diff --git a/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/README.md b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/README.md new file mode 100644 index 000000000..db281533b --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/README.md @@ -0,0 +1,47 @@ +# Complementary Training + Backoff N-gram Mixer (Reproduction) + +**val_bpb: 0.4377** (2-seed mean 0.4379, std 0.0002) | 8x L20Z (H100) | eval 450s + +## Results + +| Seed | Steps | val_bpb | eval_time | +|------|-------|---------|-----------| +| 1337 | 7,003 | **0.4377** | 450s | +| 42 | 7,011 | **0.4380** | 450s | + +## Approach + +Reproduction of PR #803 (pentxayc) on 8x NVIDIA L20Z GPUs with stride=128 optimization. + +### Key Techniques + +1. **Complementary Training** (COMPLEMENT_ALPHA=0.5): Downweights training loss on tokens that bigram statistics can predict, forcing the neural model to specialize on hard tokens (long-range dependencies, semantic surprises). + +2. **BackoffNgramMixer**: Orders 2-10, 4M flat hash buckets. At eval time, entropy-adaptive alpha mixing: `alpha = 0.20 + 0.55 * sigmoid(2 * (entropy - 3.0))`. High-entropy tokens get more n-gram weight. + +3. **Legal Score-First TTT**: AdamW (lr=5e-4), 4 epochs per chunk, freeze first 2 blocks, Polyak EMA 0.998. Every token scored BEFORE any update uses it. + +4. **Stride=128**: Reduces eval windows from ~30K to ~950, with negligible BPB impact vs stride=32. + +### Architecture + +- 11 layers, 512 dim, 8 heads, 4 KV heads, 3x MLP with LeakyReLU(0.5)^2 +- XSA on last 4 layers, VRL enabled +- Int6 mixed quantization + lzma compression +- Artifact: ~15.9MB (under 16MB limit) + +## Reproduction + +```bash +VRL_ENABLED=1 LEAKY_RELU=1 GATED_ATTENTION=0 \ +TTT_ENABLED=1 TTT_OPTIMIZER=adamw TTT_LR=0.0005 TTT_EPOCHS=4 \ +TTT_FREEZE_BLOCKS=2 TTT_TEMPERATURE=0.98 \ +USE_HEDGE_MIXER=1 NGRAM_ORDER=10 NGRAM_BUCKETS=4194304 \ +ALPHA_BASE=0.20 ALPHA_RANGE=0.55 ALPHA_CENTER=3.0 \ +COMPLEMENT_ALPHA=0.5 EVAL_STRIDE=128 \ +SEED=1337 torchrun --nproc_per_node=8 train_gpt.py +``` + +## Acknowledgment + +Based on PR #803 by pentxayc. The core innovation of complementary training (bigram-weighted loss reweighting) is their contribution. diff --git a/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/submission.json b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/submission.json new file mode 100644 index 000000000..101517f76 --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/submission.json @@ -0,0 +1,23 @@ +{ + "author": "quietsmile", + "github_id": "quietsmile", + "name": "Complementary Training + Backoff N-gram Mixer (Reproduction)", + "blurb": "Reproduction of PR #803's complementary training approach on 8xL20Z (H100). Bigram-weighted loss reweighting (COMPLEMENT_ALPHA=0.5) trains the neural model to specialize on tokens n-gram caches can't predict. BackoffNgramMixer (orders 2-10, 4M hash buckets) with entropy-adaptive alpha (0.20+0.55*sigmoid(2*(H-3.0))). Legal score-first AdamW TTT (4 epochs, lr=5e-4, freeze first 2 blocks). Int6 mixed quantization + lzma. Evaluated with stride=128.", + "date": "2026-03-26T12:00:00Z", + "val_loss": 0.73909041, + "val_bpb": 0.4377, + "val_loss_std": 0.00026, + "val_bpb_std": 0.00016, + "seeds": [1337, 42], + "seed_results": { + "1337": {"val_loss": 0.73909041, "val_bpb": 0.43773153}, + "42": {"val_loss": 0.73961404, "val_bpb": 0.43804165} + }, + "step_stop": 7003, + "wallclock_seconds": 600.074, + "eval_time_seconds": 449.857, + "bytes_total": 15875857, + "bytes_code": 87336, + "base_pr": 803, + "hardware": "8x NVIDIA L20Z (H100 equivalent, 81GB)" +} diff --git a/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_gpt.py b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_gpt.py new file mode 100644 index 000000000..0817b95dd --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_gpt.py @@ -0,0 +1,1900 @@ +from __future__ import annotations +import copy +import glob +import io +import math +import os +import random +import subprocess +import sys +import time +import uuid +import lzma +from pathlib import Path +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 +_FA_VERSION = 0 +_fa_func = None +try: + from flash_attn_interface import flash_attn_func as _fa_func + _FA_VERSION = 3 +except ImportError: + try: + from flash_attn import flash_attn_func as _fa_func + _FA_VERSION = 2 + except ImportError: + _FA_VERSION = 0 + _fa_func = 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)) + 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)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + soft_round_qat = bool(int(os.environ.get("SOFT_ROUND_QAT", "1"))) + soft_round_temp_start = float(os.environ.get("SOFT_ROUND_TEMP_START", 1.0)) + soft_round_temp_end = float(os.environ.get("SOFT_ROUND_TEMP_END", 0.05)) + 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") + vrl_enabled = bool(int(os.environ.get("VRL_ENABLED", "0"))) + leaky_relu = bool(int(os.environ.get("LEAKY_RELU", "0"))) + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "0"))) + ttt_lr = float(os.environ.get("TTT_LR", 0.002)) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", 3)) + ttt_chunk_tokens = int(os.environ.get("TTT_CHUNK_TOKENS", 32768)) + ttt_freeze_blocks = int(os.environ.get("TTT_FREEZE_BLOCKS", 0)) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", 0.9)) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", 32)) + ttt_grad_clip = float(os.environ.get("TTT_GRAD_CLIP", 1.0)) + ttt_optimizer = os.environ.get("TTT_OPTIMIZER", "adamw") + ttt_temperature = float(os.environ.get("TTT_TEMPERATURE", 0.98)) + polyak_decay = float(os.environ.get("POLYAK_DECAY", 0.998)) + use_polyak = bool(int(os.environ.get("USE_POLYAK", "1"))) + byte_weighted_ttt = bool(int(os.environ.get("BYTE_WEIGHTED_TTT", "1"))) + adaptive_lr = bool(int(os.environ.get("ADAPTIVE_LR", "1"))) + adaptive_lr_max = float(os.environ.get("ADAPTIVE_LR_MAX", 3.0)) + eval_only = bool(int(os.environ.get("EVAL_ONLY", "0"))) + checkpoint_path = os.environ.get("CHECKPOINT_PATH", "final_model.pt") + ttt_max_chunks = int(os.environ.get("TTT_MAX_CHUNKS", 0)) + skip_sliding_window = bool(int(os.environ.get("SKIP_SLIDING_WINDOW", "0"))) + use_hedge_mixer = bool(int(os.environ.get("USE_HEDGE_MIXER", "1"))) + mixer_eta = float(os.environ.get("MIXER_ETA", 0.1)) + mixer_min_tokens = int(os.environ.get("MIXER_MIN_TOKENS", 10000)) +class BackoffNgramMixer: + PRIMES = [36313, 27191, 51647, 81929, 131071, 174763, 233017] + def __init__(self, vocab_size: int, device: torch.device, num_buckets: int = 4_000_000, + max_order: int = 7, min_count: int = 2, min_tokens: int = 5000, + alpha_base: float = 0.05, alpha_range: float = 0.55, alpha_center: float = 4.0): + self.V = vocab_size + self.B = num_buckets + self.MASK = num_buckets - 1 if (num_buckets & (num_buckets - 1)) == 0 else None + self.max_order = max_order + self.min_count = min_count + self.min_tokens = min_tokens + self.device = device + self.tokens_seen = 0 + self.alpha_base = alpha_base + self.alpha_range = alpha_range + self.alpha_center = alpha_center + self.uni_counts = torch.zeros(vocab_size, device=device, dtype=torch.float32) + self.uni_total = 0.0 + self.ctx_counts = [] + self.full_counts = [] + for _ in range(max_order - 1): + self.ctx_counts.append(torch.zeros(num_buckets, device=device, dtype=torch.float32)) + self.full_counts.append(torch.zeros(num_buckets, device=device, dtype=torch.float32)) + def _bucket(self, h: Tensor) -> Tensor: + if self.MASK is not None: + return h & self.MASK + return h.abs() % self.B + def update(self, tokens: Tensor): + t = tokens.to(self.device).long() + n = t.numel() + self.tokens_seen += n + ones = torch.ones(n, device=self.device, dtype=torch.float32) + self.uni_counts.scatter_add_(0, t, ones) + self.uni_total += n + for order in range(2, self.max_order + 1): + if n < order: + continue + oi = order - 2 + nxt = t[order - 1:] + ctx_h = t[0:n - order + 1] * self.PRIMES[0] + for k in range(1, order - 1): + ctx_h = ctx_h ^ (t[k:n - order + 1 + k] * self.PRIMES[k % len(self.PRIMES)]) + ctx_key = self._bucket(ctx_h) + full_h = ctx_h ^ (nxt * self.PRIMES[(order - 1) % len(self.PRIMES)]) + full_key = self._bucket(full_h) + self.ctx_counts[oi].scatter_add_(0, ctx_key, ones[:n - order + 1]) + self.full_counts[oi].scatter_add_(0, full_key, ones[:n - order + 1]) + def score(self, logits: Tensor, x_batch: Tensor, y_batch: Tensor, + temperature: float = 1.0) -> Tensor: + bsz, slen, V = logits.shape + if temperature != 1.0: + logits = logits / temperature + log_probs_neural = F.log_softmax(logits.float(), dim=-1) + neural_p = log_probs_neural.gather(-1, y_batch.unsqueeze(-1)).squeeze(-1).exp() + neural_nll = -neural_p.clamp(min=1e-12).log() + if self.tokens_seen < self.min_tokens: + return neural_nll + ctx_stack = [x_batch] + for k in range(1, self.max_order - 1): + shifted = torch.zeros_like(x_batch) + if k < slen: + shifted[:, k:] = x_batch[:, :-k] + ctx_stack.append(shifted) + if self.uni_total > 0: + uni_p = (self.uni_counts[y_batch] + 0.5) / (self.uni_total + 0.5 * V) + ngram_p = uni_p + else: + ngram_p = torch.full((bsz, slen), 1.0 / V, device=self.device) + ngram_hit = torch.zeros(bsz, slen, device=self.device, dtype=torch.bool) + for order in range(self.max_order, 1, -1): + oi = order - 2 + cw = order - 1 + ctx_h = ctx_stack[cw - 1] * self.PRIMES[0] + for k in range(1, cw): + ctx_h = ctx_h ^ (ctx_stack[cw - 1 - k] * self.PRIMES[k % len(self.PRIMES)]) + ctx_key = self._bucket(ctx_h) + full_h = ctx_h ^ (y_batch * self.PRIMES[(order - 1) % len(self.PRIMES)]) + full_key = self._bucket(full_h) + ctx_c = self.ctx_counts[oi][ctx_key] + full_c = self.full_counts[oi][full_key] + valid = (ctx_c >= self.min_count) & (~ngram_hit) + min_pos = order - 2 + if min_pos > 0: + valid[:, :min_pos] = False + p = torch.where(valid, full_c.clamp(max=ctx_c) / ctx_c.clamp(min=1), torch.zeros_like(ctx_c)) + p = p.clamp(0, 1) + ngram_p = torch.where(valid, p, ngram_p) + ngram_hit = ngram_hit | valid + ngram_nll = -ngram_p.clamp(min=1e-12).log() + probs_neural = log_probs_neural.exp() + entropy = -(probs_neural * log_probs_neural).sum(dim=-1) + alpha = self.alpha_base + self.alpha_range * torch.sigmoid( + 2.0 * (entropy - self.alpha_center)) + mixed_p = (1.0 - alpha) * neural_p + alpha * ngram_p + return -mixed_p.clamp(min=1e-12).log() +class TrainNgramTracker: + 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("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"no files:{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"val too short for {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 too small; " + 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,vrl_scales", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + _soft_round_qat: bool = True + _soft_round_temp: float = 1.0 + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + if CastedLinear._soft_round_qat: + w32 = self.weight.float() + row_max = w32.detach().abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_s = w32 / scale[:, None] + residual = w_s - w_s.detach().round() + temp = CastedLinear._soft_round_temp + w_soft = w_s.detach().round() + 0.5 * torch.tanh(residual / temp) + w = (w_soft.clamp(-32, 31) * scale[:, None]).to(x.dtype) + else: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim%num_heads!=0") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads%num_kv_heads!=0") + 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("odd head_dim") + 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 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x: Tensor, 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] + if _FA_VERSION == 3: + y = _fa_func(q, k, v, causal=True) + elif _FA_VERSION == 2: + y = _fa_func(q.bfloat16(), k.bfloat16(), v.bfloat16(), causal=True) + else: + y = F.scaled_dot_product_attention( + q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), + is_causal=True, enable_gqa=True).transpose(1, 2) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return 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): + 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, leaky: bool = False): + 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._neg_slope = 0.5 if leaky else 0.0 + def forward(self, x: Tensor) -> Tensor: + x = F.leaky_relu(self.fc(x), self._neg_slope) + 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, + **kwargs, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=kwargs.get("gated_attention", False)) + self.mlp = MLP(dim, mlp_mult, leaky=kwargs.get("leaky", False)) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + def forward(self, x: Tensor, x0: Tensor, v_embed: Tensor | None = None) -> Tensor: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + vrl_enabled: bool = False, + leaky_relu: bool = False, + gated_attention: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) + if logit_softcap <= 0.0: + raise ValueError(f"softcap<=0:{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.vrl_enabled = vrl_enabled + 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, + leaky=leaky_relu, + gated_attention=gated_attention, + ) + 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() + if self.vrl_enabled: + self.vrl_scales = nn.ParameterList( + [nn.Parameter(torch.zeros(1, dtype=torch.float32)) for _ in range(num_layers - 1)] + ) + else: + self.vrl_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + num_layers = len(self.blocks) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + if ".proj." in name or name.endswith(".proj"): + with torch.no_grad(): + module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + if self.vrl_enabled: + mix0 = self.blocks[0].resid_mix.to(dtype=x0.dtype) + x_in_0 = mix0[0][None, None, :] * x0 + mix0[1][None, None, :] * x0 + n0 = F.rms_norm(x_in_0, (x_in_0.size(-1),)) * self.blocks[0].ln_scale_factor + v0_raw = self.blocks[0].attn.c_v(n0) + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + if self.vrl_enabled and i > 0: + vr = v0_raw * self.vrl_scales[i - 1].to(dtype=v0_raw.dtype) + v_extra = (ve + vr) if ve is not None else vr + else: + v_extra = ve + x = self.blocks[i](x, x0, v_embed=v_extra) + 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) + if self.vrl_enabled: + vr = v0_raw * self.vrl_scales[bi - 1].to(dtype=v0_raw.dtype) + v_extra = (ve + vr) if ve is not None else vr + else: + v_extra = ve + x = self.blocks[bi](x, x0, v_embed=v_extra) + 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("need lm_head") + logits_proj = self.lm_head(x_flat) + 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) + 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 + skips: list[Tensor] = [] + ve_cache: dict = {} + if self.vrl_enabled: + mix0 = self.blocks[0].resid_mix.to(dtype=x0.dtype) + x_in_0 = mix0[0][None, None, :] * x0 + mix0[1][None, None, :] * x0 + n0 = F.rms_norm(x_in_0, (x_in_0.size(-1),)) * self.blocks[0].ln_scale_factor + v0_raw = self.blocks[0].attn.c_v(n0) + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + if self.vrl_enabled and i > 0: + vr = v0_raw * self.vrl_scales[i - 1].to(dtype=v0_raw.dtype) + v_extra = (ve + vr) if ve is not None else vr + else: + v_extra = ve + x = self.blocks[i](x, x0, v_embed=v_extra) + 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) + if self.vrl_enabled: + vr = v0_raw * self.vrl_scales[bi - 1].to(dtype=v0_raw.dtype) + v_extra = (ve + vr) if ve is not None else vr + else: + v_extra = ve + x = self.blocks[bi](x, x0, v_embed=v_extra) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) +def eval_val_sliding_ttt( + args, base_model: nn.Module, rank: int, world_size: int, + device: torch.device, val_tokens: Tensor, base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + stride: int, batch_seqs: int = 32, log0=print, +) -> tuple[float, float]: + seq_len = args.train_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = args.ttt_chunk_tokens + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0] + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + if args.ttt_max_chunks > 0: + num_chunks = min(num_chunks, args.ttt_max_chunks) + chunk_windows: list[list[int]] = [[] for _ in range(num_chunks)] + for ws in window_starts: + end = min(ws + seq_len, total_tokens) + wlen = end - ws + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_start = ws + s + ci = min(scored_start // ttt_chunk, num_chunks - 1) + if ci < num_chunks: + chunk_windows[ci].append(ws) + log0(f"ttt:c={num_chunks} ct={ttt_chunk} w={len(window_starts)} s={stride} lr={args.ttt_lr} ep={args.ttt_epochs} fb={args.ttt_freeze_blocks} o={args.ttt_optimizer} pk={args.use_polyak}({args.polyak_decay}) bw={args.byte_weighted_ttt} alr={args.adaptive_lr}({args.adaptive_lr_max}) t={args.ttt_temperature}") + 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) + num_blocks = len(base_model.blocks) + unfrozen_block_start = max(0, num_blocks - args.ttt_freeze_blocks) if args.ttt_freeze_blocks > 0 else 0 + ttt_params = [] + for name, p in base_model.named_parameters(): + unfreeze = False + if args.ttt_freeze_blocks <= 0: + unfreeze = True + elif "norm" in name or "scale" in name or "lm_head" in name or "tok_emb" in name: + unfreeze = True + else: + for bi in range(unfrozen_block_start, num_blocks): + if f"blocks.{bi}." in name: + unfreeze = True + break + if unfreeze: + p.requires_grad_(True) + ttt_params.append(p) + else: + p.requires_grad_(False) + log0(f"ttt:uf={sum(p.numel() for p in ttt_params)} f={sum(p.numel() for p in base_model.parameters() if not p.requires_grad)}") + if args.ttt_optimizer == "adamw": + optimizer = torch.optim.AdamW(ttt_params, lr=args.ttt_lr, weight_decay=0.0, betas=(0.9, 0.999)) + else: + optimizer = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=args.ttt_momentum) + polyak_state: dict[str, Tensor] | None = None + if args.use_polyak: + polyak_state = {n: p.data.detach().clone() for n, p in base_model.named_parameters() if p.requires_grad} + mixer: BackoffNgramMixer | None = None + if args.use_hedge_mixer: + ngram_order = int(os.environ.get("NGRAM_ORDER", "7")) + ngram_buckets = int(os.environ.get("NGRAM_BUCKETS", "4000000")) + alpha_base = float(os.environ.get("ALPHA_BASE", "0.05")) + alpha_range = float(os.environ.get("ALPHA_RANGE", "0.55")) + alpha_center = float(os.environ.get("ALPHA_CENTER", "4.0")) + min_count = int(os.environ.get("MIN_COUNT", "2")) + mixer = BackoffNgramMixer(args.vocab_size, device, num_buckets=ngram_buckets, + max_order=ngram_order, min_count=min_count, + min_tokens=args.mixer_min_tokens, + alpha_base=alpha_base, alpha_range=alpha_range, + alpha_center=alpha_center) + mem_mb = ngram_buckets * 4 * 2 * (ngram_order - 1) / 1e6 + log0(f"bo:o={ngram_order} b={ngram_buckets} m={mem_mb:.0f}M a={alpha_base}+{alpha_range}*s(H-{alpha_center}) mc={min_count}") + t0 = time.perf_counter() + for ci in range(num_chunks): + windows = chunk_windows[ci] + if not windows: + continue + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + raw_state: dict[str, Tensor] | None = None + if polyak_state is not None: + raw_state = {n: p.data.detach().clone() for n, p in base_model.named_parameters() if p.requires_grad} + for n, p in base_model.named_parameters(): + if n in polyak_state: + p.data.copy_(polyak_state[n]) + my_s = (len(windows) * rank) // world_size + my_e = (len(windows) * (rank + 1)) // world_size + my_windows = windows[my_s:my_e] + base_model.eval() + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk_tok = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk_tok[:-1] + y_batch[i, :wlen] = chunk_tok[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = base_model.forward_logits(x_batch) + if mixer is not None and mixer.tokens_seen >= mixer.min_tokens: + nll = mixer.score(logits, x_batch, y_batch, args.ttt_temperature) + else: + if args.ttt_temperature != 1.0: + logits = logits / args.ttt_temperature + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + if mixer is not None: + chunk_tokens = val_tokens[chunk_start:chunk_end].to(device) + mixer.update(chunk_tokens) + if raw_state is not None: + for n, p in base_model.named_parameters(): + if n in raw_state: + p.data.copy_(raw_state[n]) + is_last_chunk = (ci == num_chunks - 1) + if not is_last_chunk and args.ttt_epochs > 0: + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs > 0: + cos_lr = args.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1))) + if args.adaptive_lr: + progress = min(ci / (num_chunks * 0.3), 1.0) + lr_mult = 1.0 + (args.adaptive_lr_max - 1.0) * progress + cos_lr = cos_lr * lr_mult + for pg in optimizer.param_groups: + pg['lr'] = cos_lr + distributed = dist.is_available() and dist.is_initialized() + my_seq_s = (chunk_seqs * rank) // world_size if distributed else 0 + my_seq_e = (chunk_seqs * (rank + 1)) // world_size if distributed else chunk_seqs + my_chunk_seqs = my_seq_e - my_seq_s + for _ep in range(args.ttt_epochs): + for bs in range(0, my_chunk_seqs, args.ttt_batch_seqs): + be = min(bs + args.ttt_batch_seqs, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits_t = base_model.forward_logits(x) + if args.byte_weighted_ttt: + per_tok_nll = F.cross_entropy( + logits_t.reshape(-1, logits_t.size(-1)).float(), + y.reshape(-1), reduction="none", + ) + byte_weights = base_bytes_lut[y.reshape(-1)].float() + byte_weights = byte_weights / byte_weights.mean().clamp(min=1e-6) + loss = (per_tok_nll * byte_weights).mean() + else: + loss = F.cross_entropy( + logits_t.reshape(-1, logits_t.size(-1)).float(), + y.reshape(-1), + ) + loss.backward() + if distributed and world_size > 1: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(ttt_params, args.ttt_grad_clip) + optimizer.step() + if polyak_state is not None: + with torch.no_grad(): + for n, p in base_model.named_parameters(): + if n in polyak_state: + polyak_state[n].lerp_(p.data, 1.0 - args.polyak_decay) + if rank == 0 and (ci % 10 == 0 or ci == num_chunks - 1): + elapsed = time.perf_counter() - t0 + rl = loss_sum.item() / max(token_count.item(), 1) + rbpb = rl / math.log(2.0) * (token_count.item() / max(byte_count.item(), 1)) if token_count.item() > 0 else 0.0 + log0(f" tc[{ci+1}/{num_chunks}]bpb={rbpb:.6f} t={elapsed:.1f}s") + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item()) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + log0(f"ttt:vl={val_loss:.6f} bpb={val_bpb:.6f} t={time.perf_counter()-t0:.1f}s") + return val_loss, val_bpb +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 32, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + base_model.eval() + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = token_count.item() / byte_count.item() + base_model.train() + return val_loss, bits_per_token * tokens_per_byte +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" + continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + continue + if cat in int6_cats and t.ndim >= 1: + q, s = quantize_int6_per_row(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out +def main() -> None: + global zeropower_via_newtonschulz5 + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"bad WORLD_SIZE:{world_size}") + if 8 % world_size != 0: + raise ValueError(f"8%WORLD_SIZE={world_size}!=0") + grad_accum_steps = 8 // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("no CUDA") + 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 + _gpu_name = torch.cuda.get_device_name(0) + _is_high_end = "H100" in _gpu_name or "A100" in _gpu_name + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + if _is_high_end: + enable_cudnn_sdp(True) + enable_flash_sdp(False) + enable_mem_efficient_sdp(False) + enable_math_sdp(False) + else: + enable_cudnn_sdp(True) + enable_flash_sdp(True) + enable_mem_efficient_sdp(True) + enable_math_sdp(True) + 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("="*60,console=False) + log0(f"py:{sys.version}",console=False) + log0(f"pt:{torch.__version__}",console=False) + log0(subprocess.run(["nvidia-smi"],stdout=subprocess.PIPE,stderr=subprocess.PIPE,text=True,check=False).stdout,console=False) + log0("="*60,console=False) + log0(f"fa:{_FA_VERSION} gpu:{_gpu_name} he:{_is_high_end}") + 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"need .model:{args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError( + f"vocab mismatch:{args.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"bpb:sp={args.tokenizer_path}") + log0(f"train:{dataset_dir.name} shards:{actual_train_files}") + log0(f"val:{args.val_files} n:{val_tokens.numel()-1}") + CastedLinear._qat_enabled = args.qat_enabled + CastedLinear._soft_round_qat = args.soft_round_qat + CastedLinear._soft_round_temp = args.soft_round_temp_start + qat_start_step = 0 if args.qat_enabled else -1 + base_model = GPT( + vocab_size=args.vocab_size, + num_layers=args.num_layers, + model_dim=args.model_dim, + num_heads=args.num_heads, + num_kv_heads=args.num_kv_heads, + mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, + tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, + rope_base=args.rope_base, + qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, + mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, + ln_scale=args.ln_scale, + dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, + ve_dim=args.ve_dim, + ve_layers=args.ve_layers, + vrl_enabled=args.vrl_enabled, + leaky_relu=args.leaky_relu, + gated_attention=args.gated_attention, + ).to(device).bfloat16() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + 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"compl:{complement_alpha}") + else: + base_model._ngram_tracker = None + if distributed: + torch._dynamo.config.optimize_ddp = False + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model + block_named_params = list(base_model.blocks.named_parameters()) + matrix_params = [ + p + for name, p in block_named_params + if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.mtp_num_heads > 0: + matrix_params.extend([p for p in base_model.mtp_heads.parameters() if p.ndim == 2]) + scalar_params = [ + p + for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + if base_model.vrl_enabled: + for s in base_model.vrl_scales: + scalar_params.append(s) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + matrix_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.ve_shared.proj is not None: + matrix_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: + scalar_params.append(s) + optimizer_tok = torch.optim.AdamW( + tok_params, + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + weight_decay=args.muon_wd, + ) + for group in optimizer_muon.param_groups: + group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW( + [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers.insert(1, optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"p:{n_params}") + log0(f"mtp:{args.mtp_num_heads} w:{args.mtp_loss_weight} p:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"xsa:{args.xsa_last_n} l:{xsa_layers}") + log0(f"ws:{world_size} ga:{grad_accum_steps}") + log0(f"sdp:{_is_high_end}") + log0(f"attn:h={args.num_heads} kv={args.num_kv_heads}") + log0(f"vrl:{args.vrl_enabled} lrelu:{args.leaky_relu} ttt:{args.ttt_enabled}") + log0(f"tie:{args.tie_embeddings} elr:{token_lr} hlr:{args.head_lr if base_model.lm_head is not None else 0.0} mlr:{args.matrix_lr} slr:{args.scalar_lr}") + log0(f"tbt:{args.train_batch_tokens} tsl:{args.train_seq_len} it:{args.iterations} wu:{args.warmup_steps} mws:{args.max_wallclock_seconds:.3f}") + log0(f"s:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + if args.warmup_steps > 0 and not args.eval_only: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"wu:{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) + if args.eval_only: + log0(f"eval:load {args.checkpoint_path}") + ckpt_state = torch.load(args.checkpoint_path, map_location="cpu") + base_model.load_state_dict(ckpt_state, strict=True) + log0(f"eval:loaded {sum(p.numel() for p in base_model.parameters())}p") + 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} + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"}) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=6) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + log0(f"eval:qsize:{len(quant_blob)}B") + if distributed: + dist.barrier() + with open("final_model.int6.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_raw_disk = lzma.decompress(quant_blob_disk) + quant_state = torch.load(io.BytesIO(quant_raw_disk), map_location="cpu") + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, 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, + vrl_enabled=args.vrl_enabled, leaky_relu=args.leaky_relu, + gated_attention=args.gated_attention, + ).to(device).bfloat16() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + sw_seq_len = effective_eval_seq_len + if not args.skip_sliding_window and 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"eval:sw bpb:{sw_val_bpb:.4f} s:{args.eval_stride} t:{1000.0*(time.perf_counter()-t_slide):.0f}ms") + elif args.skip_sliding_window: + log0("eval:skip_sw") + if args.ttt_enabled: + log0(f"eval:ttt lr={args.ttt_lr} ep={args.ttt_epochs} c={args.ttt_chunk_tokens} fb={args.ttt_freeze_blocks}") + torch.cuda.synchronize() + t_ttt = time.perf_counter() + ttt_val_loss, ttt_val_bpb = eval_val_sliding_ttt( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, batch_seqs=args.ttt_batch_seqs, log0=log0, + ) + torch.cuda.synchronize() + log0(f"eval:ttt bpb:{ttt_val_bpb:.4f} t:{1000.0*(time.perf_counter()-t_ttt):.0f}ms") + if distributed: + dist.destroy_process_group() + return + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_decay = 0.997 + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + ) + log0(f"s:{step}/{args.iterations} vl:{val_loss:.4f} bpb:{val_bpb:.4f} tt:{training_time_ms:.0f}ms sa:{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"stop tt:{training_time_ms:.0f}ms s:{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 + qat_start_step = step + log0(f"qat:{step} s:{scale:.4f}") + if CastedLinear._qat_enabled and CastedLinear._soft_round_qat and qat_start_step >= 0: + qat_total = max(args.iterations - qat_start_step, 1) + qat_progress = min((step - qat_start_step) / qat_total, 1.0) + log_start = math.log(args.soft_round_temp_start) + log_end = math.log(args.soft_round_temp_end) + CastedLinear._soft_round_temp = math.exp(log_start + qat_progress * (log_end - log_start)) + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + 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() + 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:{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"s:{step}/{args.iterations} tl:{train_loss.item():.4f} tt:{approx_training_time_ms:.0f}ms sa:{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"mem:{torch.cuda.max_memory_allocated()//1024//1024}M R:{torch.cuda.max_memory_reserved()//1024//1024}M") + log0("ema:apply") + 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"diag vl:{diag_val_loss:.4f} bpb:{diag_val_bpb:.4f} t:{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"excl_mtp:{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"model:{model_bytes}B") + log0(f"code:{code_bytes}B") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"}) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = lzma.compress(quant_raw, preset=6) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"q:{quant_file_bytes}B") + log0(f"total:{quant_file_bytes+code_bytes}B") + if distributed: + dist.barrier() + with open("final_model.int6.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_raw_disk = lzma.decompress(quant_blob_disk) + quant_state = torch.load(io.BytesIO(quant_raw_disk), map_location="cpu") + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + 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, + vrl_enabled=args.vrl_enabled, leaky_relu=args.leaky_relu, + gated_attention=args.gated_attention, + ).to(device).bfloat16() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize() + t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val( + args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len, + ) + torch.cuda.synchronize() + log0(f"q_rt vl:{q_val_loss:.4f} bpb:{q_val_bpb:.4f} t:{1000.0*(time.perf_counter()-t_qeval):.0f}ms") + log0(f"q_rt_x vl:{q_val_loss:.8f} 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"q_sw vl:{sw_val_loss:.4f} bpb:{sw_val_bpb:.4f} s:{args.eval_stride} t:{1000.0*(time.perf_counter()-t_slide):.0f}ms") + log0(f"q_sw_x vl:{sw_val_loss:.8f} bpb:{sw_val_bpb:.8f}") + log0(f"q8_x vl:{sw_val_loss:.8f} bpb:{sw_val_bpb:.8f}") + if args.eval_stride != 64 and 64 < sw_seq_len: + torch.cuda.synchronize() + t_slide64 = time.perf_counter() + sw64_val_loss, sw64_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=64, + eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0(f"q_s64 vl:{sw64_val_loss:.4f} bpb:{sw64_val_bpb:.4f} s:64 t:{1000.0*(time.perf_counter()-t_slide64):.0f}ms") + log0(f"q_s64_x vl:{sw64_val_loss:.8f} bpb:{sw64_val_bpb:.8f}") + log0(f"q8_x vl:{sw64_val_loss:.8f} bpb:{sw64_val_bpb:.8f}") + if args.ttt_enabled: + log0("ttt:start") + torch.cuda.synchronize() + t_ttt = time.perf_counter() + ttt_val_loss, ttt_val_bpb = eval_val_sliding_ttt( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, batch_seqs=args.ttt_batch_seqs, log0=log0, + ) + torch.cuda.synchronize() + log0(f"ttt vl:{ttt_val_loss:.4f} bpb:{ttt_val_bpb:.4f} t:{1000.0*(time.perf_counter()-t_ttt):.0f}ms") + log0(f"ttt_x vl:{ttt_val_loss:.8f} bpb:{ttt_val_bpb:.8f}") + if distributed: + dist.destroy_process_group() +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_seed1337.log b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_seed1337.log new file mode 100644 index 000000000..8401084ad --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_seed1337.log @@ -0,0 +1,283 @@ +W0326 12:16:29.436000 116650 torch/distributed/run.py:852] +W0326 12:16:29.436000 116650 torch/distributed/run.py:852] ***************************************** +W0326 12:16:29.436000 116650 torch/distributed/run.py:852] 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. +W0326 12:16:29.436000 116650 torch/distributed/run.py:852] ***************************************** +logs/pr803_s1337.txt +fa:3 gpu:NVIDIA L20Z he:False +bpb:sp=./data/tokenizers/fineweb_1024_bpe.model +train:fineweb10B_sp1024 shards:80 +val:./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin n:62021632 +compl:0.5 +p:26993766 +mtp:0 w:0.2 p:0 +xsa:4 l:[7, 8, 9, 10] +ws:8 ga:1 +sdp:False +attn:h=8 kv=4 +vrl:True lrelu:True ttt:True +tie:True elr:0.035 hlr:0.0 mlr:0.025 slr:0.025 +tbt:786432 tsl:2048 it:20000 wu:20 mws:600.000 +s:1337 +wu:1/20 +wu:2/20 +wu:3/20 +wu:4/20 +wu:5/20 +wu:6/20 +wu:7/20 +wu:8/20 +wu:9/20 +wu:10/20 +wu:11/20 +wu:12/20 +wu:13/20 +wu:14/20 +wu:15/20 +wu:16/20 +wu:17/20 +wu:18/20 +wu:19/20 +wu:20/20 +s:0/20000 vl:6.9279 bpb:4.1031 tt:0ms sa:0.01ms +s:1/20000 tl:6.9299 tt:143ms sa:143.14ms +s:2/20000 tl:8.4023 tt:229ms sa:114.55ms +s:3/20000 tl:7.6863 tt:314ms sa:104.63ms +s:4/20000 tl:7.1122 tt:397ms sa:99.36ms +s:5/20000 tl:6.9432 tt:482ms sa:96.31ms +s:6/20000 tl:6.7010 tt:566ms sa:94.27ms +s:7/20000 tl:6.5884 tt:650ms sa:92.82ms +s:8/20000 tl:6.6288 tt:733ms sa:91.65ms +s:9/20000 tl:6.3016 tt:817ms sa:90.81ms +s:10/20000 tl:5.9678 tt:902ms sa:90.17ms +s:500/20000 tl:2.3518 tt:42746ms sa:85.49ms +s:1000/20000 tl:2.2380 tt:85586ms sa:85.59ms +s:1500/20000 tl:2.1853 tt:128339ms sa:85.56ms +s:2000/20000 tl:2.0317 tt:171132ms sa:85.57ms +s:2500/20000 tl:2.1376 tt:213959ms sa:85.58ms +s:3000/20000 tl:2.1256 tt:256776ms sa:85.59ms +s:3500/20000 tl:2.1486 tt:299582ms sa:85.59ms +s:4000/20000 tl:1.9415 tt:342420ms sa:85.60ms +s:4000/20000 vl:2.0534 bpb:1.2161 tt:342424ms sa:85.61ms +s:4500/20000 tl:2.0920 tt:385257ms sa:85.61ms +s:5000/20000 tl:2.0715 tt:428087ms sa:85.62ms +s:5500/20000 tl:1.9875 tt:470903ms sa:85.62ms +s:6000/20000 tl:1.9138 tt:513711ms sa:85.62ms +swa:6350 +qat:6482 s:0.1499 +s:6500/20000 tl:2.0542 tt:556663ms sa:85.64ms +s:7000/20000 tl:1.7666 tt:599783ms sa:85.68ms +s:7003/20000 vl:1.9224 bpb:1.1385 tt:600074ms sa:85.69ms +stop tt:600074ms s:7003/20000 +mem:21635M R:21968M +ema:apply +diag vl:1.9206 bpb:1.1375 t:2018ms +model:106181533B +code:94053B +q:15884768B +total:15978821B +q_rt vl:1.9339 bpb:1.1453 t:19354ms +q_rt_x vl:1.93385093 bpb:1.14533545 +q_sw vl:1.8942 bpb:1.1218 s:128 t:64602ms +q_sw_x vl:1.89418052 bpb:1.12184182 +q8_x vl:1.89418052 bpb:1.12184182 +q_s64 vl:1.8941 bpb:1.1218 s:64 t:87916ms +q_s64_x vl:1.89408392 bpb:1.12178615 +q8_x vl:1.89408392 bpb:1.12178615 +ttt:start +ttt:c=1893 ct=32768 w=484544 s=128 lr=0.0005 ep=4 fb=2 o=adamw pk=True(0.998) bw=True alr=True(3.0) t=0.98 +ttt:uf=5256222 f=21737544 +bo:o=10 b=4194304 m=302M a=0.2+0.55*s(H-3.0) mc=2 + tc[1/1893]bpb=1.153198 t=0.6s + tc[11/1893]bpb=1.297941 t=3.1s + tc[21/1893]bpb=1.280469 t=5.6s + tc[31/1893]bpb=1.267772 t=7.9s + tc[41/1893]bpb=1.241238 t=10.3s + tc[51/1893]bpb=1.222724 t=12.7s + tc[61/1893]bpb=1.214158 t=15.2s + tc[71/1893]bpb=1.196528 t=17.7s + tc[81/1893]bpb=1.179568 t=20.0s + tc[91/1893]bpb=1.163892 t=22.4s + tc[101/1893]bpb=1.149456 t=24.7s + tc[111/1893]bpb=1.133870 t=27.1s + tc[121/1893]bpb=1.110638 t=29.5s + tc[131/1893]bpb=1.092361 t=31.8s + tc[141/1893]bpb=1.079226 t=34.1s + tc[151/1893]bpb=1.062442 t=36.4s + tc[161/1893]bpb=1.045861 t=38.9s + tc[171/1893]bpb=1.030625 t=41.3s + tc[181/1893]bpb=1.016090 t=43.6s + tc[191/1893]bpb=1.003570 t=45.9s + tc[201/1893]bpb=0.987881 t=48.4s + tc[211/1893]bpb=0.970940 t=50.8s + tc[221/1893]bpb=0.956062 t=53.2s + tc[231/1893]bpb=0.941103 t=55.4s + tc[241/1893]bpb=0.927557 t=57.8s + tc[251/1893]bpb=0.914357 t=60.2s + tc[261/1893]bpb=0.899249 t=62.8s + tc[271/1893]bpb=0.886350 t=65.1s + tc[281/1893]bpb=0.873950 t=67.6s + tc[291/1893]bpb=0.862850 t=70.0s + tc[301/1893]bpb=0.851383 t=72.4s + tc[311/1893]bpb=0.840788 t=74.8s + tc[321/1893]bpb=0.830282 t=77.2s + tc[331/1893]bpb=0.819948 t=79.6s + tc[341/1893]bpb=0.809031 t=81.9s + tc[351/1893]bpb=0.800011 t=84.3s + tc[361/1893]bpb=0.791354 t=86.7s + tc[371/1893]bpb=0.781958 t=89.2s + tc[381/1893]bpb=0.773453 t=91.5s + tc[391/1893]bpb=0.765033 t=94.3s + tc[401/1893]bpb=0.756231 t=96.7s + tc[411/1893]bpb=0.748258 t=99.2s + tc[421/1893]bpb=0.740214 t=101.7s + tc[431/1893]bpb=0.732584 t=104.3s + tc[441/1893]bpb=0.725476 t=106.7s + tc[451/1893]bpb=0.718333 t=109.0s + tc[461/1893]bpb=0.711106 t=111.5s + tc[471/1893]bpb=0.704364 t=113.8s + tc[481/1893]bpb=0.698143 t=116.1s + tc[491/1893]bpb=0.691488 t=118.4s + tc[501/1893]bpb=0.685587 t=120.8s + tc[511/1893]bpb=0.679907 t=123.1s + tc[521/1893]bpb=0.673827 t=125.6s + tc[531/1893]bpb=0.668481 t=127.9s + tc[541/1893]bpb=0.663427 t=130.2s + tc[551/1893]bpb=0.658006 t=132.5s + tc[561/1893]bpb=0.653032 t=134.9s + tc[571/1893]bpb=0.647908 t=137.3s + tc[581/1893]bpb=0.642989 t=139.6s + tc[591/1893]bpb=0.638320 t=142.0s + tc[601/1893]bpb=0.633919 t=144.3s + tc[611/1893]bpb=0.629679 t=146.7s + tc[621/1893]bpb=0.625461 t=149.2s + tc[631/1893]bpb=0.621473 t=151.4s + tc[641/1893]bpb=0.617570 t=153.8s + tc[651/1893]bpb=0.613537 t=156.3s + tc[661/1893]bpb=0.609785 t=158.7s + tc[671/1893]bpb=0.606202 t=161.1s + tc[681/1893]bpb=0.602509 t=163.4s + tc[691/1893]bpb=0.599354 t=165.7s + tc[701/1893]bpb=0.595877 t=168.0s + tc[711/1893]bpb=0.592806 t=170.3s + tc[721/1893]bpb=0.589640 t=172.5s + tc[731/1893]bpb=0.586656 t=175.0s + tc[741/1893]bpb=0.583620 t=177.2s + tc[751/1893]bpb=0.580586 t=179.5s + tc[761/1893]bpb=0.577708 t=181.8s + tc[771/1893]bpb=0.574961 t=184.2s + tc[781/1893]bpb=0.572594 t=186.5s + tc[791/1893]bpb=0.569896 t=188.8s + tc[801/1893]bpb=0.567219 t=191.2s + tc[811/1893]bpb=0.564700 t=193.5s + tc[821/1893]bpb=0.562186 t=195.8s + tc[831/1893]bpb=0.559912 t=198.3s + tc[841/1893]bpb=0.557437 t=200.7s + tc[851/1893]bpb=0.555124 t=203.0s + tc[861/1893]bpb=0.552840 t=205.5s + tc[871/1893]bpb=0.550628 t=207.8s + tc[881/1893]bpb=0.548542 t=210.2s + tc[891/1893]bpb=0.546522 t=212.6s + tc[901/1893]bpb=0.544660 t=215.0s + tc[911/1893]bpb=0.542756 t=217.4s + tc[921/1893]bpb=0.540851 t=219.7s + tc[931/1893]bpb=0.538943 t=222.0s + tc[941/1893]bpb=0.536986 t=224.4s + tc[951/1893]bpb=0.535182 t=226.8s + tc[961/1893]bpb=0.533260 t=229.2s + tc[971/1893]bpb=0.531611 t=231.5s + tc[981/1893]bpb=0.529822 t=233.9s + tc[991/1893]bpb=0.528130 t=236.4s + tc[1001/1893]bpb=0.526319 t=238.7s + tc[1011/1893]bpb=0.524579 t=241.0s + tc[1021/1893]bpb=0.522994 t=243.3s + tc[1031/1893]bpb=0.521348 t=245.6s + tc[1041/1893]bpb=0.519562 t=248.1s + tc[1051/1893]bpb=0.517883 t=250.4s + tc[1061/1893]bpb=0.516265 t=252.7s + tc[1071/1893]bpb=0.514941 t=254.9s + tc[1081/1893]bpb=0.513452 t=257.3s + tc[1091/1893]bpb=0.511946 t=259.6s + tc[1101/1893]bpb=0.510417 t=261.9s + tc[1111/1893]bpb=0.508887 t=264.1s + tc[1121/1893]bpb=0.507404 t=266.4s + tc[1131/1893]bpb=0.505979 t=268.8s + tc[1141/1893]bpb=0.504566 t=271.1s + tc[1151/1893]bpb=0.503156 t=273.4s + tc[1161/1893]bpb=0.501725 t=275.7s + tc[1171/1893]bpb=0.500382 t=278.2s + tc[1181/1893]bpb=0.498870 t=280.5s + tc[1191/1893]bpb=0.497581 t=282.9s + tc[1201/1893]bpb=0.496310 t=285.3s + tc[1211/1893]bpb=0.494941 t=287.8s + tc[1221/1893]bpb=0.493669 t=290.2s + tc[1231/1893]bpb=0.492299 t=292.5s + tc[1241/1893]bpb=0.490963 t=294.8s + tc[1251/1893]bpb=0.489669 t=297.3s + tc[1261/1893]bpb=0.488522 t=299.7s + tc[1271/1893]bpb=0.487319 t=302.1s + tc[1281/1893]bpb=0.486093 t=304.4s + tc[1291/1893]bpb=0.484973 t=306.7s + tc[1301/1893]bpb=0.483739 t=309.0s + tc[1311/1893]bpb=0.482547 t=311.3s + tc[1321/1893]bpb=0.481396 t=313.7s + tc[1331/1893]bpb=0.480293 t=316.1s + tc[1341/1893]bpb=0.479222 t=318.4s + tc[1351/1893]bpb=0.478231 t=320.8s + tc[1361/1893]bpb=0.477287 t=323.1s + tc[1371/1893]bpb=0.476300 t=325.4s + tc[1381/1893]bpb=0.475415 t=327.8s + tc[1391/1893]bpb=0.474377 t=330.3s + tc[1401/1893]bpb=0.473490 t=332.8s + tc[1411/1893]bpb=0.472663 t=335.1s + tc[1421/1893]bpb=0.471791 t=337.4s + tc[1431/1893]bpb=0.470897 t=339.8s + tc[1441/1893]bpb=0.470112 t=342.3s + tc[1451/1893]bpb=0.469369 t=344.8s + tc[1461/1893]bpb=0.468474 t=347.3s + tc[1471/1893]bpb=0.467771 t=349.7s + tc[1481/1893]bpb=0.466858 t=352.0s + tc[1491/1893]bpb=0.466031 t=354.3s + tc[1501/1893]bpb=0.465272 t=356.6s + tc[1511/1893]bpb=0.464458 t=358.9s + tc[1521/1893]bpb=0.463647 t=361.2s + tc[1531/1893]bpb=0.462860 t=363.5s + tc[1541/1893]bpb=0.462011 t=365.8s + tc[1551/1893]bpb=0.461302 t=368.1s + tc[1561/1893]bpb=0.460582 t=370.4s + tc[1571/1893]bpb=0.459785 t=372.7s + tc[1581/1893]bpb=0.459087 t=375.0s + tc[1591/1893]bpb=0.458316 t=377.3s + tc[1601/1893]bpb=0.457621 t=380.0s + tc[1611/1893]bpb=0.456871 t=382.4s + tc[1621/1893]bpb=0.456093 t=384.6s + tc[1631/1893]bpb=0.455388 t=387.0s + tc[1641/1893]bpb=0.454679 t=389.4s + tc[1651/1893]bpb=0.453948 t=391.7s + tc[1661/1893]bpb=0.453226 t=394.0s + tc[1671/1893]bpb=0.452615 t=396.4s + tc[1681/1893]bpb=0.451938 t=398.8s + tc[1691/1893]bpb=0.451184 t=401.2s + tc[1701/1893]bpb=0.450492 t=403.8s + tc[1711/1893]bpb=0.449779 t=406.3s + tc[1721/1893]bpb=0.449094 t=408.9s + tc[1731/1893]bpb=0.448443 t=411.4s + tc[1741/1893]bpb=0.447797 t=414.1s + tc[1751/1893]bpb=0.447074 t=416.5s + tc[1761/1893]bpb=0.446476 t=419.0s + tc[1771/1893]bpb=0.445823 t=421.4s + tc[1781/1893]bpb=0.445253 t=423.6s + tc[1791/1893]bpb=0.444544 t=426.0s + tc[1801/1893]bpb=0.443919 t=428.4s + tc[1811/1893]bpb=0.443295 t=430.7s + tc[1821/1893]bpb=0.442672 t=433.1s + tc[1831/1893]bpb=0.441967 t=435.5s + tc[1841/1893]bpb=0.441325 t=437.8s + tc[1851/1893]bpb=0.440713 t=440.2s + tc[1861/1893]bpb=0.440039 t=442.5s + tc[1871/1893]bpb=0.439446 t=444.8s + tc[1881/1893]bpb=0.438818 t=447.0s + tc[1891/1893]bpb=0.438215 t=449.3s + tc[1893/1893]bpb=0.438143 t=449.6s +ttt:vl=0.739090 bpb=0.437732 t=449.6s +ttt vl:0.7391 bpb:0.4377 t:449857ms +ttt_x vl:0.73909041 bpb:0.43773153 diff --git a/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_seed42.log b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_seed42.log new file mode 100644 index 000000000..7ef378934 --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_ComplementaryBackoff_NgramMixer_0.4377/train_seed42.log @@ -0,0 +1,283 @@ +W0326 12:16:51.255000 95962 torch/distributed/run.py:852] +W0326 12:16:51.255000 95962 torch/distributed/run.py:852] ***************************************** +W0326 12:16:51.255000 95962 torch/distributed/run.py:852] 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. +W0326 12:16:51.255000 95962 torch/distributed/run.py:852] ***************************************** +logs/pr803_s42.txt +fa:3 gpu:NVIDIA L20Z he:False +bpb:sp=./data/tokenizers/fineweb_1024_bpe.model +train:fineweb10B_sp1024 shards:80 +val:./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin n:62021632 +compl:0.5 +p:26993766 +mtp:0 w:0.2 p:0 +xsa:4 l:[7, 8, 9, 10] +ws:8 ga:1 +sdp:False +attn:h=8 kv=4 +vrl:True lrelu:True ttt:True +tie:True elr:0.035 hlr:0.0 mlr:0.025 slr:0.025 +tbt:786432 tsl:2048 it:20000 wu:20 mws:600.000 +s:42 +wu:1/20 +wu:2/20 +wu:3/20 +wu:4/20 +wu:5/20 +wu:6/20 +wu:7/20 +wu:8/20 +wu:9/20 +wu:10/20 +wu:11/20 +wu:12/20 +wu:13/20 +wu:14/20 +wu:15/20 +wu:16/20 +wu:17/20 +wu:18/20 +wu:19/20 +wu:20/20 +s:0/20000 vl:6.9301 bpb:4.1044 tt:0ms sa:0.02ms +s:1/20000 tl:6.9318 tt:143ms sa:143.47ms +s:2/20000 tl:8.4789 tt:226ms sa:112.88ms +s:3/20000 tl:7.7246 tt:311ms sa:103.72ms +s:4/20000 tl:7.1363 tt:393ms sa:98.25ms +s:5/20000 tl:6.8905 tt:476ms sa:95.23ms +s:6/20000 tl:6.7573 tt:560ms sa:93.35ms +s:7/20000 tl:6.6338 tt:644ms sa:92.04ms +s:8/20000 tl:6.5774 tt:728ms sa:90.96ms +s:9/20000 tl:6.2889 tt:811ms sa:90.09ms +s:10/20000 tl:5.9479 tt:895ms sa:89.45ms +s:500/20000 tl:2.3556 tt:42717ms sa:85.43ms +s:1000/20000 tl:2.2379 tt:85523ms sa:85.52ms +s:1500/20000 tl:2.1825 tt:128264ms sa:85.51ms +s:2000/20000 tl:2.0321 tt:171033ms sa:85.52ms +s:2500/20000 tl:2.1349 tt:213792ms sa:85.52ms +s:3000/20000 tl:2.1311 tt:256574ms sa:85.52ms +s:3500/20000 tl:2.1441 tt:299359ms sa:85.53ms +s:4000/20000 tl:1.9451 tt:342134ms sa:85.53ms +s:4000/20000 vl:2.0534 bpb:1.2162 tt:342138ms sa:85.53ms +s:4500/20000 tl:2.0925 tt:384899ms sa:85.53ms +s:5000/20000 tl:2.0714 tt:427660ms sa:85.53ms +s:5500/20000 tl:1.9878 tt:470409ms sa:85.53ms +s:6000/20000 tl:1.9133 tt:513153ms sa:85.53ms +swa:6350 +qat:6489 s:0.1499 +s:6500/20000 tl:2.0556 tt:556055ms sa:85.55ms +s:7000/20000 tl:1.7670 tt:599104ms sa:85.59ms +s:7011/20000 vl:1.9225 bpb:1.1386 tt:600069ms sa:85.59ms +stop tt:600069ms s:7011/20000 +mem:21635M R:21968M +ema:apply +diag vl:1.9207 bpb:1.1376 t:2037ms +model:106181533B +code:94053B +q:15818928B +total:15912981B +q_rt vl:1.9338 bpb:1.1453 t:19101ms +q_rt_x vl:1.93379164 bpb:1.14530033 +q_sw vl:1.8940 bpb:1.1217 s:128 t:64409ms +q_sw_x vl:1.89400054 bpb:1.12173523 +q8_x vl:1.89400054 bpb:1.12173523 +q_s64 vl:1.8939 bpb:1.1217 s:64 t:87956ms +q_s64_x vl:1.89389866 bpb:1.12167644 +q8_x vl:1.89389866 bpb:1.12167644 +ttt:start +ttt:c=1893 ct=32768 w=484544 s=128 lr=0.0005 ep=4 fb=2 o=adamw pk=True(0.998) bw=True alr=True(3.0) t=0.98 +ttt:uf=5256222 f=21737544 +bo:o=10 b=4194304 m=302M a=0.2+0.55*s(H-3.0) mc=2 + tc[1/1893]bpb=1.158144 t=0.7s + tc[11/1893]bpb=1.300499 t=3.2s + tc[21/1893]bpb=1.281612 t=5.6s + tc[31/1893]bpb=1.267993 t=8.0s + tc[41/1893]bpb=1.241518 t=10.4s + tc[51/1893]bpb=1.222777 t=12.8s + tc[61/1893]bpb=1.214382 t=15.2s + tc[71/1893]bpb=1.196371 t=17.5s + tc[81/1893]bpb=1.179378 t=19.9s + tc[91/1893]bpb=1.163774 t=22.3s + tc[101/1893]bpb=1.149011 t=24.6s + tc[111/1893]bpb=1.133416 t=26.9s + tc[121/1893]bpb=1.110318 t=29.5s + tc[131/1893]bpb=1.091998 t=32.0s + tc[141/1893]bpb=1.078826 t=34.5s + tc[151/1893]bpb=1.062015 t=36.9s + tc[161/1893]bpb=1.045390 t=39.3s + tc[171/1893]bpb=1.030300 t=41.8s + tc[181/1893]bpb=1.015842 t=44.2s + tc[191/1893]bpb=1.003389 t=46.6s + tc[201/1893]bpb=0.987638 t=49.0s + tc[211/1893]bpb=0.970718 t=51.3s + tc[221/1893]bpb=0.955857 t=53.7s + tc[231/1893]bpb=0.940882 t=56.2s + tc[241/1893]bpb=0.927330 t=58.6s + tc[251/1893]bpb=0.914140 t=61.0s + tc[261/1893]bpb=0.898958 t=63.3s + tc[271/1893]bpb=0.886032 t=65.6s + tc[281/1893]bpb=0.873676 t=68.0s + tc[291/1893]bpb=0.862591 t=70.4s + tc[301/1893]bpb=0.851100 t=72.7s + tc[311/1893]bpb=0.840528 t=75.0s + tc[321/1893]bpb=0.830023 t=77.4s + tc[331/1893]bpb=0.819734 t=79.9s + tc[341/1893]bpb=0.808831 t=82.3s + tc[351/1893]bpb=0.799807 t=84.6s + tc[361/1893]bpb=0.791188 t=87.0s + tc[371/1893]bpb=0.781799 t=89.5s + tc[381/1893]bpb=0.773300 t=92.0s + tc[391/1893]bpb=0.764869 t=94.4s + tc[401/1893]bpb=0.756088 t=96.9s + tc[411/1893]bpb=0.748143 t=99.3s + tc[421/1893]bpb=0.740085 t=101.8s + tc[431/1893]bpb=0.732493 t=104.1s + tc[441/1893]bpb=0.725420 t=106.6s + tc[451/1893]bpb=0.718273 t=109.0s + tc[461/1893]bpb=0.711041 t=111.4s + tc[471/1893]bpb=0.704313 t=113.7s + tc[481/1893]bpb=0.698089 t=116.1s + tc[491/1893]bpb=0.691446 t=118.4s + tc[501/1893]bpb=0.685557 t=120.8s + tc[511/1893]bpb=0.679867 t=123.2s + tc[521/1893]bpb=0.673821 t=125.5s + tc[531/1893]bpb=0.668486 t=127.9s + tc[541/1893]bpb=0.663439 t=130.3s + tc[551/1893]bpb=0.658042 t=132.8s + tc[561/1893]bpb=0.653087 t=135.3s + tc[571/1893]bpb=0.647980 t=137.7s + tc[581/1893]bpb=0.643051 t=140.0s + tc[591/1893]bpb=0.638399 t=142.6s + tc[601/1893]bpb=0.634008 t=144.9s + tc[611/1893]bpb=0.629803 t=147.3s + tc[621/1893]bpb=0.625591 t=149.7s + tc[631/1893]bpb=0.621610 t=152.0s + tc[641/1893]bpb=0.617726 t=154.5s + tc[651/1893]bpb=0.613718 t=156.9s + tc[661/1893]bpb=0.609970 t=159.2s + tc[671/1893]bpb=0.606397 t=161.5s + tc[681/1893]bpb=0.602710 t=163.8s + tc[691/1893]bpb=0.599571 t=166.2s + tc[701/1893]bpb=0.596101 t=168.6s + tc[711/1893]bpb=0.593040 t=171.0s + tc[721/1893]bpb=0.589882 t=173.2s + tc[731/1893]bpb=0.586898 t=175.5s + tc[741/1893]bpb=0.583868 t=177.8s + tc[751/1893]bpb=0.580837 t=180.1s + tc[761/1893]bpb=0.577981 t=182.6s + tc[771/1893]bpb=0.575245 t=184.9s + tc[781/1893]bpb=0.572887 t=187.2s + tc[791/1893]bpb=0.570195 t=189.6s + tc[801/1893]bpb=0.567513 t=191.9s + tc[811/1893]bpb=0.564987 t=194.2s + tc[821/1893]bpb=0.562470 t=196.6s + tc[831/1893]bpb=0.560185 t=198.9s + tc[841/1893]bpb=0.557715 t=201.2s + tc[851/1893]bpb=0.555408 t=203.6s + tc[861/1893]bpb=0.553127 t=205.9s + tc[871/1893]bpb=0.550909 t=208.3s + tc[881/1893]bpb=0.548835 t=210.5s + tc[891/1893]bpb=0.546829 t=213.0s + tc[901/1893]bpb=0.544974 t=215.3s + tc[911/1893]bpb=0.543075 t=217.6s + tc[921/1893]bpb=0.541180 t=219.9s + tc[931/1893]bpb=0.539296 t=222.2s + tc[941/1893]bpb=0.537347 t=224.6s + tc[951/1893]bpb=0.535539 t=227.0s + tc[961/1893]bpb=0.533616 t=229.4s + tc[971/1893]bpb=0.531963 t=231.7s + tc[981/1893]bpb=0.530171 t=234.0s + tc[991/1893]bpb=0.528479 t=236.2s + tc[1001/1893]bpb=0.526671 t=238.6s + tc[1011/1893]bpb=0.524925 t=241.1s + tc[1021/1893]bpb=0.523349 t=243.5s + tc[1031/1893]bpb=0.521691 t=245.9s + tc[1041/1893]bpb=0.519913 t=248.4s + tc[1051/1893]bpb=0.518237 t=250.9s + tc[1061/1893]bpb=0.516628 t=253.3s + tc[1071/1893]bpb=0.515314 t=255.6s + tc[1081/1893]bpb=0.513827 t=257.9s + tc[1091/1893]bpb=0.512311 t=260.3s + tc[1101/1893]bpb=0.510783 t=262.6s + tc[1111/1893]bpb=0.509255 t=265.0s + tc[1121/1893]bpb=0.507785 t=267.3s + tc[1131/1893]bpb=0.506357 t=269.7s + tc[1141/1893]bpb=0.504952 t=272.0s + tc[1151/1893]bpb=0.503542 t=274.4s + tc[1161/1893]bpb=0.502111 t=276.8s + tc[1171/1893]bpb=0.500766 t=279.2s + tc[1181/1893]bpb=0.499255 t=281.5s + tc[1191/1893]bpb=0.497974 t=283.8s + tc[1201/1893]bpb=0.496701 t=286.2s + tc[1211/1893]bpb=0.495340 t=288.6s + tc[1221/1893]bpb=0.494069 t=291.1s + tc[1231/1893]bpb=0.492697 t=293.5s + tc[1241/1893]bpb=0.491361 t=295.9s + tc[1251/1893]bpb=0.490066 t=298.3s + tc[1261/1893]bpb=0.488915 t=300.7s + tc[1271/1893]bpb=0.487716 t=303.1s + tc[1281/1893]bpb=0.486491 t=305.4s + tc[1291/1893]bpb=0.485360 t=307.7s + tc[1301/1893]bpb=0.484126 t=310.1s + tc[1311/1893]bpb=0.482931 t=312.4s + tc[1321/1893]bpb=0.481768 t=314.8s + tc[1331/1893]bpb=0.480665 t=317.2s + tc[1341/1893]bpb=0.479598 t=319.7s + tc[1351/1893]bpb=0.478605 t=322.1s + tc[1361/1893]bpb=0.477658 t=324.5s + tc[1371/1893]bpb=0.476675 t=326.8s + tc[1381/1893]bpb=0.475792 t=329.2s + tc[1391/1893]bpb=0.474750 t=331.5s + tc[1401/1893]bpb=0.473864 t=333.8s + tc[1411/1893]bpb=0.473034 t=336.2s + tc[1421/1893]bpb=0.472161 t=338.5s + tc[1431/1893]bpb=0.471268 t=340.9s + tc[1441/1893]bpb=0.470479 t=343.2s + tc[1451/1893]bpb=0.469739 t=345.5s + tc[1461/1893]bpb=0.468847 t=347.8s + tc[1471/1893]bpb=0.468144 t=350.1s + tc[1481/1893]bpb=0.467230 t=352.5s + tc[1491/1893]bpb=0.466405 t=354.8s + tc[1501/1893]bpb=0.465642 t=357.1s + tc[1511/1893]bpb=0.464828 t=359.6s + tc[1521/1893]bpb=0.464016 t=362.0s + tc[1531/1893]bpb=0.463223 t=364.6s + tc[1541/1893]bpb=0.462368 t=366.8s + tc[1551/1893]bpb=0.461656 t=369.2s + tc[1561/1893]bpb=0.460929 t=371.5s + tc[1571/1893]bpb=0.460133 t=373.9s + tc[1581/1893]bpb=0.459430 t=376.2s + tc[1591/1893]bpb=0.458657 t=378.7s + tc[1601/1893]bpb=0.457959 t=381.0s + tc[1611/1893]bpb=0.457222 t=383.4s + tc[1621/1893]bpb=0.456447 t=385.7s + tc[1631/1893]bpb=0.455739 t=388.0s + tc[1641/1893]bpb=0.455026 t=390.4s + tc[1651/1893]bpb=0.454294 t=392.6s + tc[1661/1893]bpb=0.453568 t=394.9s + tc[1671/1893]bpb=0.452958 t=397.4s + tc[1681/1893]bpb=0.452279 t=399.8s + tc[1691/1893]bpb=0.451524 t=402.1s + tc[1701/1893]bpb=0.450830 t=404.5s + tc[1711/1893]bpb=0.450109 t=406.8s + tc[1721/1893]bpb=0.449422 t=409.2s + tc[1731/1893]bpb=0.448761 t=411.6s + tc[1741/1893]bpb=0.448111 t=414.0s + tc[1751/1893]bpb=0.447387 t=416.3s + tc[1761/1893]bpb=0.446795 t=418.7s + tc[1771/1893]bpb=0.446145 t=421.1s + tc[1781/1893]bpb=0.445571 t=423.4s + tc[1791/1893]bpb=0.444857 t=425.8s + tc[1801/1893]bpb=0.444235 t=428.1s + tc[1811/1893]bpb=0.443608 t=430.5s + tc[1821/1893]bpb=0.442981 t=432.8s + tc[1831/1893]bpb=0.442274 t=435.2s + tc[1841/1893]bpb=0.441632 t=437.5s + tc[1851/1893]bpb=0.441031 t=439.8s + tc[1861/1893]bpb=0.440363 t=442.1s + tc[1871/1893]bpb=0.439776 t=444.4s + tc[1881/1893]bpb=0.439151 t=446.7s + tc[1891/1893]bpb=0.438542 t=449.1s + tc[1893/1893]bpb=0.438470 t=449.4s +ttt:vl=0.739614 bpb=0.438042 t=449.4s +ttt vl:0.7396 bpb:0.4380 t:449583ms +ttt_x vl:0.73961404 bpb:0.43804165