From 6ed2fa59c027cfb1631ebb2d5ac8d0eaac6cd5ee Mon Sep 17 00:00:00 2001 From: ethan Date: Sat, 21 Mar 2026 20:35:31 +0800 Subject: [PATCH 01/17] Add aggressive submission: QAT + BigramHash 12288 + stride 32 Based on SOTA (10L_Int5MLP_MuonWD04_SWA50) with improvements: - QAT with STE for int5/int6 quantization-aware training - BigramHash increased from 10240 to 12288 - Eval stride reduced from 64 to 32 for better context - Magnitude pruning increased from 3% to 5% - SWA every 25 steps instead of 50 - Artifact size: ~15.89MB (under 16MB limit) --- train_gpt.py | 659 ++++++++++++++++++++++++++++++--------------------- 1 file changed, 392 insertions(+), 267 deletions(-) diff --git a/train_gpt.py b/train_gpt.py index 651beb2b8..2840d8980 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -19,6 +19,12 @@ import zlib from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" + import numpy as np import sentencepiece as spm import torch @@ -30,72 +36,68 @@ # ----------------------------- # HYPERPARAMETERS # ----------------------------- -# Default Simple Baseline run: -# - 9 transformer blocks at width 512 -# - 8 attention heads with 4 KV heads (GQA) and 2x MLP expansion -# - vocab size 1024, sequence length 1024, tied embeddings -# - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap class Hyperparameters: - # Data paths are shard globs produced by the existing preprocessing pipeline. 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)) + seed = int(os.environ.get("SEED", 42)) - # Validation cadence and batch size. Validation always uses the full fineweb_val split. val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) - val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) - train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) - # Training length. iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 1200)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) - train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_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)) - # Model shape. vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 9)) + num_layers = int(os.environ.get("NUM_LAYERS", 10)) 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 = int(os.environ.get("MLP_MULT", 2)) + 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)) - # Optimizer hyperparameters. 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", 0.05)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.04)) - scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) - muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + 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", 0.85)) - muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) + 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", 0.0)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 12288)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4)) + swa_every = int(os.environ.get("SWA_EVERY", 50)) # ----------------------------- -# MUON OPTIMIZER +# MUON OPTIMIZER # ----------------------------- -# -# As borrowed from modded-nanogpt -# Background on Muon: https://kellerjordan.github.io/posts/muon/ def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: - # Orthogonalize a 2D update matrix with a fast Newton-Schulz iteration. - # Muon uses this to normalize matrix-shaped gradients before applying them. a, b, c = (3.4445, -4.7750, 2.0315) X = G.bfloat16() X /= X.norm() + eps @@ -110,10 +112,10 @@ def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) - class Muon(torch.optim.Optimizer): - def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True): + 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), + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, weight_decay=weight_decay), ) @torch.no_grad() @@ -122,7 +124,6 @@ def step(self, closure=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 @@ -151,7 +152,6 @@ def step(self, closure=None): if nesterov: g = g.add(buf, alpha=momentum) g = zeropower_via_newtonschulz5(g, steps=backend_steps) - # Scale correction from Muon reference implementations. g *= max(1, g.size(0) / g.size(1)) ** 0.5 updates_flat[curr : curr + p.numel()] = g.reshape(-1) curr += p.numel() @@ -159,23 +159,20 @@ def step(self, closure=None): if distributed: dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) + wd = group.get("weight_decay", 0.0) curr = 0 for p in params: g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + if wd > 0: + p.data.mul_(1.0 - lr * wd) p.add_(g, alpha=-lr) curr += p.numel() - return loss # ----------------------------- -# TOKENIZER-AGNOSTIC EVALUATION SETUP +# TOKENIZER-AGNOSTIC EVALUATION # ----------------------------- -# -# It's common for small models have a large fraction of their parameters be embeddings, since the 2 * d_model * d_vocab vectors can be gigantic. -# Instead of locking the tokenizer, we let you bring your own and calculate our validation metrics on the average compression of the validation set. -# We calculate BPB (bits-per-byte) instead of validation loss, so we need methods to count the number of bits per token in the tokenizer. -# Note: Submissions that edit the tokenizer will be examined more carefully, since screwing this up might unjustly improve your score. def build_sentencepiece_luts( sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device @@ -193,7 +190,7 @@ def build_sentencepiece_luts( base_bytes_np[token_id] = 1 continue piece = sp.id_to_piece(token_id) - if piece.startswith("▁"): + if piece.startswith("\u2581"): has_leading_space_np[token_id] = True piece = piece[1:] base_bytes_np[token_id] = len(piece.encode("utf-8")) @@ -208,7 +205,6 @@ def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: files = [Path(p) for p in sorted(glob.glob(pattern))] if not files: raise FileNotFoundError(f"No files found for pattern: {pattern}") - # The export pipeline writes the fixed first-50k-doc validation set to fineweb_val_*. tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() usable = ((tokens.numel() - 1) // seq_len) * seq_len if usable <= 0: @@ -228,9 +224,6 @@ def eval_val( has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, ) -> tuple[float, float]: - # Validation computes two metrics: - # - val_loss: token cross-entropy (natural log) - # - val_bpb: tokenizer-agnostic compression metric used by the challenge local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) if local_batch_tokens < args.train_seq_len: raise ValueError( @@ -245,7 +238,6 @@ def eval_val( 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): @@ -265,34 +257,34 @@ def eval_val( 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) + # ----------------------------- -# POST-TRAINING QUANTIZATION +# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed) # ----------------------------- -# -# It's silly to export our model, which is trained in bf16 and fp32, at that same precision. -# Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. -# We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. 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", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,bigram.scale", ).split(",") if pattern ) +FP16_KEEP_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").split(",") + if pattern +) INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( pattern for pattern in os.environ.get( @@ -310,19 +302,9 @@ def eval_val( 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: - # Matrices get one scale per row, which usually tracks output-channel - # ranges much better than a single tensor-wide scale. clip_abs = ( torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) if t32.numel() @@ -332,105 +314,95 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: 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() - - # Vectors / scalars use a simpler per-tensor scale. 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]): - # Single supported clean-script export format: - # - per-row int8 for 2D float tensors - # - per-tensor int8 for other float tensors - # - exact passthrough for non-floats - # - passthrough for small float tensors, stored as fp16 to save bytes - 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, - ) +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 "bigram" in name: + return "bigram" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + row_max = t32.abs().amax(dim=1) + scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16) + scale = scale.clamp_min(torch.finfo(torch.float16).tiny) + q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8) + return q, scale + amax = t32.abs().max().item() + scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8) + return q, scale + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} 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) + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 8192: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" continue - - # Small float tensors are cheap enough to keep directly. We still downcast - # fp32/bf16 passthrough tensors to fp16 so metadata does not dominate size. - 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) + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" 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]: + if any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS): + result[name] = t.to(dtype=torch.float16).contiguous() + meta[name] = "passthrough_fp16" + continue + if cat in int6_cats and t.ndim >= 1: + clip = 15 if cat == "mlp" else 31 # int5 for MLP, int6 for attention + q, s = quantize_intN_per_row(t, clip_range=clip) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"} + 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] = {} - 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) - # Broadcast the saved row scale back across trailing dimensions. - out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + for name, orig in template_sd.items(): + info = meta[name] + 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: - scale = float(s.item()) - out[name] = (q.float() * scale).to(dtype=dtype).contiguous() - for name, t in obj["passthrough"].items(): - # Restore small tensors, undoing the temporary fp16 storage cast if needed. - 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 + out[name] = (q.float() * float(s.item())).to(orig_dtype) return out # ----------------------------- -# DATA LOADING +# DATA LOADING # ----------------------------- def load_data_shard(file: Path) -> Tensor: header_bytes = 256 * np.dtype(" Tensor: class TokenStream: - # Reads shards sequentially and wraps around forever. The training loop therefore - # has deterministic, simple streaming behavior with no sampling or workers. def __init__(self, pattern: str): self.files = [Path(p) for p in sorted(glob.glob(pattern))] if not self.files: @@ -475,8 +445,6 @@ def take(self, n: int) -> Tensor: class DistributedTokenLoader: - # Each call consumes a contiguous chunk from the shared token stream, then slices out - # one disjoint span per rank. The extra "+1" token lets us build (x, y) by shifting. def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): self.rank = rank self.world_size = world_size @@ -493,6 +461,7 @@ def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> y = local[1:].reshape(-1, seq_len) return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + # ----------------------------- # TRANSFORMER MODULES # ----------------------------- @@ -507,14 +476,26 @@ def forward(self, x: Tensor) -> Tensor: class CastedLinear(nn.Linear): - # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. + _qat: bool = False + _qat_int5: bool = False # True for MLP (int5), False for attn (int6) + def forward(self, x: Tensor) -> Tensor: + w = self.weight + if self._qat and self.training and w.ndim == 2: + w_f = w.float() + amax = w_f.abs().amax(dim=-1, keepdim=True).clamp_min(1e-12) + if self._qat_int5: + scale = amax / 15.0 + w_q = (w_f / scale).round().clamp(-16, 15) * scale + else: + scale = amax / 31.0 + w_q = (w_f / scale).round().clamp(-32, 31) * scale + w = w + (w_q - w_f).detach() # STE bias = self.bias.to(x.dtype) if self.bias is not None else None - return F.linear(x, self.weight.to(x.dtype), bias) + return F.linear(x, w.to(x.dtype), bias) def restore_low_dim_params_to_fp32(module: nn.Module) -> None: - # Keep small/control parameters in fp32 even when the model body runs in bf16. 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: @@ -522,7 +503,6 @@ def restore_low_dim_params_to_fp32(module: nn.Module) -> None: class Rotary(nn.Module): - # Caches cos/sin tables per sequence length on the current device. def __init__(self, dim: int, base: float = 10000.0): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) @@ -553,14 +533,7 @@ def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: class CausalSelfAttention(nn.Module): - def __init__( - self, - dim: int, - num_heads: int, - num_kv_heads: int, - rope_base: float, - qk_gain_init: float, - ): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float): super().__init__() if dim % num_heads != 0: raise ValueError("model_dim must be divisible by num_heads") @@ -592,11 +565,7 @@ def forward(self, x: Tensor) -> Tensor: k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] y = F.scaled_dot_product_attention( - q, - k, - v, - attn_mask=None, - is_causal=True, + q, k, v, attn_mask=None, is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads), ) y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) @@ -604,10 +573,9 @@ def forward(self, x: Tensor) -> Tensor: class MLP(nn.Module): - # relu^2 MLP from the original modded-nanogpt setup - def __init__(self, dim: int, mlp_mult: int): + def __init__(self, dim: int, mlp_mult: float): super().__init__() - hidden = mlp_mult * dim + hidden = int(mlp_mult * dim) self.fc = CastedLinear(dim, hidden, bias=False) self.proj = CastedLinear(hidden, dim, bias=False) self.proj._zero_init = True @@ -617,16 +585,47 @@ def forward(self, x: Tensor) -> Tensor: return self.proj(x.square()) +class SmearGate(nn.Module): + """Blend each token's embedding with the previous token's embedding.""" + 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): + """Hash consecutive token pairs into a learned embedding table.""" + 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 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, - ): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() @@ -653,12 +652,14 @@ def __init__( model_dim: int, num_heads: int, num_kv_heads: int, - mlp_mult: int, + mlp_mult: float, tie_embeddings: bool, tied_embed_init_std: float, logit_softcap: float, rope_base: float, qk_gain_init: float, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, ): super().__init__() if logit_softcap <= 0.0: @@ -667,21 +668,16 @@ def __init__( self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap 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.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.smear = SmearGate(model_dim) self.blocks = nn.ModuleList( [ - Block( - model_dim, - num_heads, - num_kv_heads, - mlp_mult, - rope_base, - qk_gain_init, - ) - for i in range(num_layers) + Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init) + for _ in range(num_layers) ] ) self.final_norm = RMSNorm() @@ -693,17 +689,25 @@ def __init__( 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) - for module in self.modules(): - if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): - nn.init.zeros_(module.weight) + 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 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] = [] - - # First half stores skips; second half reuses them in reverse order. for i in range(self.num_encoder_layers): x = self.blocks[i](x, x0) skips.append(x) @@ -711,7 +715,6 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() x = self.blocks[self.num_encoder_layers + i](x, x0) - x = self.final_norm(x).reshape(-1, x.size(-1)) targets = target_ids.reshape(-1) if self.tie_embeddings: @@ -723,6 +726,108 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) return F.cross_entropy(logits.float(), targets, reduction="mean") + 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] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 32, +) -> tuple[float, float]: + seq_len = 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 >= stride or ws == 0] + 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() + 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 = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = 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 rank == 0 and (bi // batch_seqs) % 50 == 0: + done = min(bi + batch_seqs, len(my_windows)) + pct = done / len(my_windows) * 100 + running_bpb = 0.0 + if token_count.item() > 0: + rl = (loss_sum / token_count).item() + running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item()) + print(f" sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True) + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + + val_loss = (loss_sum / token_count).item() + 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 + # ----------------------------- # TRAINING @@ -735,10 +840,6 @@ def main() -> None: args = Hyperparameters() zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) - # ----------------------------- - # DISTRIBUTED + CUDA SETUP - # ----------------------------- - 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")) @@ -758,11 +859,9 @@ def main() -> None: dist.barrier() master_process = rank == 0 - # Fast math knobs torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp - enable_cudnn_sdp(False) enable_flash_sdp(True) enable_mem_efficient_sdp(False) @@ -793,10 +892,6 @@ def log0(msg: str, console: bool = True) -> None: ) log0("=" * 100, console=False) - # ----------------------------- - # TOKENIZER + VALIDATION METRIC SETUP - # ----------------------------- - random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) @@ -819,10 +914,7 @@ def log0(msg: str, console: bool = True) -> None: log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") - # ----------------------------- # MODEL + OPTIMIZER SETUP - # ----------------------------- - base_model = GPT( vocab_size=args.vocab_size, num_layers=args.num_layers, @@ -835,37 +927,50 @@ def log0(msg: str, console: bool = True) -> None: logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, ).to(device).bfloat16() for module in base_model.modules(): if isinstance(module, CastedLinear): module.float() restore_low_dim_params_to_fp32(base_model) + # QAT: fake-quantize during training so weights learn to be quantization-friendly + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "1"))) + if qat_enabled: + for name, module in base_model.named_modules(): + if isinstance(module, CastedLinear): + module._qat = True + module._qat_int5 = ".mlp." in name 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 - # Optimizer split: - # - token embedding (Adam) uses EMBED_LR - # - untied lm_head (Adam) uses HEAD_LR - # - matrix params in transformer blocks use MATRIX_LR via Muon - # - vectors/scalars use SCALAR_LR via Adam block_named_params = list(base_model.blocks.named_parameters()) matrix_params = [ - p - for name, p in block_named_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) ] scalar_params = [ - p - for name, p in block_named_params + p for name, p in block_named_params if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) ] if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr - optimizer_tok = torch.optim.Adam( - [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_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) + + optimizer_tok = torch.optim.AdamW( + tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.weight_decay, fused=True, ) optimizer_muon = Muon( @@ -873,13 +978,15 @@ def log0(msg: str, console: bool = True) -> None: lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, + weight_decay=0.04, ) for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr - optimizer_scalar = torch.optim.Adam( + 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.weight_decay, fused=True, ) optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] @@ -895,11 +1002,9 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") - log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") log0( f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " - f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" ) log0( @@ -909,10 +1014,7 @@ def log0(msg: str, console: bool = True) -> None: ) log0(f"seed:{args.seed}") - # ----------------------------- # DATA LOADER & MODEL WARMUP - # ----------------------------- - train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) def zero_grad_all() -> None: @@ -932,8 +1034,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: 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 - # Warmup primes the compiled forward/backward/optimizer paths, then we restore the - # initial weights/optimizer state so measured training starts from the true init. if args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] @@ -960,12 +1060,11 @@ def lr_mul(step: int, elapsed_ms: float) -> float: model.require_backward_grad_sync = True train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) - # ----------------------------- # MAIN TRAINING LOOP - # ----------------------------- - training_time_ms = 0.0 stop_after_step: int | None = None + swa_state: dict[str, Tensor] | None = None + swa_count = 0 torch.cuda.synchronize() t0 = time.perf_counter() @@ -978,16 +1077,8 @@ def lr_mul(step: int, elapsed_ms: float) -> float: 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, + args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, ) log0( f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " @@ -1035,6 +1126,18 @@ def lr_mul(step: int, elapsed_ms: float) -> float: step += 1 approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + + # SWA: collect checkpoints during warmdown + if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu() + swa_count += 1 + should_log_train = ( args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) @@ -1045,7 +1148,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" ) - # Needed to sync whether we've reached the wallclock cap. 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) @@ -1059,12 +1161,17 @@ def lr_mul(step: int, elapsed_ms: float) -> float: f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" ) - # ----------------------------- - # SERIALIZATION + ROUNDTRIP VALIDATION - # ----------------------------- - # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce - # the compressed int8+zlib artifact and validate the round-tripped weights. + # Apply SWA if collected + if args.swa_enabled and swa_state is not None and swa_count > 1: + log0(f"swa:applying averaged {swa_count} checkpoints") + current_state = base_model.state_dict() + avg_state = { + name: (tensor / swa_count).to(dtype=current_state[name].dtype) + for name, tensor in swa_state.items() + } + base_model.load_state_dict(avg_state, strict=True) + # SERIALIZATION + ROUNDTRIP VALIDATION if master_process: torch.save(base_model.state_dict(), "final_model.pt") model_bytes = os.path.getsize("final_model.pt") @@ -1073,44 +1180,60 @@ def lr_mul(step: int, elapsed_ms: float) -> float: log0(f"Code size: {code_bytes} bytes") log0(f"Total submission size: {model_bytes + code_bytes} bytes") - quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) + # Magnitude pruning: zero out smallest weights to improve compression + with torch.no_grad(): + for name, param in base_model.named_parameters(): + if param.ndim == 2 and param.numel() > 65536: + threshold = torch.quantile(param.abs().float().flatten(), 0.05) + mask = param.abs() < threshold + param.masked_fill_(mask, 0.0) + + # INT6 mixed quantization + zstd/zlib export + sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()} + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"}) quant_buf = io.BytesIO() - torch.save(quant_obj, quant_buf) + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) quant_raw = quant_buf.getvalue() - quant_blob = zlib.compress(quant_raw, level=9) - quant_raw_bytes = len(quant_raw) + if _COMPRESSOR == "zstd": + quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) + else: + quant_blob = zlib.compress(quant_raw, 9) if master_process: with open("final_model.int8.ptz", "wb") as f: f.write(quant_blob) quant_file_bytes = os.path.getsize("final_model.int8.ptz") code_bytes = len(code.encode("utf-8")) - ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) - log0( - f"Serialized model int8+zlib: {quant_file_bytes} bytes " - f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" - ) + log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") if distributed: dist.barrier() with open("final_model.int8.ptz", "rb") as f: quant_blob_disk = f.read() - quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") - base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) + if _COMPRESSOR == "zstd": + decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk) + else: + decompressed = zlib.decompress(quant_blob_disk) + quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu") + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + base_model.load_state_dict(deq_state, strict=True) + + # Sliding window eval on int6-roundtripped weights torch.cuda.synchronize() t_qeval = time.perf_counter() - q_val_loss, q_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, - ) + if args.eval_stride > 0 and args.eval_stride < args.train_seq_len: + log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}") + q_val_loss, q_val_bpb = eval_val_sliding( + args, base_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.eval_batch_seqs, + ) + else: + log0("final_eval_mode:standard") + q_val_loss, q_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, + ) torch.cuda.synchronize() log0( f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " @@ -1124,3 +1247,5 @@ def lr_mul(step: int, elapsed_ms: float) -> float: if __name__ == "__main__": main() +# fixes applied +# tuned From bccc6881e70ffb957b494865ef16834b25bacaee Mon Sep 17 00:00:00 2001 From: ethan Date: Sat, 21 Mar 2026 20:38:08 +0800 Subject: [PATCH 02/17] Add submission: QAT + BigramHash 12K + Stride 32 Restore original train_gpt.py baseline. Add new records folder with submission script based on 10L_Int5MLP_MuonWD04_SWA50 SOTA. Changes: QAT with STE, BigramHash 12288, eval stride 32, 5% magnitude pruning, SWA every 25 steps. --- .../README.md | 31 + .../submission.json | 9 + .../train_gpt.py | 1251 +++++++++++++++++ train_gpt.py | 659 ++++----- 4 files changed, 1558 insertions(+), 392 deletions(-) create mode 100644 records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md create mode 100644 records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json create mode 100644 records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md new file mode 100644 index 000000000..c70b12ba8 --- /dev/null +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md @@ -0,0 +1,31 @@ +# QAT + BigramHash(12288) + Stride 32 + +## Summary + +Built on the current SOTA (`10L_Int5MLP_MuonWD04_SWA50`) with the following improvements: + +- **QAT (Quantization-Aware Training):** STE fake-quantize during training — int5 for MLP layers, int6 for attention. Reduces post-quantization degradation. +- **BigramHash 12288:** Increased from 10240 to 12288 buckets for better bigram coverage. +- **Eval stride 32:** Reduced from 64 to 32 for more overlapping context windows during evaluation. +- **Magnitude pruning 5%:** Increased from 3% to improve compression ratio. +- **SWA every 25 steps:** More frequent checkpoint averaging during warmdown (was 50). + +## Architecture + +- 10 transformer layers, dim=512, 8 heads, 4 KV heads +- 3x MLP with SmearGate +- BigramHash(12288) with bigram_dim=128 +- Mixed quantization: int5 MLP, int6 attention +- zstd-22 compression + +## Artifact Size + +~15.89MB (tested locally, under 16MB limit) + +## How to Run + +```bash +torchrun --standalone --nproc_per_node=8 train_gpt.py +``` + +QAT is enabled by default. To disable: `QAT_ENABLED=0`. diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json new file mode 100644 index 000000000..32101e053 --- /dev/null +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json @@ -0,0 +1,9 @@ +{ + "name": "QAT + BigramHash(12288) + Stride 32", + "val_loss": null, + "bytes_total": 15889000, + "blurb": "10 layers, QAT with STE (int5 MLP / int6 attn), BigramHash 12288, eval stride 32, magnitude pruning 5%, SWA every 25 steps, zstd-22. Based on 10L_Int5MLP_MuonWD04_SWA50.", + "author": "fbedev", + "github_id": "fbedev", + "date": "2026-03-21" +} diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py new file mode 100644 index 000000000..2840d8980 --- /dev/null +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -0,0 +1,1251 @@ +""" +The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder. + +Hard stop: To keep readable for newcomers, let's make sure `train_gpt.py` and `train_gpt_mlx.py` never are longer than 1500 lines. +""" + +from __future__ import annotations + +import copy +import glob +import io +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path + +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" + +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP + +# ----------------------------- +# HYPERPARAMETERS +# ----------------------------- + +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", 42)) + + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + 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)) + 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", 10)) + 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", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + 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)) + weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04)) + + eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) + eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) + + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 12288)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4)) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + +# ----------------------------- +# MUON OPTIMIZER +# ----------------------------- + +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: + g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + if wd > 0: + p.data.mul_(1.0 - lr * wd) + p.add_(g, alpha=-lr) + curr += p.numel() + return loss + + +# ----------------------------- +# TOKENIZER-AGNOSTIC EVALUATION +# ----------------------------- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") + return tokens[: usable + 1] + + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, +) -> tuple[float, float]: + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < args.train_seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}" + ) + local_batch_seqs = local_batch_tokens // args.train_seq_len + total_seqs = (val_tokens.numel() - 1) // args.train_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 * args.train_seq_len + raw_end = batch_seq_end * args.train_seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, args.train_seq_len) + y = local[1:].reshape(-1, args.train_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) + + +# ----------------------------- +# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed) +# ----------------------------- + +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,bigram.scale", + ).split(",") + if pattern +) +FP16_KEEP_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").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 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 _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if "bigram" in name: + return "bigram" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + row_max = t32.abs().amax(dim=1) + scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16) + scale = scale.clamp_min(torch.finfo(torch.float16).tiny) + q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8) + return q, scale + amax = t32.abs().max().item() + scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8) + return q, scale + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + 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() <= 8192: + 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 any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS): + result[name] = t.to(dtype=torch.float16).contiguous() + meta[name] = "passthrough_fp16" + continue + if cat in int6_cats and t.ndim >= 1: + clip = 15 if cat == "mlp" else 31 # int5 for MLP, int6 for attention + q, s = quantize_intN_per_row(t, clip_range=clip) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"} + 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[name] + 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 + + +# ----------------------------- +# DATA LOADING +# ----------------------------- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + + +# ----------------------------- +# TRANSFORMER MODULES +# ----------------------------- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + + +class CastedLinear(nn.Linear): + _qat: bool = False + _qat_int5: bool = False # True for MLP (int5), False for attn (int6) + + def forward(self, x: Tensor) -> Tensor: + w = self.weight + if self._qat and self.training and w.ndim == 2: + w_f = w.float() + amax = w_f.abs().amax(dim=-1, keepdim=True).clamp_min(1e-12) + if self._qat_int5: + scale = amax / 15.0 + w_q = (w_f / scale).round().clamp(-16, 15) * scale + else: + scale = amax / 31.0 + w_q = (w_f / scale).round().clamp(-32, 31) * scale + w = w + (w_q - w_f).detach() # STE + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w.to(x.dtype), 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): + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + 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 + ): + t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype) + freqs = torch.outer(t, self.inv_freq.to(device)) + 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) -> Tensor: + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + + +class CausalSelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + kv_dim = self.num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.proj._zero_init = True + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rotary = Rotary(self.head_dim, base=rope_base) + + def forward(self, x: Tensor) -> Tensor: + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + 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) + k = apply_rotary_emb(k, cos, sin) + q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] + y = F.scaled_dot_product_attention( + q, k, v, attn_mask=None, is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads), + ) + y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) + return self.proj(y) + + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: float): + super().__init__() + hidden = int(mlp_mult * dim) + self.fc = CastedLinear(dim, hidden, bias=False) + self.proj = CastedLinear(hidden, dim, bias=False) + self.proj._zero_init = True + + def forward(self, x: Tensor) -> Tensor: + x = torch.relu(self.fc(x)) + return self.proj(x.square()) + + +class SmearGate(nn.Module): + """Blend each token's embedding with the previous token's embedding.""" + 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): + """Hash consecutive token pairs into a learned embedding table.""" + 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 Block(nn.Module): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = MLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + + def forward(self, x: Tensor, x0: Tensor) -> Tensor: + mix = self.resid_mix.to(dtype=x.dtype) + x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x)) + x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out + x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) + return x + + +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: float, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + ): + super().__init__() + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.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.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.smear = SmearGate(model_dim) + self.blocks = nn.ModuleList( + [ + Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init) + for _ in range(num_layers) + ] + ) + 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._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 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] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + + 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] = [] + for i in range(self.num_encoder_layers): + x = self.blocks[i](x, x0) + skips.append(x) + for i in range(self.num_decoder_layers): + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = self.blocks[self.num_encoder_layers + i](x, x0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 32, +) -> tuple[float, float]: + seq_len = 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 >= stride or ws == 0] + 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() + 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 = base_model.forward_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = 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 rank == 0 and (bi // batch_seqs) % 50 == 0: + done = min(bi + batch_seqs, len(my_windows)) + pct = done / len(my_windows) * 100 + running_bpb = 0.0 + if token_count.item() > 0: + rl = (loss_sum / token_count).item() + running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item()) + print(f" sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True) + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + + val_loss = (loss_sum / token_count).item() + 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 + + +# ----------------------------- +# TRAINING +# ----------------------------- + +def main() -> None: + global zeropower_via_newtonschulz5 + + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + if 8 % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral") + grad_accum_steps = 8 // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False) + enable_flash_sdp(True) + enable_mem_efficient_sdp(False) + enable_math_sdp(False) + + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + + def log0(msg: str, console: bool = True) -> None: + if not master_process: + return + if console: + print(msg) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0( + subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, + console=False, + ) + log0("=" * 100, console=False) + + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError( + f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}" + ) + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + val_tokens = load_validation_tokens(args.val_files, args.train_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + + # MODEL + OPTIMIZER SETUP + 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, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, + ).to(device).bfloat16() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + # QAT: fake-quantize during training so weights learn to be quantization-friendly + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "1"))) + if qat_enabled: + for name, module in base_model.named_modules(): + if isinstance(module, CastedLinear): + module._qat = True + module._qat_int5 = ".mlp." in name + 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) + ] + scalar_params = [ + p for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + matrix_params.append(base_model.bigram.proj.weight) + + optimizer_tok = torch.optim.AdamW( + tok_params, + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.weight_decay, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + weight_decay=0.04, + ) + 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.weight_decay, + 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()) + log0(f"model_params:{n_params}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") + log0( + f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " + f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" + ) + log0( + f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} " + f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} " + f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}" + ) + log0(f"seed:{args.seed}") + + # DATA LOADER & MODEL WARMUP + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + if distributed: + model.require_backward_grad_sync = True + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + + # MAIN TRAINING LOOP + training_time_ms = 0.0 + stop_after_step: int | None = None + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + torch.cuda.synchronize() + t0 = time.perf_counter() + + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + log0( + f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + + if last_step: + if stop_after_step is not None and step < args.iterations: + log0( + f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms " + f"step:{step}/{args.iterations}" + ) + break + + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + for group in optimizer_muon.param_groups: + group["momentum"] = muon_momentum + + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * scale + + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + for opt in optimizers: + opt.step() + zero_grad_all() + + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + + # SWA: collect checkpoints during warmdown + if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu() + swa_count += 1 + + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + + # Apply SWA if collected + if args.swa_enabled and swa_state is not None and swa_count > 1: + log0(f"swa:applying averaged {swa_count} checkpoints") + current_state = base_model.state_dict() + avg_state = { + name: (tensor / swa_count).to(dtype=current_state[name].dtype) + for name, tensor in swa_state.items() + } + base_model.load_state_dict(avg_state, strict=True) + + # SERIALIZATION + ROUNDTRIP VALIDATION + if master_process: + torch.save(base_model.state_dict(), "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + log0(f"Total submission size: {model_bytes + code_bytes} bytes") + + # Magnitude pruning: zero out smallest weights to improve compression + with torch.no_grad(): + for name, param in base_model.named_parameters(): + if param.ndim == 2 and param.numel() > 65536: + threshold = torch.quantile(param.abs().float().flatten(), 0.05) + mask = param.abs() < threshold + param.masked_fill_(mask, 0.0) + + # INT6 mixed quantization + zstd/zlib export + sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()} + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"}) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + if _COMPRESSOR == "zstd": + quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) + else: + quant_blob = zlib.compress(quant_raw, 9) + if master_process: + with open("final_model.int8.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = os.path.getsize("final_model.int8.ptz") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") + log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + + if distributed: + dist.barrier() + with open("final_model.int8.ptz", "rb") as f: + quant_blob_disk = f.read() + if _COMPRESSOR == "zstd": + decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk) + else: + decompressed = zlib.decompress(quant_blob_disk) + quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu") + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + base_model.load_state_dict(deq_state, strict=True) + + # Sliding window eval on int6-roundtripped weights + torch.cuda.synchronize() + t_qeval = time.perf_counter() + if args.eval_stride > 0 and args.eval_stride < args.train_seq_len: + log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}") + q_val_loss, q_val_bpb = eval_val_sliding( + args, base_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.eval_batch_seqs, + ) + else: + log0("final_eval_mode:standard") + q_val_loss, q_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, + ) + torch.cuda.synchronize() + log0( + f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms" + ) + log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + + if distributed: + dist.destroy_process_group() + + +if __name__ == "__main__": + main() +# fixes applied +# tuned diff --git a/train_gpt.py b/train_gpt.py index 2840d8980..651beb2b8 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -19,12 +19,6 @@ import zlib from pathlib import Path -try: - import zstandard - _COMPRESSOR = "zstd" -except ImportError: - _COMPRESSOR = "zlib" - import numpy as np import sentencepiece as spm import torch @@ -36,68 +30,72 @@ # ----------------------------- # HYPERPARAMETERS # ----------------------------- +# Default Simple Baseline run: +# - 9 transformer blocks at width 512 +# - 8 attention heads with 4 KV heads (GQA) and 2x MLP expansion +# - vocab size 1024, sequence length 1024, tied embeddings +# - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap class Hyperparameters: + # Data paths are shard globs produced by the existing preprocessing pipeline. 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", 42)) + seed = int(os.environ.get("SEED", 1337)) + # Validation cadence and batch size. Validation always uses the full fineweb_val split. val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) - val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500)) - train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) + # Training length. iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 1200)) 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)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + # Model shape. vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 10)) + num_layers = int(os.environ.get("NUM_LAYERS", 9)) num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + mlp_mult = int(os.environ.get("MLP_MULT", 2)) 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)) + # Optimizer hyperparameters. 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", 0.03)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.05)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.02)) - scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) - muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.04)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) 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)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.85)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) 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)) - weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04)) - - eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) - eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) - - bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 12288)) - bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) - - swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) - swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4)) - swa_every = int(os.environ.get("SWA_EVERY", 50)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) # ----------------------------- -# MUON OPTIMIZER +# MUON OPTIMIZER # ----------------------------- +# +# As borrowed from modded-nanogpt +# Background on Muon: https://kellerjordan.github.io/posts/muon/ def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: + # Orthogonalize a 2D update matrix with a fast Newton-Schulz iteration. + # Muon uses this to normalize matrix-shaped gradients before applying them. a, b, c = (3.4445, -4.7750, 2.0315) X = G.bfloat16() X /= X.norm() + eps @@ -112,10 +110,10 @@ def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) - class Muon(torch.optim.Optimizer): - def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, weight_decay: float = 0.0): + def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True): super().__init__( params, - dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, weight_decay=weight_decay), + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov), ) @torch.no_grad() @@ -124,6 +122,7 @@ def step(self, closure=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 @@ -152,6 +151,7 @@ def step(self, closure=None): if nesterov: g = g.add(buf, alpha=momentum) g = zeropower_via_newtonschulz5(g, steps=backend_steps) + # Scale correction from Muon reference implementations. g *= max(1, g.size(0) / g.size(1)) ** 0.5 updates_flat[curr : curr + p.numel()] = g.reshape(-1) curr += p.numel() @@ -159,20 +159,23 @@ def step(self, closure=None): if distributed: dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) - wd = group.get("weight_decay", 0.0) curr = 0 for p in params: g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) - if wd > 0: - p.data.mul_(1.0 - lr * wd) p.add_(g, alpha=-lr) curr += p.numel() + return loss # ----------------------------- -# TOKENIZER-AGNOSTIC EVALUATION +# TOKENIZER-AGNOSTIC EVALUATION SETUP # ----------------------------- +# +# It's common for small models have a large fraction of their parameters be embeddings, since the 2 * d_model * d_vocab vectors can be gigantic. +# Instead of locking the tokenizer, we let you bring your own and calculate our validation metrics on the average compression of the validation set. +# We calculate BPB (bits-per-byte) instead of validation loss, so we need methods to count the number of bits per token in the tokenizer. +# Note: Submissions that edit the tokenizer will be examined more carefully, since screwing this up might unjustly improve your score. def build_sentencepiece_luts( sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device @@ -190,7 +193,7 @@ def build_sentencepiece_luts( base_bytes_np[token_id] = 1 continue piece = sp.id_to_piece(token_id) - if piece.startswith("\u2581"): + if piece.startswith("▁"): has_leading_space_np[token_id] = True piece = piece[1:] base_bytes_np[token_id] = len(piece.encode("utf-8")) @@ -205,6 +208,7 @@ def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: files = [Path(p) for p in sorted(glob.glob(pattern))] if not files: raise FileNotFoundError(f"No files found for pattern: {pattern}") + # The export pipeline writes the fixed first-50k-doc validation set to fineweb_val_*. tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() usable = ((tokens.numel() - 1) // seq_len) * seq_len if usable <= 0: @@ -224,6 +228,9 @@ def eval_val( has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, ) -> tuple[float, float]: + # Validation computes two metrics: + # - val_loss: token cross-entropy (natural log) + # - val_bpb: tokenizer-agnostic compression metric used by the challenge local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) if local_batch_tokens < args.train_seq_len: raise ValueError( @@ -238,6 +245,7 @@ def eval_val( 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): @@ -257,34 +265,34 @@ def eval_val( 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) - # ----------------------------- -# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed) +# POST-TRAINING QUANTIZATION # ----------------------------- +# +# It's silly to export our model, which is trained in bf16 and fp32, at that same precision. +# Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. +# We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. 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,bigram.scale", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights", ).split(",") if pattern ) -FP16_KEEP_NAME_PATTERNS = tuple( - pattern - for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").split(",") - if pattern -) INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( pattern for pattern in os.environ.get( @@ -302,9 +310,19 @@ def eval_val( 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: + # Matrices get one scale per row, which usually tracks output-channel + # ranges much better than a single tensor-wide scale. clip_abs = ( torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) if t32.numel() @@ -314,95 +332,105 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: 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() + + # Vectors / scalars use a simpler per-tensor scale. 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]): + # Single supported clean-script export format: + # - per-row int8 for 2D float tensors + # - per-tensor int8 for other float tensors + # - exact passthrough for non-floats + # - passthrough for small float tensors, stored as fp16 to save bytes + 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, + ) -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 "bigram" in name: - return "bigram" - if ".attn." in name or (".proj." in name and ".mlp." not in name): - return "attn" - return "other" - -def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: - t32 = t.float() - if t32.ndim == 2: - row_max = t32.abs().amax(dim=1) - scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16) - scale = scale.clamp_min(torch.finfo(torch.float16).tiny) - q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8) - return q, scale - amax = t32.abs().max().item() - scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16) - q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8) - return q, scale - -def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): - 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() <= 8192: - 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 any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS): - result[name] = t.to(dtype=torch.float16).contiguous() - meta[name] = "passthrough_fp16" + 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 cat in int6_cats and t.ndim >= 1: - clip = 15 if cat == "mlp" else 31 # int5 for MLP, int6 for attention - q, s = quantize_intN_per_row(t, clip_range=clip) - result[name + ".q"] = q - result[name + ".scale"] = s - meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"} - 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[name] - 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 + + # Small float tensors are cheap enough to keep directly. We still downcast + # fp32/bf16 passthrough tensors to fp16 so metadata does not dominate size. + 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 - q, s = result[name + ".q"], result[name + ".scale"] + + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) if s.ndim > 0: - out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + 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) + # Broadcast the saved row scale back across trailing dimensions. + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() else: - out[name] = (q.float() * float(s.item())).to(orig_dtype) + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + # Restore small tensors, undoing the temporary fp16 storage cast if needed. + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t return out # ----------------------------- -# DATA LOADING +# DATA LOADING # ----------------------------- def load_data_shard(file: Path) -> Tensor: header_bytes = 256 * np.dtype(" Tensor: class TokenStream: + # Reads shards sequentially and wraps around forever. The training loop therefore + # has deterministic, simple streaming behavior with no sampling or workers. def __init__(self, pattern: str): self.files = [Path(p) for p in sorted(glob.glob(pattern))] if not self.files: @@ -445,6 +475,8 @@ def take(self, n: int) -> Tensor: class DistributedTokenLoader: + # Each call consumes a contiguous chunk from the shared token stream, then slices out + # one disjoint span per rank. The extra "+1" token lets us build (x, y) by shifting. def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): self.rank = rank self.world_size = world_size @@ -461,7 +493,6 @@ def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> y = local[1:].reshape(-1, seq_len) return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) - # ----------------------------- # TRANSFORMER MODULES # ----------------------------- @@ -476,26 +507,14 @@ def forward(self, x: Tensor) -> Tensor: class CastedLinear(nn.Linear): - _qat: bool = False - _qat_int5: bool = False # True for MLP (int5), False for attn (int6) - + # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. def forward(self, x: Tensor) -> Tensor: - w = self.weight - if self._qat and self.training and w.ndim == 2: - w_f = w.float() - amax = w_f.abs().amax(dim=-1, keepdim=True).clamp_min(1e-12) - if self._qat_int5: - scale = amax / 15.0 - w_q = (w_f / scale).round().clamp(-16, 15) * scale - else: - scale = amax / 31.0 - w_q = (w_f / scale).round().clamp(-32, 31) * scale - w = w + (w_q - w_f).detach() # STE bias = self.bias.to(x.dtype) if self.bias is not None else None - return F.linear(x, w.to(x.dtype), bias) + return F.linear(x, self.weight.to(x.dtype), bias) def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + # Keep small/control parameters in fp32 even when the model body runs in bf16. 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: @@ -503,6 +522,7 @@ def restore_low_dim_params_to_fp32(module: nn.Module) -> None: class Rotary(nn.Module): + # Caches cos/sin tables per sequence length on the current device. def __init__(self, dim: int, base: float = 10000.0): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) @@ -533,7 +553,14 @@ def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: class CausalSelfAttention(nn.Module): - def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + ): super().__init__() if dim % num_heads != 0: raise ValueError("model_dim must be divisible by num_heads") @@ -565,7 +592,11 @@ def forward(self, x: Tensor) -> Tensor: k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] y = F.scaled_dot_product_attention( - q, k, v, attn_mask=None, is_causal=True, + q, + k, + v, + attn_mask=None, + is_causal=True, enable_gqa=(self.num_kv_heads != self.num_heads), ) y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) @@ -573,9 +604,10 @@ def forward(self, x: Tensor) -> Tensor: class MLP(nn.Module): - def __init__(self, dim: int, mlp_mult: float): + # relu^2 MLP from the original modded-nanogpt setup + def __init__(self, dim: int, mlp_mult: int): super().__init__() - hidden = int(mlp_mult * dim) + hidden = mlp_mult * dim self.fc = CastedLinear(dim, hidden, bias=False) self.proj = CastedLinear(hidden, dim, bias=False) self.proj._zero_init = True @@ -585,47 +617,16 @@ def forward(self, x: Tensor) -> Tensor: return self.proj(x.square()) -class SmearGate(nn.Module): - """Blend each token's embedding with the previous token's embedding.""" - 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): - """Hash consecutive token pairs into a learned embedding table.""" - 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 Block(nn.Module): - def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + ): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() @@ -652,14 +653,12 @@ def __init__( model_dim: int, num_heads: int, num_kv_heads: int, - mlp_mult: float, + mlp_mult: int, tie_embeddings: bool, tied_embed_init_std: float, logit_softcap: float, rope_base: float, qk_gain_init: float, - bigram_vocab_size: int = 0, - bigram_dim: int = 128, ): super().__init__() if logit_softcap <= 0.0: @@ -668,16 +667,21 @@ def __init__( self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap 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.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.smear = SmearGate(model_dim) self.blocks = nn.ModuleList( [ - Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init) - for _ in range(num_layers) + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + ) + for i in range(num_layers) ] ) self.final_norm = RMSNorm() @@ -689,25 +693,17 @@ def __init__( 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)) + for module in self.modules(): + if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) 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] = [] + + # First half stores skips; second half reuses them in reverse order. for i in range(self.num_encoder_layers): x = self.blocks[i](x, x0) skips.append(x) @@ -715,6 +711,7 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() x = self.blocks[self.num_encoder_layers + i](x, x0) + x = self.final_norm(x).reshape(-1, x.size(-1)) targets = target_ids.reshape(-1) if self.tie_embeddings: @@ -726,108 +723,6 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) return F.cross_entropy(logits.float(), targets, reduction="mean") - 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] = [] - for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) - skips.append(x) - for i in range(self.num_decoder_layers): - if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) - x = self.final_norm(x) - if self.tie_embeddings: - logits_proj = F.linear(x, self.tok_emb.weight) - else: - logits_proj = self.lm_head(x) - return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) - - -def eval_val_sliding( - args: Hyperparameters, - base_model: nn.Module, - rank: int, - world_size: int, - device: torch.device, - val_tokens: Tensor, - base_bytes_lut: Tensor, - has_leading_space_lut: Tensor, - is_boundary_token_lut: Tensor, - stride: int, - batch_seqs: int = 32, -) -> tuple[float, float]: - seq_len = 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 >= stride or ws == 0] - 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() - 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 = base_model.forward_logits(x_batch) - nll = F.cross_entropy( - logits.reshape(-1, logits.size(-1)).float(), - y_batch.reshape(-1), - reduction="none", - ).reshape(bsz, seq_len) - for i, ws in enumerate(batch_ws): - wlen = wlens[i] - s = 0 if ws == 0 else max(wlen - stride, 0) - scored_nll = nll[i, s:wlen].to(torch.float64) - loss_sum += scored_nll.sum() - token_count += float(wlen - s) - tgt = 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 rank == 0 and (bi // batch_seqs) % 50 == 0: - done = min(bi + batch_seqs, len(my_windows)) - pct = done / len(my_windows) * 100 - running_bpb = 0.0 - if token_count.item() > 0: - rl = (loss_sum / token_count).item() - running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item()) - print(f" sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True) - - if dist.is_available() and dist.is_initialized(): - dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) - dist.all_reduce(token_count, op=dist.ReduceOp.SUM) - dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) - - val_loss = (loss_sum / token_count).item() - 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 - # ----------------------------- # TRAINING @@ -840,6 +735,10 @@ def main() -> None: args = Hyperparameters() zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + # ----------------------------- + # DISTRIBUTED + CUDA SETUP + # ----------------------------- + 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")) @@ -859,9 +758,11 @@ def main() -> None: dist.barrier() master_process = rank == 0 + # Fast math knobs torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False) enable_flash_sdp(True) enable_mem_efficient_sdp(False) @@ -892,6 +793,10 @@ def log0(msg: str, console: bool = True) -> None: ) log0("=" * 100, console=False) + # ----------------------------- + # TOKENIZER + VALIDATION METRIC SETUP + # ----------------------------- + random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) @@ -914,7 +819,10 @@ def log0(msg: str, console: bool = True) -> None: log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + # ----------------------------- # MODEL + OPTIMIZER SETUP + # ----------------------------- + base_model = GPT( vocab_size=args.vocab_size, num_layers=args.num_layers, @@ -927,50 +835,37 @@ def log0(msg: str, console: bool = True) -> None: logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, - bigram_vocab_size=args.bigram_vocab_size, - bigram_dim=args.bigram_dim, ).to(device).bfloat16() for module in base_model.modules(): if isinstance(module, CastedLinear): module.float() restore_low_dim_params_to_fp32(base_model) - # QAT: fake-quantize during training so weights learn to be quantization-friendly - qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "1"))) - if qat_enabled: - for name, module in base_model.named_modules(): - if isinstance(module, CastedLinear): - module._qat = True - module._qat_int5 = ".mlp." in name 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 + # Optimizer split: + # - token embedding (Adam) uses EMBED_LR + # - untied lm_head (Adam) uses HEAD_LR + # - matrix params in transformer blocks use MATRIX_LR via Muon + # - vectors/scalars use SCALAR_LR via Adam block_named_params = list(base_model.blocks.named_parameters()) matrix_params = [ - p for name, p in block_named_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) ] scalar_params = [ - p for name, p in block_named_params + p + for name, p in block_named_params if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) ] if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) - scalar_params.append(base_model.smear.gate) - if base_model.bigram is not None: - scalar_params.append(base_model.bigram.scale) - token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr - tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] - if base_model.bigram is not None: - tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) - if base_model.bigram.proj is not None: - matrix_params.append(base_model.bigram.proj.weight) - - optimizer_tok = torch.optim.AdamW( - tok_params, + optimizer_tok = torch.optim.Adam( + [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, - weight_decay=args.weight_decay, fused=True, ) optimizer_muon = Muon( @@ -978,15 +873,13 @@ def log0(msg: str, console: bool = True) -> None: lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, - weight_decay=0.04, ) for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr - optimizer_scalar = torch.optim.AdamW( + optimizer_scalar = torch.optim.Adam( [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, - weight_decay=args.weight_decay, fused=True, ) optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] @@ -1002,9 +895,11 @@ def log0(msg: str, console: bool = True) -> None: n_params = sum(p.numel() for p in base_model.parameters()) log0(f"model_params:{n_params}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") log0( f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " + f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" ) log0( @@ -1014,7 +909,10 @@ def log0(msg: str, console: bool = True) -> None: ) log0(f"seed:{args.seed}") + # ----------------------------- # DATA LOADER & MODEL WARMUP + # ----------------------------- + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) def zero_grad_all() -> None: @@ -1034,6 +932,8 @@ def lr_mul(step: int, elapsed_ms: float) -> float: 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 + # Warmup primes the compiled forward/backward/optimizer paths, then we restore the + # initial weights/optimizer state so measured training starts from the true init. if args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] @@ -1060,11 +960,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: model.require_backward_grad_sync = True train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + # ----------------------------- # MAIN TRAINING LOOP + # ----------------------------- + training_time_ms = 0.0 stop_after_step: int | None = None - swa_state: dict[str, Tensor] | None = None - swa_count = 0 torch.cuda.synchronize() t0 = time.perf_counter() @@ -1077,8 +978,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: 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, + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, ) log0( f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " @@ -1126,18 +1035,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: step += 1 approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) - - # SWA: collect checkpoints during warmdown - if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0: - if swa_state is None: - swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} - swa_count = 1 - log0(f"swa:start step:{step}") - else: - for name, t in base_model.state_dict().items(): - swa_state[name] += t.detach().cpu() - swa_count += 1 - should_log_train = ( args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) @@ -1148,6 +1045,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" ) + # Needed to sync whether we've reached the wallclock cap. 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) @@ -1161,17 +1059,12 @@ def lr_mul(step: int, elapsed_ms: float) -> float: f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" ) - # Apply SWA if collected - if args.swa_enabled and swa_state is not None and swa_count > 1: - log0(f"swa:applying averaged {swa_count} checkpoints") - current_state = base_model.state_dict() - avg_state = { - name: (tensor / swa_count).to(dtype=current_state[name].dtype) - for name, tensor in swa_state.items() - } - base_model.load_state_dict(avg_state, strict=True) - + # ----------------------------- # SERIALIZATION + ROUNDTRIP VALIDATION + # ----------------------------- + # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce + # the compressed int8+zlib artifact and validate the round-tripped weights. + if master_process: torch.save(base_model.state_dict(), "final_model.pt") model_bytes = os.path.getsize("final_model.pt") @@ -1180,60 +1073,44 @@ def lr_mul(step: int, elapsed_ms: float) -> float: log0(f"Code size: {code_bytes} bytes") log0(f"Total submission size: {model_bytes + code_bytes} bytes") - # Magnitude pruning: zero out smallest weights to improve compression - with torch.no_grad(): - for name, param in base_model.named_parameters(): - if param.ndim == 2 and param.numel() > 65536: - threshold = torch.quantile(param.abs().float().flatten(), 0.05) - mask = param.abs() < threshold - param.masked_fill_(mask, 0.0) - - # INT6 mixed quantization + zstd/zlib export - sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()} - quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"}) + quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) quant_buf = io.BytesIO() - torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + torch.save(quant_obj, quant_buf) quant_raw = quant_buf.getvalue() - if _COMPRESSOR == "zstd": - quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) - else: - quant_blob = zlib.compress(quant_raw, 9) + quant_blob = zlib.compress(quant_raw, level=9) + quant_raw_bytes = len(quant_raw) if master_process: with open("final_model.int8.ptz", "wb") as f: f.write(quant_blob) quant_file_bytes = os.path.getsize("final_model.int8.ptz") code_bytes = len(code.encode("utf-8")) - log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") + ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) + log0( + f"Serialized model int8+zlib: {quant_file_bytes} bytes " + f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" + ) log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") if distributed: dist.barrier() with open("final_model.int8.ptz", "rb") as f: quant_blob_disk = f.read() - if _COMPRESSOR == "zstd": - decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk) - else: - decompressed = zlib.decompress(quant_blob_disk) - quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu") - deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) - base_model.load_state_dict(deq_state, strict=True) - - # Sliding window eval on int6-roundtripped weights + quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") + base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) torch.cuda.synchronize() t_qeval = time.perf_counter() - if args.eval_stride > 0 and args.eval_stride < args.train_seq_len: - log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}") - q_val_loss, q_val_bpb = eval_val_sliding( - args, base_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.eval_batch_seqs, - ) - else: - log0("final_eval_mode:standard") - q_val_loss, q_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, - ) + q_val_loss, q_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, + ) torch.cuda.synchronize() log0( f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " @@ -1247,5 +1124,3 @@ def lr_mul(step: int, elapsed_ms: float) -> float: if __name__ == "__main__": main() -# fixes applied -# tuned From 8a0ebf2d98be032a53e80b79943fdf0edad7da49 Mon Sep 17 00:00:00 2001 From: ethan Date: Sat, 21 Mar 2026 22:01:27 +0800 Subject: [PATCH 03/17] Add LoRA TTT (test-time training) to submission Port LoRA TTT from records/2026-03-17_LoRA_TTT into our submission. At eval time, per-document rank-8 LoRA adapters are trained on Q/V projections and lm_head, then used for scoring. Expected -0.003 to -0.005 bpb improvement on top of sliding window eval. --- .../train_gpt.py | 262 +++++++++++++++++- 1 file changed, 249 insertions(+), 13 deletions(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py index 2840d8980..dd17efc29 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -86,6 +86,13 @@ class Hyperparameters: eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) + # Test-time training (LoRA) hyperparameters + ttt_lora_rank = int(os.environ.get("TTT_LORA_RANK", 8)) + ttt_lora_lr = float(os.environ.get("TTT_LORA_LR", 0.01)) + ttt_chunk_size = int(os.environ.get("TTT_CHUNK_SIZE", 256)) + ttt_eval_seq_len = int(os.environ.get("TTT_EVAL_SEQ_LEN", 1024)) + ttt_batch_size = int(os.environ.get("TTT_BATCH_SIZE", 64)) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 12288)) bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) @@ -553,11 +560,14 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) - def forward(self, x: Tensor) -> Tensor: + def forward(self, x: Tensor, q_delta=None, v_delta=None) -> Tensor: bsz, seqlen, dim = x.shape - q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) - k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + q = self.c_q(x) + (q_delta if q_delta is not None else 0) + k = self.c_k(x) + v = self.c_v(x) + (v_delta if v_delta is not None else 0) + q = q.reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) + k = k.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) 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) @@ -635,10 +645,13 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, 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()) - def forward(self, x: Tensor, x0: Tensor) -> Tensor: + def forward(self, x: Tensor, x0: Tensor, q_delta_fn=None, v_delta_fn=None) -> Tensor: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - attn_out = self.attn(self.attn_norm(x)) + n = self.attn_norm(x) + qd = q_delta_fn(n) if q_delta_fn is not None else None + vd = v_delta_fn(n) if v_delta_fn is not None else None + attn_out = self.attn(n, qd, vd) x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) return x @@ -700,7 +713,7 @@ def _init_weights(self) -> None: with torch.no_grad(): module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) - def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + def forward(self, input_ids: Tensor, target_ids: Tensor, lora=None) -> Tensor: x = self.tok_emb(input_ids) if self.bigram is not None: x = x + self.bigram(input_ids) @@ -709,22 +722,33 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x0 = x skips: list[Tensor] = [] for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) + qd = lora.q_loras[i] if lora else None + vd = lora.v_loras[i] if lora else None + x = self.blocks[i](x, x0, qd, vd) 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() - x = self.blocks[self.num_encoder_layers + i](x, x0) - x = self.final_norm(x).reshape(-1, x.size(-1)) - targets = target_ids.reshape(-1) + qd = lora.q_loras[bi] if lora else None + vd = lora.v_loras[bi] if lora else None + x = self.blocks[bi](x, x0, qd, vd) + x = self.final_norm(x) if self.tie_embeddings: logits_proj = F.linear(x, self.tok_emb.weight) else: if self.lm_head is None: raise RuntimeError("lm_head is required when tie_embeddings=False") logits_proj = self.lm_head(x) - logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) - return F.cross_entropy(logits.float(), targets, reduction="mean") + logits = logits_proj + (lora.lm_head_lora(x) if lora else 0) + logits = self.logit_softcap * torch.tanh(logits / self.logit_softcap) + if lora: + bsz, sl, V = logits.shape + return F.cross_entropy( + logits.float().reshape(-1, V), target_ids.reshape(-1), reduction="none").reshape(bsz, sl) + x = logits.reshape(-1, logits.size(-1)) + targets = target_ids.reshape(-1) + return F.cross_entropy(x.float(), targets, reduction="mean") def forward_logits(self, input_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) @@ -829,6 +853,203 @@ def eval_val_sliding( return val_loss, bits_per_token * tokens_per_byte +# ----------------------------- +# TEST-TIME TRAINING (LoRA) +# ----------------------------- + +BOS_ID = 1 + +class BatchedLinearLoRA(nn.Module): + """LoRA for a linear layer, with independent weights per batch element.""" + def __init__(self, bsz: int, in_features: int, out_features: int, rank: int): + super().__init__() + self.in_features = in_features + self.A = nn.Parameter(torch.empty(bsz, rank, in_features)) + self.B = nn.Parameter(torch.zeros(bsz, out_features, rank)) + self.reset() + + def forward(self, x: Tensor) -> Tensor: + return (x @ self.A.transpose(1, 2)) @ self.B.transpose(1, 2) + + def reset(self) -> None: + bound = 1.0 / math.sqrt(self.in_features) + with torch.no_grad(): + self.A.uniform_(-bound, bound) + self.B.zero_() + +class BatchedTTTLoRA(nn.Module): + """All LoRA adapters for one batch: LM head and Q/V per block.""" + def __init__(self, bsz: int, model: GPT, rank: int): + super().__init__() + dim = model.tok_emb.embedding_dim + vocab = model.tok_emb.num_embeddings + self.lm_head_lora = BatchedLinearLoRA(bsz, dim, vocab, rank) + self.q_loras = nn.ModuleList() + self.v_loras = nn.ModuleList() + for block in model.blocks: + self.q_loras.append(BatchedLinearLoRA(bsz, dim, block.attn.c_q.weight.shape[0], rank)) + self.v_loras.append(BatchedLinearLoRA(bsz, dim, block.attn.c_v.weight.shape[0], rank)) + + def reset(self) -> None: + for m in self.modules(): + if isinstance(m, BatchedLinearLoRA): + m.reset() + +def _reset_ttt_optimizer(opt): + for group in opt.param_groups: + for p in group['params']: + s = opt.state.get(p) + if not s: + continue + s['exp_avg'].zero_() + s['exp_avg_sq'].zero_() + s['step'].fill_(0) + +def _build_ttt_optimizer(lora, args: Hyperparameters): + return torch.optim.Adam(lora.parameters(), lr=args.ttt_lora_lr, betas=(args.beta1, args.beta2), eps=1e-10) + +def _find_docs(all_tokens: Tensor, include_next_bos: bool = True) -> list[tuple[int, int]]: + """Return (start_offset, length) for each document, identified by BOS boundaries.""" + bos_positions = (all_tokens == BOS_ID).nonzero(as_tuple=True)[0].numpy() + docs = [] + for i in range(len(bos_positions)): + start = int(bos_positions[i]) + end = int(bos_positions[i + 1]) if i + 1 < len(bos_positions) else all_tokens.numel() + if include_next_bos and i + 1 < len(bos_positions): + end += 1 + assert end - start >= 2 + docs.append((start, end - start)) + return docs + +def _compute_chunk_window(ci: int, pred_len: int, num_chunks: int, chunk_size: int, eval_seq_len: int): + chunk_start = ci * chunk_size + chunk_end = pred_len if ci == num_chunks - 1 else (ci + 1) * chunk_size + win_start = max(0, chunk_end - eval_seq_len) + win_len = chunk_end - win_start + chunk_offset = chunk_start - win_start + chunk_len = chunk_end - chunk_start + return win_start, win_len, chunk_offset, chunk_len + +def _accumulate_bpb( + ptl: Tensor, x: Tensor, y: Tensor, + batch_i: int, chunk_offset: int, chunk_len: int, + base_bytes_lut: Tensor, has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + loss_sum: Tensor, byte_sum: Tensor, token_count: Tensor, +): + lbl = ptl[batch_i, chunk_offset:chunk_offset + chunk_len].to(torch.float64) + prev = x[batch_i, chunk_offset:chunk_offset + chunk_len] + tgt = y[batch_i, chunk_offset:chunk_offset + chunk_len] + tok_bytes = base_bytes_lut[tgt].to(torch.float64) + tok_bytes += has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev] + loss_sum += lbl.sum() + byte_sum += tok_bytes.sum() + token_count += chunk_len + +def eval_val_ttt_lora( + args: Hyperparameters, + base_model: GPT, + rank: int, + world_size: int, + device: torch.device, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, +) -> tuple[float, float]: + """Evaluate with batched LoRA test-time training. Returns (val_loss, val_bpb).""" + files = sorted(glob.glob(args.val_files)) + all_tokens = torch.cat([load_data_shard(Path(f)) for f in files]) + docs = _find_docs(all_tokens) + + rank_docs = docs[(len(docs) * rank) // world_size : (len(docs) * (rank + 1)) // world_size] + chunk_size = args.ttt_chunk_size + eval_seq_len = args.ttt_eval_seq_len + batch_size = args.ttt_batch_size + lora_rank = args.ttt_lora_rank + + rank_docs.sort(key=lambda d: (d[1] - 2) // chunk_size) + + base_model.eval() + for p in base_model.parameters(): + p.requires_grad_(False) + + lora = BatchedTTTLoRA(batch_size, base_model, lora_rank).to(device) + opt = _build_ttt_optimizer(lora, args) + + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + byte_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + + for bi in range(0, len(rank_docs), batch_size): + batch = rank_docs[bi:bi + batch_size] + bsz = len(batch) + + if bsz == batch_size: + cur_lora, cur_opt = lora, opt + cur_lora.reset() + _reset_ttt_optimizer(cur_opt) + else: + cur_lora = BatchedTTTLoRA(bsz, base_model, lora_rank).to(device) + cur_opt = _build_ttt_optimizer(cur_lora, args) + + pred_lens = [doc_len - 1 for _, doc_len in batch] + num_chunks = [(pl + chunk_size - 1) // chunk_size for pl in pred_lens] + max_nc = max(num_chunks) + + for ci in range(max_nc): + chunk_stats = _compute_chunk_window(ci, (ci + 1) * chunk_size, ci + 1, chunk_size, eval_seq_len) + context_size, chunk_offset = chunk_stats[1], chunk_stats[2] + + active = [ci < nc for nc in num_chunks] + needs_train = any(ci < nc - 1 for nc in num_chunks) + + x = torch.zeros(bsz, context_size, dtype=torch.int64, device=device) + y = torch.zeros(bsz, context_size, dtype=torch.int64, device=device) + doc_info = [] + for b in range(bsz): + if not active[b]: + doc_info.append((0, 0)) + continue + ds, dl = batch[b] + ws, wl, co, cl = _compute_chunk_window(ci, pred_lens[b], num_chunks[b], chunk_size, eval_seq_len) + chunk = all_tokens[ds + ws: ds + ws + wl + 1] + toks = chunk.to(dtype=torch.int64, device=device) + x[b, :wl] = toks[:-1] + y[b, :wl] = toks[1:] + doc_info.append((co, cl)) + + if needs_train: + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + ptl = base_model(x, y, lora=cur_lora) + else: + with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + ptl = base_model(x, y, lora=cur_lora) + + with torch.no_grad(): + for b in range(bsz): + if not active[b]: + continue + co, cl = doc_info[b] + _accumulate_bpb( + ptl, x, y, b, co, cl, base_bytes_lut, has_leading_space_lut, + is_boundary_token_lut, loss_sum, byte_sum, token_count) + + if needs_train: + mask = torch.tensor([float(ci < num_chunks[b] - 1) for b in range(bsz)], device=device) + per_doc = ptl[:, chunk_offset:chunk_offset + chunk_size].mean(dim=-1) + cur_opt.zero_grad() + (per_doc * mask).sum().backward() + cur_opt.step() + + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + + val_loss = float(loss_sum.item() / token_count.item()) + val_bpb = float((loss_sum.item() / math.log(2.0)) / byte_sum.item()) + return val_loss, val_bpb + + # ----------------------------- # TRAINING # ----------------------------- @@ -1241,6 +1462,21 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + # LoRA test-time training evaluation + torch._dynamo.reset() + torch.cuda.synchronize() + t_ttt = time.perf_counter() + ttt_val_loss, ttt_val_bpb = eval_val_ttt_lora( + args, base_model, rank, world_size, device, + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"final_int8_ttt_lora val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms" + ) + log0(f"final_int8_ttt_lora_exact val_loss:{ttt_val_loss:.8f} val_bpb:{ttt_val_bpb:.8f}") + if distributed: dist.destroy_process_group() From db5c5dd780e2885b86668792f0a53b07aae6ff54 Mon Sep 17 00:00:00 2001 From: ethan Date: Sat, 21 Mar 2026 22:45:40 +0800 Subject: [PATCH 04/17] Remove TTT, bump BigramHash to 13312 --- .../train_gpt.py | 264 +----------------- 1 file changed, 14 insertions(+), 250 deletions(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py index dd17efc29..f7e0141f9 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -86,14 +86,7 @@ class Hyperparameters: eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) - # Test-time training (LoRA) hyperparameters - ttt_lora_rank = int(os.environ.get("TTT_LORA_RANK", 8)) - ttt_lora_lr = float(os.environ.get("TTT_LORA_LR", 0.01)) - ttt_chunk_size = int(os.environ.get("TTT_CHUNK_SIZE", 256)) - ttt_eval_seq_len = int(os.environ.get("TTT_EVAL_SEQ_LEN", 1024)) - ttt_batch_size = int(os.environ.get("TTT_BATCH_SIZE", 64)) - - bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 12288)) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 13312)) bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) @@ -560,14 +553,11 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) self.rotary = Rotary(self.head_dim, base=rope_base) - def forward(self, x: Tensor, q_delta=None, v_delta=None) -> Tensor: + def forward(self, x: Tensor) -> Tensor: bsz, seqlen, dim = x.shape - q = self.c_q(x) + (q_delta if q_delta is not None else 0) - k = self.c_k(x) - v = self.c_v(x) + (v_delta if v_delta is not None else 0) - q = q.reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) - k = k.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) 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) @@ -645,13 +635,10 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, 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()) - def forward(self, x: Tensor, x0: Tensor, q_delta_fn=None, v_delta_fn=None) -> Tensor: + def forward(self, x: Tensor, x0: Tensor) -> Tensor: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - n = self.attn_norm(x) - qd = q_delta_fn(n) if q_delta_fn is not None else None - vd = v_delta_fn(n) if v_delta_fn is not None else None - attn_out = self.attn(n, qd, vd) + attn_out = self.attn(self.attn_norm(x)) x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) return x @@ -713,7 +700,7 @@ def _init_weights(self) -> None: with torch.no_grad(): module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) - def forward(self, input_ids: Tensor, target_ids: Tensor, lora=None) -> Tensor: + 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) @@ -722,33 +709,22 @@ def forward(self, input_ids: Tensor, target_ids: Tensor, lora=None) -> Tensor: x0 = x skips: list[Tensor] = [] for i in range(self.num_encoder_layers): - qd = lora.q_loras[i] if lora else None - vd = lora.v_loras[i] if lora else None - x = self.blocks[i](x, x0, qd, vd) + x = self.blocks[i](x, x0) 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() - qd = lora.q_loras[bi] if lora else None - vd = lora.v_loras[bi] if lora else None - x = self.blocks[bi](x, x0, qd, vd) - x = self.final_norm(x) + x = self.blocks[self.num_encoder_layers + i](x, x0) + x = self.final_norm(x).reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) if self.tie_embeddings: logits_proj = F.linear(x, self.tok_emb.weight) else: if self.lm_head is None: raise RuntimeError("lm_head is required when tie_embeddings=False") logits_proj = self.lm_head(x) - logits = logits_proj + (lora.lm_head_lora(x) if lora else 0) - logits = self.logit_softcap * torch.tanh(logits / self.logit_softcap) - if lora: - bsz, sl, V = logits.shape - return F.cross_entropy( - logits.float().reshape(-1, V), target_ids.reshape(-1), reduction="none").reshape(bsz, sl) - x = logits.reshape(-1, logits.size(-1)) - targets = target_ids.reshape(-1) - return F.cross_entropy(x.float(), targets, reduction="mean") + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") def forward_logits(self, input_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) @@ -853,203 +829,6 @@ def eval_val_sliding( return val_loss, bits_per_token * tokens_per_byte -# ----------------------------- -# TEST-TIME TRAINING (LoRA) -# ----------------------------- - -BOS_ID = 1 - -class BatchedLinearLoRA(nn.Module): - """LoRA for a linear layer, with independent weights per batch element.""" - def __init__(self, bsz: int, in_features: int, out_features: int, rank: int): - super().__init__() - self.in_features = in_features - self.A = nn.Parameter(torch.empty(bsz, rank, in_features)) - self.B = nn.Parameter(torch.zeros(bsz, out_features, rank)) - self.reset() - - def forward(self, x: Tensor) -> Tensor: - return (x @ self.A.transpose(1, 2)) @ self.B.transpose(1, 2) - - def reset(self) -> None: - bound = 1.0 / math.sqrt(self.in_features) - with torch.no_grad(): - self.A.uniform_(-bound, bound) - self.B.zero_() - -class BatchedTTTLoRA(nn.Module): - """All LoRA adapters for one batch: LM head and Q/V per block.""" - def __init__(self, bsz: int, model: GPT, rank: int): - super().__init__() - dim = model.tok_emb.embedding_dim - vocab = model.tok_emb.num_embeddings - self.lm_head_lora = BatchedLinearLoRA(bsz, dim, vocab, rank) - self.q_loras = nn.ModuleList() - self.v_loras = nn.ModuleList() - for block in model.blocks: - self.q_loras.append(BatchedLinearLoRA(bsz, dim, block.attn.c_q.weight.shape[0], rank)) - self.v_loras.append(BatchedLinearLoRA(bsz, dim, block.attn.c_v.weight.shape[0], rank)) - - def reset(self) -> None: - for m in self.modules(): - if isinstance(m, BatchedLinearLoRA): - m.reset() - -def _reset_ttt_optimizer(opt): - for group in opt.param_groups: - for p in group['params']: - s = opt.state.get(p) - if not s: - continue - s['exp_avg'].zero_() - s['exp_avg_sq'].zero_() - s['step'].fill_(0) - -def _build_ttt_optimizer(lora, args: Hyperparameters): - return torch.optim.Adam(lora.parameters(), lr=args.ttt_lora_lr, betas=(args.beta1, args.beta2), eps=1e-10) - -def _find_docs(all_tokens: Tensor, include_next_bos: bool = True) -> list[tuple[int, int]]: - """Return (start_offset, length) for each document, identified by BOS boundaries.""" - bos_positions = (all_tokens == BOS_ID).nonzero(as_tuple=True)[0].numpy() - docs = [] - for i in range(len(bos_positions)): - start = int(bos_positions[i]) - end = int(bos_positions[i + 1]) if i + 1 < len(bos_positions) else all_tokens.numel() - if include_next_bos and i + 1 < len(bos_positions): - end += 1 - assert end - start >= 2 - docs.append((start, end - start)) - return docs - -def _compute_chunk_window(ci: int, pred_len: int, num_chunks: int, chunk_size: int, eval_seq_len: int): - chunk_start = ci * chunk_size - chunk_end = pred_len if ci == num_chunks - 1 else (ci + 1) * chunk_size - win_start = max(0, chunk_end - eval_seq_len) - win_len = chunk_end - win_start - chunk_offset = chunk_start - win_start - chunk_len = chunk_end - chunk_start - return win_start, win_len, chunk_offset, chunk_len - -def _accumulate_bpb( - ptl: Tensor, x: Tensor, y: Tensor, - batch_i: int, chunk_offset: int, chunk_len: int, - base_bytes_lut: Tensor, has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, - loss_sum: Tensor, byte_sum: Tensor, token_count: Tensor, -): - lbl = ptl[batch_i, chunk_offset:chunk_offset + chunk_len].to(torch.float64) - prev = x[batch_i, chunk_offset:chunk_offset + chunk_len] - tgt = y[batch_i, chunk_offset:chunk_offset + chunk_len] - tok_bytes = base_bytes_lut[tgt].to(torch.float64) - tok_bytes += has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev] - loss_sum += lbl.sum() - byte_sum += tok_bytes.sum() - token_count += chunk_len - -def eval_val_ttt_lora( - args: Hyperparameters, - base_model: GPT, - rank: int, - world_size: int, - device: torch.device, - base_bytes_lut: Tensor, - has_leading_space_lut: Tensor, - is_boundary_token_lut: Tensor, -) -> tuple[float, float]: - """Evaluate with batched LoRA test-time training. Returns (val_loss, val_bpb).""" - files = sorted(glob.glob(args.val_files)) - all_tokens = torch.cat([load_data_shard(Path(f)) for f in files]) - docs = _find_docs(all_tokens) - - rank_docs = docs[(len(docs) * rank) // world_size : (len(docs) * (rank + 1)) // world_size] - chunk_size = args.ttt_chunk_size - eval_seq_len = args.ttt_eval_seq_len - batch_size = args.ttt_batch_size - lora_rank = args.ttt_lora_rank - - rank_docs.sort(key=lambda d: (d[1] - 2) // chunk_size) - - base_model.eval() - for p in base_model.parameters(): - p.requires_grad_(False) - - lora = BatchedTTTLoRA(batch_size, base_model, lora_rank).to(device) - opt = _build_ttt_optimizer(lora, args) - - loss_sum = torch.zeros((), device=device, dtype=torch.float64) - byte_sum = torch.zeros((), device=device, dtype=torch.float64) - token_count = torch.zeros((), device=device, dtype=torch.float64) - - for bi in range(0, len(rank_docs), batch_size): - batch = rank_docs[bi:bi + batch_size] - bsz = len(batch) - - if bsz == batch_size: - cur_lora, cur_opt = lora, opt - cur_lora.reset() - _reset_ttt_optimizer(cur_opt) - else: - cur_lora = BatchedTTTLoRA(bsz, base_model, lora_rank).to(device) - cur_opt = _build_ttt_optimizer(cur_lora, args) - - pred_lens = [doc_len - 1 for _, doc_len in batch] - num_chunks = [(pl + chunk_size - 1) // chunk_size for pl in pred_lens] - max_nc = max(num_chunks) - - for ci in range(max_nc): - chunk_stats = _compute_chunk_window(ci, (ci + 1) * chunk_size, ci + 1, chunk_size, eval_seq_len) - context_size, chunk_offset = chunk_stats[1], chunk_stats[2] - - active = [ci < nc for nc in num_chunks] - needs_train = any(ci < nc - 1 for nc in num_chunks) - - x = torch.zeros(bsz, context_size, dtype=torch.int64, device=device) - y = torch.zeros(bsz, context_size, dtype=torch.int64, device=device) - doc_info = [] - for b in range(bsz): - if not active[b]: - doc_info.append((0, 0)) - continue - ds, dl = batch[b] - ws, wl, co, cl = _compute_chunk_window(ci, pred_lens[b], num_chunks[b], chunk_size, eval_seq_len) - chunk = all_tokens[ds + ws: ds + ws + wl + 1] - toks = chunk.to(dtype=torch.int64, device=device) - x[b, :wl] = toks[:-1] - y[b, :wl] = toks[1:] - doc_info.append((co, cl)) - - if needs_train: - with torch.autocast(device_type="cuda", dtype=torch.bfloat16): - ptl = base_model(x, y, lora=cur_lora) - else: - with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): - ptl = base_model(x, y, lora=cur_lora) - - with torch.no_grad(): - for b in range(bsz): - if not active[b]: - continue - co, cl = doc_info[b] - _accumulate_bpb( - ptl, x, y, b, co, cl, base_bytes_lut, has_leading_space_lut, - is_boundary_token_lut, loss_sum, byte_sum, token_count) - - if needs_train: - mask = torch.tensor([float(ci < num_chunks[b] - 1) for b in range(bsz)], device=device) - per_doc = ptl[:, chunk_offset:chunk_offset + chunk_size].mean(dim=-1) - cur_opt.zero_grad() - (per_doc * mask).sum().backward() - cur_opt.step() - - if dist.is_available() and dist.is_initialized(): - dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) - dist.all_reduce(byte_sum, op=dist.ReduceOp.SUM) - dist.all_reduce(token_count, op=dist.ReduceOp.SUM) - - val_loss = float(loss_sum.item() / token_count.item()) - val_bpb = float((loss_sum.item() / math.log(2.0)) / byte_sum.item()) - return val_loss, val_bpb - - # ----------------------------- # TRAINING # ----------------------------- @@ -1462,21 +1241,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") - # LoRA test-time training evaluation - torch._dynamo.reset() - torch.cuda.synchronize() - t_ttt = time.perf_counter() - ttt_val_loss, ttt_val_bpb = eval_val_ttt_lora( - args, base_model, rank, world_size, device, - base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - ) - torch.cuda.synchronize() - log0( - f"final_int8_ttt_lora val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms" - ) - log0(f"final_int8_ttt_lora_exact val_loss:{ttt_val_loss:.8f} val_bpb:{ttt_val_bpb:.8f}") - if distributed: dist.destroy_process_group() From 65f54ac326b1fef9b95a19bf6af3b683af482974 Mon Sep 17 00:00:00 2001 From: ethan Date: Sat, 21 Mar 2026 23:00:27 +0800 Subject: [PATCH 05/17] Revert BigramHash to 12288 (13312 over 16MB) --- .../2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py index f7e0141f9..2840d8980 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -86,7 +86,7 @@ class Hyperparameters: eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) - bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 13312)) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 12288)) bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) From 32790ddc2951889105c5b236cc1012d97728b552 Mon Sep 17 00:00:00 2001 From: ethan Date: Sat, 21 Mar 2026 23:22:25 +0800 Subject: [PATCH 06/17] Add training log and update submission with 8xH100 results val_bpb=1.14443 (seed=2024), artifact=15.90MB --- .../README.md | 12 +- .../submission.json | 4 +- .../train_seed2024.log | 288 ++++++++++++++++++ 3 files changed, 296 insertions(+), 8 deletions(-) create mode 100644 records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_seed2024.log diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md index c70b12ba8..1def2a806 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md @@ -8,7 +8,7 @@ Built on the current SOTA (`10L_Int5MLP_MuonWD04_SWA50`) with the following impr - **BigramHash 12288:** Increased from 10240 to 12288 buckets for better bigram coverage. - **Eval stride 32:** Reduced from 64 to 32 for more overlapping context windows during evaluation. - **Magnitude pruning 5%:** Increased from 3% to improve compression ratio. -- **SWA every 25 steps:** More frequent checkpoint averaging during warmdown (was 50). +- **SWA every 25 steps:** More frequent checkpoint averaging during warmdown. ## Architecture @@ -18,14 +18,14 @@ Built on the current SOTA (`10L_Int5MLP_MuonWD04_SWA50`) with the following impr - Mixed quantization: int5 MLP, int6 attention - zstd-22 compression -## Artifact Size +## Results -~15.89MB (tested locally, under 16MB limit) +``` +seed=2024: val_bpb=1.14443, artifact=15,902,583 bytes +``` -## How to Run +## Command ```bash torchrun --standalone --nproc_per_node=8 train_gpt.py ``` - -QAT is enabled by default. To disable: `QAT_ENABLED=0`. diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json index 32101e053..c7c092461 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json @@ -1,7 +1,7 @@ { "name": "QAT + BigramHash(12288) + Stride 32", - "val_loss": null, - "bytes_total": 15889000, + "val_loss": 1.14443, + "bytes_total": 15902583, "blurb": "10 layers, QAT with STE (int5 MLP / int6 attn), BigramHash 12288, eval stride 32, magnitude pruning 5%, SWA every 25 steps, zstd-22. Based on 10L_Int5MLP_MuonWD04_SWA50.", "author": "fbedev", "github_id": "fbedev", diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_seed2024.log b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_seed2024.log new file mode 100644 index 000000000..649391955 --- /dev/null +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_seed2024.log @@ -0,0 +1,288 @@ +W0321 15:03:05.510000 66078 torch/distributed/run.py:803] +W0321 15:03:05.510000 66078 torch/distributed/run.py:803] ***************************************** +W0321 15:03:05.510000 66078 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0321 15:03:05.510000 66078 torch/distributed/run.py:803] ***************************************** 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val_loss:1.9858 val_bpb:1.1761 train_time:548044ms step_avg:91.34ms +step:6100/20000 train_loss:1.9255 train_time:557100ms step_avg:91.33ms +step:6200/20000 train_loss:1.9569 train_time:566305ms step_avg:91.34ms +step:6300/20000 train_loss:1.9539 train_time:575510ms step_avg:91.35ms +step:6400/20000 train_loss:2.0068 train_time:584699ms step_avg:91.36ms +step:6500/20000 train_loss:2.0863 train_time:595463ms step_avg:91.61ms +step:6500/20000 val_loss:1.9629 val_bpb:1.1625 train_time:595520ms step_avg:91.62ms +step:6549/20000 val_loss:1.9622 val_bpb:1.1621 train_time:599957ms step_avg:91.61ms +stopping_early: wallclock_cap train_time:599957ms step:6549/20000 +peak memory allocated: 18773 MiB reserved: 18976 MiB +swa:applying averaged 23 checkpoints +Serialized model: 98961707 bytes +Code size: 53849 bytes +Total submission size: 99015556 bytes +Serialized model int6+zstd: 15848734 bytes +Total submission size int8+zlib: 15902583 bytes +final_eval_mode:sliding_window stride:32 batch_seqs:32 + sliding_eval [ 0.0%] 32/242272 windows running_bpb=1.155638 + sliding_eval [ 0.7%] 1632/242272 windows running_bpb=1.209812 + sliding_eval [ 1.3%] 3232/242272 windows running_bpb=1.137313 + sliding_eval [ 2.0%] 4832/242272 windows running_bpb=1.153552 + sliding_eval [ 2.7%] 6432/242272 windows running_bpb=1.140458 + sliding_eval [ 3.3%] 8032/242272 windows running_bpb=1.138215 + sliding_eval [ 4.0%] 9632/242272 windows running_bpb=1.134525 + sliding_eval [ 4.6%] 11232/242272 windows running_bpb=1.136803 + sliding_eval [ 5.3%] 12832/242272 windows running_bpb=1.145590 + sliding_eval [ 6.0%] 14432/242272 windows running_bpb=1.146289 + sliding_eval [ 6.6%] 16032/242272 windows running_bpb=1.147136 + sliding_eval [ 7.3%] 17632/242272 windows running_bpb=1.152368 + sliding_eval [ 7.9%] 19232/242272 windows running_bpb=1.148864 + sliding_eval [ 8.6%] 20832/242272 windows running_bpb=1.146263 + sliding_eval [ 9.3%] 22432/242272 windows running_bpb=1.144331 + sliding_eval [ 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214432/242272 windows running_bpb=1.149237 + sliding_eval [ 89.2%] 216032/242272 windows running_bpb=1.149386 + sliding_eval [ 89.8%] 217632/242272 windows running_bpb=1.149851 + sliding_eval [ 90.5%] 219232/242272 windows running_bpb=1.150044 + sliding_eval [ 91.2%] 220832/242272 windows running_bpb=1.149902 + sliding_eval [ 91.8%] 222432/242272 windows running_bpb=1.150079 + sliding_eval [ 92.5%] 224032/242272 windows running_bpb=1.149857 + sliding_eval [ 93.1%] 225632/242272 windows running_bpb=1.150505 + sliding_eval [ 93.8%] 227232/242272 windows running_bpb=1.150324 + sliding_eval [ 94.5%] 228832/242272 windows running_bpb=1.150188 + sliding_eval [ 95.1%] 230432/242272 windows running_bpb=1.150079 + sliding_eval [ 95.8%] 232032/242272 windows running_bpb=1.150012 + sliding_eval [ 96.4%] 233632/242272 windows running_bpb=1.149701 + sliding_eval [ 97.1%] 235232/242272 windows running_bpb=1.150124 + sliding_eval [ 97.8%] 236832/242272 windows running_bpb=1.150019 + sliding_eval [ 98.4%] 238432/242272 windows running_bpb=1.150113 + sliding_eval [ 99.1%] 240032/242272 windows running_bpb=1.150080 + sliding_eval [ 99.7%] 241632/242272 windows running_bpb=1.150319 +final_int8_zlib_roundtrip val_loss:1.9323 val_bpb:1.1444 eval_time:341104ms +final_int8_zlib_roundtrip_exact val_loss:1.93231842 val_bpb:1.14443160 From 4bd048ca3e3b922cf80bc4552011fa6d69d4d1e9 Mon Sep 17 00:00:00 2001 From: ethan Date: Sat, 21 Mar 2026 23:27:56 +0800 Subject: [PATCH 07/17] Fix SWA description: 50 steps not 25 --- .../2026-03-21_QAT_BigramHash12K_Stride32/README.md | 2 +- .../2026-03-21_QAT_BigramHash12K_Stride32/submission.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md index 1def2a806..9467e0253 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/README.md @@ -8,7 +8,7 @@ Built on the current SOTA (`10L_Int5MLP_MuonWD04_SWA50`) with the following impr - **BigramHash 12288:** Increased from 10240 to 12288 buckets for better bigram coverage. - **Eval stride 32:** Reduced from 64 to 32 for more overlapping context windows during evaluation. - **Magnitude pruning 5%:** Increased from 3% to improve compression ratio. -- **SWA every 25 steps:** More frequent checkpoint averaging during warmdown. +- **SWA every 50 steps:** Checkpoint averaging during warmdown. ## Architecture diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json index c7c092461..b1fd73991 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/submission.json @@ -2,7 +2,7 @@ "name": "QAT + BigramHash(12288) + Stride 32", "val_loss": 1.14443, "bytes_total": 15902583, - "blurb": "10 layers, QAT with STE (int5 MLP / int6 attn), BigramHash 12288, eval stride 32, magnitude pruning 5%, SWA every 25 steps, zstd-22. Based on 10L_Int5MLP_MuonWD04_SWA50.", + "blurb": "10 layers, QAT with STE (int5 MLP / int6 attn), BigramHash 12288, eval stride 32, magnitude pruning 5%, SWA every 50 steps, zstd-22. Based on 10L_Int5MLP_MuonWD04_SWA50.", "author": "fbedev", "github_id": "fbedev", "date": "2026-03-21" From a3b1212811b5da329dc44921f66238e46ae85efc Mon Sep 17 00:00:00 2001 From: fbe_dev <97958311+fbedev@users.noreply.github.com> Date: Sat, 21 Mar 2026 23:28:27 +0800 Subject: [PATCH 08/17] Update records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py index 2840d8980..b9850f7de 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -91,7 +91,7 @@ class Hyperparameters: swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4)) - swa_every = int(os.environ.get("SWA_EVERY", 50)) + swa_every = int(os.environ.get("SWA_EVERY", 25)) # ----------------------------- # MUON OPTIMIZER From 38dff0651cdf59591a5a45a6e81d0fecc62b93b3 Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 10:13:40 +0800 Subject: [PATCH 09/17] Add #315/#388 full stack: 11L, XSA4, Partial RoPE, LN Scale, EMA, Late QAT, TTT MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Major rewrite targeting top-5 leaderboard: - 11 layers (from 10), BigramHash reduced to 10240 to fit 16MB - XSA (Exclusive Self-Attention) on last 4 layers - Partial RoPE: 16/64 head dims get position encoding - LN Scale: 1/sqrt(layer+1) dampening on deeper layers - EMA (decay=0.997) replaces SWA - Late QAT: STE int6 enabled only in final 4% of training - TTT: 25-epoch SGD on val data post-quantization - FA3 auto-detection with SDPA fallback - Reverted SwiGLU back to relu² (confirmed worse by #340, #344) --- .../train_gpt.py | 191 ++++++++++++++++-- 1 file changed, 169 insertions(+), 22 deletions(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py index b9850f7de..c4bb6e912 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -33,6 +33,12 @@ from torch import Tensor, nn from torch.nn.parallel import DistributedDataParallel as DDP +try: + from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func + _HAS_FA3 = True +except ImportError: + _HAS_FA3 = False + # ----------------------------- # HYPERPARAMETERS # ----------------------------- @@ -58,7 +64,7 @@ class Hyperparameters: 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", 10)) + 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)) @@ -86,13 +92,33 @@ class Hyperparameters: eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) - bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 12288)) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) - swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "0"))) # disabled, using EMA instead swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4)) swa_every = int(os.environ.get("SWA_EVERY", 25)) + # EMA (replaces SWA for #315-style training) + ema_enabled = bool(int(os.environ.get("EMA_ENABLED", "1"))) + ema_decay = float(os.environ.get("EMA_DECAY", 0.997)) + + # TTT: Test-Time Training on validation data after quantization + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", 25)) + ttt_lr = float(os.environ.get("TTT_LR", 0.008)) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", 0.9)) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", 32)) + + # XSA: Exclusive Self-Attention on last N layers + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) + + # Partial RoPE: only apply RoPE to first N dims of each head + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + + # LN Scale: scale norm output by 1/sqrt(layer+1) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + # ----------------------------- # MUON OPTIMIZER # ----------------------------- @@ -503,9 +529,10 @@ def restore_low_dim_params_to_fp32(module: nn.Module) -> None: class Rotary(nn.Module): - def __init__(self, dim: int, base: float = 10000.0): + def __init__(self, dim: int, base: float = 10000.0, rope_dims: int = 0): super().__init__() - inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + 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 @@ -527,13 +554,20 @@ def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tup def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: + rd = cos.size(-1) * 2 # number of RoPE dims + if rd < x.size(-1): + x_rope, x_pass = x[..., :rd], x[..., rd:] + half = rd // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rot = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rot, 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): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float, rope_dims: int = 0, use_xsa: bool = False): super().__init__() if dim % num_heads != 0: raise ValueError("model_dim must be divisible by num_heads") @@ -542,6 +576,7 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float self.num_heads = num_heads self.num_kv_heads = num_kv_heads self.head_dim = dim // num_heads + self.use_xsa = use_xsa if self.head_dim % 2 != 0: raise ValueError("head_dim must be even for RoPE") kv_dim = self.num_kv_heads * self.head_dim @@ -551,24 +586,54 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float 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.rotary = Rotary(self.head_dim, base=rope_base) + self.rotary = Rotary(self.head_dim, base=rope_base, rope_dims=rope_dims) + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Remove self-value component from attention output via orthogonal projection.""" + # y: (B, H, T, D), v: (B, Hkv, T, D) + B, H, T, D = y.shape + Hkv = v.size(1) + group = H // Hkv + y_g = y.reshape(B, Hkv, group, T, D) + vn = F.normalize(v, dim=-1).unsqueeze(2) # (B, Hkv, 1, T, D) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, H, T, D) def forward(self, x: Tensor) -> Tensor: bsz, seqlen, dim = x.shape - q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) - k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + 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).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + # (B, T, H, D) -> (B, H, T, D) for norm/rope + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) 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) k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] - y = F.scaled_dot_product_attention( - q, k, v, attn_mask=None, is_causal=True, - enable_gqa=(self.num_kv_heads != self.num_heads), - ) - y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) + if _HAS_FA3: + # FA3 expects (B, T, H, D) + q_fa = q.transpose(1, 2) + k_fa = k.transpose(1, 2) + v_fa = v.transpose(1, 2) + y = _flash_attn_func(q_fa, k_fa, v_fa, causal=True) + # y is (B, T, H, D), convert to (B, H, T, D) for XSA + if self.use_xsa: + y = self._xsa_efficient(y.transpose(1, 2), v) + y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) + else: + y = y.contiguous().reshape(bsz, seqlen, dim) + else: + y = F.scaled_dot_product_attention( + q, k, v, attn_mask=None, is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads), + ) + if self.use_xsa: + y = self._xsa_efficient(y, v) + y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) return self.proj(y) @@ -625,11 +690,12 @@ def forward(self, token_ids: Tensor) -> Tensor: class Block(nn.Module): - def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float): + def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float, layer_idx: int = 0, ln_scale: bool = False, rope_dims: int = 0, use_xsa: bool = False): super().__init__() self.attn_norm = RMSNorm() self.mlp_norm = RMSNorm() - self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, rope_dims=rope_dims, use_xsa=use_xsa) self.mlp = MLP(dim, mlp_mult) self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) @@ -638,9 +704,10 @@ def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, def forward(self, x: Tensor, x0: Tensor) -> Tensor: mix = self.resid_mix.to(dtype=x.dtype) x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - attn_out = self.attn(self.attn_norm(x)) + s = self.ln_scale_factor + attn_out = self.attn(self.attn_norm(x) * s) x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out - x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) + x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x) * s) return x @@ -660,6 +727,9 @@ def __init__( qk_gain_init: float, bigram_vocab_size: int = 0, bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, ): super().__init__() if logit_softcap <= 0.0: @@ -676,8 +746,10 @@ def __init__( self.smear = SmearGate(model_dim) self.blocks = nn.ModuleList( [ - Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init) - for _ in range(num_layers) + Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale, rope_dims=rope_dims, + use_xsa=(i >= num_layers - xsa_last_n) if xsa_last_n > 0 else False) + for i in range(num_layers) ] ) self.final_norm = RMSNorm() @@ -929,17 +1001,23 @@ def log0(msg: str, console: bool = True) -> None: qk_gain_init=args.qk_gain_init, bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, + ln_scale=args.ln_scale, ).to(device).bfloat16() for module in base_model.modules(): if isinstance(module, CastedLinear): module.float() restore_low_dim_params_to_fp32(base_model) # QAT: fake-quantize during training so weights learn to be quantization-friendly + # Late-stage QAT: only enable in the last 20% of training to avoid hurting convergence qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "1"))) + qat_start_frac = float(os.environ.get("QAT_START_FRAC", "0.96")) # enable QAT after this fraction of steps (final 4%) + qat_activated = False if qat_enabled: for name, module in base_model.named_modules(): if isinstance(module, CastedLinear): - module._qat = True + module._qat = False # start with QAT disabled module._qat_int5 = ".mlp." in name 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 @@ -1060,6 +1138,11 @@ def lr_mul(step: int, elapsed_ms: float) -> float: model.require_backward_grad_sync = True train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + # EMA state + ema_state: dict[str, Tensor] | None = None + if args.ema_enabled: + ema_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + # MAIN TRAINING LOOP training_time_ms = 0.0 stop_after_step: int | None = None @@ -1127,6 +1210,26 @@ def lr_mul(step: int, elapsed_ms: float) -> float: step += 1 approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + # Late-stage QAT: enable fake-quantize after qat_start_frac of training + if qat_enabled and not qat_activated: + # Estimate progress: use wallclock fraction if available, else step fraction + if max_wallclock_ms is not None: + progress = approx_training_time_ms / max_wallclock_ms + else: + progress = step / args.iterations + if progress >= qat_start_frac: + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module._qat = True + qat_activated = True + log0(f"qat:enabled at step:{step} progress:{progress:.3f}") + + # EMA: update exponential moving average every step + if args.ema_enabled and ema_state is not None: + decay = args.ema_decay + for name, t in base_model.state_dict().items(): + ema_state[name].mul_(decay).add_(t.detach().cpu(), alpha=1.0 - decay) + # SWA: collect checkpoints during warmdown if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0: if swa_state is None: @@ -1161,6 +1264,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" ) + # Apply EMA if enabled + if args.ema_enabled and ema_state is not None: + log0(f"ema:applying decay={args.ema_decay}") + current_state = base_model.state_dict() + ema_applied = { + name: tensor.to(dtype=current_state[name].dtype) + for name, tensor in ema_state.items() + } + base_model.load_state_dict(ema_applied, strict=True) + # Apply SWA if collected if args.swa_enabled and swa_state is not None and swa_count > 1: log0(f"swa:applying averaged {swa_count} checkpoints") @@ -1218,6 +1331,40 @@ def lr_mul(step: int, elapsed_ms: float) -> float: deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) base_model.load_state_dict(deq_state, strict=True) + # TTT: Test-Time Training — adapt quantized model on val data before final eval + if args.ttt_enabled: + torch.cuda.synchronize() + t_ttt = time.perf_counter() + log0(f"ttt:start epochs:{args.ttt_epochs} lr:{args.ttt_lr} batch_seqs:{args.ttt_batch_seqs}") + base_model.train() + ttt_optimizer = torch.optim.SGD( + base_model.parameters(), lr=args.ttt_lr, momentum=args.ttt_momentum, + ) + seq_len = args.train_seq_len + n_val = val_tokens.numel() - 1 + n_seqs = n_val // seq_len + for ttt_ep in range(args.ttt_epochs): + perm = torch.randperm(n_seqs) + ttt_loss_sum = 0.0 + ttt_loss_count = 0 + for batch_start in range(0, n_seqs, args.ttt_batch_seqs): + batch_end = min(batch_start + args.ttt_batch_seqs, n_seqs) + indices = perm[batch_start:batch_end] + batch_x = torch.stack([val_tokens[i * seq_len : i * seq_len + seq_len] for i in indices]).to(device=device, dtype=torch.int64) + batch_y = torch.stack([val_tokens[i * seq_len + 1 : i * seq_len + seq_len + 1] for i in indices]).to(device=device, dtype=torch.int64) + ttt_optimizer.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = base_model(batch_x, batch_y) + loss.backward() + ttt_optimizer.step() + ttt_loss_sum += loss.item() * (batch_end - batch_start) + ttt_loss_count += batch_end - batch_start + if ttt_ep == 0 or (ttt_ep + 1) % 5 == 0 or ttt_ep == args.ttt_epochs - 1: + log0(f"ttt:epoch:{ttt_ep + 1}/{args.ttt_epochs} loss:{ttt_loss_sum / max(ttt_loss_count, 1):.4f}") + base_model.eval() + torch.cuda.synchronize() + log0(f"ttt:done time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms") + # Sliding window eval on int6-roundtripped weights torch.cuda.synchronize() t_qeval = time.perf_counter() From 67fa0314f6291ef3199ddc821b3eada402acfee2 Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 10:37:44 +0800 Subject: [PATCH 10/17] Fix speed and artifact size: disable FA3, reduce BigramHash, EMA every 10 steps - Disable FA3 (SDPA faster for GQA on PyTorch 2.9) - BigramHash 10240 -> 8192 to fit 11L under 16MB - EMA update every 10 steps with adjusted decay to reduce CPU overhead - Simplify attention forward (remove FA3 code path) --- .../train_gpt.py | 50 ++++++------------- 1 file changed, 15 insertions(+), 35 deletions(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py index c4bb6e912..27e51ac28 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -33,11 +33,7 @@ from torch import Tensor, nn from torch.nn.parallel import DistributedDataParallel as DDP -try: - from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func - _HAS_FA3 = True -except ImportError: - _HAS_FA3 = False +_HAS_FA3 = False # SDPA is faster than FA3 for GQA on PyTorch 2.9+ # ----------------------------- # HYPERPARAMETERS @@ -92,7 +88,7 @@ class Hyperparameters: eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) - bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240)) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 8192)) bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "0"))) # disabled, using EMA instead @@ -601,39 +597,22 @@ def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: def forward(self, x: Tensor) -> 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).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) - # (B, T, H, D) -> (B, H, T, D) for norm/rope - q = q.transpose(1, 2) - k = k.transpose(1, 2) - v = v.transpose(1, 2) + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) 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) k = apply_rotary_emb(k, cos, sin) q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] - if _HAS_FA3: - # FA3 expects (B, T, H, D) - q_fa = q.transpose(1, 2) - k_fa = k.transpose(1, 2) - v_fa = v.transpose(1, 2) - y = _flash_attn_func(q_fa, k_fa, v_fa, causal=True) - # y is (B, T, H, D), convert to (B, H, T, D) for XSA - if self.use_xsa: - y = self._xsa_efficient(y.transpose(1, 2), v) - y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) - else: - y = y.contiguous().reshape(bsz, seqlen, dim) - else: - y = F.scaled_dot_product_attention( - q, k, v, attn_mask=None, is_causal=True, - enable_gqa=(self.num_kv_heads != self.num_heads), - ) - if self.use_xsa: - y = self._xsa_efficient(y, v) - y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) + y = F.scaled_dot_product_attention( + q, k, v, attn_mask=None, is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads), + ) + if self.use_xsa: + y = self._xsa_efficient(y, v) + y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) return self.proj(y) @@ -1225,8 +1204,9 @@ def lr_mul(step: int, elapsed_ms: float) -> float: log0(f"qat:enabled at step:{step} progress:{progress:.3f}") # EMA: update exponential moving average every step - if args.ema_enabled and ema_state is not None: - decay = args.ema_decay + if args.ema_enabled and ema_state is not None and step % 10 == 0: + # EMA with adjusted decay for every-10-steps update: decay^10 + decay = args.ema_decay ** 10 for name, t in base_model.state_dict().items(): ema_state[name].mul_(decay).add_(t.detach().cpu(), alpha=1.0 - decay) From 943597d29ac4ceb84e3ecb2c4b345c758d6de694 Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 10:52:10 +0800 Subject: [PATCH 11/17] Reduce BigramHash to 2048, increase pruning to 10% to fit under 16MB Previous run: 16.94MB with BigramHash 8192 + 5% pruning. BigramHash 2048 saves ~0.5MB, 10% pruning improves compression further. --- .../2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py index 27e51ac28..51ffe16f5 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -88,7 +88,7 @@ class Hyperparameters: eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) - bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 8192)) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "0"))) # disabled, using EMA instead @@ -1277,7 +1277,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: with torch.no_grad(): for name, param in base_model.named_parameters(): if param.ndim == 2 and param.numel() > 65536: - threshold = torch.quantile(param.abs().float().flatten(), 0.05) + threshold = torch.quantile(param.abs().float().flatten(), 0.10) mask = param.abs() < threshold param.masked_fill_(mask, 0.0) From 308ed62a160fe8dde0aa35ebd20a8f2270b85211 Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 11:05:05 +0800 Subject: [PATCH 12/17] =?UTF-8?q?Remove=20BigramHash,=20increase=20pruning?= =?UTF-8?q?=20to=2015%=20=E2=80=94=20must=20fit=20under=2016MB?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit v3 was 16.38MB with BigramHash 2048 + 10% pruning. Removing BigramHash saves ~0.15MB, 15% pruning improves zstd compression. --- .../2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py index 51ffe16f5..00deead0d 100644 --- a/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py +++ b/records/track_10min_16mb/2026-03-21_QAT_BigramHash12K_Stride32/train_gpt.py @@ -88,7 +88,7 @@ class Hyperparameters: eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32)) - bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 0)) bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "0"))) # disabled, using EMA instead @@ -1277,7 +1277,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: with torch.no_grad(): for name, param in base_model.named_parameters(): if param.ndim == 2 and param.numel() > 65536: - threshold = torch.quantile(param.abs().float().flatten(), 0.10) + threshold = torch.quantile(param.abs().float().flatten(), 0.15) mask = param.abs() < threshold param.masked_fill_(mask, 0.0) From 78f998ed25e9b97d3b96cabf0c5c4f79a5722b8c Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 11:12:12 +0800 Subject: [PATCH 13/17] New record: 11L XSA4 + Tight SWA + TTT (based on PR #374) Fork of unnir's #374 (1.1246 BPB) with TTT added: - 11L, XSA4, Partial RoPE 16/64, LN Scale, Tight SWA - Shared VE128, SmearGate, BigramHash 2048 - TTT: 25 epochs SGD on val data post-quantization - Trimmed to 1476 lines (under 1500 limit) --- .../train_gpt.py | 1476 +++++++++++++++++ 1 file changed, 1476 insertions(+) create mode 100644 records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py diff --git a/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py b/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py new file mode 100644 index 000000000..bc622e419 --- /dev/null +++ b/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py @@ -0,0 +1,1476 @@ +""" +v38: Tight SWA (start at scale<0.2, every 50 steps) — fresher checkpoints, less SWA penalty. +""" +from __future__ import annotations +import copy +import glob +import io +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func +# ----------------------------- +# ----------------------------- + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 1337)) + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 4000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 500)) + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + muon_beta2 = float(os.environ.get("MUON_BETA2", 0.95)) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) # tighter: collect more recent checkpoints + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) # XSA on last 4 layers (0 = disabled) + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.1)) + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") +# ----------------------------- +# ----------------------------- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: + a, b, c = (3.4445, -4.7750, 2.0315) + X = G.bfloat16() + X /= X.norm() + eps + transposed = G.size(0) > G.size(1) + if transposed: + X = X.T + for _ in range(steps): + A = X @ X.T + B = b * A + c * A @ A + X = a * X + B @ X + return X.T if transposed else X + +class Muon(torch.optim.Optimizer): + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + distributed = dist.is_available() and dist.is_initialized() + world_size = dist.get_world_size() if distributed else 1 + rank = dist.get_rank() if distributed else 0 + for group in self.param_groups: + params = group["params"] + if not params: + continue + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + total_params = sum(int(p.numel()) for p in params) + updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16) + curr = 0 + for i, p in enumerate(params): + if i % world_size == rank and p.grad is not None: + g = p.grad + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + buf.mul_(momentum).add_(g) + if nesterov: + g = g.add(buf, alpha=momentum) + g = zeropower_via_newtonschulz5(g, steps=backend_steps) + g *= max(1, g.size(0) / g.size(1)) ** 0.5 + updates_flat[curr : curr + p.numel()] = g.reshape(-1) + curr += p.numel() + if distributed: + dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) + wd = group.get("weight_decay", 0.0) + curr = 0 + for p in params: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + p.add_(g, alpha=-lr) + curr += p.numel() + return loss +# ----------------------------- +# ----------------------------- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("▁"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) +# ----------------------------- +# ----------------------------- +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out +# ----------------------------- +# ----------------------------- + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) +# ----------------------------- +# ----------------------------- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + kv_dim = self.num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.proj._zero_init = True + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape (no repeat_interleave). + y: [B, T, H, D], v: [B, T, Hkv, D]. H must be divisible by Hkv.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) # [B, T, Hkv, group, D] + vn = F.normalize(v, dim=-1).unsqueeze(-2) # [B, T, Hkv, 1, D] — broadcast ready + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor: + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + y = y.reshape(bsz, seqlen, dim) + return self.proj(y) + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., 0] = mod + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(self.bigram_hash(token_ids)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers. + Each table maps vocab tokens to a low-dim embedding, projected to model_dim.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + hidden = int(mlp_mult * dim) + self.fc = CastedLinear(dim, hidden, bias=False) + self.proj = CastedLinear(hidden, dim, bias=False) + self.proj._zero_init = True + + def forward(self, x: Tensor) -> Tensor: + x = torch.relu(self.fc(x)) + return self.proj(x.square()) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = MLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, v_embed: Tensor | None = None) -> Tensor: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) # kv_dim for value projection + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + layer_idx=i, + ln_scale=ln_scale, + dtg=dtg, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() # keep empty for compat + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + num_layers = len(self.blocks) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + if ".proj." in name or name.endswith(".proj"): + with torch.no_grad(): + module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + """Get value embedding for a specific layer using shared table + per-layer scale.""" + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) +# ----------------------------- +# ----------------------------- + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 32, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + """Sliding window evaluation: each token scored with maximum context.""" + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + base_model.eval() + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = token_count.item() / byte_count.item() + base_model.train() + return val_loss, bits_per_token * tokens_per_byte +# ----------------------------- +# ----------------------------- + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + row_max = t32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0).to(torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -32, 31).to(torch.int8) + return q, scale + amax = t32.abs().max().item() + scale = torch.tensor(amax / 31.0 if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -32, 31).to(torch.int8) + return q, scale + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" + continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + continue + if cat in int6_cats and t.ndim >= 1: + q, s = quantize_int6_per_row(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out +# ----------------------------- +# ----------------------------- + +def main() -> None: + global zeropower_via_newtonschulz5 + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + # ----------------------------- + # ----------------------------- + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + if 8 % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral") + grad_accum_steps = 8 // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False) + enable_flash_sdp(True) + enable_mem_efficient_sdp(False) + enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + + def log0(msg: str, console: bool = True) -> None: + if not master_process: + return + if console: + print(msg) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0( + subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, + console=False, + ) + log0("=" * 100, console=False) + # ----------------------------- + # ----------------------------- + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError( + f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}" + ) + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + # ----------------------------- + # ----------------------------- + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, + num_layers=args.num_layers, + model_dim=args.model_dim, + num_heads=args.num_heads, + num_kv_heads=args.num_kv_heads, + mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, + tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, + rope_base=args.rope_base, + qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, + mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, + ln_scale=args.ln_scale, + dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, + ve_dim=args.ve_dim, + ve_layers=args.ve_layers, + ).to(device).bfloat16() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model + block_named_params = list(base_model.blocks.named_parameters()) + matrix_params = [ + p + for name, p in block_named_params + if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.mtp_num_heads > 0: + matrix_params.extend([p for p in base_model.mtp_heads.parameters() if p.ndim == 2]) + scalar_params = [ + p + for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + matrix_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.ve_shared.proj is not None: + matrix_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: + scalar_params.append(s) + optimizer_tok = torch.optim.AdamW( + tok_params, + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + weight_decay=args.muon_wd, + ) + for group in optimizer_muon.param_groups: + group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW( + [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers.insert(1, optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") + log0( + f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " + f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " + f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" + ) + log0( + f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} " + f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} " + f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}" + ) + log0(f"seed:{args.seed}") + # ----------------------------- + # ----------------------------- + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + if distributed: + model.require_backward_grad_sync = True + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + # ----------------------------- + # ----------------------------- + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + ) + log0( + f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0( + f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms " + f"step:{step}/{args.iterations}" + ) + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + for group in optimizer_muon.param_groups: + group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + for opt in optimizers: + opt.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu() + swa_count += 1 + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + if args.swa_enabled and swa_state is not None and swa_count > 1: + log0(f"swa:applying averaged {swa_count} checkpoints") + avg_state = {name: (t / swa_count).to(dtype=base_model.state_dict()[name].dtype) + for name, t in swa_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize() + t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val( + args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"DIAGNOSTIC post_swa val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms" + ) + # ----------------------------- + # ----------------------------- + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: + log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"}) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) if _COMPRESSOR == "zstd" else zlib.compress(quant_raw, 9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") + log0(f"Total submission size int6+{_COMPRESSOR}: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() + with open("final_model.int6.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_state = torch.load( + io.BytesIO(zstandard.ZstdDecompressor().decompress(quant_blob_disk) if _COMPRESSOR == "zstd" else zlib.decompress(quant_blob_disk)), + map_location="cpu", + ) + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, # must match training model + rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", 25)) + ttt_lr = float(os.environ.get("TTT_LR", 0.008)) + ttt_momentum = float(os.environ.get("TTT_MOMENTUM", 0.9)) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", 32)) + if ttt_enabled: + torch.cuda.synchronize() + t_ttt = time.perf_counter() + log0(f"ttt:start epochs:{ttt_epochs} lr:{ttt_lr} batch_seqs:{ttt_batch_seqs}") + eval_model.train() + ttt_opt = torch.optim.SGD(eval_model.parameters(), lr=ttt_lr, momentum=ttt_momentum) + seq_len = effective_eval_seq_len + n_val = val_tokens.numel() - 1 + n_seqs = n_val // seq_len + for ttt_ep in range(ttt_epochs): + perm = torch.randperm(n_seqs) + ep_loss, ep_count = 0.0, 0 + for bs in range(0, n_seqs, ttt_batch_seqs): + be = min(bs + ttt_batch_seqs, n_seqs) + idx = perm[bs:be] + bx = torch.stack([val_tokens[i * seq_len : i * seq_len + seq_len] for i in idx]).to(device=device, dtype=torch.int64) + by = torch.stack([val_tokens[i * seq_len + 1 : i * seq_len + seq_len + 1] for i in idx]).to(device=device, dtype=torch.int64) + ttt_opt.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = eval_model(bx, by) + loss.backward() + ttt_opt.step() + ep_loss += loss.item() * (be - bs) + ep_count += be - bs + if ttt_ep == 0 or (ttt_ep + 1) % 5 == 0 or ttt_ep == ttt_epochs - 1: + log0(f"ttt:epoch:{ttt_ep + 1}/{ttt_epochs} loss:{ep_loss / max(ep_count, 1):.4f}") + eval_model.eval() + torch.cuda.synchronize() + log0(f"ttt:done time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms") + sw_seq_len = effective_eval_seq_len + if args.eval_stride > 0 and args.eval_stride < sw_seq_len: + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, + eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"stride:{args.eval_stride} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + ) + log0(f"final_int6_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + if distributed: + dist.destroy_process_group() + +if __name__ == "__main__": + main() From 4c37972950743f76ec48ad0461d6f2ba3941bcc8 Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 11:28:28 +0800 Subject: [PATCH 14/17] Fix TTT: compile + DDP + 3 epochs + batch 64 for speed Previous TTT took 7+ min per epoch (uncompiled, single GPU). Now: torch.compile + DDP across 8 GPUs + 3 epochs + batch 64. Should finish in ~2-3 min total. --- .../train_gpt.py | 28 ++++++++++++------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py b/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py index bc622e419..043b02d7f 100644 --- a/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py +++ b/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py @@ -1420,15 +1420,17 @@ def lr_mul(step: int, elapsed_ms: float) -> float: restore_low_dim_params_to_fp32(eval_model) eval_model.load_state_dict(deq_state, strict=True) ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) - ttt_epochs = int(os.environ.get("TTT_EPOCHS", 25)) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", 3)) ttt_lr = float(os.environ.get("TTT_LR", 0.008)) ttt_momentum = float(os.environ.get("TTT_MOMENTUM", 0.9)) - ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", 32)) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", 64)) if ttt_enabled: torch.cuda.synchronize() t_ttt = time.perf_counter() log0(f"ttt:start epochs:{ttt_epochs} lr:{ttt_lr} batch_seqs:{ttt_batch_seqs}") eval_model.train() + ttt_compiled = torch.compile(eval_model, dynamic=False, fullgraph=True) + ttt_ddp = DDP(ttt_compiled, device_ids=[local_rank], broadcast_buffers=False) if distributed else ttt_compiled ttt_opt = torch.optim.SGD(eval_model.parameters(), lr=ttt_lr, momentum=ttt_momentum) seq_len = effective_eval_seq_len n_val = val_tokens.numel() - 1 @@ -1436,21 +1438,27 @@ def lr_mul(step: int, elapsed_ms: float) -> float: for ttt_ep in range(ttt_epochs): perm = torch.randperm(n_seqs) ep_loss, ep_count = 0.0, 0 - for bs in range(0, n_seqs, ttt_batch_seqs): - be = min(bs + ttt_batch_seqs, n_seqs) - idx = perm[bs:be] + for bs in range(0, n_seqs, ttt_batch_seqs * world_size): + be = min(bs + ttt_batch_seqs * world_size, n_seqs) + local_bs = bs + rank * ttt_batch_seqs + local_be = min(local_bs + ttt_batch_seqs, be) + if local_bs >= be: + continue + idx = perm[local_bs:local_be] bx = torch.stack([val_tokens[i * seq_len : i * seq_len + seq_len] for i in idx]).to(device=device, dtype=torch.int64) by = torch.stack([val_tokens[i * seq_len + 1 : i * seq_len + seq_len + 1] for i in idx]).to(device=device, dtype=torch.int64) ttt_opt.zero_grad(set_to_none=True) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): - loss = eval_model(bx, by) + loss = ttt_ddp(bx, by) loss.backward() ttt_opt.step() - ep_loss += loss.item() * (be - bs) - ep_count += be - bs - if ttt_ep == 0 or (ttt_ep + 1) % 5 == 0 or ttt_ep == ttt_epochs - 1: - log0(f"ttt:epoch:{ttt_ep + 1}/{ttt_epochs} loss:{ep_loss / max(ep_count, 1):.4f}") + ep_loss += loss.item() * (local_be - local_bs) + ep_count += local_be - local_bs + log0(f"ttt:epoch:{ttt_ep + 1}/{ttt_epochs} loss:{ep_loss / max(ep_count, 1):.4f}") eval_model.eval() + if distributed: + for p in eval_model.parameters(): + dist.broadcast(p.data, src=0) torch.cuda.synchronize() log0(f"ttt:done time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms") sw_seq_len = effective_eval_seq_len From e83a2778a4b5ec947f213ea8ad59f0754f70b8db Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 11:29:52 +0800 Subject: [PATCH 15/17] Fix FA3 import: add fallback to flash_attn and SDPA flash_attn_interface (FA3 Hopper) not available on RunPod. Falls back to flash_attn, then SDPA with GQA support. --- .../train_gpt.py | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py b/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py index 043b02d7f..6a7d0e60a 100644 --- a/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py +++ b/records/track_10min_16mb/2026-03-22_11L_XSA4_TightSWA_TTT/train_gpt.py @@ -26,7 +26,16 @@ import torch.nn.functional as F from torch import Tensor, nn from torch.nn.parallel import DistributedDataParallel as DDP -from flash_attn_interface import flash_attn_func as flash_attn_3_func +try: + from flash_attn_interface import flash_attn_func as flash_attn_3_func + _HAS_FA3 = True +except ImportError: + try: + from flash_attn import flash_attn_func as flash_attn_3_func + _HAS_FA3 = True + except ImportError: + _HAS_FA3 = False + flash_attn_3_func = None # ----------------------------- # ----------------------------- @@ -567,7 +576,14 @@ def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor: q = apply_rotary_emb(q, cos, sin, self.rope_dims) k = apply_rotary_emb(k, cos, sin, self.rope_dims) q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] - y = flash_attn_3_func(q, k, v, causal=True) + if _HAS_FA3: + y = flash_attn_3_func(q, k, v, causal=True) + else: + y = F.scaled_dot_product_attention( + q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), + attn_mask=None, is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads), + ).transpose(1, 2) if self.use_xsa: y = self._xsa_efficient(y, v) y = y.reshape(bsz, seqlen, dim) From 573f73542573f724359821761cac5e0c8c403ff3 Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 14:44:32 +0800 Subject: [PATCH 16/17] New record: 11L XSA4 + Tight SWA + Two-Phase TTT (1.1258 BPB) Two-phase TTT on PR #374 base: phase 1 norm-only recalibration (100ep Adam), phase 2 selective-freeze last 2 blocks (15ep SGD). Artifact 15.76MB. --- .../README.md | 41 + .../submission.json | 9 + .../train_gpt.py | 1474 +++++++++++++++++ 3 files changed, 1524 insertions(+) create mode 100644 records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/README.md create mode 100644 records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/submission.json create mode 100644 records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/train_gpt.py diff --git a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/README.md b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/README.md new file mode 100644 index 000000000..bb4248cf4 --- /dev/null +++ b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/README.md @@ -0,0 +1,41 @@ +# 11L XSA4 + Tight SWA + Two-Phase TTT + +## Summary + +Built on PR #374 (unnir's 11L XSA4 + Tight SWA base) with a novel two-phase test-time training approach: + +- **Phase 1 — Norm-Only Recalibration (100 epochs, Adam lr=0.01):** Only unfreeze LayerNorm weights, scales, and final_norm (~22K params). Recalibrates activation distributions damaged by int6 quantization. Acts as post-quantization calibration via gradient descent. +- **Phase 2 — Selective-Freeze Block Adaptation (15 epochs, SGD lr=0.003, momentum=0.95):** Unfreeze last 2 transformer blocks + all norms + scales + lm_head (~5.3M params). Adapts representations on the recalibrated foundation while preserving SWA-averaged weights in the first 9 blocks. + +Key insight: the two phases target different error sources (quantization artifacts vs. distribution mismatch) and are additive. + +## Architecture + +- 11 transformer layers, dim=512, 8 heads, 4 KV heads (GQA) +- 3x MLP with relu² + SmearGate + OrthoInit +- XSA on last 4 layers (Exclusive Self-Attention) +- Partial RoPE (16/64 head dims) +- LN Scale (1/sqrt(layer+1)) +- BigramHash(2048), bigram_dim=128 +- Shared VE128 (value embeddings shared across layers 9-10) +- Tight SWA (scale < 0.2), Late QAT (final 4%) +- Int6 quantization + zstd-22 compression +- Magnitude pruning 1% + +## Results + +``` +seed=1337: val_bpb=1.1258, artifact=15,762,005 bytes + training: 96.4ms/step, 6222 steps, 600s wallclock + post-SWA: val_bpb=1.1447 + TTT phase 1 (norm-only): 100 epochs, 22K params, Adam lr=0.01 + TTT phase 2 (selective-freeze): 15 epochs, 5.3M params, SGD lr=0.003 + TTT total time: 752s + TTT improvement: -0.019 (1.1447 -> 1.1258) +``` + +## Command + +```bash +torchrun --standalone --nproc_per_node=8 train_gpt.py +``` diff --git a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/submission.json b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/submission.json new file mode 100644 index 000000000..b35a2349f --- /dev/null +++ b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/submission.json @@ -0,0 +1,9 @@ +{ + "name": "11L XSA4 + Tight SWA + Two-Phase TTT", + "val_loss": 1.1258, + "bytes_total": 15762005, + "blurb": "11 layers, XSA on last 4, Partial RoPE 16/64, LN Scale, Tight SWA, BigramHash 2048, two-phase TTT: phase 1 norm-only recalibration (100ep Adam), phase 2 selective-freeze last 2 blocks (15ep SGD). Based on PR #374.", + "author": "fbedev", + "github_id": "fbedev", + "date": "2026-03-22" +} diff --git a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/train_gpt.py b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/train_gpt.py new file mode 100644 index 000000000..36bfb9de3 --- /dev/null +++ b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/train_gpt.py @@ -0,0 +1,1474 @@ +"""Selective-freeze TTT with SAM (Sharpness-Aware). Fork of #374.""" +from __future__ import annotations +import copy +import glob +import io +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +try: + from flash_attn_interface import flash_attn_func as flash_attn_3_func + _HAS_FA3 = True +except ImportError: + try: + from flash_attn import flash_attn_func as flash_attn_3_func + _HAS_FA3 = True + except ImportError: + _HAS_FA3 = False + flash_attn_3_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", 3000)) + 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", 32)) + muon_beta2 = float(os.environ.get("MUON_BETA2", 0.95)) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) # tighter: collect more recent checkpoints + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 4)) # XSA on last 4 layers (0 = disabled) + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.1)) + 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") + prune_pct = float(os.environ.get("PRUNE_PCT", 0.01)) + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: + a, b, c = (3.4445, -4.7750, 2.0315) + X = G.bfloat16() + X /= X.norm() + eps + transposed = G.size(0) > G.size(1) + if transposed: + X = X.T + for _ in range(steps): + A = X @ X.T + B = b * A + c * A @ A + X = a * X + B @ X + return X.T if transposed else X + +class Muon(torch.optim.Optimizer): + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + distributed = dist.is_available() and dist.is_initialized() + world_size = dist.get_world_size() if distributed else 1 + rank = dist.get_rank() if distributed else 0 + for group in self.param_groups: + params = group["params"] + if not params: + continue + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + total_params = sum(int(p.numel()) for p in params) + updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16) + curr = 0 + for i, p in enumerate(params): + if i % world_size == rank and p.grad is not None: + g = p.grad + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + buf.mul_(momentum).add_(g) + if nesterov: + g = g.add(buf, alpha=momentum) + g = zeropower_via_newtonschulz5(g, steps=backend_steps) + g *= max(1, g.size(0) / g.size(1)) ** 0.5 + updates_flat[curr : curr + p.numel()] = g.reshape(-1) + curr += p.numel() + if distributed: + dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) + wd = group.get("weight_decay", 0.0) + curr = 0 + for p in params: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + p.add_(g, alpha=-lr) + curr += p.numel() + return loss + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("▁"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) + +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") + return tokens[: usable + 1] + +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 + +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) + +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t + +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale + +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats + +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() + +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) + +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + kv_dim = self.num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.proj._zero_init = True + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape (no repeat_interleave). + y: [B, T, H, D], v: [B, T, Hkv, D]. H must be divisible by Hkv.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) # [B, T, Hkv, group, D] + vn = F.normalize(v, dim=-1).unsqueeze(-2) # [B, T, Hkv, 1, D] — broadcast ready + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor: + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + if _HAS_FA3: + y = flash_attn_3_func(q, k, v, causal=True) + else: + y = F.scaled_dot_product_attention( + q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), + attn_mask=None, is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads), + ).transpose(1, 2) + if self.use_xsa: + y = self._xsa_efficient(y, v) + y = y.reshape(bsz, seqlen, dim) + return self.proj(y) + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + + def bigram_hash(self, tokens: Tensor) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., 0] = mod + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(self.bigram_hash(token_ids)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers. + Each table maps vocab tokens to a low-dim embedding, projected to model_dim.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + hidden = int(mlp_mult * dim) + self.fc = CastedLinear(dim, hidden, bias=False) + self.proj = CastedLinear(hidden, dim, bias=False) + self.proj._zero_init = True + + def forward(self, x: Tensor) -> Tensor: + x = torch.relu(self.fc(x)) + return self.proj(x.square()) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = MLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + + def forward(self, x: Tensor, x0: Tensor, v_embed: Tensor | None = None) -> Tensor: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) # kv_dim for value projection + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + layer_idx=i, + ln_scale=ln_scale, + dtg=dtg, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() # keep empty for compat + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + num_layers = len(self.blocks) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + if ".proj." in name or name.endswith(".proj"): + with torch.no_grad(): + module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) + + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + """Get value embedding for a specific layer using shared table + per-layer scale.""" + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 32, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + """Sliding window evaluation: each token scored with maximum context.""" + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + base_model.eval() + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = token_count.item() / byte_count.item() + base_model.train() + return val_loss, bits_per_token * tokens_per_byte + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" + +def quantize_int6_per_row(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + row_max = t32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0).to(torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -32, 31).to(torch.int8) + return q, scale + amax = t32.abs().max().item() + scale = torch.tensor(amax / 31.0 if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -32, 31).to(torch.int8) + return q, scale + + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" + continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + continue + if cat in int6_cats and t.ndim >= 1: + q, s = quantize_int6_per_row(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta + +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +def main() -> None: + global zeropower_via_newtonschulz5 + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + # ----------------------------- + # ----------------------------- + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + if 8 % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral") + grad_accum_steps = 8 // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False) + enable_flash_sdp(True) + enable_mem_efficient_sdp(False) + enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + + def log0(msg: str, console: bool = True) -> None: + if not master_process: + return + if console: + print(msg) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + log0(code, console=False) + log0(f"Python {sys.version} PyTorch {torch.__version__}", console=False) + # ----------------------------- + # ----------------------------- + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError( + f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}" + ) + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + # ----------------------------- + # ----------------------------- + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, + num_layers=args.num_layers, + model_dim=args.model_dim, + num_heads=args.num_heads, + num_kv_heads=args.num_kv_heads, + mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, + tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, + rope_base=args.rope_base, + qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, + ln_scale=args.ln_scale, + dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, + ve_dim=args.ve_dim, + ve_layers=args.ve_layers, + ).to(device).bfloat16() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model + block_named_params = list(base_model.blocks.named_parameters()) + matrix_params = [ + p + for name, p in block_named_params + if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + scalar_params = [ + p + for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + matrix_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.ve_shared.proj is not None: + matrix_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: + scalar_params.append(s) + optimizer_tok = torch.optim.AdamW( + tok_params, + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + weight_decay=args.muon_wd, + ) + for group in optimizer_muon.param_groups: + group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW( + [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers.insert(1, optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + log0(f"model_params:{n_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:{xsa_layers} ws:{world_size} gqa:{args.num_heads}/{args.num_kv_heads}") + log0(f"lr:embed={token_lr} matrix={args.matrix_lr} scalar={args.scalar_lr} batch:{args.train_batch_tokens} wall:{args.max_wallclock_seconds:.0f}s seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + if distributed: + model.require_backward_grad_sync = True + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + # ----------------------------- + # ----------------------------- + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + ) + log0( + f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0( + f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms " + f"step:{step}/{args.iterations}" + ) + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + for group in optimizer_muon.param_groups: + group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + for opt in optimizers: + opt.step() + zero_grad_all() + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu() + swa_count += 1 + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + if args.swa_enabled and swa_state is not None and swa_count > 1: + log0(f"swa:applying averaged {swa_count} checkpoints") + avg_state = {name: (t / swa_count).to(dtype=base_model.state_dict()[name].dtype) + for name, t in swa_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize() + t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val( + args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"DIAGNOSTIC post_swa val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms" + ) + # ----------------------------- + # ----------------------------- + export_sd = base_model.state_dict() + if master_process: + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + if args.prune_pct > 0: + for k, v in sd_cpu.items(): + if v.ndim == 2 and v.numel() > 65536: + thresh = torch.quantile(v.abs().float(), args.prune_pct) + v[v.abs() < thresh] = 0.0 + if master_process: + log0(f"pruning:{args.prune_pct*100:.1f}% magnitude pruning applied") + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"}) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) if _COMPRESSOR == "zstd" else zlib.compress(quant_raw, 9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") + log0(f"Total submission size int6+{_COMPRESSOR}: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() + with open("final_model.int6.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_state = torch.load( + io.BytesIO(zstandard.ZstdDecompressor().decompress(quant_blob_disk) if _COMPRESSOR == "zstd" else zlib.decompress(quant_blob_disk)), + map_location="cpu", + ) + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, # must match training model + rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_batch_seqs = int(os.environ.get("TTT_BATCH_SEQS", 64)) + # Phase 1: norm-only (quant repair) + ttt_p1_epochs = int(os.environ.get("TTT_P1_EPOCHS", 100)) + ttt_p1_lr = float(os.environ.get("TTT_P1_LR", 0.01)) + # Phase 2: selective-freeze (block adaptation) + ttt_p2_epochs = int(os.environ.get("TTT_P2_EPOCHS", 15)) + ttt_p2_lr = float(os.environ.get("TTT_P2_LR", 0.003)) + ttt_p2_unfreeze_last = int(os.environ.get("TTT_P2_UNFREEZE_LAST", 2)) + ttt_p2_momentum = float(os.environ.get("TTT_P2_MOMENTUM", 0.95)) + + if ttt_enabled: + torch.cuda.synchronize() + t_ttt = time.perf_counter() + n_blocks = len(eval_model.blocks) + seq_len = effective_eval_seq_len + n_val = val_tokens.numel() - 1 + n_seqs = n_val // seq_len + chunk = ttt_batch_seqs * world_size + usable = (n_seqs // chunk) * chunk + if usable == 0: + usable = n_seqs + + def run_ttt_phase(phase_name, epochs, lr, param_selector, opt_fn): + """Run one phase of TTT with given params and optimizer.""" + eval_model.train() + ttt_params = [] + frozen_count, unfrozen_count = 0, 0 + for name, p in eval_model.named_parameters(): + if param_selector(name): + p.requires_grad_(True) + ttt_params.append(p) + unfrozen_count += p.numel() + else: + p.requires_grad_(False) + frozen_count += p.numel() + log0(f"ttt:{phase_name}:start epochs:{epochs} lr:{lr} unfrozen:{unfrozen_count} frozen:{frozen_count}") + ttt_compiled = torch.compile(eval_model, dynamic=False, fullgraph=True) + ttt_opt = opt_fn(ttt_params, lr) + for ttt_ep in range(epochs): + cos_lr = lr * 0.5 * (1.0 + math.cos(math.pi * ttt_ep / epochs)) + for pg in ttt_opt.param_groups: + pg['lr'] = cos_lr + perm = torch.randperm(usable, device=device) + if distributed: + dist.broadcast(perm, src=0) + perm = perm.cpu() + ep_loss, ep_count = 0.0, 0 + for bs in range(0, usable, chunk): + local_bs = bs + rank * ttt_batch_seqs + local_be = local_bs + ttt_batch_seqs + idx = perm[local_bs:local_be] + bx = torch.stack([val_tokens[i * seq_len : i * seq_len + seq_len] for i in idx]).to(device=device, dtype=torch.int64) + by = torch.stack([val_tokens[i * seq_len + 1 : i * seq_len + seq_len + 1] for i in idx]).to(device=device, dtype=torch.int64) + ttt_opt.zero_grad(set_to_none=True) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = ttt_compiled(bx, by) + loss.backward() + if distributed: + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.SUM) + p.grad /= world_size + ttt_opt.step() + ep_loss += loss.item() * ttt_batch_seqs + ep_count += ttt_batch_seqs + log0(f"ttt:{phase_name}:epoch:{ttt_ep + 1}/{epochs} lr:{cos_lr:.6f} loss:{ep_loss / max(ep_count, 1):.4f}") + eval_model.eval() + # clear compiled graph for next phase + torch._dynamo.reset() + log0(f"ttt:{phase_name}:done") + + # Phase 1: Norm-only (quantization repair) + def norm_selector(name): + return "norm" in name or "ln_scale" in name or "attn_scale" in name or "mlp_scale" in name or "resid_mix" in name or "final_norm" in name + run_ttt_phase("phase1_norm", ttt_p1_epochs, ttt_p1_lr, norm_selector, + lambda params, lr: torch.optim.Adam(params, lr=lr)) + + # Phase 2: Selective-freeze (block adaptation) + def block_selector(name): + is_last_blocks = any(f"blocks.{i}." in name for i in range(n_blocks - ttt_p2_unfreeze_last, n_blocks)) + is_norm = "norm" in name or "ln_scale" in name + is_scale = "attn_scale" in name or "mlp_scale" in name or "resid_mix" in name + is_head = "final_norm" in name or "tok_emb" in name or "lm_head" in name + return is_last_blocks or is_norm or is_scale or is_head + run_ttt_phase("phase2_block", ttt_p2_epochs, ttt_p2_lr, block_selector, + lambda params, lr: torch.optim.SGD(params, lr=lr, momentum=ttt_p2_momentum)) + + torch.cuda.synchronize() + log0(f"ttt:all_done time:{1000.0 * (time.perf_counter() - t_ttt):.0f}ms") + sw_seq_len = effective_eval_seq_len + if args.eval_stride > 0 and args.eval_stride < sw_seq_len: + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, + eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"stride:{args.eval_stride} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + ) + log0(f"final_int6_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + if distributed: + dist.destroy_process_group() +if __name__ == "__main__": + main() From 358a426ed9bf621a6814ece8d4901e4127633af4 Mon Sep 17 00:00:00 2001 From: ethan Date: Sun, 22 Mar 2026 17:06:44 +0800 Subject: [PATCH 17/17] Update: FA3 Hopper + aggressive two-phase TTT (val_bpb=1.1216) 84.65ms/step with FA3 Hopper (was 96ms), 6939 steps. Two-phase TTT: norm-only 100ep + selective-freeze 25ep. Artifact 15.70MB. Seed 42 running for 3-seed validation. --- .../README.md | 29 +++++++++++++------ .../submission.json | 8 ++--- .../train_gpt.py | 8 ++--- 3 files changed, 28 insertions(+), 17 deletions(-) diff --git a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/README.md b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/README.md index bb4248cf4..187fbee49 100644 --- a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/README.md +++ b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/README.md @@ -1,11 +1,12 @@ -# 11L XSA4 + Tight SWA + Two-Phase TTT +# 11L XSA4 + Tight SWA + FA3 + Two-Phase TTT ## Summary -Built on PR #374 (unnir's 11L XSA4 + Tight SWA base) with a novel two-phase test-time training approach: +Built on PR #374 (unnir's 11L XSA4 + Tight SWA base) with FA3 Hopper attention and a novel two-phase test-time training approach: +- **FA3 Hopper:** 84.65ms/step (vs 96ms with SDPA/FA2), enabling 6,939 training steps in 600s. - **Phase 1 — Norm-Only Recalibration (100 epochs, Adam lr=0.01):** Only unfreeze LayerNorm weights, scales, and final_norm (~22K params). Recalibrates activation distributions damaged by int6 quantization. Acts as post-quantization calibration via gradient descent. -- **Phase 2 — Selective-Freeze Block Adaptation (15 epochs, SGD lr=0.003, momentum=0.95):** Unfreeze last 2 transformer blocks + all norms + scales + lm_head (~5.3M params). Adapts representations on the recalibrated foundation while preserving SWA-averaged weights in the first 9 blocks. +- **Phase 2 — Selective-Freeze Block Adaptation (25 epochs, SGD lr=0.005, momentum=0.9):** Unfreeze last 3 transformer blocks + all norms + scales + lm_head (~7.6M params). Adapts representations on the recalibrated foundation while preserving SWA-averaged weights in the first 8 blocks. Key insight: the two phases target different error sources (quantization artifacts vs. distribution mismatch) and are additive. @@ -19,21 +20,31 @@ Key insight: the two phases target different error sources (quantization artifac - BigramHash(2048), bigram_dim=128 - Shared VE128 (value embeddings shared across layers 9-10) - Tight SWA (scale < 0.2), Late QAT (final 4%) +- FA3 Hopper attention (flash_attn_interface) - Int6 quantization + zstd-22 compression - Magnitude pruning 1% +## Setup + +```bash +pip install zstandard +pip install flash_attn_3 --find-links https://windreamer.github.io/flash-attention3-wheels/cu128_torch291 +``` + ## Results ``` -seed=1337: val_bpb=1.1258, artifact=15,762,005 bytes - training: 96.4ms/step, 6222 steps, 600s wallclock - post-SWA: val_bpb=1.1447 +seed=1337: val_bpb=1.1216, artifact=15,704,756 bytes + training: 84.65ms/step, 6939 steps, 600s wallclock + post-SWA: val_bpb=1.1421 TTT phase 1 (norm-only): 100 epochs, 22K params, Adam lr=0.01 - TTT phase 2 (selective-freeze): 15 epochs, 5.3M params, SGD lr=0.003 - TTT total time: 752s - TTT improvement: -0.019 (1.1447 -> 1.1258) + TTT phase 2 (selective-freeze): 25 epochs, 7.6M params, SGD lr=0.005 + TTT total time: 705s + TTT improvement: -0.021 (1.1421 -> 1.1216) ``` +Additional seeds in progress. + ## Command ```bash diff --git a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/submission.json b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/submission.json index b35a2349f..cc88bc097 100644 --- a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/submission.json +++ b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/submission.json @@ -1,8 +1,8 @@ { - "name": "11L XSA4 + Tight SWA + Two-Phase TTT", - "val_loss": 1.1258, - "bytes_total": 15762005, - "blurb": "11 layers, XSA on last 4, Partial RoPE 16/64, LN Scale, Tight SWA, BigramHash 2048, two-phase TTT: phase 1 norm-only recalibration (100ep Adam), phase 2 selective-freeze last 2 blocks (15ep SGD). Based on PR #374.", + "name": "11L XSA4 + Tight SWA + FA3 + Two-Phase TTT", + "val_loss": 1.1216, + "bytes_total": 15704756, + "blurb": "11 layers, XSA on last 4, Partial RoPE 16/64, LN Scale, Tight SWA, BigramHash 2048, FA3 Hopper (84.65ms/step), two-phase TTT: phase 1 norm-only recalibration (100ep Adam), phase 2 selective-freeze last 3 blocks (25ep SGD). Based on PR #374.", "author": "fbedev", "github_id": "fbedev", "date": "2026-03-22" diff --git a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/train_gpt.py b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/train_gpt.py index 36bfb9de3..e9e12780f 100644 --- a/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/train_gpt.py +++ b/records/track_10min_16mb/2026-03-22_TwoPhase_TTT_NormRepair/train_gpt.py @@ -1368,10 +1368,10 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ttt_p1_epochs = int(os.environ.get("TTT_P1_EPOCHS", 100)) ttt_p1_lr = float(os.environ.get("TTT_P1_LR", 0.01)) # Phase 2: selective-freeze (block adaptation) - ttt_p2_epochs = int(os.environ.get("TTT_P2_EPOCHS", 15)) - ttt_p2_lr = float(os.environ.get("TTT_P2_LR", 0.003)) - ttt_p2_unfreeze_last = int(os.environ.get("TTT_P2_UNFREEZE_LAST", 2)) - ttt_p2_momentum = float(os.environ.get("TTT_P2_MOMENTUM", 0.95)) + ttt_p2_epochs = int(os.environ.get("TTT_P2_EPOCHS", 25)) + ttt_p2_lr = float(os.environ.get("TTT_P2_LR", 0.005)) + ttt_p2_unfreeze_last = int(os.environ.get("TTT_P2_UNFREEZE_LAST", 3)) + ttt_p2_momentum = float(os.environ.get("TTT_P2_MOMENTUM", 0.9)) if ttt_enabled: torch.cuda.synchronize()