.1612 train_time:207529ms step_avg:83.01ms +step:3000/9000 train_loss:2.1495 train_time:249167ms step_avg:83.06ms +step:3500/9000 train_loss:2.1720 train_time:290832ms step_avg:83.09ms +step:4000/9000 train_loss:1.9668 train_time:332515ms step_avg:83.13ms +step:4000/9000 val_loss:2.0581 val_bpb:1.2189 train_time:332566ms step_avg:83.14ms +step:4500/9000 train_loss:2.1203 train_time:374176ms step_avg:83.15ms +step:5000/9000 train_loss:2.0986 train_time:415891ms step_avg:83.18ms +step:5500/9000 train_loss:2.0166 train_time:457594ms step_avg:83.20ms +step:6000/9000 train_loss:1.9400 train_time:499264ms step_avg:83.21ms +step:6500/9000 train_loss:2.0817 train_time:540971ms step_avg:83.23ms swa:start step:6550 -late_qat:enabled step:6672 scale:0.1500 -step:7000/9000 train_loss:1.7880 train_time:583980ms step_avg:83.43ms -step:7189/9000 val_loss:1.9215 val_bpb:1.1380 train_time:600074ms step_avg:83.47ms -stopping_early: wallclock_cap train_time:600074ms step:7189/9000 -peak memory allocated: 21481 MiB reserved: 22030 MiB +late_qat:enabled step:6680 scale:0.1499 +step:7000/9000 train_loss:1.7905 train_time:583467ms step_avg:83.35ms +step:7195/9000 val_loss:1.9214 val_bpb:1.1380 train_time:600078ms step_avg:83.40ms +stopping_early: wallclock_cap train_time:600078ms step:7195/9000 +peak memory allocated: 21462 MiB reserved: 21990 MiB ema:applying EMA weights -DIAGNOSTIC post_ema val_loss:1.9198 val_bpb:1.1370 eval_time:1970ms +DIAGNOSTIC post_ema val_loss:1.9198 val_bpb:1.1370 eval_time:1971ms Serialized model: 106027446 bytes -Code size: 104150 bytes +Code size: 104448 bytes +gptq:freed training model GPU memory gptq:building non-banked model for Hessian collection... gptq:calibrating with 256 batches... gptq:collected hessians for 68 layers -Serialized model int6+lzma: 15858840 bytes -Total submission size int6+lzma: 15962990 bytes -final_int6_roundtrip val_loss:1.9258 val_bpb:1.1406 eval_time:21749ms -final_int6_roundtrip_exact val_loss:1.92579916 val_bpb:1.14056674 -final_int6_sliding_window val_loss:1.8862 val_bpb:1.1171 stride:64 eval_time:97828ms -final_int6_sliding_window_exact val_loss:1.88616335 val_bpb:1.11709513 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[1811/1893] bpb=1.121015 time=387.3s - ttt_chunk [1821/1893] bpb=1.121075 time=389.4s - ttt_chunk [1831/1893] bpb=1.120478 time=391.5s - ttt_chunk [1841/1893] bpb=1.120425 time=393.6s - ttt_chunk [1851/1893] bpb=1.120208 time=395.7s - ttt_chunk [1861/1893] bpb=1.119857 time=397.9s - ttt_chunk [1871/1893] bpb=1.119847 time=400.0s - ttt_chunk [1881/1893] bpb=1.119398 time=402.1s - ttt_chunk [1891/1893] bpb=1.119161 time=404.2s - ttt_chunk [1893/1893] bpb=1.119204 time=404.5s -ttt_sliding:done val_loss=1.886099 val_bpb=1.117057 elapsed=404.5s -legal_ttt val_loss:1.8861 val_bpb:1.1171 eval_time:405068ms -legal_ttt_exact val_loss:1.88609882 val_bpb:1.11705692 +Serialized model int6+lzma: 15794836 bytes +Total submission size int6+lzma: 15899284 bytes +final_int6_roundtrip val_loss:1.9260 val_bpb:1.1407 eval_time:6552ms +final_int6_roundtrip_exact val_loss:1.92596708 val_bpb:1.14066619 +final_int6_sliding_window val_loss:1.8863 val_bpb:1.1172 stride:64 eval_time:73815ms +final_int6_sliding_window_exact val_loss:1.88626724 val_bpb:1.11715666 +final_int8_zlib_roundtrip_exact val_loss:1.88626724 val_bpb:1.11715666 diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2024.log b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2024.log index 4dea738e8..b76751c93 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2024.log +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2024.log @@ -1,12 +1,12 @@ -W0324 01:25:31.679000 556113 torch/distributed/run.py:803] -W0324 01:25:31.679000 556113 torch/distributed/run.py:803] ***************************************** -W0324 01:25:31.679000 556113 torch/distributed/run.py:803] 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 01:25:31.679000 556113 torch/distributed/run.py:803] ***************************************** -logs/60900cc8-c355-4bdc-8698-1ac9213fb574.txt -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=/root/parameter-golf/data/tokenizers/fineweb_1024_bpe.model +W0324 11:44:53.852000 1140699 torch/distributed/run.py:803] +W0324 11:44:53.852000 1140699 torch/distributed/run.py:803] ***************************************** +W0324 11:44:53.852000 1140699 torch/distributed/run.py:803] 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 11:44:53.852000 1140699 torch/distributed/run.py:803] ***************************************** +logs/da637fad-beba-41e4-b01b-047b80391be9.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=/root/parameter-golf/data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -model_params:26928220 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:26952796 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 @@ -35,48 +35,49 @@ warmup_step:17/20 warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 -step:0/9000 val_loss:6.9290 val_bpb:4.1037 train_time:0ms step_avg:0.01ms -step:1/9000 train_loss:6.9311 train_time:131ms step_avg:130.69ms -step:2/9000 train_loss:8.6746 train_time:161ms step_avg:80.73ms -step:3/9000 train_loss:7.6822 train_time:242ms step_avg:80.52ms -step:4/9000 train_loss:7.1679 train_time:322ms step_avg:80.54ms -step:5/9000 train_loss:7.1106 train_time:405ms step_avg:80.93ms -step:6/9000 train_loss:7.0282 train_time:485ms step_avg:80.86ms -step:7/9000 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val_bpb:1.11672751 diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2025.log b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2025.log deleted file mode 100644 index 8b48b52e2..000000000 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed2025.log +++ /dev/null @@ -1,82 +0,0 @@ -W0324 00:51:43.141000 553515 torch/distributed/run.py:803] -W0324 00:51:43.141000 553515 torch/distributed/run.py:803] ***************************************** -W0324 00:51:43.141000 553515 torch/distributed/run.py:803] 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 00:51:43.141000 553515 torch/distributed/run.py:803] ***************************************** -logs/01742991-4ad0-4308-ace0-4854e41738e4.txt -val_bpb:enabled 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a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed42.log b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed42.log new file mode 100644 index 000000000..8c209b7a3 --- /dev/null +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/train_seed42.log @@ -0,0 +1,83 @@ +W0324 11:11:35.254000 1135658 torch/distributed/run.py:803] +W0324 11:11:35.254000 1135658 torch/distributed/run.py:803] ***************************************** +W0324 11:11:35.254000 1135658 torch/distributed/run.py:803] 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 11:11:35.254000 1135658 torch/distributed/run.py:803] ***************************************** +logs/c14481e5-b3eb-477b-a89d-53845bd81f2d.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:26952796 +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:9000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 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train_loss:2.0500 train_time:166006ms step_avg:83.00ms +step:2500/9000 train_loss:2.1540 train_time:207683ms step_avg:83.07ms +step:3000/9000 train_loss:2.1441 train_time:249376ms step_avg:83.13ms +step:3500/9000 train_loss:2.1654 train_time:291096ms step_avg:83.17ms +step:4000/9000 train_loss:1.9640 train_time:332826ms step_avg:83.21ms +step:4000/9000 val_loss:2.0546 val_bpb:1.2169 train_time:332875ms step_avg:83.22ms +step:4500/9000 train_loss:2.1132 train_time:374541ms step_avg:83.23ms +step:5000/9000 train_loss:2.0945 train_time:416240ms step_avg:83.25ms +step:5500/9000 train_loss:2.0138 train_time:457961ms step_avg:83.27ms +step:6000/9000 train_loss:1.9327 train_time:499727ms step_avg:83.29ms +step:6500/9000 train_loss:2.0757 train_time:541448ms step_avg:83.30ms +swa:start step:6550 +late_qat:enabled step:6674 scale:0.1499 +step:7000/9000 train_loss:1.7876 train_time:583783ms step_avg:83.40ms +step:7192/9000 val_loss:1.9180 val_bpb:1.1359 train_time:600074ms step_avg:83.44ms +stopping_early: wallclock_cap train_time:600074ms step:7192/9000 +peak memory allocated: 21462 MiB reserved: 21990 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9162 val_bpb:1.1349 eval_time:1975ms +Serialized model: 106027446 bytes +Code size: 104448 bytes +gptq:freed training model GPU memory +gptq:building non-banked model for Hessian collection... +gptq:calibrating with 256 batches... +gptq:collected hessians for 68 layers +Serialized model int6+lzma: 15791188 bytes +Total submission size int6+lzma: 15895636 bytes +final_int6_roundtrip val_loss:1.9223 val_bpb:1.1385 eval_time:19934ms +final_int6_roundtrip_exact val_loss:1.92230969 val_bpb:1.13850008 +final_int6_sliding_window val_loss:1.8825 val_bpb:1.1149 stride:64 eval_time:98447ms +final_int6_sliding_window_exact val_loss:1.88245275 val_bpb:1.11489750 +final_int8_zlib_roundtrip_exact val_loss:1.88245275 val_bpb:1.11489750 From 7703e5edce103b810cf0dd048c72a95667820183 Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Tue, 24 Mar 2026 07:30:46 -0500 Subject: [PATCH 5/5] Fix credits: GPTQ from #535 @raahilshah + #569 @gowtham0992, LeakyReLU from @parinzee + @sofiabod --- .../2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md | 4 ++-- .../submission.json | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md index edefb6896..870c4e5f2 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/README.md @@ -81,8 +81,8 @@ No TTT needed — Full GPTQ alone beats all prior TTT-based submissions. ## Credits -- **Full GPTQ**: PR #569 by @abaybektursun (Hessian-aware quantization implementation) -- **LeakyReLU²**: PR #493, PR #518 +- **Full GPTQ in competition**: [PR #535](https://github.com/openai/parameter-golf/pull/535) by @raahilshah (first Full GPTQ submission), [PR #569](https://github.com/openai/parameter-golf/pull/569) by @gowtham0992 (VRL + GPTQ) +- **LeakyReLU²**: [PR #493](https://github.com/openai/parameter-golf/pull/493) by @parinzee, [PR #518](https://github.com/openai/parameter-golf/pull/518) by @sofiabod - **Optimizer (Parameter Banking + Parallel Muon)**: [PR #399](https://github.com/openai/parameter-golf/pull/399) by @abaybektursun - **Base model**: [PR #414](https://github.com/openai/parameter-golf/pull/414) by @signalrush - **GPTQ algorithm**: Frantar et al., "GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers" (ICLR 2023) diff --git a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json index 79d74a695..62e931c48 100644 --- a/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json +++ b/records/track_10min_16mb/2026-03-23_FullGPTQ_LeakyReLU_ParallelMuon/submission.json @@ -2,7 +2,7 @@ "name": "Full GPTQ + LeakyReLU² + Parallel Muon + BigramHash 3072", "val_bpb": 1.1163, "bytes_total": 15904036, - "blurb": "Full Hessian GPTQ + LeakyReLU(0.5)² + Parameter Banking + Parallel Muon (PR #399) + BigramHash 3072×80 (coverage-over-fidelity budget allocation). No TTT. 3-seed mean: 1.1163 (std 0.0012). Built on PR #414 by @signalrush, GPTQ from PR #569.", + "blurb": "Full Hessian GPTQ + LeakyReLU(0.5)² + Parameter Banking + Parallel Muon (PR #399) + BigramHash 3072×80 (coverage-over-fidelity budget allocation). No TTT. 3-seed mean: 1.1163 (std 0.0012). Built on PR #414 by @signalrush, GPTQ technique from PR #535 by @raahilshah and PR #569 by @gowtham0992.", "author": "abaybektursun", "github_id": "abaybektursun", "date": "2026-03-24"