File size: 4,464 Bytes
2866d3d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from pathlib import Path
from datasets import load_dataset
from bit_transformer import (
BitTransformerLM,
configure_optimizer,
expand_model,
text_to_bits,
)
from bit_transformer.training import train_loop as basic_train
def _build_memmap(lines, path: Path, max_len: int) -> None:
"""Precompute bit tensors into a memory-mapped file."""
arr = np.memmap(path, mode="w+", shape=(len(lines), max_len), dtype="uint8")
for idx, text in enumerate(lines):
bits = text_to_bits(text)[:max_len]
if len(bits) < max_len:
bits.extend([0] * (max_len - len(bits)))
arr[idx] = np.array(bits, dtype="uint8")
arr.flush()
class MemmapDataset(Dataset):
"""Dataset backed by a memory-mapped array."""
def __init__(self, path: Path, length: int, max_len: int) -> None:
self.path = path
self.length = length
self.max_len = max_len
self._arr = np.memmap(path, mode="r", shape=(length, max_len), dtype="uint8")
def __len__(self) -> int: # pragma: no cover - trivial
return self.length
def __getitem__(self, idx: int) -> torch.Tensor:
return torch.from_numpy(self._arr[idx].astype("int64"))
def progressive_scale_schedule(steps=12, max_len=64, dataset_size=128):
"""Run deterministic scale-up on WikiText data."""
ds = load_dataset("wikitext", "wikitext-2-raw-v1")
train_lines = [t for t in ds["train"]["text"] if t.strip()][:dataset_size]
valid_lines = [t for t in ds["validation"]["text"] if t.strip()][: dataset_size // 4]
train_path = Path("wikitext_train.memmap")
valid_path = Path("wikitext_valid.memmap")
_build_memmap(train_lines, train_path, max_len)
_build_memmap(valid_lines, valid_path, max_len)
train = MemmapDataset(train_path, len(train_lines), max_len)
valid = torch.from_numpy(
np.memmap(valid_path, mode="r", shape=(len(valid_lines), max_len), dtype="uint8")
).long()
layers = 1
width = 32
params = dict(
d_model=width,
nhead=4,
num_layers=layers,
dim_feedforward=width * 2,
max_seq_len=max_len,
reversible=True,
chunk_size=max_len,
use_autocast=True,
use_act=True,
act_threshold=0.9,
)
model = BitTransformerLM(**params)
steps_per_epoch = max(1, (len(train) + 7) // 8)
optimizer, scheduler = configure_optimizer(model, lr=1e-3, total_steps=(steps + 1) * steps_per_epoch)
results = []
for step in range(steps + 1):
basic_train(
model,
train,
epochs=1,
compress_prob=0.5,
log=False,
forward_kwargs=None,
num_workers=2,
)
with torch.no_grad():
logits, _ = model(valid)
pred = logits[:, :-1, :].reshape(-1, 2)
target = valid[:, 1:].reshape(-1)
val_loss = F.cross_entropy(pred, target).item()
print(f"Step {step} validation loss: {val_loss:.4f}")
results.append((step, val_loss))
if step < steps:
if step % 2 == 0:
layers *= 2
else:
width *= 2
params = dict(
d_model=width,
nhead=4,
num_layers=layers,
dim_feedforward=width * 2,
max_seq_len=max_len,
reversible=True,
chunk_size=max_len,
use_autocast=True,
use_act=True,
act_threshold=0.9,
)
model = expand_model(model, params)
optimizer, scheduler = configure_optimizer(model, lr=1e-3, total_steps=(steps - step) * steps_per_epoch)
print(f"Scaled model to {layers} layers and width {width}")
return results
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Deterministic scale-up benchmark")
parser.add_argument("--steps", type=int, default=12, help="number of scale-up steps")
parser.add_argument("--max-len", type=int, default=64, help="sequence length")
parser.add_argument("--dataset-size", type=int, default=128, help="number of training lines")
args = parser.parse_args()
progressive_scale_schedule(steps=args.steps, max_len=args.max_len, dataset_size=args.dataset_size)
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