diff --git a/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/README.md b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/README.md new file mode 100644 index 0000000000..d0ba2a2367 --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/README.md @@ -0,0 +1,71 @@ +# Medusa: Unstable — DeltaNet Crawler, Frugendorff Continuation + +**val_bpb: PENDING** (3-seed mean) | **~9.96MB** | 8xH100 SXM | Successor to PR #990 (ClownCar, 1.1813) + +> **Catalyst:** PR #875 (@shalyhinpavel, Pure Neural GDN, 1.0226 BPB) proved that Gated DeltaNet +> is the dominant architecture for this competition. Medusa's DeltaNet integration is directly +> symbiotic: the same `chunk_delta_rule` kernel powering GDN's state updates is active inside +> the Frugendorff crawler topology here. Different architectures, same foundational mechanism. + +> **Stability note:** This submission shows significant cross-seed variance (see results table). +> The DeltaNet heads introduce sensitivity not present in ClownCar (variance 0.00015). +> Best seed is a genuine improvement. Research into stabilization is ongoing — Medusa_VII next. + +## Results + +| Seed | BPB (sliding window) | Size (int6+zstd) | Post-EMA BPB | Steps | +|------|---------------------:|-----------------:|-------------:|------:| +| 42 | **0.8104** ← best | 9.96MB | 0.2519 | 4872 | +| 300 | 0.9578 | 9.97MB | 0.3882 | 4880 | +| 1337 | 1.2269 | 9.96MB | 0.7126 | 4876 | +| **Mean** | **0.9984** | | | | +| **Std dev** | **0.1724** | | | | + +## What Changed vs PR #990 (ClownCar) + +| Change | Reason | +|--------|--------| +| `DELTA_NET_HEADS=4` | Canonical FLA DeltaNet enabled (vs 0 in ClownCar) | +| `LOOP_AWARE_GPTQ=1` | 2-phase GPTQ calibration: phase 1 collects flat-layer Hessians, phase 2 collects crawler Hessians with quantized-flat activations — better approximation of inference conditions | +| `EMA_START_STEP=4400` + `EMA_DECAY=0.99` | Late-start EMA re-initialized at warmdown onset, fast decay tracks warmdown weights closely | + +## Architecture + +- **Topology**: 4 flat layers + 1 crawler layer × 4 loops (Frugendorff compression) +- **INST_DIM**: 32 (flow instructions) +- **DeltaNet**: 4 heads, canonical `chunk_delta_rule` from `fla.ops.delta_rule` +- **Quantization**: int6+zstd + CRAWLER_QUANT_INT8=1, loop-aware GPTQ (41 layers) +- **Dims**: XSA_LAST_N=11, BIGRAM_VOCAB_SIZE=2048, ROPE_DIMS=16 +- **Schedule**: WARMDOWN_ITERS=2000, SWA_EVERY=50, EMA_START_STEP=4400 +- **N-gram eval**: DISABLED (sliding window only) + +## Known Issues + +The DeltaNet heads introduce cross-seed instability. Investigation identified two causes: +1. **State dtype bug**: `chunk_delta_rule` returns Float32 `new_state` in BF16 training — fixed in follow-on work (Medusa_V: `new_state.to(dtype)`) +2. **Quantization unravel**: DeltaNet weight errors compound through 4 crawler loops — active research area + +## Legality + +1. No n-gram eval — sliding window only +2. No val data used during training +3. int6 quantization runs inside training wallclock +4. Score-first protocol not applicable (no n-gram cache) + +## Reproduce + +```bash +SEED=300 bash experiments/Medusa_IV/run.sh +SEED=1337 bash experiments/Medusa_IV/run.sh +SEED=42 bash experiments/Medusa_IV/run.sh +``` + +8xH100 SXM, 600s training per seed. + +## Credits + +- **Gated DeltaNet (GDN) — primary catalyst**: @shalyhinpavel (PR #875) — proved GDN is the architecture for this competition at 1.0226 BPB pure neural. Medusa's DeltaNet integration is directly symbiotic: same `chunk_delta_rule` mechanism, applied inside the crawler topology. +- **Canonical DeltaNet kernel**: `fla.ops.delta_rule` (flash-linear-attention) +- **Loop-aware GPTQ**: @newjordan (Medusa series) +- **Frugendorff crawler architecture + flow instructions**: @newjordan (PR #990) +- **FX_Wing_Delta base**: @newjordan diff --git a/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/submission.json b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/submission.json new file mode 100644 index 0000000000..4c5298d000 --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/submission.json @@ -0,0 +1,36 @@ +{ + "author": "Frosty40", + "github_id": "newjordan", + "name": "Medusa: DeltaNet (DELTA_NET_HEADS=4) + Loop-Aware GPTQ + Late-Start EMA", + "blurb": "Successor to PR #990 (ClownCar, 1.1813 BPB). Catalyzed by PR #875 (@shalyhinpavel, GDN 1.0226). Adds DELTA_NET_HEADS=4 (canonical chunk_delta_rule), loop-aware 2-phase GPTQ, late-start EMA (step 4400, decay=0.99). 4 flat + 1 crawler x 4 loops, INST_DIM=32. NOTE: this variant (Medusa_IV) has state dtype bug in eval path — see Medusa_V for fix.", + "date": "2026-03-28", + "seed_300": { + "val_bpb": 0.3736, + "sliding_window_bpb": 0.95777934, + "post_ema_bpb": 0.3882, + "steps": 4880, + "train_time_s": 600, + "eval_time_s": "~110s" + }, + "seed_1337": { + "val_bpb": 0.6989, + "sliding_window_bpb": 1.22693269, + "post_ema_bpb": 0.7126, + "steps": 4876, + "train_time_s": 600, + "eval_time_s": "~108s" + }, + "seed_42": { + "val_bpb": 0.2441, + "sliding_window_bpb": 0.81041025, + "post_ema_bpb": 0.2519, + "steps": 4872, + "train_time_s": 600, + "eval_time_s": "~124s" + }, + "val_bpb": 0.9984, + "bytes_total": 10031847, + "bytes_code": 180226, + "hardware": "8xH100 SXM", + "notes": "High cross-seed variance (std dev 0.1724 vs ClownCar 0.00015). Best seed: 42 at 0.8104. DeltaNet heads introduce seed sensitivity. Stabilization ongoing in Medusa_VII." +} diff --git a/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_gpt.py b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_gpt.py new file mode 100644 index 0000000000..24c5175bad --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_gpt.py @@ -0,0 +1,3534 @@ +from __future__ import annotations +import copy +import glob +import importlib.util +import io +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + import warnings + warnings.warn("zstandard not found — falling back to zlib. Artifact will be ~1.5MB larger! pip install zstandard") + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +try: + from flash_attn_interface import flash_attn_func as flash_attn_3_func +except ImportError: + def flash_attn_3_func(q, k, v, causal=False): + # q: (B, T, Hq, D), k/v: (B, T, Hkv, D) — expand KV for GQA + q2 = q.transpose(1, 2) # (B, Hq, T, D) + k2 = k.transpose(1, 2) # (B, Hkv, T, D) + v2 = v.transpose(1, 2) + if k2.size(1) != q2.size(1): + rep = q2.size(1) // k2.size(1) + k2 = k2.repeat_interleave(rep, dim=1) + v2 = v2.repeat_interleave(rep, dim=1) + out = torch.nn.functional.scaled_dot_product_attention(q2, k2, v2, is_causal=causal) + return out.transpose(1, 2) +# Canonical FLA delta rule kernel — replaces Python token loop in DeltaNetMemory +# chunk_delta_rule: parallelized over sequence chunks on CUDA (arxiv 2406.06484) +try: + from fla.ops.delta_rule import chunk_delta_rule as _fla_chunk_delta_rule + _HAS_FLA_OPS = True +except ImportError: + _fla_chunk_delta_rule = None + _HAS_FLA_OPS = False + +NITRUST_ENABLE = bool(int(os.environ.get("NITRUST_ENABLE", "0"))) +NITRUST_STRICT = bool(int(os.environ.get("NITRUST_STRICT", "0"))) +NITRUST_SO_PATH = os.environ.get("NITRUST_SO_PATH", "Nitrust/rust/target/release/libnitrust_py.so") +_NITRUST_IMPORT_ERROR: str | None = None +_NITRUST_RUNTIME_FALLBACK_WARNED = False + + +def _load_nitrust_bridge(): + global _NITRUST_IMPORT_ERROR + if not NITRUST_ENABLE: + return None + try: + import nitrust_py as mod + return mod + except Exception as e: + _NITRUST_IMPORT_ERROR = f"import nitrust_py failed: {e}" + so_path = Path(NITRUST_SO_PATH) + if not so_path.is_absolute(): + so_path = (Path.cwd() / so_path).resolve() + if not so_path.exists(): + _NITRUST_IMPORT_ERROR = f"{_NITRUST_IMPORT_ERROR}; missing shared object at {so_path}" + return None + try: + spec = importlib.util.spec_from_file_location("nitrust_py", so_path) + if spec is None or spec.loader is None: + raise RuntimeError(f"unable to create import spec for {so_path}") + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + except Exception as e: + _NITRUST_IMPORT_ERROR = f"direct load from {so_path} failed: {e}" + return None + + +_NITRUST = _load_nitrust_bridge() +NITRUST_ACTIVE = bool(NITRUST_ENABLE and _NITRUST is not None) + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 1337)) + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 4000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 500)) + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + mlp_act = os.environ.get("MLP_ACT", "relu_sq").lower() + mlp_leaky_slope = float(os.environ.get("MLP_LEAKY_SLOPE", 0.5)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + muon_beta2 = float(os.environ.get("MUON_BETA2", 0.95)) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) # tighter: collect more recent checkpoints + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) # XSA on ALL 11 layers + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.5)) + 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") + # F1 capacity add-on: low-rank correction head (active at inference). + # Approx extra params ~= rank * (model_dim + vocab_size). + f1_corr_rank = int(os.environ.get("F1_CORR_RANK", 0)) + f1_corr_scale_init = float(os.environ.get("F1_CORR_SCALE_INIT", 0.10)) + # Post-train self-distillation: EMA teacher -> student. + distill_enabled = bool(int(os.environ.get("DISTILL_ENABLED", "0"))) + distill_steps = int(os.environ.get("DISTILL_STEPS", 24)) + distill_lr_factor = float(os.environ.get("DISTILL_LR_FACTOR", 0.02)) + distill_temperature = float(os.environ.get("DISTILL_TEMPERATURE", 1.5)) + distill_alpha = float(os.environ.get("DISTILL_ALPHA", 0.60)) + distill_kl_clip = float(os.environ.get("DISTILL_KL_CLIP", 10.0)) + # Optional legal score-first hashed n-gram interpolation at eval time. + # Multi-order backoff (2..max_order) with entropy-adaptive alpha. + # Alpha depends only on model entropy (no target/label access). + ngram_eval_order = int(os.environ.get("NGRAM_EVAL_ORDER", 0)) # 0=off, max order for backoff + ngram_eval_min_order = int(os.environ.get("NGRAM_EVAL_MIN_ORDER", 2)) # min order for backoff + ngram_eval_alpha = float(os.environ.get("NGRAM_EVAL_ALPHA", 0.30)) # base alpha (or fixed if adaptive off) + ngram_eval_adaptive = bool(int(os.environ.get("NGRAM_EVAL_ADAPTIVE", "1"))) # entropy-adaptive alpha + ngram_eval_alpha_min = float(os.environ.get("NGRAM_EVAL_ALPHA_MIN", 0.05)) # alpha floor (confident model) + ngram_eval_alpha_max = float(os.environ.get("NGRAM_EVAL_ALPHA_MAX", 0.60)) # alpha ceiling (uncertain model) + ngram_eval_entropy_center = float(os.environ.get("NGRAM_EVAL_ENTROPY_CENTER", 4.0)) # sigmoid center + ngram_eval_entropy_scale = float(os.environ.get("NGRAM_EVAL_ENTROPY_SCALE", 2.0)) # sigmoid steepness + ngram_eval_min_count = int(os.environ.get("NGRAM_EVAL_MIN_COUNT", 2)) + ngram_eval_buckets = int(os.environ.get("NGRAM_EVAL_BUCKETS", 4_194_304)) + ngram_eval_max_seconds = float(os.environ.get("NGRAM_EVAL_MAX_SECONDS", 0.0)) + ngram_entropy_shift = bool(int(os.environ.get("NGRAM_ENTROPY_SHIFT", "0"))) # per-order center shift + ngram_order_mults_str = os.environ.get("NGRAM_ORDER_MULTS", "") # fixed per-order multipliers (comma-sep) + cubric_cadence = int(os.environ.get("CUBRIC_CADENCE", 0)) + # F-Wing: Frugendorff crawler architecture (USE_CRAWLER=1 to activate) + use_crawler = bool(int(os.environ.get("USE_CRAWLER", "0"))) + num_flat_layers = int(os.environ.get("NUM_FLAT_LAYERS", 4)) # unique blocks, run once + num_crawler_layers = int(os.environ.get("NUM_CRAWLER_LAYERS", 1)) # shared blocks, looped + crawler_loops = int(os.environ.get("CRAWLER_LOOPS", 2)) # how many times shared blocks fire + crawler_mlp_mult = float(os.environ.get("CRAWLER_MLP_MULT", 4.0)) # MLP width multiplier for crawler + inst_dim = int(os.environ.get("INST_DIM", "32")) # instruction bottleneck dim per loop (0=disabled, use legacy loop_pos) + crawler_quant_int8 = bool(int(os.environ.get("CRAWLER_QUANT_INT8", "0"))) # use int8 for shared crawler block (multi-context quant resilience) + delta_net_heads = int(os.environ.get("DELTA_NET_HEADS", "0")) # DeltaNet heads in crawler (0=disabled); state carried between loops + # Purple-1: Dirichlet-Multinomial smoothing (PR #900 — replaces linear alpha) + ngram_dirichlet = bool(int(os.environ.get("NGRAM_DIRICHLET", "0"))) + ngram_dirichlet_conc = float(os.environ.get("NGRAM_DIRICHLET_CONC", "5.0")) + # Purple-1: variable-length phrase suffix cache (PR #880/900 — legal) + phrase_cache_enabled = bool(int(os.environ.get("PHRASE_CACHE", "0"))) + phrase_buckets = int(os.environ.get("PHRASE_BUCKETS", 4_194_304)) + phrase_probe_lengths_str = os.environ.get("PHRASE_PROBE_LENGTHS", "48,36,28,20,16") + phrase_concentration = float(os.environ.get("PHRASE_CONCENTRATION", "2.0")) + phrase_min_count = int(os.environ.get("PHRASE_MIN_COUNT", "1")) + # Purple-1: regime tracker (PR #880 — scales cache trust for repetitive vs novel text) + regime_tracker_enabled = bool(int(os.environ.get("REGIME_TRACKER", "0"))) + # Artifact ngram: training corpus oracle (disabled by default — legality pending) + artifact_ngram = bool(int(os.environ.get("ARTIFACT_NGRAM", "0"))) + artifact_ngram_max_shards = int(os.environ.get("ARTIFACT_NGRAM_MAX_SHARDS", "2")) + # Learned mixer head: train a tiny linear head to predict per-token expert weights + mixer_enabled = bool(int(os.environ.get("MIXER_ENABLED", "0"))) + mixer_n_orders = int(os.environ.get("MIXER_N_ORDERS", 11)) # n-gram orders 2..12 + mixer_loss_weight = float(os.environ.get("MIXER_LOSS_WEIGHT", 0.1)) + mixer_neural_floor = float(os.environ.get("MIXER_NEURAL_FLOOR", 0.05)) + mixer_buckets = int(os.environ.get("MIXER_BUCKETS", 8_388_608)) # 8M for training oracle + mixer_prefill_max_shards = int(os.environ.get("MIXER_PREFILL_MAX_SHARDS", 80)) + mixer_prefill_max_seconds = float(os.environ.get("MIXER_PREFILL_MAX_SECONDS", 0.0)) # 0 = unlimited + mixer_prefill_min_shards = int(os.environ.get("MIXER_PREFILL_MIN_SHARDS", 1)) + mixer_prefill_tokens_per_shard = int(os.environ.get("MIXER_PREFILL_TOKENS_PER_SHARD", 0)) # 0 = full shard + mixer_gpu_mode = bool(int(os.environ.get("MIXER_GPU_MODE", "1"))) # GPU oracle/prefill on CUDA + mixer_prefill_pos_chunk = int(os.environ.get("MIXER_PREFILL_POS_CHUNK", 1_000_000)) + compile_enabled = bool(int(os.environ.get("COMPILE_ENABLED", "1"))) + compile_fullgraph = bool(int(os.environ.get("COMPILE_FULLGRAPH", "1"))) + # Workaround for torch.compile + DDP higher-order-op backend issue on H100 runs. + # Keeps compile enabled while avoiding the DDPOptimizer path that throws NotImplementedError. + torchdynamo_optimize_ddp = bool(int(os.environ.get("TORCHDYNAMO_OPTIMIZE_DDP", "0"))) + # FX paths can leave some params unused in specific phases; enable DDP unused-param tracking by default. + ddp_find_unused_parameters = bool(int(os.environ.get("DDP_FIND_UNUSED_PARAMETERS", "1"))) +def maybe_torch_compile(obj, args: Hyperparameters): + if not args.compile_enabled: + return obj + return torch.compile(obj, dynamic=False, fullgraph=args.compile_fullgraph) +class TrainNgramTracker: + """Complementary training: track bigram stats, downweight tokens n-grams can predict.""" + def __init__(self, vocab_size: int, device: torch.device, complement_alpha: float = 0.5): + self.V = vocab_size + self.alpha = complement_alpha + self.bi_counts = torch.zeros(vocab_size, vocab_size, device=device, dtype=torch.float32) + self.bi_totals = torch.zeros(vocab_size, device=device, dtype=torch.float32) + @torch.no_grad() + def update(self, x: Tensor, y: Tensor): + xf = x.reshape(-1) + yf = y.reshape(-1) + ones = torch.ones(xf.numel(), device=xf.device, dtype=torch.float32) + self.bi_counts.reshape(-1).scatter_add_(0, xf * self.V + yf, ones) + self.bi_totals.scatter_add_(0, xf, ones) + def get_weights(self, x: Tensor, y: Tensor) -> Tensor: + xf = x.reshape(-1) + yf = y.reshape(-1) + total = self.bi_totals[xf] + count = self.bi_counts.reshape(-1)[xf * self.V + yf] + ngram_prob = count / (total + 1) + return (1.0 - self.alpha * ngram_prob).clamp(min=0.1) +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: + a, b, c = (3.4445, -4.7750, 2.0315) + X = G.bfloat16() + X /= X.norm() + eps + transposed = G.size(0) > G.size(1) + if transposed: + X = X.T + for _ in range(steps): + A = X @ X.T + B = b * A + c * A @ A + X = a * X + B @ X + return X.T if transposed else X +class Muon(torch.optim.Optimizer): + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + distributed = dist.is_available() and dist.is_initialized() + world_size = dist.get_world_size() if distributed else 1 + rank = dist.get_rank() if distributed else 0 + for group in self.param_groups: + params = group["params"] + if not params: + continue + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + total_params = sum(int(p.numel()) for p in params) + updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16) + curr = 0 + for i, p in enumerate(params): + if i % world_size == rank and p.grad is not None: + g = p.grad + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + buf.mul_(momentum).add_(g) + if nesterov: + g = g.add(buf, alpha=momentum) + g = zeropower_via_newtonschulz5(g, steps=backend_steps) + g *= max(1, g.size(0) / g.size(1)) ** 0.5 + updates_flat[curr : curr + p.numel()] = g.reshape(-1) + curr += p.numel() + if distributed: + dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) + wd = group.get("weight_decay", 0.0) + curr = 0 + for p in params: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + p.add_(g, alpha=-lr) + curr += p.numel() + return loss +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("▁"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") + return tokens[: usable + 1] +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out +def load_data_shard(file: Path) -> Tensor: + global _NITRUST_RUNTIME_FALLBACK_WARNED + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + # Use 99.95th percentile clipping to match GPTQ export quantizer + row_clip = torch.quantile(w32.abs(), 0.9995, dim=1) + scale = (row_clip / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + kv_dim = self.num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.proj._zero_init = True + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape (no repeat_interleave). + y: [B, T, H, D], v: [B, T, Hkv, D]. H must be divisible by Hkv.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) # [B, T, Hkv, group, D] + vn = F.normalize(v, dim=-1).unsqueeze(-2) # [B, T, Hkv, 1, D] — broadcast ready + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor: + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + # Some pod images route this path through fp32; flash-attn kernels require fp16/bf16. + if q.is_cuda and (q.dtype not in (torch.float16, torch.bfloat16) or k.dtype not in (torch.float16, torch.bfloat16) or v.dtype not in (torch.float16, torch.bfloat16)): + q = q.to(torch.bfloat16) + k = k.to(torch.bfloat16) + v = v.to(torch.bfloat16) + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + y = y.reshape(bsz, seqlen, dim) + return self.proj(y) +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev +class BigramHashEmbedding(nn.Module): + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + def bigram_hash(self, tokens: Tensor) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., 0] = mod + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(self.bigram_hash(token_ids)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers. + Each table maps vocab tokens to a low-dim embedding, projected to model_dim.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int, mlp_act: str = "relu_sq", mlp_leaky_slope: float = 0.5): + super().__init__() + hidden = int(mlp_mult * dim) + self.fc = CastedLinear(dim, hidden, bias=False) + self.proj = CastedLinear(hidden, dim, bias=False) + self.proj._zero_init = True + self.mlp_act = mlp_act + self.mlp_leaky_slope = mlp_leaky_slope + if self.mlp_act not in {"relu_sq", "leaky_relu_sq"}: + raise ValueError(f"Unsupported MLP_ACT '{self.mlp_act}'. Use 'relu_sq' or 'leaky_relu_sq'.") + def forward(self, x: Tensor) -> Tensor: + x = self.fc(x) + if self.mlp_act == "leaky_relu_sq": + x = F.leaky_relu(x, negative_slope=self.mlp_leaky_slope) + else: + x = F.relu(x) + return self.proj(x.square()) +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + mlp_act: str = "relu_sq", + mlp_leaky_slope: float = 0.5, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = MLP(dim, mlp_mult, mlp_act=mlp_act, mlp_leaky_slope=mlp_leaky_slope) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + def forward(self, x: Tensor, x0: Tensor, v_embed: Tensor | None = None) -> Tensor: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out +# 12 primes for XOR hashing — shared between training oracle and eval tables +NGRAM_PRIMES = np.array( + [np.uint64(36313), np.uint64(27191), np.uint64(51647), np.uint64(81929), + np.uint64(131071), np.uint64(174763), np.uint64(233017), np.uint64(283721), + np.uint64(347237), np.uint64(401519), np.uint64(479909), np.uint64(541267)], + dtype=np.uint64, +) + +class TrainNgramOracle: + """Training-time n-gram oracle: prefilled from training data, frozen during training. + Used to supervise the learned mixer head — NOT used at eval time.""" + def __init__(self, buckets: int, min_order: int = 2, max_order: int = 12, min_count: int = 2): + self.buckets = buckets + self.min_order = min_order + self.max_order = max_order + self.min_count = min_count + self.mask = np.uint64(buckets - 1) + self.primes = NGRAM_PRIMES + self.n_orders = max_order - min_order + 1 + self.ctx_tables = {n: np.zeros(buckets, dtype=np.uint32) for n in range(min_order, max_order + 1)} + self.full_tables = {n: np.zeros(buckets, dtype=np.uint32) for n in range(min_order, max_order + 1)} + self.total_tokens = 0 + + def prefill_shard(self, filepath: str, max_tokens: int = 0) -> int: + """Load a training shard and update hash tables. Returns token count.""" + count = int(max_tokens) if max_tokens and max_tokens > 0 else -1 + raw = np.fromfile(filepath, dtype=np.uint16, count=count) + t = raw.astype(np.uint64) + n = len(t) + self.total_tokens += n + for order in range(self.min_order, self.max_order + 1): + if n < order: + continue + ctx_width = order - 1 + length = n - order + 1 + ctx_hash = np.zeros(length, dtype=np.uint64) + for k in range(ctx_width): + ctx_hash ^= t[k:k + length] * self.primes[k % len(self.primes)] + ctx_key = (ctx_hash & self.mask).astype(np.int64) + tgt = t[order - 1:order - 1 + length] + full_key = ((ctx_hash ^ (tgt * self.primes[ctx_width % len(self.primes)])) & self.mask).astype(np.int64) + self.ctx_tables[order] += np.bincount(ctx_key, minlength=self.buckets).astype(np.uint32) + self.full_tables[order] += np.bincount(full_key, minlength=self.buckets).astype(np.uint32) + return n + + def get_ngram_probs(self, x_batch: Tensor, y_batch: Tensor) -> tuple[Tensor, Tensor]: + """Get per-order n-gram probabilities for a training batch. + Returns (order_p, order_valid) both shaped (bsz, seq_len, n_orders). + order_p[..., i] is probability from order (min_order+i). + order_valid[..., i] is True where ctx_count >= min_count.""" + x_np = x_batch.cpu().numpy().astype(np.uint64) + y_np = y_batch.cpu().numpy().astype(np.uint64) + bsz, slen = x_np.shape + order_p = np.full((bsz, slen, self.n_orders), 1.0 / 1024.0, dtype=np.float32) + order_valid = np.zeros((bsz, slen, self.n_orders), dtype=np.bool_) + for oi, order in enumerate(range(self.min_order, self.max_order + 1)): + ctx_width = order - 1 + if slen < ctx_width: + continue + # Build context hash from x_batch (context tokens) + # For order n, context is x[pos-cw+1:pos+1], target is y[pos] + # x_batch[b, j] is input at position j, y_batch[b, j] is target at position j + # Context for position j: tokens at positions j-cw+1 .. j (= x[j-cw+1], ..., x[j]) + # But x_batch is the input sequence, where x[j] predicts y[j] + # For n-gram: we need the last (order-1) input tokens as context, and y[j] as target + ctx_hash = np.zeros((bsz, slen), dtype=np.uint64) + for k in range(ctx_width): + shift = ctx_width - 1 - k + if shift > 0: + ctx_hash[:, shift:] ^= x_np[:, :slen - shift] * self.primes[k % len(self.primes)] + else: + ctx_hash ^= x_np * self.primes[k % len(self.primes)] + ctx_key = (ctx_hash & self.mask).astype(np.int64) + full_key = ((ctx_hash ^ (y_np * self.primes[ctx_width % len(self.primes)])) & self.mask).astype(np.int64) + ctx_c = self.ctx_tables[order][ctx_key.ravel()].astype(np.float32).reshape(bsz, slen) + full_c = self.full_tables[order][full_key.ravel()].astype(np.float32).reshape(bsz, slen) + p = np.minimum(full_c, ctx_c) / np.maximum(ctx_c, 1.0) + p = np.clip(p, 0.0, 1.0) + valid = ctx_c >= self.min_count + if ctx_width > 0: + valid[:, :ctx_width] = False + order_p[:, :, oi] = np.where(valid, p, order_p[:, :, oi]) + order_valid[:, :, oi] = valid + return ( + torch.from_numpy(order_p), + torch.from_numpy(order_valid), + ) + + +class TrainNgramOracleGPU: + """GPU-native training-time n-gram oracle for mixer supervision.""" + def __init__( + self, + buckets: int, + min_order: int = 2, + max_order: int = 12, + min_count: int = 2, + device: torch.device | None = None, + pos_chunk: int = 1_000_000, + ): + if device is None: + raise ValueError("TrainNgramOracleGPU requires an explicit CUDA device") + self.device = device + self.buckets = buckets + self.min_order = min_order + self.max_order = max_order + self.min_count = min_count + self.n_orders = max_order - min_order + 1 + self.pos_chunk = max(1, int(pos_chunk)) + self.total_tokens = 0 + self.mask = int(buckets - 1) + self.mask_t = torch.tensor(self.mask, device=device, dtype=torch.int64) + self.primes = torch.tensor(NGRAM_PRIMES.astype(np.int64), device=device, dtype=torch.int64) + self.ctx_tables = {n: torch.zeros(buckets, device=device, dtype=torch.int64) for n in range(min_order, max_order + 1)} + self.full_tables = {n: torch.zeros(buckets, device=device, dtype=torch.int64) for n in range(min_order, max_order + 1)} + + def prefill_shard(self, filepath: str, max_tokens: int = 0) -> int: + count = int(max_tokens) if max_tokens and max_tokens > 0 else -1 + raw = np.fromfile(filepath, dtype=np.uint16, count=count) + if raw.size == 0: + return 0 + t = torch.from_numpy(raw.astype(np.int64, copy=False)).to(device=self.device, dtype=torch.int64) + n = int(t.numel()) + self.total_tokens += n + npr = int(self.primes.numel()) + + for order in range(self.min_order, self.max_order + 1): + if n < order: + continue + ctx_width = order - 1 + length = n - order + 1 + p_ctx = self.primes[ctx_width % npr] + for pos0 in range(0, length, self.pos_chunk): + m = min(self.pos_chunk, length - pos0) + ctx_hash = torch.zeros(m, device=self.device, dtype=torch.int64) + for k in range(ctx_width): + tok = t[k + pos0 : k + pos0 + m] + ctx_hash.bitwise_xor_(tok * self.primes[k % npr]) + ctx_key = torch.bitwise_and(ctx_hash, self.mask_t) + tgt = t[order - 1 + pos0 : order - 1 + pos0 + m] + full_key = torch.bitwise_and(torch.bitwise_xor(ctx_hash, tgt * p_ctx), self.mask_t) + self.ctx_tables[order].add_(torch.bincount(ctx_key, minlength=self.buckets)) + self.full_tables[order].add_(torch.bincount(full_key, minlength=self.buckets)) + return n + + def get_ngram_probs(self, x_batch: Tensor, y_batch: Tensor) -> tuple[Tensor, Tensor]: + x = x_batch.to(device=self.device, dtype=torch.int64, non_blocking=True) + y = y_batch.to(device=self.device, dtype=torch.int64, non_blocking=True) + bsz, slen = x.shape + order_p = torch.full((bsz, slen, self.n_orders), 1.0 / 1024.0, device=self.device, dtype=torch.float32) + order_valid = torch.zeros((bsz, slen, self.n_orders), device=self.device, dtype=torch.bool) + npr = int(self.primes.numel()) + + for oi, order in enumerate(range(self.min_order, self.max_order + 1)): + ctx_width = order - 1 + if slen < ctx_width: + continue + ctx_hash = torch.zeros((bsz, slen), device=self.device, dtype=torch.int64) + for k in range(ctx_width): + shift = ctx_width - 1 - k + p = self.primes[k % npr] + if shift > 0: + ctx_hash[:, shift:].bitwise_xor_(x[:, :slen - shift] * p) + else: + ctx_hash.bitwise_xor_(x * p) + ctx_key = torch.bitwise_and(ctx_hash, self.mask_t) + full_key = torch.bitwise_and( + torch.bitwise_xor(ctx_hash, y * self.primes[ctx_width % npr]), + self.mask_t, + ) + ctx_c = self.ctx_tables[order].gather(0, ctx_key.reshape(-1)).reshape(bsz, slen).to(dtype=torch.float32) + full_c = self.full_tables[order].gather(0, full_key.reshape(-1)).reshape(bsz, slen).to(dtype=torch.float32) + p = torch.minimum(full_c, ctx_c) / torch.maximum(ctx_c, torch.ones_like(ctx_c)) + p = p.clamp_(0.0, 1.0) + valid = ctx_c >= float(self.min_count) + if ctx_width > 0: + valid[:, :ctx_width] = False + order_p[:, :, oi] = torch.where(valid, p, order_p[:, :, oi]) + order_valid[:, :, oi] = valid + return order_p, order_valid + + +def broadcast_train_mixer_tables(train_mixer: TrainNgramOracle, rank: int, device: torch.device): + """Broadcast rank-0 prefilled mixer tables to all ranks via NCCL.""" + if not (dist.is_available() and dist.is_initialized()): + return + if rank == 0: + meta = torch.tensor([train_mixer.total_tokens], device=device, dtype=torch.int64) + else: + meta = torch.zeros(1, device=device, dtype=torch.int64) + dist.broadcast(meta, src=0) + train_mixer.total_tokens = int(meta.item()) + + for order in range(train_mixer.min_order, train_mixer.max_order + 1): + if rank == 0: + ctx_src = train_mixer.ctx_tables[order].view(np.int32) + full_src = train_mixer.full_tables[order].view(np.int32) + ctx_t = torch.from_numpy(ctx_src).to(device=device, dtype=torch.int32, non_blocking=True) + full_t = torch.from_numpy(full_src).to(device=device, dtype=torch.int32, non_blocking=True) + else: + ctx_t = torch.empty(train_mixer.buckets, device=device, dtype=torch.int32) + full_t = torch.empty(train_mixer.buckets, device=device, dtype=torch.int32) + dist.broadcast(ctx_t, src=0) + dist.broadcast(full_t, src=0) + train_mixer.ctx_tables[order] = ctx_t.cpu().numpy().view(np.uint32).copy() + train_mixer.full_tables[order] = full_t.cpu().numpy().view(np.uint32).copy() + + +def all_reduce_train_mixer_tables_gpu(train_mixer: TrainNgramOracleGPU, device: torch.device): + """All-reduce GPU-resident mixer tables across ranks.""" + if not (dist.is_available() and dist.is_initialized()): + return + total = torch.tensor([train_mixer.total_tokens], device=device, dtype=torch.int64) + dist.all_reduce(total, op=dist.ReduceOp.SUM) + train_mixer.total_tokens = int(total.item()) + for order in range(train_mixer.min_order, train_mixer.max_order + 1): + dist.all_reduce(train_mixer.ctx_tables[order], op=dist.ReduceOp.SUM) + dist.all_reduce(train_mixer.full_tables[order], op=dist.ReduceOp.SUM) + +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", + mlp_act: str = "relu_sq", + mlp_leaky_slope: float = 0.5, + f1_corr_rank: int = 0, + f1_corr_scale_init: float = 0.10, + mixer_n_experts: int = 0, + mixer_loss_weight: float = 0.1, + mixer_neural_floor: float = 0.05, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) # kv_dim for value projection + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + layer_idx=i, + ln_scale=ln_scale, + dtg=dtg, + mlp_act=mlp_act, + mlp_leaky_slope=mlp_leaky_slope, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() # keep empty for compat + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + # Low-rank correction path for extra capacity under size budget. + self.f1_corr_rank = f1_corr_rank + if f1_corr_rank > 0: + self.f1_corr_in = CastedLinear(model_dim, f1_corr_rank, bias=False) + self.f1_corr_out = CastedLinear(f1_corr_rank, vocab_size, bias=False) + self.f1_corr_out._zero_init = True + self.f1_corr_scale = nn.Parameter(torch.tensor(f1_corr_scale_init, dtype=torch.float32)) + else: + self.f1_corr_in = None + self.f1_corr_out = None + self.f1_corr_scale = None + # Learned mixer head: predicts per-token expert weights for n-gram blending + self.mixer_n_experts = mixer_n_experts + self.mixer_loss_weight = mixer_loss_weight + self.mixer_neural_floor = mixer_neural_floor + if mixer_n_experts > 0: + self.alpha_head = nn.Linear(model_dim, mixer_n_experts, bias=True) + else: + self.alpha_head = None + 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() + # Special init for alpha_head: zeros + bias[0]=2.0 (favor neural initially) + if self.alpha_head is not None: + nn.init.zeros_(self.alpha_head.weight) + nn.init.zeros_(self.alpha_head.bias) + with torch.no_grad(): + self.alpha_head.bias[0] = 2.0 + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + num_layers = len(self.blocks) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + if ".proj." in name or name.endswith(".proj"): + with torch.no_grad(): + module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + """Get value embedding for a specific layer using shared table + per-layer scale.""" + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + def forward(self, input_ids: Tensor, target_ids: Tensor, + ngram_expert_p: Tensor | None = None, ngram_valid_mask: Tensor | None = None) -> Tensor: + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + if self.f1_corr_in is not None and self.f1_corr_out is not None and self.f1_corr_scale is not None: + corr_hidden = F.silu(self.f1_corr_in(x_flat)) + corr_proj = self.f1_corr_out(corr_hidden) + logits_proj = logits_proj + self.f1_corr_scale.to(dtype=logits_proj.dtype) * corr_proj + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + if hasattr(self, '_ngram_tracker') and self._ngram_tracker is not None and self.training: + per_tok_loss = F.cross_entropy(logits.float(), targets, reduction="none") + weights = self._ngram_tracker.get_weights(input_ids, target_ids) + main_loss = (per_tok_loss * weights).mean() + else: + 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) + # Mixer loss: train alpha_head to blend neural + n-gram experts + if (self.training and self.alpha_head is not None and self.mixer_loss_weight > 0 + and ngram_expert_p is not None and ngram_valid_mask is not None): + alpha_raw = self.alpha_head(x_flat.float()) # (N, n_experts) + # Neural probability for the correct target token + with torch.no_grad(): + neural_p = F.softmax(logits.float(), dim=-1).gather(1, targets.unsqueeze(1)).squeeze(1) + # Stack experts: [neural, order2, order3, ..., orderN] + ngram_p_flat = ngram_expert_p.reshape(-1, ngram_expert_p.size(-1)) # (N, n_orders) + ngram_v_flat = ngram_valid_mask.reshape(-1, ngram_valid_mask.size(-1)) # (N, n_orders) + expert_p = torch.cat([neural_p.unsqueeze(1), ngram_p_flat.to(dtype=neural_p.dtype)], dim=1) + full_mask = torch.cat([ + torch.ones(targets.size(0), 1, device=targets.device, dtype=torch.bool), + ngram_v_flat.to(device=targets.device), + ], dim=1) + gate = alpha_raw.masked_fill(~full_mask, -1e9) + weights = F.softmax(gate, dim=-1) + # Neural floor: ensure ≥ mixer_neural_floor for neural expert + nf = self.mixer_neural_floor + neural_w = nf + (1.0 - nf) * weights[:, :1] + other_w = (1.0 - nf) * weights[:, 1:] + weights = torch.cat([neural_w, other_w], dim=1) + mixed_p = (weights * expert_p.clamp(min=1e-12)).sum(dim=1) + mixer_loss = -torch.log(mixed_p.clamp(min=1e-12)).mean() + main_loss = main_loss + self.mixer_loss_weight * mixer_loss + return main_loss + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + if self.f1_corr_in is not None and self.f1_corr_out is not None and self.f1_corr_scale is not None: + corr_hidden = F.silu(self.f1_corr_in(x)) + corr_proj = self.f1_corr_out(corr_hidden) + logits_proj = logits_proj + self.f1_corr_scale.to(dtype=logits_proj.dtype) * corr_proj + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + def forward_logits_and_alpha(self, input_ids: Tensor) -> tuple[Tensor, Tensor | None]: + """Return (logits, alpha_raw) — alpha_raw is gate logits for mixer head.""" + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + if self.f1_corr_in is not None and self.f1_corr_out is not None and self.f1_corr_scale is not None: + corr_hidden = F.silu(self.f1_corr_in(x)) + corr_proj = self.f1_corr_out(corr_hidden) + logits_proj = logits_proj + self.f1_corr_scale.to(dtype=logits_proj.dtype) * corr_proj + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + alpha_raw = self.alpha_head(x.float()) if self.alpha_head is not None else None + return logits, alpha_raw + + +# ────────────────────────────────────────────────────────────────────────────── +# F-Wing: Frugendorff Crawler GPT +# ────────────────────────────────────────────────────────────────────────────── +# DeltaNet associative memory — delta rule update, state carried between loops +# Update rule: S_t += β_t * outer(v_t - S_t @ k_t, k_t) (error correction) +# The state S accumulates pattern associations across crawler loop iterations, +# giving each loop genuine new information rather than repeating the same pass. +# ────────────────────────────────────────────────────────────────────────────── +class DeltaNetMemory(nn.Module): + """Delta-rule associative memory for the FX-Wing crawler reservoir. + + State S (shape [B, H, Dh, Dh]) is carried between crawler loop iterations. + Each pass corrects prediction errors, progressively refining associations. + Output projection is zero-initialized so it starts as a residual no-op. + """ + def __init__(self, model_dim: int, n_heads: int): + super().__init__() + assert model_dim % n_heads == 0 + self.n_heads = n_heads + self.head_dim = model_dim // n_heads + d = model_dim + Dh = self.head_dim + H = n_heads + self.k_proj = nn.Linear(d, H * Dh, bias=False) + self.v_proj = nn.Linear(d, H * Dh, bias=False) + self.q_proj = nn.Linear(d, H * Dh, bias=False) + self.b_proj = nn.Linear(d, H, bias=True) # per-head beta (learning rate) + self.o_proj = nn.Linear(H * Dh, d, bias=False) + self.norm = RMSNorm() + nn.init.zeros_(self.o_proj.weight) # start as identity (no-op) + + @torch.compiler.disable # T-loop unrolled by dynamo → OOM; run in eager instead + def forward(self, x: Tensor, state: Tensor) -> tuple[Tensor, Tensor]: + """ + x: [B, T, D] + state: [B, H, Dh, Dh] — carried from previous loop iteration + returns (x_out [B, T, D], new_state [B, H, Dh, Dh]) + """ + B, T, D = x.shape + H, Dh = self.n_heads, self.head_dim + k = F.normalize(self.k_proj(x).reshape(B, T, H, Dh), dim=-1) # [B,T,H,Dh] + v = self.v_proj(x).reshape(B, T, H, Dh) # [B,T,H,Dh] + q = F.normalize(self.q_proj(x).reshape(B, T, H, Dh), dim=-1) # [B,T,H,Dh] + beta = torch.sigmoid(self.b_proj(x)) # [B,T,H] + # Sequential delta rule — process each token, carry state forward + S = state # [B, H, Dh, Dh] + outs: list[Tensor] = [] + for t in range(T): + k_t = k[:, t] # [B, H, Dh] + v_t = v[:, t] + q_t = q[:, t] + b_t = beta[:, t, :, None, None] # [B, H, 1, 1] + # Read: y = S @ q + y_t = torch.einsum("bhij,bhj->bhi", S, q_t) # [B, H, Dh] + # Delta rule write: S += β * outer(v - S@k, k) + pred = torch.einsum("bhij,bhj->bhi", S, k_t) # [B, H, Dh] + S = S + b_t * torch.einsum("bhi,bhj->bhij", v_t - pred, k_t) + outs.append(y_t) + y = torch.stack(outs, dim=1).reshape(B, T, H * Dh) # [B, T, H*Dh] + return self.norm(x + self.o_proj(y)), S + + +class CanonicalDeltaNet(nn.Module): + """Delta rule associative memory using FLA's chunk_delta_rule CUDA kernel. + + Replaces DeltaNetMemory's Python token-by-token loop with the parallelized + chunk implementation from flash-linear-attention (arxiv 2406.06484). + Adds causal short convolutions on Q/K/V — proven quality gain from the paper. + + State API is identical to DeltaNetMemory: forward(x, state) -> (x_out, new_state) + so _run_crawler state threading requires no changes. + Output projection is zero-initialized so it starts as a residual no-op. + """ + def __init__(self, model_dim: int, n_heads: int, conv_size: int = 4): + super().__init__() + assert model_dim % n_heads == 0 + self.n_heads = n_heads + self.head_dim = model_dim // n_heads + self._conv_size = conv_size + d = model_dim + H = n_heads + Dh = self.head_dim + inner = H * Dh + self.k_proj = nn.Linear(d, inner, bias=False) + self.v_proj = nn.Linear(d, inner, bias=False) + self.q_proj = nn.Linear(d, inner, bias=False) + self.b_proj = nn.Linear(d, H, bias=True) # per-head beta (learning rate) + self.o_proj = nn.Linear(inner, d, bias=False) + nn.init.zeros_(self.o_proj.weight) # start as identity (no-op) + # Causal depthwise short convolutions per Q/K/V (canonical per paper) + # padding=0 + explicit left-pad in forward ensures strict causality + self.q_conv = nn.Conv1d(inner, inner, conv_size, padding=0, groups=inner, bias=False) + self.k_conv = nn.Conv1d(inner, inner, conv_size, padding=0, groups=inner, bias=False) + self.v_conv = nn.Conv1d(inner, inner, conv_size, padding=0, groups=inner, bias=False) + self.norm = RMSNorm() + + def _causal_conv(self, conv: nn.Conv1d, x: Tensor) -> Tensor: + """Left-pad then convolve: output[t] depends only on inputs[t-k+1..t].""" + T = x.size(1) + xT = F.pad(x.transpose(1, 2), (self._conv_size - 1, 0)) # [B, C, T+k-1] + return conv(xT).transpose(1, 2) # [B, T, C] + + def forward(self, x: Tensor, state: Tensor | None) -> tuple[Tensor, Tensor]: + """ + x: [B, T, D] + state: [B, H, Dh, Dh] or None — carried from previous loop iteration + returns (x_out [B, T, D], new_state [B, H, Dh, Dh]) + """ + B, T, D = x.shape + H, Dh = self.n_heads, self.head_dim + # Project + causal short conv + q = self._causal_conv(self.q_conv, self.q_proj(x)) # [B, T, H*Dh] + k = self._causal_conv(self.k_conv, self.k_proj(x)) + v = self._causal_conv(self.v_conv, self.v_proj(x)) + beta = torch.sigmoid(self.b_proj(x)) # [B, T, H] + # L2-normalize Q/K (canonical qk_norm='l2') + q = F.normalize(q.reshape(B, T, H, Dh), dim=-1) # [B, T, H, Dh] + k = F.normalize(k.reshape(B, T, H, Dh), dim=-1) + v = v.reshape(B, T, H, Dh) + # chunk_delta_rule requires q/k/v/beta to share dtype — mixed precision can diverge + dtype = x.dtype + q, k, v, beta = q.to(dtype), k.to(dtype), v.to(dtype), beta.to(dtype) + # Chunked CUDA delta rule — parallel over sequence, correct over loops + o, new_state = _fla_chunk_delta_rule( + q=q, k=k, v=v, beta=beta, + initial_state=state, + output_final_state=True, + ) + y = o.reshape(B, T, H * Dh) + return self.norm(x + self.o_proj(y)), new_state + + +# flat blocks (unique, U-Net enc/dec) + crawler blocks (shared, looped K times) +# Compression: fewer unique blocks → same BPB → smaller artifact → freed budget +# ────────────────────────────────────────────────────────────────────────────── +class CrawlerGPT(nn.Module): + """Frugendorff architecture: flat U-Net + shared crawler blocks at bottleneck.""" + def __init__( + self, + vocab_size: int, + num_flat_layers: int, + num_crawler_layers: int, + crawler_loops: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: float, + crawler_mlp_mult: float, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "0", + mlp_act: str = "relu_sq", + mlp_leaky_slope: float = 0.5, + mixer_n_experts: int = 0, + mixer_loss_weight: float = 0.1, + mixer_neural_floor: float = 0.05, + inst_dim: int = 32, + delta_net_heads: int = 0, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.num_flat_layers = num_flat_layers + self.num_crawler_layers = num_crawler_layers + self.crawler_loops = crawler_loops + self.inst_dim = inst_dim + self.mixer_n_experts = mixer_n_experts + self.mixer_loss_weight = mixer_loss_weight + self.mixer_neural_floor = mixer_neural_floor + # Compatibility stubs + self.mtp_num_heads = 0 + self.mtp_loss_weight = 0.0 + self.mtp_heads = nn.ModuleList() + self.f1_corr_in = None + self.f1_corr_out = None + self.f1_corr_scale = None + # Embeddings + 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) + # Flat section: U-Net encoder / decoder with skip connections + self.flat_encoder_layers = num_flat_layers // 2 + self.flat_decoder_layers = num_flat_layers - self.flat_encoder_layers + self.num_flat_skips = min(self.flat_encoder_layers, self.flat_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_flat_skips, model_dim, dtype=torch.float32)) + self.flat_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=False, + mlp_act=mlp_act, mlp_leaky_slope=mlp_leaky_slope) + for i in range(num_flat_layers) + ]) + # Crawler section: shared blocks, looped crawler_loops times at bottleneck + self.crawler_blocks = nn.ModuleList([ + Block(model_dim, num_heads, num_kv_heads, crawler_mlp_mult, rope_base, qk_gain_init, + layer_idx=num_flat_layers + i, ln_scale=ln_scale, dtg=False, + mlp_act=mlp_act, mlp_leaky_slope=mlp_leaky_slope) + for i in range(num_crawler_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in list(self.flat_blocks) + list(self.crawler_blocks): + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + # Instructed recurrence — FLOW version (FX_Wing_Delta): + # Instructions are recomputed from CURRENT x at each loop (not pre-planned from x_enc). + # perturbation→flow: each loop's instruction responds to what the previous loop produced. + # loop_inst_proj: model_dim → inst_dim (shared bottleneck, applied per loop) + # loop_inst_up[k]: inst_dim → model_dim (loop-specific expansion) + if num_crawler_layers > 0 and crawler_loops > 1 and inst_dim > 0: + self.loop_pos = None + # Single projection → inst_dim; reused at each loop on current x + self.loop_inst_proj = nn.Linear(model_dim, inst_dim, bias=False) + self.loop_inst_up = nn.ModuleList([ + nn.Linear(inst_dim, model_dim, bias=False) + for _ in range(crawler_loops) + ]) + # Initialize small so instructions start near zero (warm start near original behavior) + nn.init.normal_(self.loop_inst_proj.weight, std=0.01) + for up in self.loop_inst_up: + nn.init.zeros_(up.weight) + elif num_crawler_layers > 0 and crawler_loops > 1: + # Fallback: legacy fixed orthogonal offsets (UT-style) + raw = torch.randn(crawler_loops, model_dim) + Q, _ = torch.linalg.qr(raw.T) + ortho = Q.T[:crawler_loops] + self.loop_pos = nn.ParameterList([ + nn.Parameter(ortho[i] * 0.01) for i in range(crawler_loops) + ]) + self.loop_inst_proj = None + self.loop_inst_up = None + else: + self.loop_pos = None + self.loop_inst_proj = None + self.loop_inst_up = None + # DeltaNet memory — state carried between crawler loop iterations + # Uses canonical FLA chunk_delta_rule when available (CUDA parallel + short conv) + # Falls back to DeltaNetMemory (Python loop) if fla.ops not installed + if delta_net_heads > 0 and num_crawler_layers > 0: + if _HAS_FLA_OPS: + self.delta_net = CanonicalDeltaNet(model_dim, delta_net_heads) + else: + self.delta_net = DeltaNetMemory(model_dim, delta_net_heads) + else: + self.delta_net = None + # VE on crawler blocks + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() + # XSA on last N of crawler blocks + if xsa_last_n > 0: + for i in range(max(0, num_crawler_layers - xsa_last_n), num_crawler_layers): + self.crawler_blocks[i].attn.use_xsa = True + 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 + # Learned mixer head + if mixer_n_experts > 0: + self.alpha_head = nn.Linear(model_dim, mixer_n_experts, bias=True) + else: + self.alpha_head = None + 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) + total_layers = self.num_flat_layers + self.num_crawler_layers + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + if ".proj." in name or name.endswith(".proj"): + with torch.no_grad(): + module.weight.mul_(1.0 / math.sqrt(2 * total_layers)) + if self.alpha_head is not None: + nn.init.zeros_(self.alpha_head.weight) + nn.init.zeros_(self.alpha_head.bias) + if self.mixer_n_experts > 0: + self.alpha_head.bias[0] = 2.0 + + def _get_crawler_ve(self, crawler_idx: int, input_ids: Tensor, ve_cache: dict) -> Tensor | None: + if self.ve_shared is None or crawler_idx not in self.ve_layer_indices: + return None + if 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] + ve_idx = self.ve_layer_indices.index(crawler_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def _run_encoder(self, x: Tensor, x0: Tensor) -> tuple[Tensor, list[Tensor]]: + skips: list[Tensor] = [] + for i in range(self.flat_encoder_layers): + x = self.flat_blocks[i](x, x0) + skips.append(x) + return x, skips + + def _run_decoder(self, x: Tensor, x0: Tensor, skips: list[Tensor]) -> Tensor: + for i in range(self.flat_decoder_layers): + bi = self.flat_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.flat_blocks[bi](x, x0) + return x + + def _run_crawler(self, x: Tensor, x0: Tensor, input_ids: Tensor, ve_cache: dict) -> Tensor: + # FLOW instructions: recompute from current x at each loop (not static x_enc pre-plan). + # This makes each loop's instruction respond to what the previous loop produced, + # reducing gradient conflict and activation distribution drift across loops. + + # DeltaNet state — initialized to zero, carried across loop iterations + if self.delta_net is not None: + B, T, D = x.shape + delta_state = torch.zeros( + B, self.delta_net.n_heads, self.delta_net.head_dim, self.delta_net.head_dim, + device=x.device, dtype=x.dtype, + ) + else: + delta_state = None + + for loop in range(self.crawler_loops): + if self.loop_inst_proj is not None: + # Flow: project CURRENT x through shared bottleneck, expand with loop-specific up + inst_k = self.loop_inst_up[loop](self.loop_inst_proj(x)) # [B, T, model_dim] + x_loop = x + inst_k + elif self.loop_pos is not None: + x_loop = x + self.loop_pos[loop] + else: + x_loop = x + for ci, block in enumerate(self.crawler_blocks): + ve = self._get_crawler_ve(ci, input_ids, ve_cache) + x_loop = block(x_loop, x0, v_embed=ve) + # DeltaNet: correct prediction errors, carry refined state to next loop + if self.delta_net is not None: + x_loop, delta_state = self.delta_net(x_loop, delta_state) + x = x_loop + return x + + def _compute_logits(self, x: Tensor) -> Tensor: + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward(self, input_ids: Tensor, target_ids: Tensor, + ngram_expert_p: Tensor | None = None, + ngram_valid_mask: Tensor | None = None) -> Tensor: + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + x, skips = self._run_encoder(x, x0) + ve_cache: dict = {} + if self.num_crawler_layers > 0: + x = self._run_crawler(x, x0, input_ids, ve_cache) + x = self._run_decoder(x, x0, skips) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + logits = self._compute_logits(x_flat) + if hasattr(self, '_ngram_tracker') and self._ngram_tracker is not None and self.training: + per_tok_loss = F.cross_entropy(logits.float(), targets, reduction="none") + weights = self._ngram_tracker.get_weights(input_ids, target_ids) + main_loss = (per_tok_loss * weights).mean() + else: + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + # Mixer loss + if (self.training and self.alpha_head is not None and self.mixer_loss_weight > 0 + and ngram_expert_p is not None and ngram_valid_mask is not None): + alpha_raw = self.alpha_head(x_flat.float()) + with torch.no_grad(): + neural_p = F.softmax(logits.float(), dim=-1).gather(1, targets.unsqueeze(1)).squeeze(1) + ngram_p_flat = ngram_expert_p.reshape(-1, ngram_expert_p.size(-1)) + ngram_v_flat = ngram_valid_mask.reshape(-1, ngram_valid_mask.size(-1)) + expert_p = torch.cat([neural_p.unsqueeze(1), ngram_p_flat.to(dtype=neural_p.dtype)], dim=1) + full_mask = torch.cat([ + torch.ones(targets.size(0), 1, device=targets.device, dtype=torch.bool), + ngram_v_flat.to(device=targets.device), + ], dim=1) + gate = alpha_raw.masked_fill(~full_mask, -1e9) + weights_gate = F.softmax(gate, dim=-1) + nf = self.mixer_neural_floor + neural_w = nf + (1.0 - nf) * weights_gate[:, :1] + other_w = (1.0 - nf) * weights_gate[:, 1:] + weights_gate = torch.cat([neural_w, other_w], dim=1) + mixed_p = (weights_gate * expert_p.clamp(min=1e-12)).sum(dim=1) + mixer_loss = -torch.log(mixed_p.clamp(min=1e-12)).mean() + main_loss = main_loss + self.mixer_loss_weight * mixer_loss + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + x, skips = self._run_encoder(x, x0) + ve_cache: dict = {} + if self.num_crawler_layers > 0: + x = self._run_crawler(x, x0, input_ids, ve_cache) + x = self._run_decoder(x, x0, skips) + x = self.final_norm(x) + return self._compute_logits(x) + + def forward_logits_and_alpha(self, input_ids: Tensor) -> tuple[Tensor, Tensor | None]: + 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 + x, skips = self._run_encoder(x, x0) + ve_cache: dict = {} + if self.num_crawler_layers > 0: + x = self._run_crawler(x, x0, input_ids, ve_cache) + x = self._run_decoder(x, x0, skips) + x = self.final_norm(x) + logits = self._compute_logits(x) + alpha_raw = self.alpha_head(x.float()) if self.alpha_head is not None else None + return logits, alpha_raw + + +def _get_block_named_params(model: nn.Module) -> list: + """Return named parameters from all transformer blocks, compatible with both GPT and CrawlerGPT.""" + if isinstance(model, CrawlerGPT): + return list(model.flat_blocks.named_parameters()) + list(model.crawler_blocks.named_parameters()) + return list(model.blocks.named_parameters()) + + +def build_model(args: Hyperparameters, device: torch.device) -> nn.Module: + """Instantiate GPT or CrawlerGPT based on USE_CRAWLER env var.""" + mixer_n_experts = (1 + args.mixer_n_orders) if args.mixer_enabled else 0 + if args.use_crawler: + model = CrawlerGPT( + vocab_size=args.vocab_size, + num_flat_layers=args.num_flat_layers, + num_crawler_layers=args.num_crawler_layers, + crawler_loops=args.crawler_loops, + model_dim=args.model_dim, + num_heads=args.num_heads, + num_kv_heads=args.num_kv_heads, + mlp_mult=args.mlp_mult, + crawler_mlp_mult=args.crawler_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, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, + ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, + ve_dim=args.ve_dim, + ve_layers=args.ve_layers, + mlp_act=args.mlp_act, + mlp_leaky_slope=args.mlp_leaky_slope, + mixer_n_experts=mixer_n_experts, + mixer_loss_weight=args.mixer_loss_weight, + mixer_neural_floor=args.mixer_neural_floor, + inst_dim=args.inst_dim, + delta_net_heads=args.delta_net_heads, + ) + else: + 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, + mlp_act=args.mlp_act, + mlp_leaky_slope=args.mlp_leaky_slope, + f1_corr_rank=args.f1_corr_rank, + f1_corr_scale_init=args.f1_corr_scale_init, + mixer_n_experts=mixer_n_experts, + mixer_loss_weight=args.mixer_loss_weight, + mixer_neural_floor=args.mixer_neural_floor, + ) + return model.to(device).bfloat16() + + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 128, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + """Sliding window evaluation: each token scored with maximum context.""" + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + base_model.eval() + compiled_logits = maybe_torch_compile(base_model.forward_logits, args) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = token_count.item() / byte_count.item() + base_model.train() + return val_loss, bits_per_token * tokens_per_byte +class RegimeTracker: + """Adapts phrase cache concentration based on content repetitiveness (PR #880). + + High match rate (boilerplate/code) → lower concentration → trust cache more. + Low match rate (novel prose) → higher concentration → trust neural more. + Multiplier range: [0.7, 1.5]. + """ + def __init__(self, window: int = 4096): + self._max = max(1, window // 64) + self._match: list[float] = [] + self._div: list[float] = [] + self.mult = 1.0 + + def update(self, n_match: int, n_total: int, tokens: np.ndarray) -> None: + if n_total == 0: + return + self._match.append(n_match / n_total) + if len(tokens) > 0: + self._div.append(float(len(np.unique(tokens))) / len(tokens)) + if len(self._match) > self._max: + self._match.pop(0) + if len(self._div) > self._max: + self._div.pop(0) + if len(self._match) >= 3: + r_match = float(np.mean(self._match[-10:])) + r_div = float(np.mean(self._div[-10:])) if self._div else 0.5 + rep = r_match * (1.0 - r_div * 0.5) + self.mult = 0.7 + 0.8 * float(np.clip(rep, 0.0, 1.0)) + + def effective_concentration(self, base_c: float) -> float: + """Divide base_c by mult: repetitive text → lower c → more cache weight.""" + return base_c / self.mult + + +def _build_training_ngram_oracle( + data_path: str, + min_order: int, + max_order: int, + buckets: int, + max_shards: int = 2, +) -> dict: + """Build n-gram count tables from training shards (PR #931 idea). + + Uses identical XOR hash scheme as eval tables so they seed the eval cache. + Small buckets (e.g. 131072) give a warm prior even with collisions -- + any prior beats a cold-start empty table. + """ + primes = np.array( + [np.uint64(36313), np.uint64(27191), np.uint64(51647), np.uint64(81929), + np.uint64(131071), np.uint64(174763), np.uint64(233017)], + dtype=np.uint64, + ) + mask = np.uint64(buckets - 1) + ctx_tbl = {n: np.zeros(buckets, dtype=np.uint32) for n in range(min_order, max_order + 1)} + full_tbl = {n: np.zeros(buckets, dtype=np.uint32) for n in range(min_order, max_order + 1)} + train_files = sorted(glob.glob(os.path.join(data_path, "fineweb_train_*.bin")))[:max_shards] + total_toks = 0 + t0 = time.perf_counter() + for fpath in train_files: + header = np.fromfile(fpath, dtype=" identical tables everywhere.""" + t = val_np[start:end].astype(np.uint64) + n = len(t) + for order in range(min_order, max_order + 1): + if n < order: + continue + ctx_width = order - 1 + ctx_hash = np.zeros(n - order + 1, dtype=np.uint64) + for k in range(ctx_width): + ctx_hash ^= t[k:n - order + 1 + k] * primes[k % len(primes)] + ctx_key = (ctx_hash & mask).astype(np.int64) + tgt = t[order - 1:] + full_key = ((ctx_hash ^ (tgt * primes[ctx_width % len(primes)])) & mask).astype(np.int64) + ctx_tables[order] += np.bincount(ctx_key, minlength=len(ctx_tables[order])).astype(np.uint32) + full_tables[order] += np.bincount(full_key, minlength=len(full_tables[order])).astype(np.uint32) + +def eval_val_sliding_hashed_ngram( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + order: int, + alpha: float, + min_count: int, + buckets: int, + max_seconds: float = 0.0, + batch_seqs: int = 128, + eval_seq_len: int | None = None, + oracle_state: dict | None = None, +) -> tuple[float, float, float]: + """Score-first sliding eval with chunk-based SHARED n-gram tables + cubric. + + Key design: all ranks share identical n-gram tables via bulk chunk updates. + Each chunk's windows are distributed across ranks for scoring, then ALL ranks + update tables with the same contiguous token range. Every rank sees the full + n-gram picture (not 1/world_size like per-segment updates). + + Legal: entire chunk scored before its tokens update the tables. + """ + min_order = max(args.ngram_eval_min_order, 2) + max_order = max(order, min_order) + adaptive = args.ngram_eval_adaptive + alpha_min = args.ngram_eval_alpha_min + alpha_max = args.ngram_eval_alpha_max + ent_center = args.ngram_eval_entropy_center + ent_scale = args.ngram_eval_entropy_scale + + # Parse fixed per-order multipliers (PR #809 style) + _fixed_order_mults = None + if args.ngram_order_mults_str: + _fixed_order_mults = np.array([float(x) for x in args.ngram_order_mults_str.split(",")], dtype=np.float64) + + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + + # Build all windows and total scored tokens + all_window_starts = [ws for ws in range(0, total_tokens, stride) if min(ws + seq_len, total_tokens) - ws >= 1] + total_scored_tokens = 0.0 + for ws in all_window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + total_scored_tokens += float(max(wlen - s, 0)) + + # Group windows into chunks by scored position -- all ranks share this grouping + chunk_tokens = int(os.environ.get("NGRAM_CHUNK_TOKENS", "1048576")) # 1M default + num_chunks = (total_tokens + chunk_tokens - 1) // chunk_tokens + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in all_window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // chunk_tokens, num_chunks - 1) + chunk_windows[ci].append(ws) + + val_np = val_tokens.numpy() + ctx_tables = {n: np.zeros((buckets,), dtype=np.uint32) for n in range(min_order, max_order + 1)} + full_tables = {n: np.zeros((buckets,), dtype=np.uint32) for n in range(min_order, max_order + 1)} + mask = np.uint64(buckets - 1) + primes = NGRAM_PRIMES + + # Purple-1 (PR #931): seed tables from pre-built training oracle if provided + if oracle_state is not None and oracle_state.get("buckets") == buckets: + for n in range(min_order, max_order + 1): + if n in oracle_state["ctx_tables"]: + ctx_tables[n][:] = oracle_state["ctx_tables"][n] + full_tables[n][:] = oracle_state["full_tables"][n] + if rank == 0: + print(f"oracle:seeded_eval_tables from {oracle_state.get('total_tokens', 0)} " + f"training tokens buckets={buckets}", flush=True) + elif oracle_state is not None and rank == 0: + print(f"oracle:bucket_mismatch oracle_buckets={oracle_state.get('buckets')} " + f"eval_buckets={buckets} (no seeding)", flush=True) + + loss_sum = 0.0 + token_count = 0.0 + byte_count = 0.0 + + # Cubric 3D: per (order × entropy_bin × count_bin) adaptive alpha scaling + _NUM_ENT_BINS = 3 # low / mid / high entropy + _NUM_CNT_BINS = 3 # low / mid / high count + _ENT_EDGES = np.array([ent_center - 1.0, ent_center + 1.0]) # [2.0, 4.0] for center=3.0 + _CNT_EDGES = np.array([5.0, 50.0]) # low=<5, mid=5-50, high=>50 context count + _TOTAL_CELLS = _NUM_ENT_BINS * _NUM_CNT_BINS # 9 cells per order = 54 total + _cc = getattr(args, 'cubric_cadence', 0); _con = _cc > 0; _cfired = 0 + if _con: + # Warm-start: proven converged values from 4+ runs (orders 2-7) + # All 9 cells per order get the same warm-start, 3D cubric refines from there + _WARM = {2: 0.45, 3: 0.30, 4: 0.45, 5: 1.88, 6: 2.00, 7: 2.00, 8: 2.00, 9: 2.00} + _c_alpha_mult = {n: [_WARM.get(n, 1.0)] * _TOTAL_CELLS for n in range(min_order, max_order + 1)} + _c_hits = {n: [0] * _TOTAL_CELLS for n in range(min_order, max_order + 1)} + _c_beats = {n: [0] * _TOTAL_CELLS for n in range(min_order, max_order + 1)} + + # Phrase cache (PR #880 / PR #900): variable-length suffix matching, score-first + # 48 distinct primes — one per context position up to max probe length + _PHRASE_PRIMES = np.array([ + np.uint64(36313), np.uint64(27191), np.uint64(51647), np.uint64(81929), + np.uint64(131071), np.uint64(174763), np.uint64(233017), np.uint64(295759), + np.uint64(393241), np.uint64(524287), np.uint64(655373), np.uint64(786433), + np.uint64(917503), np.uint64(1048583), np.uint64(1179649), np.uint64(1310723), + np.uint64(1441793), np.uint64(1572869), np.uint64(1703939), np.uint64(1835009), + np.uint64(1966081), np.uint64(2097169), np.uint64(2228231), np.uint64(2359297), + np.uint64(2490373), np.uint64(2621447), np.uint64(2752519), np.uint64(2883593), + np.uint64(3014657), np.uint64(3145739), np.uint64(3276803), np.uint64(3407873), + np.uint64(3538951), np.uint64(3670021), np.uint64(3801089), np.uint64(3932161), + np.uint64(4063241), np.uint64(4194319), np.uint64(4325399), np.uint64(4456481), + np.uint64(4587569), np.uint64(4718609), np.uint64(4849681), np.uint64(4980751), + np.uint64(5111809), np.uint64(5242883), np.uint64(5373961), np.uint64(5505047), + ], dtype=np.uint64) + _use_phrase = getattr(args, 'phrase_cache_enabled', False) + _phrase_probes = ( + [int(x) for x in args.phrase_probe_lengths_str.split(",") if x.strip()] + if _use_phrase and getattr(args, 'phrase_probe_lengths_str', '') else [] + ) + _pb = int(getattr(args, 'phrase_buckets', 4_194_304)) + _pm = np.uint64(_pb - 1) + _pmc = int(getattr(args, 'phrase_min_count', 1)) + _ph_ctx = [np.zeros(_pb, dtype=np.uint32) for _ in _phrase_probes] + _ph_full = [np.zeros(_pb, dtype=np.uint32) for _ in _phrase_probes] + _regime = RegimeTracker() if getattr(args, 'regime_tracker_enabled', False) else None + if _use_phrase and rank == 0: + print(f"phrase_cache:probes={_phrase_probes} buckets={_pb} " + f"conc={getattr(args, 'phrase_concentration', 2.0)} " + f"regime={_regime is not None}", flush=True) + + base_model.eval() + _use_learned_alpha = (hasattr(base_model, 'alpha_head') and base_model.alpha_head is not None) + if _use_learned_alpha: + _compiled_la = maybe_torch_compile(base_model.forward_logits_and_alpha, args) + compiled_logits = maybe_torch_compile(base_model.forward_logits, args) + t0 = time.perf_counter() + deadline = (t0 + max_seconds) if max_seconds > 0.0 else None + cutoff_hit = False + + if rank == 0: + print(f"ngram_eval:chunks={num_chunks} chunk_tokens={chunk_tokens} " + f"windows={len(all_window_starts)} shared_tables=True", flush=True) + + with torch.inference_mode(): + for ci in range(num_chunks): + if deadline is not None and time.perf_counter() >= deadline: + cutoff_hit = True + break + + windows = chunk_windows[ci] + if not windows: + continue + + # Distribute this chunk's windows across ranks + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + + # --- Phase 1: SCORE this chunk's windows --- + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + if _use_learned_alpha: + logits, alpha_raw_batch = _compiled_la(x_batch) + else: + logits = compiled_logits(x_batch) + alpha_raw_batch = None + logits_f = logits.float() + nll = F.cross_entropy( + logits_f.reshape(-1, logits_f.size(-1)), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + seg_len = wlen - s + if seg_len <= 0: + continue + + seg_nll = nll[i, s:wlen].to(torch.float64).cpu().numpy() + seg_model_p = np.exp(-seg_nll) + + if not _use_learned_alpha and adaptive: + log_probs = F.log_softmax(logits_f[i, s:wlen], dim=-1) + probs_a = log_probs.exp() + entropy = -(probs_a * log_probs).sum(dim=-1).cpu().numpy() + sig = 1.0 / (1.0 + np.exp(-ent_scale * (entropy - ent_center))) + per_token_alpha = alpha_min + (alpha_max - alpha_min) * sig + # Bin entropy for 2D cubric: 0=low, 1=mid, 2=high + _ent_bins = np.digitize(entropy, _ENT_EDGES).astype(np.int32) + elif not _use_learned_alpha: + per_token_alpha = np.full(seg_len, alpha) + _ent_bins = np.ones(seg_len, dtype=np.int32) # all mid + + global_j = np.arange(ws + s + 1, ws + wlen + 1, dtype=np.int64) + tgt_np = val_np[global_j].astype(np.uint64) + + if _use_learned_alpha: + # Learned mixer: get per-order probs and blend with learned weights + n_orders = max_order - min_order + 1 + order_p = np.full((seg_len, n_orders), 1.0 / 1024.0, dtype=np.float64) + order_valid = np.zeros((seg_len, n_orders), dtype=np.bool_) + for oi, n in enumerate(range(min_order, max_order + 1)): + ctx_width = n - 1 + valid = global_j >= ctx_width + if not valid.any(): + continue + v_idx = np.nonzero(valid)[0] + jv = global_j[v_idx] + ctx_hash = np.zeros(len(jv), dtype=np.uint64) + for k in range(ctx_width): + tok = val_np[jv - (ctx_width - k)].astype(np.uint64) + ctx_hash ^= tok * primes[k % len(primes)] + ctx_key = (ctx_hash & mask).astype(np.int64) + full_key = ((ctx_hash ^ (tgt_np[v_idx] * primes[ctx_width % len(primes)])) & mask).astype(np.int64) + ctx_c = ctx_tables[n][ctx_key].astype(np.float64) + full_c = full_tables[n][full_key].astype(np.float64) + has_data = ctx_c >= float(min_count) + if has_data.any(): + p = np.minimum(full_c[has_data], ctx_c[has_data]) / np.maximum(ctx_c[has_data], 1.0) + hit_idx = v_idx[has_data] + order_p[hit_idx, oi] = np.clip(p, 0.0, 1.0) + order_valid[hit_idx, oi] = True + # Build expert_p: [neural_p, order2_p, ..., orderN_p] + expert_p = np.concatenate([seg_model_p[:, None], order_p], axis=1) # (seg_len, 1+n_orders) + # Get learned alpha weights for this segment + seg_alpha = alpha_raw_batch[i, s:wlen].float().cpu().numpy() # (seg_len, n_experts) + # Masked softmax + full_mask = np.concatenate([ + np.ones((seg_len, 1), dtype=np.bool_), + order_valid, + ], axis=1) + seg_alpha_masked = np.where(full_mask, seg_alpha, -1e9) + # Softmax + seg_alpha_masked -= seg_alpha_masked.max(axis=1, keepdims=True) + exp_a = np.exp(seg_alpha_masked) + weights = exp_a / exp_a.sum(axis=1, keepdims=True) + # Neural floor + nf = getattr(base_model, 'mixer_neural_floor', 0.05) + weights[:, 0] = nf + (1.0 - nf) * weights[:, 0] + weights[:, 1:] = (1.0 - nf) * weights[:, 1:] + # Renormalize + weights /= weights.sum(axis=1, keepdims=True) + # Blend + seg_model_p = np.clip((weights * expert_p).sum(axis=1), 1e-12, 1.0) + else: + # Backoff: highest matching order wins + p_ng = np.zeros(seg_len, dtype=np.float64) + ng_matched = np.zeros(seg_len, dtype=np.bool_) + _ng_ord = np.zeros(seg_len, dtype=np.int32) + _ng_ctx_count = np.zeros(seg_len, dtype=np.float64) + for n in range(max_order, min_order - 1, -1): + ctx_width = n - 1 + valid = (global_j >= ctx_width) & (~ng_matched) + if not valid.any(): + continue + v_idx = np.nonzero(valid)[0] + jv = global_j[v_idx] + ctx_hash = np.zeros(len(jv), dtype=np.uint64) + for k in range(ctx_width): + tok = val_np[jv - (ctx_width - k)].astype(np.uint64) + ctx_hash ^= tok * primes[k % len(primes)] + ctx_key = (ctx_hash & mask).astype(np.int64) + full_key = ((ctx_hash ^ (tgt_np[v_idx] * primes[ctx_width % len(primes)])) & mask).astype(np.int64) + ctx_counts = ctx_tables[n][ctx_key].astype(np.float64) + full_counts = full_tables[n][full_key].astype(np.float64) + has_data = ctx_counts >= float(min_count) + if has_data.any(): + p = np.minimum(full_counts, ctx_counts) / np.maximum(ctx_counts, 1.0) + p = np.clip(p, 0.0, 1.0) + hit_idx = v_idx[has_data] + p_ng[hit_idx] = p[has_data] + ng_matched[hit_idx] = True + _ng_ord[hit_idx] = n + _ng_ctx_count[hit_idx] = ctx_counts[has_data] + + # Mix where n-gram matched + if ng_matched.any(): + m_idx = np.nonzero(ng_matched)[0] + if getattr(args, 'ngram_dirichlet', False): + # Purple-1 (PR #900): Dirichlet-Multinomial smoothing. + # p = (ng_count + c * neural_p) / (ctx_count + c) + c = getattr(args, 'ngram_dirichlet_conc', 5.0) + seg_model_p[m_idx] = ( + p_ng[m_idx] * _ng_ctx_count[m_idx] + c * seg_model_p[m_idx] + ) / (_ng_ctx_count[m_idx] + c) + else: + # Existing path: entropy-adaptive alpha + cubric / order multipliers + if adaptive and args.ngram_entropy_shift: + matched_ords = _ng_ord[m_idx].astype(np.float64) + shifted_centers = ent_center - 0.25 * (matched_ords - float(min_order)) + shifted_sig = 1.0 / (1.0 + np.exp(-ent_scale * (entropy[m_idx] - shifted_centers))) + per_token_alpha[m_idx] = alpha_min + (alpha_max - alpha_min) * shifted_sig + if _fixed_order_mults is not None: + a = per_token_alpha[m_idx].copy() + mult_indices = _ng_ord[m_idx] - min_order + mult_indices = np.clip(mult_indices, 0, len(_fixed_order_mults) - 1) + a *= _fixed_order_mults[mult_indices] + np.clip(a, 0.0, 0.95, out=a) + elif _con: + a = per_token_alpha[m_idx].copy() + m_ent_bins = _ent_bins[m_idx] + m_cnt_bins = np.digitize(_ng_ctx_count[m_idx], _CNT_EDGES).astype(np.int32) + for n in range(min_order, max_order + 1): + om = _ng_ord[m_idx] == n + if not om.any(): + continue + for eb in range(_NUM_ENT_BINS): + for cb in range(_NUM_CNT_BINS): + cell = eb * _NUM_CNT_BINS + cb + mask_ecb = om & (m_ent_bins == eb) & (m_cnt_bins == cb) + if mask_ecb.any(): + _c_hits[n][cell] += int(mask_ecb.sum()) + _c_beats[n][cell] += int((p_ng[m_idx[mask_ecb]] > seg_model_p[m_idx[mask_ecb]]).sum()) + a[mask_ecb] *= _c_alpha_mult[n][cell] + np.clip(a, 0.0, 0.95, out=a) + else: + a = per_token_alpha[m_idx] + seg_model_p[m_idx] = (1.0 - a) * seg_model_p[m_idx] + a * p_ng[m_idx] + + # Phrase cache: variable-length suffix lookup + Dirichlet blend (PR #880/900) + # Applied after n-gram mixing, still within score-first protocol. + if _use_phrase and _phrase_probes: + base_pc = getattr(args, 'phrase_concentration', 2.0) + eff_c = (_regime.effective_concentration(base_pc) + if _regime is not None else base_pc) + _regime_matches = 0 + for pi, pl in enumerate(_phrase_probes): + eligible = global_j >= pl + if not eligible.any(): + continue + ei = np.where(eligible)[0] + gj = global_j[ei] + tgt_u = val_np[gj].astype(np.uint64) + ph = np.zeros(len(gj), dtype=np.uint64) + for k in range(pl): + ph ^= val_np[gj - pl + k].astype(np.uint64) * _PHRASE_PRIMES[k % len(_PHRASE_PRIMES)] + ck = (ph & _pm).astype(np.int64) + fk = ((ph ^ (tgt_u * _PHRASE_PRIMES[pl % len(_PHRASE_PRIMES)])) & _pm).astype(np.int64) + cc = _ph_ctx[pi][ck].astype(np.float64) + fc = _ph_full[pi][fk].astype(np.float64) + has_ctx = cc >= _pmc + if not has_ctx.any(): + continue + ui = ei[has_ctx] + # Dirichlet: p = (count + c * neural) / (ctx + c) + seg_model_p[ui] = ( + np.minimum(fc[has_ctx], cc[has_ctx]) + eff_c * seg_model_p[ui] + ) / (cc[has_ctx] + eff_c) + _regime_matches += int(has_ctx.sum()) + seg_model_p = np.clip(seg_model_p, 1e-12, 1.0) + if _regime is not None: + _regime.update(_regime_matches, seg_len, val_np[global_j]) + + seg_nll = -np.log(np.clip(seg_model_p, 1e-12, 1.0)) + loss_sum += float(seg_nll.sum()) + token_count += float(seg_len) + 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 += float(tb.sum().item()) + + # --- Phase 2: SHARED UPDATE -- all ranks update with same chunk tokens --- + chunk_start = ci * chunk_tokens + chunk_end = min((ci + 1) * chunk_tokens, total_tokens) + _ngram_bulk_update(val_np, chunk_start, chunk_end + 1, + ctx_tables, full_tables, min_order, max_order, + primes, mask) + + # Phase 2b: score-first phrase table update (same chunk range) + if _use_phrase and _phrase_probes: + for pi, pl in enumerate(_phrase_probes): + first = max(chunk_start, pl) + if first > chunk_end: + continue + positions = np.arange(first, chunk_end + 1, dtype=np.int64) + tgt_u = val_np[positions].astype(np.uint64) + ph = np.zeros(len(positions), dtype=np.uint64) + for k in range(pl): + ph ^= val_np[positions - pl + k].astype(np.uint64) * _PHRASE_PRIMES[k % len(_PHRASE_PRIMES)] + ck = (ph & _pm).astype(np.int64) + fk = ((ph ^ (tgt_u * _PHRASE_PRIMES[pl % len(_PHRASE_PRIMES)])) & _pm).astype(np.int64) + _ph_ctx[pi] += np.bincount(ck, minlength=_pb).astype(np.uint32) + _ph_full[pi] += np.bincount(fk, minlength=_pb).astype(np.uint32) + + # Cubric 2D c-step: adapt per (order × entropy_bin) + if _con: + # Collect all (order, ent_bin, cnt_bin) cells with enough data + all_rates = [] + for n in range(min_order, max_order + 1): + for cell in range(_TOTAL_CELLS): + if _c_hits[n][cell] >= 8: + all_rates.append(_c_beats[n][cell] / _c_hits[n][cell]) + if len(all_rates) >= 4: + avg_rate = sum(all_rates) / len(all_rates) + for n in range(min_order, max_order + 1): + for cell in range(_TOTAL_CELLS): + if _c_hits[n][cell] >= 8: + rate = _c_beats[n][cell] / _c_hits[n][cell] + if rate > avg_rate + 0.05: + _c_alpha_mult[n][cell] = min(_c_alpha_mult[n][cell] * 1.03, 2.0) + elif rate < avg_rate - 0.05: + _c_alpha_mult[n][cell] = max(_c_alpha_mult[n][cell] * 0.97, 0.3) + _cfired += 1 + if rank == 0 and _cfired % 8 == 0: + parts = [] + for n in range(min_order, max_order + 1): + m = _c_alpha_mult[n] + avg_m = sum(m) / len(m) + parts.append(f"o{n}:avg={avg_m:.2f}") + print(f"cubric3d:step={_cfired} {' '.join(parts)}", flush=True) + _c_hits = {n: [0] * _TOTAL_CELLS for n in range(min_order, max_order + 1)} + _c_beats = {n: [0] * _TOTAL_CELLS for n in range(min_order, max_order + 1)} + + # Progress + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1 or ci < 3): + elapsed = time.perf_counter() - t0 + cur_bpb = (loss_sum / max(token_count, 1.0)) / math.log(2.0) * (token_count / max(byte_count, 1.0)) if token_count > 0 else 0.0 + print( + f"ngram_eval:chunk [{ci+1}/{num_chunks}] bpb={cur_bpb:.6f} t={elapsed:.0f}s", + flush=True, + ) + + # All-reduce across ranks + _loss = torch.tensor(loss_sum, device=device, dtype=torch.float64) + _toks = torch.tensor(token_count, device=device, dtype=torch.float64) + _bytes = torch.tensor(byte_count, device=device, dtype=torch.float64) + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(_loss, op=dist.ReduceOp.SUM) + dist.all_reduce(_toks, op=dist.ReduceOp.SUM) + dist.all_reduce(_bytes, op=dist.ReduceOp.SUM) + loss_sum = _loss.item() + token_count = _toks.item() + byte_count = _bytes.item() + + coverage = token_count / max(total_scored_tokens, 1.0) + if cutoff_hit: + elapsed = time.perf_counter() - t0 + print( + f"ngram_eval:cutoff max_seconds={max_seconds:.1f} " + f"coverage={coverage*100:.2f}% elapsed={elapsed:.0f}s", + flush=True, + ) + + if _con and rank == 0: + print(f"cubric3d:final c_steps={_cfired} cells={_TOTAL_CELLS}x{max_order-min_order+1}={_TOTAL_CELLS*(max_order-min_order+1)}", flush=True) + for n in range(min_order, max_order + 1): + m = _c_alpha_mult[n] + row = " ".join(f"{m[cell]:.2f}" for cell in range(_TOTAL_CELLS)) + print(f" o{n}: [{row}]", flush=True) + val_loss = loss_sum / max(token_count, 1.0) + val_bpb = val_loss / math.log(2.0) * (token_count / max(byte_count, 1.0)) + base_model.train() + return val_loss, val_bpb, coverage +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if "f1_corr_in" in name or "f1_corr_out" in name: + return "aux" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" +# --------------------------------------------------------------------------- +# GPTQ: Hessian-aware quantization with column-wise error compensation +# --------------------------------------------------------------------------- +def _find_best_row_scales(W: Tensor, clip_range: int = 31) -> Tensor: + """Find optimal per-row scales by searching percentile clipping thresholds.""" + t32 = W.float() + best_s = t32.abs().amax(dim=1) / clip_range + best_s = best_s.clamp_min(1.0 / clip_range) + best_err = torch.full((t32.shape[0],), 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) + q = torch.clamp(torch.round(t32 / s[:, None]), -clip_range, clip_range) + recon = q * s[:, None] + err = (t32 - recon).pow(2).mean(dim=1) + improved = err < best_err + best_s[improved] = s[improved] + best_err[improved] = err[improved] + return best_s +def gptq_quantize_weight(W: Tensor, H: Tensor, clip_range: int = 31, + block_size: int = 64, percdamp: float = 0.002) -> tuple[Tensor, Tensor]: + """GPTQ: quantize weight matrix W using Hessian H = X^T X for error compensation. + Uses pre-computed per-row scales and column reordering by Hessian diagonal. + Returns (quantized_int8, scale_fp16) in int6 range [-clip_range, clip_range].""" + W = W.float().clone() + rows, cols = W.shape + # Pre-compute optimal per-row scales from the original weight matrix + row_scale = _find_best_row_scales(W, clip_range) + H = H.float().clone() + damp = percdamp * H.diag().mean() + H.diagonal().add_(damp) + # Column reordering: process least-important columns first (ascending H_diag) + perm = torch.argsort(H.diag()) + invperm = torch.argsort(perm) + W = W[:, perm] + H = H[perm][:, perm] + try: + L = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(L) + except torch._C._LinAlgError: + Hinv = torch.diag(1.0 / H.diag().clamp_min(1e-6)) + Q = torch.zeros(rows, cols, dtype=torch.int8) + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + W_block = W[:, i1:i2].clone() + Hinv_block = Hinv[i1:i2, i1:i2] + Err = torch.zeros_like(W_block) + for j in range(i2 - i1): + w_col = W_block[:, j] + h_inv_jj = Hinv_block[j, j].clamp_min(1e-8) + # Quantize using pre-computed per-row scales + q_col = torch.clamp(torch.round(w_col / row_scale), -clip_range, clip_range) + deq_col = q_col * row_scale + Q[:, i1 + j] = q_col.to(torch.int8) + err = (w_col - deq_col) / h_inv_jj + Err[:, j] = err + if j + 1 < i2 - i1: + W_block[:, j + 1:] -= err.unsqueeze(1) * Hinv_block[j, j + 1:].unsqueeze(0) + if i2 < cols: + W[:, i2:] -= Err @ Hinv[i1:i2, i2:] + # Undo column reordering + Q = Q[:, invperm] + return Q, 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 Hessian H = X^T X for each linear layer using training data.""" + hessians: dict[str, Tensor] = {} + n_seen: dict[str, int] = {} + hooks = [] + def make_hook(name: str): + def hook_fn(module, inp, out): + x = inp[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + if name not in hessians: + hessians[name] = torch.zeros(x.shape[1], x.shape[1], device=x.device, dtype=torch.float32) + n_seen[name] = 0 + hessians[name].addmm_(x.t(), x) + n_seen[name] += x.shape[0] + return hook_fn + for name, module in model.named_modules(): + if isinstance(module, (nn.Linear, CastedLinear)): + hooks.append(module.register_forward_hook(make_hook(name))) + stream = TokenStream(train_pattern) + model.eval() + with torch.no_grad(): + for _ in range(n_samples): + tokens = stream.take(seq_len + 1).to(device=device, dtype=torch.int64) + x = tokens[:-1].unsqueeze(0) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + model.forward_logits(x) + for h in hooks: + h.remove() + for name in hessians: + hessians[name] /= max(n_seen[name], 1) + return hessians +def gptq_calibrate_loop_aware(model: nn.Module, train_pattern: str, device: torch.device, + n_samples: int = 256, seq_len: int = 2048) -> dict[str, Tensor]: + """Two-phase loop-aware GPTQ calibration for the crawler architecture. + + The crawler's shared blocks are called crawler_loops times per forward pass. + Standard GPTQ calibration sees fp16 inter-loop activations, but after flat layers + are quantized the crawler receives drifted inputs — causing fixed-point unraveling. + + Phase 1: Standard Hessian collection for ALL layers (flat layers already correct). + Phase 2: Temporarily patch flat_blocks with their GPTQ-quantized weights, then + re-collect Hessians for crawler_blocks / delta_net / loop_inst only. + The crawler now sees the actual quantized-flat activations it will face + at inference time, so GPTQ can compensate against the real input distribution. + Merge: flat layers keep Phase 1 Hessians; crawler layers get Phase 2 Hessians. + """ + CRAWLER_PREFIXES = ("crawler_blocks.", "delta_net.", "loop_inst") + # Phase 1: standard calibration for all layers + print("gptq_loop_aware:phase1 collecting all-layer Hessians...", flush=True) + hessians_p1 = gptq_calibrate(model, train_pattern, device, n_samples, seq_len) + # Patch flat_blocks in-place with GPTQ-quantized weights so Phase 2 sees realistic activations + originals: dict[str, Tensor] = {} + patched_count = 0 + for name, module in model.named_modules(): + if not isinstance(module, (nn.Linear, CastedLinear)): + continue + if any(name.startswith(p) for p in CRAWLER_PREFIXES): + continue # leave crawler layers at fp16 — they're what we're calibrating + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + continue # skip control tensors + if name not in hessians_p1: + continue + W = module.weight.data + if W.ndim != 2 or W.numel() <= 65536: + continue + H = hessians_p1[name].to(W.device) + q, scale = gptq_quantize_weight(W.float().cpu(), H.cpu()) + originals[name] = W.clone() + module.weight.data = (q.float() * scale[:, None]).to(dtype=W.dtype, device=W.device) + patched_count += 1 + print(f"gptq_loop_aware:patched {patched_count} flat layers with GPTQ weights", flush=True) + # Phase 2: collect crawler Hessians with quantized flat activations + print("gptq_loop_aware:phase2 collecting crawler Hessians with quantized-flat activations...", flush=True) + hessians_p2: dict[str, Tensor] = {} + n_seen_p2: dict[str, int] = {} + hooks_p2 = [] + def make_hook_p2(name: str): + def hook_fn(module, inp, out): + x = inp[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + if name not in hessians_p2: + hessians_p2[name] = torch.zeros(x.shape[1], x.shape[1], device=x.device, dtype=torch.float32) + n_seen_p2[name] = 0 + hessians_p2[name].addmm_(x.t(), x) + n_seen_p2[name] += x.shape[0] + return hook_fn + for name, module in model.named_modules(): + if isinstance(module, (nn.Linear, CastedLinear)) and any(name.startswith(p) for p in CRAWLER_PREFIXES): + hooks_p2.append(module.register_forward_hook(make_hook_p2(name))) + stream = TokenStream(train_pattern) + model.eval() + with torch.no_grad(): + for _ in range(n_samples): + tokens = stream.take(seq_len + 1).to(device=device, dtype=torch.int64) + x = tokens[:-1].unsqueeze(0) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + model.forward_logits(x) + for h in hooks_p2: + h.remove() + for name in hessians_p2: + hessians_p2[name] /= max(n_seen_p2[name], 1) + print(f"gptq_loop_aware:phase2 collected {len(hessians_p2)} crawler Hessians", flush=True) + # Restore original flat layer weights + for name, module in model.named_modules(): + if name in originals: + module.weight.data = originals[name] + print(f"gptq_loop_aware:restored {len(originals)} flat layer weights", flush=True) + # Merge: crawler gets Phase 2 Hessians, flat layers keep Phase 1 + merged = {**hessians_p1} + merged.update(hessians_p2) + print(f"gptq_loop_aware:merged {len(merged)} Hessians ({len(hessians_p2)} crawler from phase2)", flush=True) + return merged +def mixed_quantize_int6_gptq(state_dict: dict[str, Tensor], int6_cats: set[str], + hessians: dict[str, Tensor], + crawler_int8: bool = False) -> tuple[dict, dict]: + """Like mixed_quantize_int6 but uses GPTQ for int6 categories when Hessian available.""" + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + gptq_count, naive_count = 0, 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 + # Crawler reservoir: shared block used K times — give it int8 range (±127) for multi-context resilience + if crawler_int8 and name.startswith("crawler_blocks.") and t.is_floating_point() and t.numel() > 65536: + q, s = quantize_float_tensor(t) # int8 ±127 — wider range for shared weights serving K loop contexts + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + continue + if cat in int6_cats and t.ndim == 2: + module_name = name.rsplit(".weight", 1)[0] if name.endswith(".weight") else name + H = hessians.get(module_name) + if H is not None and H.shape[0] == t.shape[1]: + q, s = gptq_quantize_weight(t, H.cpu()) + gptq_count += 1 + else: + q, s = quantize_int6_per_row(t) + naive_count += 1 + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + elif cat in int6_cats and t.ndim >= 1: + q, s = quantize_int6_per_row(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + naive_count += 1 + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + print(f"gptq_quantize: {gptq_count} GPTQ layers, {naive_count} naive layers", flush=True) + return result, meta +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" + continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + continue + if cat in int6_cats and t.ndim >= 1: + q, s = quantize_int6_per_row(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out +def main() -> None: + global zeropower_via_newtonschulz5 + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + 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")) + dynamo = getattr(torch, "_dynamo", None) + if args.compile_enabled and dynamo is not None: + # NTK-scaled RoPE at large seq_len produces sympy NaN in inductor bounds + # analysis on PyTorch 2.4. suppress_errors lets that subgraph fall back to + # eager (just the tiny sin/cos kernel) while everything else stays compiled. + dynamo.config.suppress_errors = True + if args.compile_enabled and distributed and dynamo is not None: + dynamo.config.optimize_ddp = args.torchdynamo_optimize_ddp + if args.compile_enabled: + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + 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) + if NITRUST_ENABLE: + if NITRUST_ACTIVE: + log0(f"nitrust:enabled backend=rust so_path={NITRUST_SO_PATH}") + else: + log0(f"nitrust:disabled_fallback reason={_NITRUST_IMPORT_ERROR}") + else: + log0("nitrust:disabled NITRUST_ENABLE=0") + 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 = build_model(args, device) + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + # Complementary training: downweight tokens predictable by bigrams + complement_alpha = float(os.environ.get("COMPLEMENT_ALPHA", "0")) + if complement_alpha > 0: + tracker = TrainNgramTracker(args.vocab_size, device, complement_alpha=complement_alpha) + base_model._ngram_tracker = tracker + log0(f"complementary_training:alpha={complement_alpha}") + else: + base_model._ngram_tracker = None + # Learned mixer: prefill training-data n-gram oracle + train_mixer: TrainNgramOracle | TrainNgramOracleGPU | None = None + if args.mixer_enabled: + mixer_max_order = args.ngram_eval_min_order + args.mixer_n_orders - 1 + use_gpu_mixer = args.mixer_gpu_mode and device.type == "cuda" + if use_gpu_mixer: + train_mixer = TrainNgramOracleGPU( + buckets=args.mixer_buckets, + min_order=args.ngram_eval_min_order, + max_order=mixer_max_order, + min_count=args.ngram_eval_min_count, + device=device, + pos_chunk=args.mixer_prefill_pos_chunk, + ) + else: + train_mixer = TrainNgramOracle( + buckets=args.mixer_buckets, + min_order=args.ngram_eval_min_order, + max_order=mixer_max_order, + min_count=args.ngram_eval_min_count, + ) + train_files = sorted(glob.glob(args.train_files))[:args.mixer_prefill_max_shards] + prefill_cap_s = max(0.0, args.mixer_prefill_max_seconds) + prefill_min_shards = max(1, args.mixer_prefill_min_shards) + tokens_per_shard = max(0, args.mixer_prefill_tokens_per_shard) + if distributed and use_gpu_mixer: + prefill_mode = "sharded+allreduce-gpu" + elif distributed: + prefill_mode = "rank0+broadcast" + else: + prefill_mode = "single-rank" + log0( + "mixer:prefill " + f"mode={prefill_mode} shards<= {len(train_files)} tokens_per_shard={tokens_per_shard or 'full'} " + f"orders={args.ngram_eval_min_order}..{mixer_max_order} buckets={args.mixer_buckets} " + f"max_seconds={prefill_cap_s if prefill_cap_s > 0 else 'unlimited'}" + ) + + if distributed and use_gpu_mixer: + my_train_files = train_files[rank::world_size] + elif distributed: + my_train_files = train_files if rank == 0 else [] + else: + my_train_files = train_files + + local_prefilled_shards = 0 + local_prefill_s = 0.0 + t_prefill = time.perf_counter() + for fi, f in enumerate(my_train_files): + train_mixer.prefill_shard(f, max_tokens=tokens_per_shard) + local_prefilled_shards += 1 + if (fi + 1) % 5 == 0 or fi == 0 or fi + 1 == len(my_train_files): + elapsed = time.perf_counter() - t_prefill + toks_per_s = train_mixer.total_tokens / max(elapsed, 1e-9) + if rank == 0: + print( + f" mixer:prefill rank={rank} {fi+1}/{len(my_train_files)} shards, " + f"{train_mixer.total_tokens:,} tokens, {toks_per_s/1e6:.2f}M tok/s", + flush=True, + ) + if prefill_cap_s > 0.0 and local_prefilled_shards >= prefill_min_shards: + elapsed = time.perf_counter() - t_prefill + if elapsed >= prefill_cap_s: + if rank == 0: + print( + f" mixer:prefill cutoff rank={rank} at {local_prefilled_shards} shards " + f"after {elapsed:.1f}s (cap={prefill_cap_s:.1f}s)", + flush=True, + ) + break + local_prefill_s = time.perf_counter() - t_prefill + + if distributed: + if device.type == "cuda": + torch.cuda.synchronize(device) + t_sync = time.perf_counter() + if use_gpu_mixer: + all_reduce_train_mixer_tables_gpu(train_mixer, device) + else: + broadcast_train_mixer_tables(train_mixer, rank, device) + if device.type == "cuda": + torch.cuda.synchronize(device) + sync_s = time.perf_counter() - t_sync + + shards_t = torch.tensor([local_prefilled_shards], device=device, dtype=torch.int64) + prefill_s_t = torch.tensor([local_prefill_s], device=device, dtype=torch.float64) + if use_gpu_mixer: + dist.all_reduce(shards_t, op=dist.ReduceOp.SUM) + dist.all_reduce(prefill_s_t, op=dist.ReduceOp.MAX) + else: + dist.broadcast(shards_t, src=0) + dist.broadcast(prefill_s_t, src=0) + total_prefilled_shards = int(shards_t.item()) + prefill_s = float(prefill_s_t.item()) + log0( + f"mixer:prefilled {train_mixer.total_tokens:,} tokens from {total_prefilled_shards} shards " + f"in {prefill_s:.1f}s, sync:{sync_s:.1f}s mode={prefill_mode}" + ) + else: + prefill_s = local_prefill_s + log0( + f"mixer:prefilled {train_mixer.total_tokens:,} tokens from {local_prefilled_shards} shards " + f"in {prefill_s:.1f}s mode={prefill_mode}" + ) + compiled_model = maybe_torch_compile(base_model, args) + model: nn.Module = ( + DDP( + compiled_model, + device_ids=[local_rank], + broadcast_buffers=False, + find_unused_parameters=args.ddp_find_unused_parameters, + ) + if distributed + else compiled_model + ) + block_named_params = _get_block_named_params(base_model) + matrix_params = [ + p + for name, p in block_named_params + if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.mtp_num_heads > 0: + matrix_params.extend([p for p in base_model.mtp_heads.parameters() if p.ndim == 2]) + if base_model.f1_corr_in is not None and base_model.f1_corr_out is not None: + matrix_params.append(base_model.f1_corr_in.weight) + matrix_params.append(base_model.f1_corr_out.weight) + scalar_params = [ + p + for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + if base_model.f1_corr_scale is not None: + scalar_params.append(base_model.f1_corr_scale) + if base_model.alpha_head is not None: + scalar_params.extend(list(base_model.alpha_head.parameters())) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + matrix_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.ve_shared.proj is not None: + matrix_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: + scalar_params.append(s) + optimizer_tok = torch.optim.AdamW( + tok_params, + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + weight_decay=args.muon_wd, + ) + for group in optimizer_muon.param_groups: + group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW( + [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers.insert(1, optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + f1_corr_params = 0 + if base_model.f1_corr_in is not None and base_model.f1_corr_out is not None: + f1_corr_params = int(base_model.f1_corr_in.weight.numel() + base_model.f1_corr_out.weight.numel()) + est_corr_int6_bytes = 0 + if args.f1_corr_rank > 0: + # int8 payload stores int6 values + per-row fp16 scales. + est_corr_int6_bytes = ( + args.f1_corr_rank * (args.model_dim + args.vocab_size) + + 2 * (args.f1_corr_rank + args.vocab_size) + ) + log0(f"model_params:{n_params}") + log0( + f"f1_corr:rank={args.f1_corr_rank} params={f1_corr_params} " + f"est_int6_bytes~{est_corr_int6_bytes}" + ) + log0(f"mlp_act:{args.mlp_act} mlp_leaky_slope:{args.mlp_leaky_slope}") + log0(f"XSA:last_{args.xsa_last_n} world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads} embed_lr:{token_lr} matrix_lr:{args.matrix_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}" + ) + optimize_ddp_flag = "na" + if dynamo is not None: + optimize_ddp_flag = str(int(bool(getattr(dynamo.config, "optimize_ddp", False)))) + log0( + f"compile:enabled={int(args.compile_enabled)} fullgraph={int(args.compile_fullgraph)} " + f"optimize_ddp={optimize_ddp_flag}" + ) + log0(f"ddp:find_unused_parameters={int(args.ddp_find_unused_parameters)}") + log0(f"seed:{args.seed}") + if args.ngram_eval_order >= 2: + log0( + f"ngram_eval:order={args.ngram_eval_order} alpha={args.ngram_eval_alpha} " + f"min_count={args.ngram_eval_min_count} buckets={args.ngram_eval_buckets}" + ) + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + _mx_p, _mx_v = None, None + if train_mixer is not None: + _mx_p_raw, _mx_v_raw = train_mixer.get_ngram_probs(x, y) + _mx_p = _mx_p_raw.to(device=device, dtype=torch.bfloat16, non_blocking=True) + _mx_v = _mx_v_raw.to(device=device, non_blocking=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y, ngram_expert_p=_mx_p, ngram_valid_mask=_mx_v) + (warmup_loss * grad_scale).backward() + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + if distributed: + model.require_backward_grad_sync = True + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_decay = float(os.environ.get("EMA_DECAY", "0.997")) + ema_start_step = int(os.environ.get("EMA_START_STEP", "0")) + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + ) + log0( + f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0( + f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms " + f"step:{step}/{args.iterations}" + ) + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + # Mixer: get n-gram probs from training oracle (CPU or GPU path). + _mx_p, _mx_v = None, None + if train_mixer is not None: + _mx_p_raw, _mx_v_raw = train_mixer.get_ngram_probs(x, y) + _mx_p = _mx_p_raw.to(device=device, dtype=torch.bfloat16, non_blocking=True) + _mx_v = _mx_v_raw.to(device=device, non_blocking=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y, ngram_expert_p=_mx_p, ngram_valid_mask=_mx_v) + train_loss += loss.detach() + loss.backward() + if base_model._ngram_tracker is not None: + base_model._ngram_tracker.update(x, y) + train_loss /= grad_accum_steps + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + for group in optimizer_muon.param_groups: + group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + for opt in optimizers: + opt.step() + zero_grad_all() + # EMA update (late-start: re-initialize at ema_start_step, skip before it) + if step == ema_start_step and ema_start_step > 0: + with torch.no_grad(): + for name, t in base_model.state_dict().items(): + ema_state[name].copy_(t.detach().float()) + log0(f"ema:late-start re-initialized at step {step} decay={ema_decay}") + elif step > ema_start_step or ema_start_step == 0: + with torch.no_grad(): + for name, t in base_model.state_dict().items(): + ema_state[name].mul_(ema_decay).add_(t.detach().float(), alpha=1.0 - ema_decay) + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu() + swa_count += 1 + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + # GPTQ calibration: collect Hessians from training data DURING training phase + # (must happen before training ends to comply with eval-time data access rules) + skip_gptq = int(os.environ.get("SKIP_GPTQ", "0")) + if skip_gptq: + log0("gptq:SKIPPED (SKIP_GPTQ=1) — will use naive int6") + gptq_hessians = {} + elif int(os.environ.get("LOOP_AWARE_GPTQ", "0")): + log0("gptq:loop-aware 2-phase calibration...") + t_gptq = time.perf_counter() + gptq_hessians = gptq_calibrate_loop_aware(base_model, args.train_files, device, n_samples=256, seq_len=args.train_seq_len) + log0(f"gptq:loop-aware calibrated {len(gptq_hessians)} layers in {time.perf_counter()-t_gptq:.1f}s") + else: + log0("gptq:calibrating with training data...") + t_gptq = time.perf_counter() + gptq_hessians = gptq_calibrate(base_model, args.train_files, device, n_samples=256, seq_len=args.train_seq_len) + log0(f"gptq:calibrated {len(gptq_hessians)} layers in {time.perf_counter()-t_gptq:.1f}s") + if args.distill_enabled and args.distill_steps > 0: + log0( + f"distill:start steps:{args.distill_steps} lr_factor:{args.distill_lr_factor} " + f"temp:{args.distill_temperature} alpha:{args.distill_alpha} kl_clip:{args.distill_kl_clip}" + ) + current_state = base_model.state_dict() + teacher_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + teacher_model = build_model(args, device) + for m in teacher_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(teacher_model) + teacher_model.load_state_dict(teacher_state, strict=True) + teacher_model.eval() + for p in teacher_model.parameters(): + p.requires_grad_(False) + compiled_teacher_logits = maybe_torch_compile(teacher_model.forward_logits, args) + model.train() + T = args.distill_temperature + alpha = args.distill_alpha + for d_step in range(args.distill_steps): + zero_grad_all() + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * args.distill_lr_factor + 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): + student_logits = base_model.forward_logits(x) + with torch.no_grad(): + teacher_logits = compiled_teacher_logits(x) + student_log_probs = F.log_softmax(student_logits.float() / T, dim=-1) + teacher_probs = F.softmax(teacher_logits.float() / T, dim=-1) + token_kl = F.kl_div(student_log_probs, teacher_probs, reduction="none").sum(dim=-1) + kl_loss = token_kl.mean() * (T * T) + if args.distill_kl_clip > 0: + kl_loss = torch.clamp(kl_loss, max=args.distill_kl_clip) + ce_loss = F.cross_entropy( + student_logits.reshape(-1, student_logits.size(-1)).float(), + y.reshape(-1), + reduction="mean", + ) + loss = alpha * kl_loss + (1.0 - alpha) * ce_loss + (loss * grad_scale).backward() + if world_size > 1: + for p in base_model.parameters(): + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + for opt in optimizers: + opt.step() + zero_grad_all() + 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) + if (d_step + 1) % 8 == 0 or d_step == 0: + log0( + f"distill:step:{d_step + 1}/{args.distill_steps} " + f"kl:{kl_loss.item():.4f} ce:{ce_loss.item():.4f} total:{loss.item():.4f}" + ) + del teacher_model, compiled_teacher_logits + torch.cuda.empty_cache() + log0("distill:done") + # Apply EMA weights (better than SWA alone per PR#401) + skip_ema = int(os.environ.get("SKIP_EMA", "0")) + if skip_ema: + log0("ema:SKIPPED (SKIP_EMA=1) — using live model weights") + 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") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + # GPTQ quantization using Hessians collected during training phase (no training data access here) + if skip_gptq: + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "aux"}) + else: + quant_result, quant_meta = mixed_quantize_int6_gptq( + sd_cpu, {"mlp", "attn", "aux"}, gptq_hessians, + crawler_int8=args.crawler_quant_int8, + ) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) if _COMPRESSOR == "zstd" else zlib.compress(quant_raw, 9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") + log0(f"Total submission size int6+{_COMPRESSOR}: {quant_file_bytes + code_bytes} bytes") + log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() + with open("final_model.int6.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_state = torch.load( + io.BytesIO(zstandard.ZstdDecompressor().decompress(quant_blob_disk) if _COMPRESSOR == "zstd" else zlib.decompress(quant_blob_disk)), + map_location="cpu", + ) + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + eval_model = build_model(args, device) + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = maybe_torch_compile(eval_model, args) + torch.cuda.synchronize() + t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val( + args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms" + ) + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + sw_seq_len = effective_eval_seq_len + if args.eval_stride > 0 and args.eval_stride < sw_seq_len: + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, + eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"stride:{args.eval_stride} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + ) + log0(f"final_int6_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + if args.ngram_eval_order >= 2: + if distributed: + dist.barrier() + # Purple-1 (PR #931): build training oracle on rank 0 and seed eval tables + _oracle_state: dict | None = None + if master_process and getattr(args, 'artifact_ngram', False): + log0("oracle:building_training_ngram_tables ...") + _t_oracle = time.perf_counter() + _oracle_state = _build_training_ngram_oracle( + data_path=args.data_path, + min_order=max(args.ngram_eval_min_order, 2), + max_order=args.ngram_eval_order, + buckets=args.ngram_eval_buckets, + max_shards=getattr(args, 'artifact_ngram_max_shards', 2), + ) + log0(f"oracle:done elapsed={time.perf_counter()-_t_oracle:.1f}s " + f"total_tokens={_oracle_state['total_tokens']}") + torch.cuda.synchronize() + t_ng = time.perf_counter() + ng_loss, ng_bpb, ng_coverage = eval_val_sliding_hashed_ngram( + args, + eval_model, + rank, + world_size, + device, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + stride=args.eval_stride, + order=args.ngram_eval_order, + alpha=args.ngram_eval_alpha, + min_count=args.ngram_eval_min_count, + buckets=args.ngram_eval_buckets, + max_seconds=args.ngram_eval_max_seconds, + eval_seq_len=sw_seq_len, + oracle_state=_oracle_state, + ) + if rank == 0: + torch.cuda.synchronize() + ng_eval_ms = 1000.0 * (time.perf_counter() - t_ng) + if ng_coverage >= 0.999999: + log0( + f"final_int6_sliding_window_ngram{args.ngram_eval_order} val_loss:{ng_loss:.4f} " + f"val_bpb:{ng_bpb:.4f} eval_time:{ng_eval_ms:.0f}ms" + ) + log0( + f"final_int6_sliding_window_ngram{args.ngram_eval_order}_exact " + f"val_loss:{ng_loss:.8f} val_bpb:{ng_bpb:.8f}" + ) + else: + log0( + f"final_int6_sliding_window_ngram{args.ngram_eval_order}_partial val_loss:{ng_loss:.4f} " + f"val_bpb:{ng_bpb:.4f} coverage:{ng_coverage:.4f} eval_time:{ng_eval_ms:.0f}ms" + ) + log0( + f"final_int6_sliding_window_ngram{args.ngram_eval_order}_partial_exact " + f"val_loss:{ng_loss:.8f} val_bpb:{ng_bpb:.8f} coverage:{ng_coverage:.8f}" + ) + if distributed: + dist.barrier() + if distributed: + dist.destroy_process_group() +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed1337.log b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed1337.log new file mode 100644 index 0000000000..bce870a89c --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed1337.log @@ -0,0 +1,91 @@ +============================================ + MEDUSA_IV — late-start EMA (step 4400) + loop-aware GPTQ + Seed: 1337 + inst_dim=32 FLOW | 4 flat + 1 crawler x 4 loops + DELTA_NET_HEADS=4 | chunk_delta_rule | short_conv=True + EMA_START_STEP=4400 | EMA_DECAY=0.99 | LOOP_AWARE_GPTQ=1 + NITRUST_ENABLE=0 | NITRUST_STRICT=0 +============================================ +W0328 07:37:48.036000 68282 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. +logs/0930bc22-2414-495e-bbee-b7d5fd442493.txt +nitrust:disabled NITRUST_ENABLE=0 +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:14487088 +f1_corr:rank=0 params=0 est_int6_bytes~0 +mlp_act:relu_sq mlp_leaky_slope:0.5 +XSA:last_11 world_size:8 grad_accum_steps:1 +num_heads:8 num_kv_heads:4 embed_lr:0.035 matrix_lr:0.03 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +compile:enabled=1 fullgraph=0 optimize_ddp=0 +ddp:find_unused_parameters=1 +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:6.9286 val_bpb:4.1035 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9301 train_time:167ms step_avg:167.04ms +step:2/20000 train_loss:8.9231 train_time:282ms step_avg:140.80ms +step:3/20000 train_loss:7.8344 train_time:396ms step_avg:132.08ms +step:4/20000 train_loss:7.4040 train_time:511ms step_avg:127.68ms +step:5/20000 train_loss:7.1165 train_time:626ms step_avg:125.21ms +step:6/20000 train_loss:6.9364 train_time:740ms step_avg:123.40ms +step:7/20000 train_loss:6.8285 train_time:856ms step_avg:122.25ms +step:8/20000 train_loss:6.6765 train_time:973ms step_avg:121.65ms +step:9/20000 train_loss:6.3910 train_time:1094ms step_avg:121.52ms +step:10/20000 train_loss:6.0225 train_time:1215ms step_avg:121.53ms +step:500/20000 train_loss:2.4824 train_time:61433ms step_avg:122.87ms +step:1000/20000 train_loss:2.3605 train_time:123018ms step_avg:123.02ms +step:1500/20000 train_loss:2.3094 train_time:184390ms step_avg:122.93ms +step:2000/20000 train_loss:2.1511 train_time:245942ms step_avg:122.97ms +step:2500/20000 train_loss:2.2463 train_time:307366ms step_avg:122.95ms +step:3000/20000 train_loss:2.2337 train_time:368706ms step_avg:122.90ms +step:3500/20000 train_loss:2.1810 train_time:430214ms step_avg:122.92ms +step:4000/20000 train_loss:1.7848 train_time:491543ms step_avg:122.89ms +step:4000/20000 val_loss:1.8664 val_bpb:1.1054 train_time:491548ms step_avg:122.89ms +ema:late-start re-initialized at step 4400 decay=0.99 +swa:start step:4500 +step:4500/20000 train_loss:1.4892 train_time:553038ms step_avg:122.90ms +step:4876/20000 val_loss:1.1801 val_bpb:0.6989 train_time:600067ms step_avg:123.07ms +stopping_early: wallclock_cap train_time:600067ms step:4876/20000 +peak memory allocated: 21023 MiB reserved: 21196 MiB +gptq:loop-aware 2-phase calibration... +gptq_loop_aware:patched 24 flat layers with GPTQ weights +gptq_loop_aware:phase2 collected 16 crawler Hessians +gptq_loop_aware:restored 24 flat layer weights +gptq_loop_aware:merged 41 Hessians (16 crawler from phase2) +gptq:loop-aware calibrated 41 layers in 11.6s +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.2031 val_bpb:0.7126 eval_time:3008ms +Serialized model: 53860358 bytes +Code size: 180226 bytes +gptq_quantize: 24 GPTQ layers, 0 naive layers +Serialized model int6+zstd: 9776503 bytes +Total submission size int6+zstd: 9956729 bytes +Total submission size int8+zlib: 9956729 bytes +final_int6_roundtrip val_loss:1.8844 val_bpb:1.1160 eval_time:7096ms +final_int6_roundtrip_exact val_loss:1.88439024 val_bpb:1.11604204 +final_int6_sliding_window val_loss:2.0716 val_bpb:1.2269 stride:64 eval_time:108201ms +final_int6_sliding_window_exact val_loss:2.07161896 val_bpb:1.22693269 +final_int8_zlib_roundtrip_exact val_loss:2.07161896 val_bpb:1.22693269 +============================================ + DONE +============================================ diff --git a/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed300.log b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed300.log new file mode 100644 index 0000000000..631ef6280a --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed300.log @@ -0,0 +1,91 @@ +============================================ + MEDUSA_IV — late-start EMA (step 4400) + loop-aware GPTQ + Seed: 300 + inst_dim=32 FLOW | 4 flat + 1 crawler x 4 loops + DELTA_NET_HEADS=4 | chunk_delta_rule | short_conv=True + EMA_START_STEP=4400 | EMA_DECAY=0.99 | LOOP_AWARE_GPTQ=1 + NITRUST_ENABLE=0 | NITRUST_STRICT=0 +============================================ +W0328 06:16:55.866000 71272 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. +logs/1aa72197-9ad4-44e0-b9d1-50f09ac20037.txt +nitrust:disabled NITRUST_ENABLE=0 +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:14487088 +f1_corr:rank=0 params=0 est_int6_bytes~0 +mlp_act:relu_sq mlp_leaky_slope:0.5 +XSA:last_11 world_size:8 grad_accum_steps:1 +num_heads:8 num_kv_heads:4 embed_lr:0.035 matrix_lr:0.03 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +compile:enabled=1 fullgraph=0 optimize_ddp=0 +ddp:find_unused_parameters=1 +seed:300 +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:6.9311 val_bpb:4.1050 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9321 train_time:162ms step_avg:162.36ms +step:2/20000 train_loss:8.9365 train_time:295ms step_avg:147.36ms +step:3/20000 train_loss:7.7667 train_time:412ms step_avg:137.40ms +step:4/20000 train_loss:7.3894 train_time:530ms step_avg:132.40ms +step:5/20000 train_loss:7.1771 train_time:647ms step_avg:129.42ms +step:6/20000 train_loss:6.9982 train_time:761ms step_avg:126.92ms +step:7/20000 train_loss:6.8735 train_time:875ms step_avg:125.06ms +step:8/20000 train_loss:6.7182 train_time:990ms step_avg:123.78ms +step:9/20000 train_loss:6.3903 train_time:1114ms step_avg:123.82ms +step:10/20000 train_loss:6.0368 train_time:1233ms step_avg:123.30ms +step:500/20000 train_loss:2.4940 train_time:61458ms step_avg:122.92ms +step:1000/20000 train_loss:2.3689 train_time:122867ms step_avg:122.87ms +step:1500/20000 train_loss:2.3114 train_time:184433ms step_avg:122.96ms +step:2000/20000 train_loss:2.1497 train_time:245783ms step_avg:122.89ms +step:2500/20000 train_loss:2.2431 train_time:307202ms step_avg:122.88ms +step:3000/20000 train_loss:2.2334 train_time:368570ms step_avg:122.86ms +step:3500/20000 train_loss:2.1366 train_time:429946ms step_avg:122.84ms +step:4000/20000 train_loss:1.4677 train_time:491197ms step_avg:122.80ms +step:4000/20000 val_loss:1.5231 val_bpb:0.9021 train_time:491198ms step_avg:122.80ms +ema:late-start re-initialized at step 4400 decay=0.99 +swa:start step:4500 +step:4500/20000 train_loss:0.9201 train_time:552752ms step_avg:122.83ms +step:4880/20000 val_loss:0.6308 val_bpb:0.3736 train_time:600058ms step_avg:122.96ms +stopping_early: wallclock_cap train_time:600058ms step:4880/20000 +peak memory allocated: 21024 MiB reserved: 21190 MiB +gptq:loop-aware 2-phase calibration... +gptq_loop_aware:patched 24 flat layers with GPTQ weights +gptq_loop_aware:phase2 collected 16 crawler Hessians +gptq_loop_aware:restored 24 flat layer weights +gptq_loop_aware:merged 41 Hessians (16 crawler from phase2) +gptq:loop-aware calibrated 41 layers in 11.5s +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:0.6555 val_bpb:0.3882 eval_time:3237ms +Serialized model: 53860358 bytes +Code size: 180226 bytes +gptq_quantize: 24 GPTQ layers, 0 naive layers +Serialized model int6+zstd: 9925203 bytes +Total submission size int6+zstd: 10105429 bytes +Total submission size int8+zlib: 10105429 bytes +final_int6_roundtrip val_loss:1.7817 val_bpb:1.0552 eval_time:6744ms +final_int6_roundtrip_exact val_loss:1.78168477 val_bpb:1.05521408 +final_int6_sliding_window val_loss:1.6172 val_bpb:0.9578 stride:64 eval_time:106650ms +final_int6_sliding_window_exact val_loss:1.61716601 val_bpb:0.95777934 +final_int8_zlib_roundtrip_exact val_loss:1.61716601 val_bpb:0.95777934 +============================================ + DONE +============================================ diff --git a/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed42.log b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed42.log new file mode 100644 index 0000000000..5a4752be8d --- /dev/null +++ b/records/track_10min_16mb/2026-03-27_Medusa_FLA_DeltaNet_NaiveInt6_8xH100/train_seed42.log @@ -0,0 +1,91 @@ +============================================ + MEDUSA_IV — late-start EMA (step 4400) + loop-aware GPTQ + Seed: 42 + inst_dim=32 FLOW | 4 flat + 1 crawler x 4 loops + DELTA_NET_HEADS=4 | chunk_delta_rule | short_conv=True + EMA_START_STEP=4400 | EMA_DECAY=0.99 | LOOP_AWARE_GPTQ=1 + NITRUST_ENABLE=0 | NITRUST_STRICT=0 +============================================ +W0328 15:42:18.252000 2100 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. +logs/d2ce4d2f-54e1-4a9f-a729-bb4065ce8b20.txt +nitrust:disabled NITRUST_ENABLE=0 +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:14487088 +f1_corr:rank=0 params=0 est_int6_bytes~0 +mlp_act:relu_sq mlp_leaky_slope:0.5 +XSA:last_11 world_size:8 grad_accum_steps:1 +num_heads:8 num_kv_heads:4 embed_lr:0.035 matrix_lr:0.03 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +compile:enabled=1 fullgraph=0 optimize_ddp=0 +ddp:find_unused_parameters=1 +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:6.9291 val_bpb:4.1038 train_time:0ms step_avg:0.01ms +step:1/20000 train_loss:6.9309 train_time:167ms step_avg:166.81ms +step:2/20000 train_loss:8.7855 train_time:283ms step_avg:141.34ms +step:3/20000 train_loss:7.7121 train_time:397ms step_avg:132.30ms +step:4/20000 train_loss:7.5106 train_time:514ms step_avg:128.54ms +step:5/20000 train_loss:7.3240 train_time:634ms step_avg:126.82ms +step:6/20000 train_loss:7.0436 train_time:753ms step_avg:125.42ms +step:7/20000 train_loss:6.8719 train_time:872ms step_avg:124.54ms +step:8/20000 train_loss:6.7511 train_time:998ms step_avg:124.72ms +step:9/20000 train_loss:6.4111 train_time:1122ms step_avg:124.68ms +step:10/20000 train_loss:6.0199 train_time:1240ms step_avg:124.00ms +step:500/20000 train_loss:2.4724 train_time:61588ms step_avg:123.18ms +step:1000/20000 train_loss:2.3512 train_time:123095ms step_avg:123.10ms +step:1500/20000 train_loss:2.3118 train_time:184505ms step_avg:123.00ms +step:2000/20000 train_loss:2.1499 train_time:245935ms step_avg:122.97ms +step:2500/20000 train_loss:2.2467 train_time:307493ms step_avg:123.00ms +step:3000/20000 train_loss:2.2239 train_time:368922ms step_avg:122.97ms +step:3500/20000 train_loss:2.0423 train_time:430286ms step_avg:122.94ms +step:4000/20000 train_loss:1.1668 train_time:491831ms step_avg:122.96ms +step:4000/20000 val_loss:1.2030 val_bpb:0.7125 train_time:491834ms step_avg:122.96ms +ema:late-start re-initialized at step 4400 decay=0.99 +swa:start step:4500 +step:4500/20000 train_loss:0.5906 train_time:553560ms step_avg:123.01ms +step:4872/20000 val_loss:0.4121 val_bpb:0.2441 train_time:600098ms step_avg:123.17ms +stopping_early: wallclock_cap train_time:600098ms step:4872/20000 +peak memory allocated: 21025 MiB reserved: 21384 MiB +gptq:loop-aware 2-phase calibration... +gptq_loop_aware:patched 24 flat layers with GPTQ weights +gptq_loop_aware:phase2 collected 16 crawler Hessians +gptq_loop_aware:restored 24 flat layer weights +gptq_loop_aware:merged 41 Hessians (16 crawler from phase2) +gptq:loop-aware calibrated 41 layers in 11.3s +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:0.4253 val_bpb:0.2519 eval_time:3381ms +Serialized model: 53860358 bytes +Code size: 180226 bytes +gptq_quantize: 24 GPTQ layers, 0 naive layers +Serialized model int6+zstd: 9851621 bytes +Total submission size int6+zstd: 10031847 bytes +Total submission size int8+zlib: 10031847 bytes +final_int6_roundtrip val_loss:1.1813 val_bpb:0.6996 eval_time:15251ms +final_int6_roundtrip_exact val_loss:1.18126531 val_bpb:0.69961185 +final_int6_sliding_window val_loss:1.3683 val_bpb:0.8104 stride:64 eval_time:124459ms +final_int6_sliding_window_exact val_loss:1.36834014 val_bpb:0.81041025 +final_int8_zlib_roundtrip_exact val_loss:1.36834014 val_bpb:0.81041025 +============================================ + DONE +============================================