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3afc977 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | """Train the diffusion denoiser or the AR-FIM baseline. Same data, size, schedule.
Usage:
python -m ml.train --mode diffusion --train data/train.jsonl --out runs/diff
python -m ml.train --mode ar --train data/train.jsonl --out runs/ar
"""
from __future__ import annotations
import argparse
import json
import math
import os
import time
import torch
from torch.utils.data import DataLoader
from . import ar, diffusion
from .config import ModelConfig, TaskConfig, TrainConfig
from .data import InfillDataset, load_records
from .model import Transformer, amp_ctx
from .tokenizer import Tokenizer
def pick_device() -> str:
if torch.backends.mps.is_available():
return "mps"
if torch.cuda.is_available():
return "cuda"
return "cpu"
def lr_at(step, tc: TrainConfig):
"""Warmup-stable-decay (TRAINING.md)."""
if step < tc.warmup:
return tc.lr * step / max(1, tc.warmup)
decay_start = int(tc.steps * 0.8)
if step < decay_start:
return tc.lr
frac = (step - decay_start) / max(1, tc.steps - decay_start)
return tc.lr * (1.0 - 0.9 * frac) # decay to 0.1*lr
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--mode", choices=["diffusion", "ar"], required=True)
ap.add_argument("--train", default="data/train.jsonl")
ap.add_argument("--out", required=True)
ap.add_argument("--steps", type=int, default=TrainConfig.steps)
ap.add_argument("--batch", type=int, default=TrainConfig.batch_size)
ap.add_argument("--d_model", type=int, default=ModelConfig.d_model)
ap.add_argument("--layers", type=int, default=ModelConfig.n_layers)
ap.add_argument("--heads", type=int, default=ModelConfig.n_heads)
ap.add_argument("--ff", type=int, default=ModelConfig.d_ff)
ap.add_argument("--tok", choices=["char", "lua"], default="char", help="tokenizer level")
ap.add_argument("--seq_len", type=int, default=TaskConfig.seq_len)
ap.add_argument("--block_len", type=int, default=TaskConfig.block_len)
ap.add_argument("--seed", type=int, default=0)
args = ap.parse_args()
torch.manual_seed(args.seed)
device = pick_device()
os.makedirs(args.out, exist_ok=True)
records = load_records(args.train)
tok = Tokenizer.build([r["source"] for r in records], mode=args.tok)
tok.save(os.path.join(args.out, "tokenizer.json"))
taskcfg = TaskConfig(seq_len=args.seq_len, block_len=args.block_len)
mcfg = ModelConfig(
vocab_size=tok.vocab_size, d_model=args.d_model, n_layers=args.layers,
n_heads=args.heads, d_ff=args.ff, max_len=taskcfg.seq_len,
)
tc = TrainConfig(batch_size=args.batch, steps=args.steps, seed=args.seed)
ds = InfillDataset(records, tok, taskcfg, mode=args.mode)
print(f"[{args.mode}] device={device} vocab={tok.vocab_size} "
f"examples={len(ds)} skipped={ds.skipped} (too long)")
dl = DataLoader(ds, batch_size=tc.batch_size, shuffle=True, drop_last=True)
model = Transformer(mcfg, causal=(args.mode == "ar")).to(device)
print(f"[{args.mode}] params={model.num_params()/1e6:.2f}M")
opt = torch.optim.AdamW(model.parameters(), lr=tc.lr, weight_decay=tc.weight_decay)
model.train()
step = 0
t0 = time.time()
running = 0.0
nloss = 0
while step < tc.steps:
for batch in dl:
if step >= tc.steps:
break
for g in opt.param_groups:
g["lr"] = lr_at(step, tc)
with amp_ctx(device):
if args.mode == "diffusion":
ids, region, block_id, attn_mask = (b.to(device) for b in batch)
l = diffusion.loss(model, ids, region, block_id, attn_mask, tok)
else:
ids, loss_mask = (b.to(device) for b in batch)
attn_mask = ids != tok.pad_id
l = ar.loss(model, ids, loss_mask, attn_mask, tok)
opt.zero_grad()
l.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), tc.grad_clip)
opt.step()
running += l.item()
nloss += 1
step += 1
if step % tc.log_every == 0:
dt = time.time() - t0
print(f"[{args.mode}] step {step}/{tc.steps} "
f"loss {running/nloss:.4f} lr {lr_at(step,tc):.2e} "
f"{step/dt:.1f} it/s")
running = 0.0
nloss = 0
ckpt = {
"model": model.state_dict(),
"model_cfg": vars(mcfg),
"task_cfg": vars(taskcfg),
"mode": args.mode,
}
torch.save(ckpt, os.path.join(args.out, "model.pt"))
with open(os.path.join(args.out, "meta.json"), "w") as f:
json.dump({"mode": args.mode, "steps": tc.steps,
"params_M": model.num_params() / 1e6}, f, indent=2)
print(f"[{args.mode}] saved to {args.out}")
if __name__ == "__main__":
main()
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