diff --git a/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/README.md b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/README.md new file mode 100644 index 0000000000..ac7f7da0b5 --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/README.md @@ -0,0 +1,89 @@ +# Record: 0.0881 BPB — 11L Int5 GPTQ + Order-12 N-gram + Phrase Cache + 65K Chunks + +**Order-12 backoff n-gram cache + long phrase cache + entropy-adaptive alpha + temperature sharpening on a fully-trained 11-layer base model** + +**val_bpb: 0.0881** (3-seed mean, std 0.0004) | **~13.0 MB** artifact | 8xH100 SXM, 600s train + <600s eval + +## Results (3 seeds, 8xH100 SXM) + +| Seed | val_bpb | Pre-quant BPB | Steps | ms/step | Eval time | Artifact | +|------|---------|---------------|-------|---------|-----------|----------| +| 1337 | 0.08855 | 1.1383 | 7,167 | 83.7 | 568s | 13,018,464 | +| 42 | 0.08793 | 1.1385 | 7,171 | 83.7 | 572s | 12,992,096 | +| 2024 | 0.08777 | 1.1391 | 7,172 | 83.7 | 563s | 13,032,072 | +| **Mean** | **0.08808** | **1.1386** | **7,170** | **83.7** | **568** | **13,014,211** | +| **Std** | **0.00041** | **0.0004** | **3** | **0.0** | **5** | **20,194** | + +## Architecture + +- 11 transformer layers, dim=512, GQA 8Q/4KV, head_dim=64 +- MLP 3.0x expansion (1536 hidden) with LeakyReLU(0.5) squared +- BigramHash(1536, dim=128), XSA-4, Partial RoPE 16/64, LN Scale +- VE128 on layers 9-10, SmearGate, Logit softcap 30.0 +- EMA decay 0.997, SWA (15 checkpoints in final warmdown) +- Parallel Muon + Parameter Banking (~84ms/step) +- ~27M parameters, tied embeddings + +## Quantization + +Full Hessian GPTQ int5 with activation-order column permutation and Cholesky error compensation. Deliberate int5 over int6: accepts ~0.02 BPB quantization penalty to reclaim ~1MB artifact headroom (13.0 MB vs ~15.9 MB at int6). LZMA preset 9 extreme compression. + +## Eval Techniques (Single-Pass, Score-First) + +### Order-12 N-gram Backoff Cache +- Orders 2-12, highest-order-first backoff with early exit +- 4M hash buckets per order, XOR-of-primes hashing +- Entropy-adaptive alpha with per-order thresholds and multipliers +- min_count=1, alpha range [0.05, 0.95] + +### Long Phrase Cache +- 7 probe lengths: [64, 56, 48, 36, 28, 20, 16] +- XOR-of-products rolling hash, 4M buckets +- Alpha 0.90–0.99 based on match length + +### Temperature Sharpening +- T=0.85 applied to logits before softmax + +### 65K Chunk Size +Default 131K-token chunks from PR #913 exceed the 600s eval budget on models beyond a few layers, as forward pass cost scales with model depth. Reducing to 65K resolves this while providing warmer cache through 2x more frequent updates. Empirically, 65K chunks also complete faster in total (568s) than 131K (606s) or 140K (613s) on the same model — contrary to the assumption that fewer chunks reduce eval time. + +### Score-First Protocol +Cache starts empty. Each 65K-token chunk: score all windows first, then update cache. Strictly backward-looking. + +## Setup and Run + +```bash +cd /workspace +git clone https://github.com/openai/parameter-golf.git pgolf +cd pgolf +pip install --break-system-packages -r requirements.txt zstandard +python data/cached_challenge_fineweb.py --variant sp1024 + +SEED=1337 torchrun --standalone --nproc_per_node=8 train_gpt.py +``` + +## Discussion: On n-gram cache dominance and the diminishing role of neural models. + +This submission applies all 295 lines of PR #913's eval stack ("Cache Is All You Need") onto a fully-trained 11-layer 512d base model (pre-quant 1.14 BPB) — a 0.64 BPB improvement over #913's original 2-layer 128d model (pre-quant 1.78 BPB, 500K parameters, 622KB artifact). The base model represents a 54x parameter increase, Full Hessian GPTQ int5 quantization, aggressive SWA, and Parallel Muon optimization. + +One practical adaptation was required: #913's default 131K-token chunk size exceeds the 600s eval budget on any model beyond a few layers, as forward pass cost scales with model depth. Reducing chunk size to 65K resolves this while providing warmer cache through more frequent updates — a necessary change for applying cache eval techniques to models built with any meaningful training investment. + +The core finding: a 0.64 BPB gap in pre-quantization model quality (1.78 vs 1.14) — representing substantial differences in architecture depth, parameter count, training compute, quantization strategy, and optimization techniques — collapses to <0.001 BPB after cache application. The n-gram cache handles approximately 97% of token predictions through pure frequency statistics. The neural model contributes meaningfully only on the narrow residual of tokens with no cache match. Beyond order-10 n-gram caching with sufficient training data, marginal returns from neural model innovation approach zero. + +This suggests the current leaderboard measures n-gram engineering quality, not language model quality. The competition's meta incentivizes cache engineering over model innovation — a dynamic where a 500K-parameter model performs equivalently to one 54x its size. This entry serves as an empirical demonstration of this limitation. + +## Compliance + +- [x] 3 seeds on 8xH100 SXM +- [x] All seeds train ≤600s +- [x] All seeds eval ≤600s (max 572s) +- [x] Artifact ≤16,000,000 bytes (~13.0 MB) +- [x] No validation data during training +- [x] Single-pass, score-first, backward-looking +- [x] No multi-pass rescoring +- [x] No TTT, no learned gate — pure cache eval +- [x] Reproducible single script + +## Credits + +Eval approach: PR #913 ("Cache Is All You Need", @RoyiRa). Base model architecture: PR #549 (@abaybektursun). Architecture foundation: PR #414 (@signalrush). Chunk size optimization concept: PR #840 (@quietsmile). diff --git a/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/requirements.txt b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/requirements.txt new file mode 100644 index 0000000000..864700d2b3 --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/requirements.txt @@ -0,0 +1 @@ +zstandard diff --git a/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/submission.json b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/submission.json new file mode 100644 index 0000000000..37d9eb11d5 --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/submission.json @@ -0,0 +1,20 @@ +{ + "author": "callithyia", + "github_id": "callithyia", + "name": "11L Int5 GPTQ + Order-12 N-gram + Phrase Cache + 65K Chunks", + "blurb": "Fully-trained 11L 512d base model (pre-quant 1.14 BPB) with order-12 backoff n-gram cache, long phrase cache, entropy-adaptive alpha, temperature sharpening (T=0.85), and 65K-token chunks. Full Hessian GPTQ int5 + LZMA. Eval approach from PR #913 adapted with 65K chunk size for properly trained models where 131K exceeds eval budget.", + "date": "2026-03-27", + "val_bpb": 0.08808, + "val_bpb_std": 0.00041, + "seeds": [1337, 42, 2024], + "seed_results": { + "1337": {"val_bpb": 0.08855, "pre_quant_val_bpb": 1.1383, "train_s": 600.132, "eval_s": 567.5, "bytes_total": 13018464}, + "42": {"val_bpb": 0.08793, "pre_quant_val_bpb": 1.1385, "train_s": 600.091, "eval_s": 572.4, "bytes_total": 12992096}, + "2024": {"val_bpb": 0.08777, "pre_quant_val_bpb": 1.1391, "train_s": 600.113, "eval_s": 563.4, "bytes_total": 13032072} + }, + "artifact_bytes_max": 13032072, + "train_time_seconds_max": 600.132, + "eval_time_seconds_max": 572.4, + "hardware": "8xH100 SXM (RunPod)", + "track": "track_10min_16mb" +} diff --git a/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_gpt.py b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_gpt.py new file mode 100644 index 0000000000..f58d1d8bee --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_gpt.py @@ -0,0 +1,2119 @@ +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 = 600.0 # HARDCODED: competition limit, never 0 + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + vocab_size = 1024 + num_layers = 11 + num_kv_heads = 4 + model_dim = 512 + num_heads = 8 + mlp_mult = 3.0 + tie_embeddings = True + rope_base = 10000.0 + 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 = 70 # Target ~13 checkpoints in final 15% (PR #895 proven optimal) + 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 = 1536 # PR #549 changed from 2048 to 1536 + 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 = 0.0 # Disabled: fullgraph=True incompatible with mid-training QAT toggle, GPTQ handles quantization + 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"))) + # No TTT -- pure cache eval with n-gram + phrase blending + +# --- 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 / 15.0).clamp_min(1.0 / 15.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -15, 15) * 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) + +# --- N-gram + Phrase cache eval (PR #913 exact code, inlined) --- + +class NgramEvalCache: + """Multi-order n-gram backoff (orders 2-12) with order-adaptive entropy gating.""" + PRIMES = np.array([36313, 27191, 51647, 81929, 131071, 196613, 262147], dtype=np.uint64) + + def __init__(self, max_order=12, buckets=4_194_304, min_count=1, + alpha_low=0.05, alpha_high=0.95, entropy_thresh=4.0): + self.max_order = max_order + self.buckets = buckets + self.min_count = min_count + self.alpha_low = alpha_low + self.alpha_high = alpha_high + self.entropy_thresh = entropy_thresh + self.mask = np.uint64(buckets - 1) + self.ctx_tables = {n: np.zeros(buckets, dtype=np.uint32) for n in range(2, max_order + 1)} + self.full_tables = {n: np.zeros(buckets, dtype=np.uint32) for n in range(2, max_order + 1)} + + def lookup(self, val_np, target_pos, targets): + seg_len = len(target_pos) + best_p = np.zeros(seg_len, dtype=np.float64) + has_match = np.zeros(seg_len, dtype=bool) + match_orders = np.zeros(seg_len, dtype=np.int32) + tgt_u64 = targets.astype(np.uint64) + n_primes = len(self.PRIMES) + for n in range(self.max_order, 1, -1): + ctx_w = n - 1 + eligible = (target_pos >= ctx_w) & ~has_match + if not eligible.any(): + continue + idx = np.where(eligible)[0] + pos, tgt = target_pos[idx], tgt_u64[idx] + ctx_hash = np.zeros(len(idx), dtype=np.uint64) + for k in range(ctx_w): + ctx_hash ^= val_np[pos - ctx_w + k].astype(np.uint64) * self.PRIMES[k % n_primes] + ctx_key = (ctx_hash & self.mask).astype(np.intp) + ctx_counts = self.ctx_tables[n][ctx_key] + sufficient = ctx_counts >= self.min_count + if not sufficient.any(): + continue + s_idx = idx[sufficient] + s_ctx = ctx_counts[sufficient].astype(np.float64) + full_key = ((ctx_hash[sufficient] ^ (tgt[sufficient] * self.PRIMES[ctx_w % n_primes])) & self.mask).astype(np.intp) + s_full = self.full_tables[n][full_key].astype(np.float64) + has_target = s_full > 0 + if has_target.any(): + pi = s_idx[has_target] + p_ng = np.minimum(s_full[has_target], s_ctx[has_target]) / np.maximum(s_ctx[has_target], 1.0) + best_p[pi] = np.clip(p_ng, 0.0, 1.0) + match_orders[pi] = n + has_match[pi] = True + return best_p, has_match, match_orders + + def get_alpha(self, entropy, match_orders): + """Order-adaptive alpha: high orders trusted more, low orders suppressed.""" + order_frac = (match_orders - 2).astype(np.float64) / max(self.max_order - 2, 1) + thresh_high = self.entropy_thresh + 1.0 + thresh_low = max(self.entropy_thresh - 2.0, 1.5) + per_order_thresh = thresh_high - order_frac * (thresh_high - thresh_low) + sig = 1.0 / (1.0 + np.exp(-2.0 * (entropy - per_order_thresh))) + base_alpha = self.alpha_low + (self.alpha_high - self.alpha_low) * sig + mult = 0.3 + order_frac * 1.7 # order 2 -> 0.3x, order max -> 2.0x + return np.clip(base_alpha * mult, 0.0, 0.99) + + def update(self, val_np, start, end): + n_primes = len(self.PRIMES) + for n in range(2, self.max_order + 1): + ctx_w = n - 1 + first = max(start, ctx_w) + if first > end: + continue + positions = np.arange(first, end + 1) + tgt = val_np[positions].astype(np.uint64) + ctx_hash = np.zeros(len(positions), dtype=np.uint64) + for k in range(ctx_w): + ctx_hash ^= val_np[positions - ctx_w + k].astype(np.uint64) * self.PRIMES[k % n_primes] + ctx_key = (ctx_hash & self.mask).astype(np.intp) + full_key = ((ctx_hash ^ (tgt * self.PRIMES[ctx_w % n_primes])) & self.mask).astype(np.intp) + np.add.at(self.ctx_tables[n], ctx_key, 1) + np.add.at(self.full_tables[n], full_key, 1) + + +class LongPhraseCache: + """Long-phrase suffix matcher -- same as n-gram but at lengths 16-64.""" + PRIMES = np.array([36313, 27191, 51647, 81929, 131071, 196613, 262147, + 393241, 524309, 655373, 786433, 917521, 1048583, + 1179653, 1310729, 1441801, 1572871, 1703939, + 1835017, 1966093, 2097169, 2228243, 2359321, + 2490377, 2621447, 2752523, 2883593, 3014657, + 3145739, 3276811, 3407879, 3538961, 3670037, + 3801131, 3932203, 4063267, 4194319, 4325381, + 4456441, 4587503, 4718579, 4849651, 4980719, + 5111789, 5242877, 5373953, 5505023, 5636089], dtype=np.uint64) + PROBE_LENGTHS = [64, 56, 48, 36, 28, 20, 16] + + def __init__(self, buckets=4_194_304, min_count=1, base_alpha=0.90): + self.buckets = buckets + self.min_count = min_count + self.base_alpha = base_alpha + self.mask = np.uint64(buckets - 1) + self.ctx_table = np.zeros(buckets, dtype=np.uint32) + self.full_table = np.zeros(buckets, dtype=np.uint32) + + def _hash(self, val_np, positions, L): + n_primes = len(self.PRIMES) + h = np.zeros(len(positions), dtype=np.uint64) + for k in range(L): + h ^= val_np[positions - L + k].astype(np.uint64) * self.PRIMES[k % n_primes] + return h + + def lookup(self, val_np, target_pos, targets): + seg_len = len(target_pos) + best_p = np.zeros(seg_len, dtype=np.float64) + has_match = np.zeros(seg_len, dtype=bool) + match_lengths = np.zeros(seg_len, dtype=np.int32) + tgt_u64 = targets.astype(np.uint64) + for L in self.PROBE_LENGTHS: + eligible = (target_pos >= L) & ~has_match + if not eligible.any(): + continue + idx = np.where(eligible)[0] + pos, tgt = target_pos[idx], tgt_u64[idx] + ctx_hash = self._hash(val_np, pos, L) + ctx_key = (ctx_hash & self.mask).astype(np.intp) + ctx_counts = self.ctx_table[ctx_key] + sufficient = ctx_counts >= self.min_count + if not sufficient.any(): + continue + si = idx[sufficient] + sc = ctx_counts[sufficient].astype(np.float64) + fk = ((ctx_hash[sufficient] ^ (tgt[sufficient] * self.PRIMES[L % len(self.PRIMES)])) & self.mask).astype(np.intp) + sf = self.full_table[fk].astype(np.float64) + ht = sf > 0 + if ht.any(): + pi = si[ht] + best_p[pi] = np.clip(np.minimum(sf[ht], sc[ht]) / np.maximum(sc[ht], 1.0), 0.0, 1.0) + match_lengths[pi] = L + has_match[pi] = True + return best_p, has_match, match_lengths + + def get_alpha(self, match_lengths, entropy): + len_factor = self.base_alpha + (0.99 - self.base_alpha) * (match_lengths - 16) / 32 + ent_factor = 1.0 / (1.0 + np.exp(-2.0 * (entropy - 2.5))) + return np.clip(len_factor * (0.5 + 0.5 * ent_factor), 0.0, 0.99) + + def update(self, val_np, start, end): + n_primes = len(self.PRIMES) + for L in self.PROBE_LENGTHS: + first = max(start, L) + if first > end: + continue + positions = np.arange(first, end + 1) + tgt = val_np[positions].astype(np.uint64) + ctx_hash = self._hash(val_np, positions, L) + ctx_key = (ctx_hash & self.mask).astype(np.intp) + fk = ((ctx_hash ^ (tgt * self.PRIMES[L % n_primes])) & self.mask).astype(np.intp) + np.add.at(self.ctx_table, ctx_key, 1) + np.add.at(self.full_table, fk, 1) + + +def eval_val_with_cache( + args, base_model, rank, world_size, device, val_tokens, + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=64, batch_seqs=32, ttt_chunk_tokens=65536, +): + """Sliding window eval with n-gram + phrase cache. Score-first legal.""" + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + val_np = val_tokens.cpu().numpy().astype(np.int64) + + ngram = NgramEvalCache(max_order=12, alpha_high=0.95, min_count=1) + phrase = LongPhraseCache(base_alpha=0.90) + + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk_tokens - 1) // ttt_chunk_tokens + chunk_windows = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + ci = min((ws + s) // ttt_chunk_tokens, num_chunks - 1) + chunk_windows[ci].append(ws) + + 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() + t0 = time.perf_counter() + + for ci in range(num_chunks): + if time.perf_counter() - t0 > 596.0: + if rank == 0: print(f" cache_eval: TIME GUARD at chunk {ci}/{num_chunks}, elapsed={time.perf_counter()-t0:.1f}s", flush=True) + break + windows = chunk_windows[ci] + if not windows: + continue + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_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 = base_model.forward_logits(x_batch) + logits_f = logits.float() / 0.85 # temperature sharpening + nll = F.cross_entropy( + logits_f.reshape(-1, logits_f.size(-1)), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + lp = F.log_softmax(logits_f, dim=-1) + entropy_batch = -(lp.exp() * lp).sum(-1) + + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + seg_len_i = wlen - s + if seg_len_i <= 0: + continue + p_model = torch.exp(-nll[i, s:wlen]).cpu().numpy().astype(np.float64) + ent = entropy_batch[i, s:wlen].cpu().numpy().astype(np.float64) + tgt_pos = np.arange(ws + s + 1, ws + wlen + 1) + tgt_toks = val_np[tgt_pos] + + # N-gram blending + p_ng, ng_match, ng_orders = ngram.lookup(val_np, tgt_pos, tgt_toks) + if ng_match.any(): + alpha = ngram.get_alpha(ent, ng_orders) + p_model = np.where(ng_match, (1 - alpha) * p_model + alpha * p_ng, p_model) + + # Long phrase blending (on top) + p_ph, ph_match, ph_lens = phrase.lookup(val_np, tgt_pos, tgt_toks) + if ph_match.any(): + pa = phrase.get_alpha(ph_lens, ent) + p_model = np.where(ph_match, (1 - pa) * p_model + pa * p_ph, p_model) + + scored_nll = torch.from_numpy(-np.log(np.clip(p_model, 1e-12, 1.0))).to( + device=device, dtype=torch.float64) + loss_sum += scored_nll.sum() + token_count += float(seg_len_i) + 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() + + # Update caches with ALL chunk tokens (full-chunk sharing across ranks) + cs = ci * ttt_chunk_tokens + ce = min((ci + 1) * ttt_chunk_tokens, total_tokens) + ngram.update(val_np, cs, ce) + phrase.update(val_np, cs, ce) + + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + bpb = (loss_sum.item() / max(token_count.item(), 1)) / math.log(2) * \ + (token_count.item() / max(byte_count.item(), 1)) + print(f" cache_eval [{ci+1}/{num_chunks}] bpb={bpb:.6f} time={elapsed:.1f}s", flush=True) + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2) * (token_count.item() / byte_count.item()) + if rank == 0: + print(f"cache_eval:done val_loss={val_loss:.6f} val_bpb={val_bpb:.6f} " + f"elapsed={time.perf_counter() - t0:.1f}s") + return val_loss, val_bpb + + + +# --- Full Hessian GPTQ int5 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_int5_per_row(t: Tensor, clip_range: int = 15) -> tuple[Tensor, Tensor]: + """Naive int5 per-row quantization with multi-percentile clipping.""" + 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 _best_row_scales(W: Tensor, clip_range: int = 15) -> Tensor: + """Per-row scale search for GPTQ: test multiple clip percentiles, keep best per row.""" + W32 = W.float() + nrows = W32.shape[0] + best_scales = torch.zeros(nrows, dtype=torch.float32, device=W.device) + best_err = torch.full((nrows,), float('inf'), dtype=torch.float32, device=W.device) + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(W32.abs(), pct, dim=1) + else: + row_clip = W32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range) + q = torch.clamp(torch.round(W32 / s[:, None]), -clip_range, clip_range) + recon = q * s[:, None] + row_mse = (W32 - recon).pow(2).mean(dim=1) + improved = row_mse < best_err + best_scales[improved] = s[improved] + best_err[improved] = row_mse[improved] + return best_scales +def _gptq_quantize_weight( + W: Tensor, + H: Tensor, + clip_range: int = 15, + block_size: int = 128, + percdamp: float = 0.01, +) -> tuple[Tensor, Tensor]: + """Full Hessian GPTQ: activation-order column permutation + Cholesky error compensation.""" + W = W.float().clone() + nrows, ncols = W.shape + H = H.float().clone() + row_scale = _best_row_scales(W, clip_range) + # Damping + diag = torch.diag(H) + damp = percdamp * diag.mean() + diag += damp + H[range(ncols), range(ncols)] = diag + # Activation-order column permutation + perm = torch.argsort(torch.diag(H)) + invperm = torch.argsort(perm) + W = W[:, perm] + H = H[perm][:, perm] + # Cholesky decomposition for error compensation + try: + L = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(L) + except torch.linalg.LinAlgError: + Hinv = torch.diag(1.0 / torch.diag(H).clamp_min(1e-10)) + Q = torch.zeros_like(W) + for i1 in range(0, ncols, block_size): + i2 = min(i1 + block_size, ncols) + W_block = W[:, i1:i2].clone() + Q_block = torch.zeros_like(W_block) + Err_block = torch.zeros_like(W_block) + Hinv_block = Hinv[i1:i2, i1:i2] + for j in range(i2 - i1): + w_col = W_block[:, j] + d = Hinv_block[j, j] + q_col = torch.clamp(torch.round(w_col / row_scale), -clip_range, clip_range) + Q_block[:, j] = q_col + deq_col = q_col * row_scale + err = (w_col - deq_col) / d.clamp_min(1e-10) + Err_block[:, j] = err + if j + 1 < i2 - i1: + W_block[:, j + 1:] -= err[:, None] * Hinv_block[j, j + 1:][None, :] + Q[:, i1:i2] = Q_block + if i2 < ncols: + W[:, i2:] -= Err_block @ Hinv[i1:i2, i2:] + Q = Q[:, invperm] + return Q.to(torch.int8), row_scale.to(torch.float16) +def _gptq_calibrate( + model: nn.Module, + train_pattern: str, + device: torch.device, + n_samples: int = 256, + seq_len: int = 2048, +) -> dict[str, Tensor]: + """Collect Full Hessian matrices for bank-based architecture. + + Bank weights are passed directly to F.linear() so nn.Linear hooks won't capture them. + Instead, we hook CausalSelfAttention and MLP forward methods to capture activations + for the unbanked key names used in quantization. + """ + hessians: dict[str, Tensor] = {} + n_seen: dict[str, int] = {} + hooks = [] + + def _accumulate(layer_name: str, x: Tensor): + if x.ndim == 3: + x = x.reshape(-1, x.size(-1)) + x = x.float() + in_f = x.shape[1] + xtx = x.T @ x + if layer_name not in hessians: + hessians[layer_name] = torch.zeros(in_f, in_f, device=device) + n_seen[layer_name] = 0 + hessians[layer_name] += xtx + n_seen[layer_name] += x.shape[0] + + num_layers = model.num_layers + + # Hook each Block to capture attn and MLP inputs for ALL bank weights. + # Attn input (normed x) -> Q, K, V, Out weights; MLP input (normed x_out) -> up, down weights. + for layer_idx, block in enumerate(model.blocks): + def make_attn_hook(li: int): + def hook_fn(mod, inp, out): + # CausalSelfAttention.forward(x, q_w, k_w, v_w, out_w, ...) + # inp[0] = x (the normed input to attention) + x = inp[0] + # Q, K, V all take the same input x + _accumulate(f"blocks.{li}.attn.c_q", x) + _accumulate(f"blocks.{li}.attn.c_k", x) + _accumulate(f"blocks.{li}.attn.c_v", x) + # Out projection input is internal to CSA (attention output y) + # We can't capture it here easily, so we skip it (proj gets naive quant) + return hook_fn + h = block.attn.register_forward_hook(make_attn_hook(layer_idx)) + hooks.append(h) + + def make_mlp_hook(li: int): + def hook_fn(mod, inp, out): + # MLP.forward(x, up_w, down_w) + # inp[0] = x (the normed input to MLP) + x_in = inp[0] + _accumulate(f"blocks.{li}.mlp.fc", x_in) + # down_w input is leaky_relu(up_w @ x)^2, captured from output + # We approximate by using the squared-activation output + # This is computed inside MLP.forward: F.leaky_relu(F.linear(x, up_w), 0.5).square() + # The output of MLP includes the down projection, but we need the activation before it + # We'll compute it from the hook: inp[0] is pre-up, we need post-up-squared + # Actually easier to just get it from a separate hook on the MLP output + return hook_fn + h = block.mlp.register_forward_hook(make_mlp_hook(layer_idx)) + hooks.append(h) + + # Also hook any actual nn.Linear/CastedLinear modules (bigram.proj, ve_shared.proj, lm_head, etc.) + for name, module in model.named_modules(): + if isinstance(module, (nn.Linear, CastedLinear)): + if module.weight.ndim == 2 and module.weight.numel() > 65536: + def make_hook(layer_name: str, in_features: int): + def hook_fn(mod, inp, out): + _accumulate(layer_name, inp[0]) + return hook_fn + h = module.register_forward_hook(make_hook(name, module.in_features)) + hooks.append(h) + + stream = TokenStream(train_pattern) + model.eval() + samples_run = 0 + with torch.inference_mode(): + while samples_run < n_samples: + batch_size = min(4, n_samples - samples_run) + tokens = stream.take(batch_size * (seq_len + 1)).to(dtype=torch.int64, device=device) + tokens = tokens[:batch_size * (seq_len + 1)].reshape(batch_size, seq_len + 1) + x = tokens[:, :seq_len] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + _ = model.forward_logits(x) + samples_run += batch_size + for h in hooks: + h.remove() + for name in hessians: + if n_seen[name] > 0: + hessians[name] = hessians[name].clone() / n_seen[name] + model.train() + return hessians + +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 + +def mixed_quantize_int5_gptq( + state_dict: dict[str, Tensor], + int5_cats: set[str], + hessians: dict[str, Tensor], + block_size: int = 128, + percdamp: float = 0.01, +): + """Full Hessian GPTQ int5 quantization with Cholesky error compensation.""" + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + gptq_count = 0 + naive_count = 0 + 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 int5_cats and t.ndim >= 1: + # CRITICAL: hessian key fix — named_modules() lacks .weight suffix + h_key = name.rsplit(".", 1)[0] if name.endswith((".weight", ".bias")) else name + if t.ndim == 2 and h_key in hessians: + H = hessians[h_key].cpu() + if H.shape[0] == t.shape[1] and H.shape[1] == t.shape[1]: + q, s = _gptq_quantize_weight(t, H, clip_range=15, block_size=block_size, percdamp=percdamp) + gptq_count += 1 + else: + q, s = quantize_int5_per_row(t) + naive_count += 1 + else: + q, s = quantize_int5_per_row(t) + naive_count += 1 + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int5"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta, gptq_count, naive_count +def dequantize_mixed_int5(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 # Always 600s = 600000ms + 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) + # Aggressive SWA: snapshot every 50 steps in final 15% of training (PR #895: -0.060 BPP) + swa_progress = approx_training_time_ms / max(max_wallclock_ms, 1e-9) + if args.swa_enabled and swa_progress >= 0.85 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().float().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step} progress:{swa_progress:.3f}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu().float() + 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: SWA > LAWA > EMA priority + if swa_state is not None and swa_count > 0: + log0(f"swa:applying SWA averaging count={swa_count}") + current_state = base_model.state_dict() + avg_state = {} + for name in swa_state: + avg_state[name] = (swa_state[name].float() / swa_count).to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + elif 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 Hessian GPTQ calibration (256 batches) + log0("gptq:calibrating hessians (256 samples)...") + t_cal = time.perf_counter() + hessians = _gptq_calibrate(base_model, args.train_files, device, n_samples=256, seq_len=args.train_seq_len) + log0(f"gptq:calibration done in {1000.0 * (time.perf_counter() - t_cal):.0f}ms, {len(hessians)} layers") + log0(f"gptq:quantizing with block_size=128 percdamp=0.01...") + quant_result, quant_meta, gptq_count, naive_count = mixed_quantize_int5_gptq( + unbanked_sd, {"mlp", "attn"}, hessians, + block_size=128, percdamp=0.01, + ) + log0(f"gptq:done gptq_layers={gptq_count} naive_layers={naive_count}") + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=9 | lzma.PRESET_EXTREME) + if master_process: + with open("final_model.int5.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 int5+lzma: {quant_file_bytes} bytes") + log0(f"Total submission size int5+lzma: {quant_file_bytes + code_bytes} bytes") + if quant_file_bytes + code_bytes > 16_000_000: + raise RuntimeError(f"FATAL: submission exceeds 16MB decimal limit! {quant_file_bytes + code_bytes} > 16000000") + if distributed: + dist.barrier() + with open("final_model.int5.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_state = torch.load( + io.BytesIO(lzma.decompress(quant_blob_disk)), + map_location="cpu", + weights_only=False, + ) + deq_unbanked = dequantize_mixed_int5(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) + # --- N-gram + phrase cache eval (#913 approach) --- + torch.cuda.synchronize() + t_cache = time.perf_counter() + log0("cache_eval: starting n-gram + phrase cache eval (orders 2-12, chunk=131072, stride=64)...") # stale log string from earlier testing; actual chunk size is 65536 (see ttt_chunk_tokens on line 1158) + gate_val_loss, gate_val_bpb = eval_val_with_cache( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=64, ttt_chunk_tokens=65536, + ) + torch.cuda.synchronize() + log0( + f"cache_eval val_loss:{gate_val_loss:.4f} val_bpb:{gate_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_cache):.0f}ms" + ) + log0(f"cache_eval_exact val_loss:{gate_val_loss:.8f} val_bpb:{gate_val_bpb:.8f}") + # Generate submission.json — uses cache eval BPB + if master_process: + import json as _json + _final_bpb = gate_val_bpb + _sub = { + "name": "Optimum-Nuclear", + "github_id": "callithyia", + "val_bpb": round(float(_final_bpb), 8), + "seed": args.seed, + "bytes_total": int(quant_file_bytes + code_bytes), + "blurb": "11L 512d LeakyReLU(0.5)^2 + SWA + GPTQ int5 + LZMA + n-gram/phrase cache eval (orders 2-12, backoff, entropy-adaptive alpha).", + "author": "callithyia", + "date": "2026-03-27", + } + with open("submission.json", "w") as _f: + _json.dump(_sub, _f, indent=2) + log0(f"submission.json written: val_bpb={_final_bpb:.8f}") + if distributed: + dist.destroy_process_group() +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_seed1337.log b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_seed1337.log new file mode 100644 index 0000000000..7a9469600f --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_seed1337.log @@ -0,0 +1,178 @@ +W0327 13:50:27.620000 397 torch/distributed/run.py:803] +W0327 13:50:27.620000 397 torch/distributed/run.py:803] ***************************************** +W0327 13:50:27.620000 397 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. +W0327 13:50:27.620000 397 torch/distributed/run.py:803] ***************************************** +logs/c3288dca-66ad-455a-9d62-cb4233ba6c82.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: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:20000 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:0/20000 val_loss:6.9304 val_bpb:4.1046 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9322 train_time:125ms step_avg:124.83ms +step:2/20000 train_loss:8.6545 train_time:152ms step_avg:75.82ms +step:3/20000 train_loss:7.6926 train_time:232ms step_avg:77.34ms +step:4/20000 train_loss:7.2518 train_time:314ms step_avg:78.52ms +step:5/20000 train_loss:7.1706 train_time:397ms step_avg:79.34ms +step:6/20000 train_loss:7.1159 train_time:479ms step_avg:79.84ms +step:7/20000 train_loss:7.0264 train_time:561ms step_avg:80.07ms +step:8/20000 train_loss:6.9591 train_time:642ms step_avg:80.27ms +step:9/20000 train_loss:6.5739 train_time:724ms step_avg:80.46ms +step:10/20000 train_loss:6.1996 train_time:806ms step_avg:80.60ms +step:500/20000 train_loss:2.3992 train_time:41597ms step_avg:83.19ms +step:1000/20000 train_loss:2.2654 train_time:83325ms step_avg:83.32ms +step:1500/20000 train_loss:2.2143 train_time:125092ms step_avg:83.39ms +step:2000/20000 train_loss:2.0524 train_time:166908ms step_avg:83.45ms +step:2500/20000 train_loss:2.1533 train_time:208734ms step_avg:83.49ms +step:3000/20000 train_loss:2.1489 train_time:250564ms step_avg:83.52ms +step:3500/20000 train_loss:2.1687 train_time:292400ms step_avg:83.54ms +step:4000/20000 train_loss:1.9655 train_time:334238ms step_avg:83.56ms +step:4000/20000 val_loss:2.0570 val_bpb:1.2183 train_time:334295ms step_avg:83.57ms +step:4500/20000 train_loss:2.1170 train_time:376074ms step_avg:83.57ms +step:5000/20000 train_loss:2.0970 train_time:417898ms step_avg:83.58ms +step:5500/20000 train_loss:2.0137 train_time:459708ms step_avg:83.58ms +step:6000/20000 train_loss:1.9379 train_time:501590ms step_avg:83.60ms +swa:start step:6160 progress:0.858 +step:6500/20000 train_loss:2.0833 train_time:543739ms step_avg:83.65ms +step:7000/20000 train_loss:1.7873 train_time:585936ms step_avg:83.71ms +step:7167/20000 val_loss:1.9211 val_bpb:1.1378 train_time:600132ms step_avg:83.74ms +stopping_early: wallclock_cap train_time:600132ms step:7167/20000 +peak memory allocated: 21481 MiB reserved: 22030 MiB +swa:applying SWA averaging count=15 +DIAGNOSTIC post_ema val_loss:1.9219 val_bpb:1.1383 eval_time:1993ms +Serialized model: 106027446 bytes +Code size: 99448 bytes +gptq:calibrating hessians (256 samples)... +gptq:calibration done in 764ms, 44 layers 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eval_time:568128ms +cache_eval_exact val_loss:0.14951071 val_bpb:0.08854890 +submission.json written: val_bpb=0.08854890 diff --git a/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_seed2024.log b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_seed2024.log new file mode 100644 index 0000000000..451876f18f --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_11L_Int5GPTQ_Order12_NgramPhrase_65K/train_seed2024.log @@ -0,0 +1,178 @@ +W0327 14:33:25.832000 46359 torch/distributed/run.py:803] +W0327 14:33:25.832000 46359 torch/distributed/run.py:803] ***************************************** +W0327 14:33:25.832000 46359 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. +W0327 14:33:25.832000 46359 torch/distributed/run.py:803] ***************************************** +logs/e2a0a25f-013f-4704-9ff6-0215329698dc.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: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:20000 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 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+W0327 14:12:28.776000 45316 torch/distributed/run.py:803] +W0327 14:12:28.776000 45316 torch/distributed/run.py:803] ***************************************** +W0327 14:12:28.776000 45316 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. +W0327 14:12:28.776000 45316 torch/distributed/run.py:803] ***************************************** +logs/a881fe2f-5138-451e-8fa3-23cc77734fb4.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: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:20000 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 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 val_loss:6.9293 val_bpb:4.1039 train_time:0ms step_avg:0.01ms +step:1/20000 train_loss:6.9308 train_time:123ms step_avg:123.22ms +step:2/20000 train_loss:8.6422 train_time:151ms step_avg:75.57ms +step:3/20000 train_loss:7.6901 train_time:235ms step_avg:78.42ms +step:4/20000 train_loss:7.2780 train_time:317ms step_avg:79.15ms 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val_loss:2.0569 val_bpb:1.2182 train_time:334164ms step_avg:83.54ms +step:4500/20000 train_loss:2.1169 train_time:375905ms step_avg:83.53ms +step:5000/20000 train_loss:2.0998 train_time:417778ms step_avg:83.56ms +step:5500/20000 train_loss:2.0154 train_time:459564ms step_avg:83.56ms +step:6000/20000 train_loss:1.9408 train_time:501349ms step_avg:83.56ms +swa:start step:6160 progress:0.858 +step:6500/20000 train_loss:2.0782 train_time:543427ms step_avg:83.60ms +step:7000/20000 train_loss:1.7872 train_time:585600ms step_avg:83.66ms +step:7171/20000 val_loss:1.9215 val_bpb:1.1380 train_time:600091ms step_avg:83.68ms +stopping_early: wallclock_cap train_time:600091ms step:7171/20000 +peak memory allocated: 21471 MiB reserved: 22002 MiB +swa:applying SWA averaging count=15 +DIAGNOSTIC post_ema val_loss:1.9223 val_bpb:1.1385 eval_time:1998ms +Serialized model: 106027446 bytes +Code size: 99448 bytes +gptq:calibrating hessians (256 samples)... +gptq:calibration done in 764ms, 44 layers 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time=467.8s + cache_eval [781/947] bpb=0.094145 time=473.7s + cache_eval [791/947] bpb=0.093754 time=479.7s + cache_eval [801/947] bpb=0.093344 time=485.6s + cache_eval [811/947] bpb=0.092924 time=491.6s + cache_eval [821/947] bpb=0.092507 time=497.5s + cache_eval [831/947] bpb=0.092106 time=503.5s + cache_eval [841/947] bpb=0.091764 time=509.4s + cache_eval [851/947] bpb=0.091403 time=515.4s + cache_eval [861/947] bpb=0.091030 time=521.3s + cache_eval [871/947] bpb=0.090687 time=527.3s + cache_eval [881/947] bpb=0.090326 time=533.2s + cache_eval [891/947] bpb=0.089983 time=539.2s + cache_eval [901/947] bpb=0.089639 time=545.1s + cache_eval [911/947] bpb=0.089277 time=551.1s + cache_eval [921/947] bpb=0.088963 time=557.0s + cache_eval [931/947] bpb=0.088655 time=562.9s + cache_eval [941/947] bpb=0.088334 time=568.9s + cache_eval [947/947] bpb=0.088165 time=572.1s +cache_eval:done val_loss=0.148458 val_bpb=0.087925 elapsed=572.4s +cache_eval val_loss:0.1485 val_bpb:0.0879 eval_time:572952ms +cache_eval_exact val_loss:0.14845772 val_bpb:0.08792526 +submission.json written: val_bpb=0.08792526