diff --git a/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/README.md b/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/README.md index a1611dbfa5..f987aad3de 100644 --- a/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/README.md +++ b/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/README.md @@ -19,5 +19,4 @@ It documents a V5 run on the official FineWeb sp1024 path using a longer sequenc - `submission.json` - `results.tsv` - `train_gpt.py` -- `train.log` - `final_model.int8.ptz` diff --git a/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/results.tsv b/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/results.tsv index d8a379b86f..69824df486 100644 --- a/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/results.tsv +++ b/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/results.tsv @@ -1,7 +1,2 @@ -run_idtrackgputokenizertrain_seq_leniterationswarmup_stepsval_loss_exactval_bpb_exactbytes_total -v5_sp1024_top10_anon-record-16mb1xH100sp102440966000302.050297521.2143016815841388 -EOFls /workspace/parameter-golf-v-5/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168 -cd /workspace/parameter-golf-v-5 -git add . -git commit -m "Add V5 non-record submission (1.2143 bpb)" -git push +run_id track gpu tokenizer train_seq_len iterations warmup_steps val_loss_exact val_bpb_exact bytes_total +v5_sp1024_top10_a non-record-16mb 1xH100 sp1024 4096 6000 30 2.05029752 1.21430168 15841388 diff --git a/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/submission.json b/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/submission.json index edaa6c1db3..39255b099f 100644 --- a/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/submission.json +++ b/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/submission.json @@ -16,6 +16,3 @@ "bytes_code": null, "gpu": "1xH100" } -EOFcat > /workspace/parameter-golf-v-5/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/results.tsv <<'EOF' -run_id track gpu tokenizer train_seq_len iterations warmup_steps val_loss_exact val_bpb_exact bytes_total -v5_sp1024_top10_a non-record-16mb 1xH100 sp1024 4096 6000 30 2.05029752 1.21430168 15841388 diff --git a/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/train.log b/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/train.log deleted file mode 100644 index b1258e76e6..0000000000 --- a/records/track_non_record_16mb/2026-04-17_V5_SP1024_SEQ4096_1xH100_121430168/train.log +++ /dev/null @@ -1,1174 +0,0 @@ -""" -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 - -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 -# ----------------------------- -# 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)) - - # 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)) - - # Training length. - iterations = int(os.environ.get("ITERATIONS", 20000)) - 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", 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", 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 = 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", 0.05)) - 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)) - 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)) - 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)) - -# ----------------------------- -# 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 - 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): - super().__init__( - params, - dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov), - ) - - @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) - # 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() - - if distributed: - dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) - - curr = 0 - for p in params: - g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) - p.add_(g, alpha=-lr) - curr += p.numel() - - return loss - - -# ----------------------------- -# 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 -) -> 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}") - # 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: - 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]: - # 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( - "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 -# ----------------------------- -# -# 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", - ).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: - # 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() - 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() - - # 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, - ) - - 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 - - # 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 - - 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) - # 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: - 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 -# ----------------------------- - -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: - # 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 - 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): - # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. - def forward(self, x: Tensor) -> Tensor: - bias = self.bias.to(x.dtype) if self.bias is not None else None - 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: - param.data = param.data.float() - - -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)) - 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): - # relu^2 MLP from the original modded-nanogpt setup - def __init__(self, dim: int, mlp_mult: int): - super().__init__() - hidden = 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, - ): - 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: int, - tie_embeddings: bool, - tied_embed_init_std: float, - logit_softcap: float, - rope_base: float, - qk_gain_init: float, - ): - 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.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, - ) - for i 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) - 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) - x = F.rms_norm(x, (x.size(-1),)) - 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) - 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") - - -# ----------------------------- -# 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 + 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")) - 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 - - # 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) - 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) - - # ----------------------------- - # TOKENIZER + VALIDATION METRIC SETUP - # ----------------------------- - - 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, - ).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 - - # 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 - 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) - 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}], - betas=(args.beta1, args.beta2), - eps=args.adam_eps, - fused=True, - ) - optimizer_muon = Muon( - matrix_params, - lr=args.matrix_lr, - momentum=args.muon_momentum, - backend_steps=args.muon_backend_steps, - ) - for group in optimizer_muon.param_groups: - group["base_lr"] = args.matrix_lr - 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, - 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("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}") - - # ----------------------------- - # 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 - - # 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] - 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 - 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) - 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" - ) - - # 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) - 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" - ) - - # ----------------------------- - # 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") - 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") - - quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) - quant_buf = io.BytesIO() - torch.save(quant_obj, quant_buf) - quant_raw = quant_buf.getvalue() - 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")) - 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() - 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() - 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() - -==================================================================================================== -Running Python 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] -Running PyTorch 2.9.1+cu128 -Fri Apr 17 13:15:52 2026 -+-----------------------------------------------------------------------------------------+ -| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 | -+-----------------------------------------+------------------------+----------------------+ -| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | -| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | -| | | MIG M. | -|=========================================+========================+======================| -| 0 NVIDIA H100 80GB HBM3 On | 00000000:5D:00.0 Off | 0 | -| N/A 43C P0 99W / 700W | 1185MiB / 81559MiB | 9% Default | -| | | Disabled | -+-----------------------------------------+------------------------+----------------------+ - -+-----------------------------------------------------------------------------------------+ -| Processes: | -| GPU GI CI PID Type Process name GPU Memory | -| ID ID Usage | -|=========================================================================================| -| No running processes found | -+-----------------------------------------------------------------------------------------+ - -==================================================================================================== -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:80 -val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -model_params:17059912 -world_size:1 grad_accum_steps:8 -sdp_backends:cudnn=False flash=True mem_efficient=False math=False -attention_mode:gqa num_heads:8 num_kv_heads:4 -tie_embeddings:True embed_lr:0.05 head_lr:0.0 matrix_lr:0.04 scalar_lr:0.04 -train_batch_tokens:524288 train_seq_len:4096 iterations:6000 warmup_steps:30 max_wallclock_seconds:0.000 -seed:1337 -warmup_step:10/30 -warmup_step:20/30 -warmup_step:30/30 -step:6000/6000 val_loss:2.0411 val_bpb:1.2089 train_time:3218242ms step_avg:536.37ms -peak memory allocated: 10242 MiB reserved: 10556 MiB -Serialized model: 67224983 bytes -Code size: 47686 bytes -Total submission size: 67272669 bytes -Serialized model int8+zlib: 15793702 bytes (payload:17178912 raw_torch:17224025 payload_ratio:3.91x) -Total submission size int8+zlib: 15841388 bytes -final_int8_zlib_roundtrip val_loss:2.0503 val_bpb:1.2143 eval_time:16861ms -final_int8_zlib_roundtrip_exact val_loss:2.05029752 val_bpb:1.21430168