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"""Train the dual-rate physics model: frozen Qwen3.5 semantic path + flow expert."""
import argparse
import json
import math
import sys
import time
from functools import partial
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader
sys.path.insert(0, str(Path(__file__).parent.parent))
from src.dualrate.config import DualRateConfig # noqa: E402
from src.dualrate.data import DualRateDataset, collate_dualrate # noqa: E402
from src.dualrate.model import DualRatePhysicsModel # noqa: E402
from src.dualrate.normalization import StateNormalizer # noqa: E402
def lr_lambda(step, cfg):
if step < cfg.warmup_steps:
return step / max(cfg.warmup_steps, 1)
p = (step - cfg.warmup_steps) / max(cfg.total_steps - cfg.warmup_steps, 1)
p = min(p, 1.0)
floor = cfg.min_lr / cfg.lr
return floor + (1 - floor) * 0.5 * (1 + math.cos(math.pi * p))
@torch.no_grad()
def validate(model, val_batches, norm, cfg, device):
"""Single-step position error (px, Euclidean) vs copy-current-frame baseline."""
model.eval()
errs, copy_errs, losses = [], [], []
for batch in val_batches:
b = {k: v.to(device) for k, v in batch.items()}
latent = model.compute_latent(b["input_ids"], b["attention_mask"])
losses.append(model.flow_loss(latent, b["cur_state"], b["static"], b["obj_mask"],
b["target_delta"], b["gravity_dt"]).item())
gen = torch.Generator(device=device)
gen.manual_seed(777)
delta_norm = model.sample_delta(latent, b["cur_state"], b["static"],
b["obj_mask"], b["gravity_dt"], generator=gen)
scale = torch.as_tensor(norm.delta_scale[:2], device=device)
pred_dpos = delta_norm[..., :2] * scale
true_dpos = b["target_delta"][..., :2] * scale
mask = b["obj_mask"]
err = ((pred_dpos - true_dpos).norm(dim=-1) * mask).sum() / mask.sum()
copy_err = (true_dpos.norm(dim=-1) * mask).sum() / mask.sum()
errs.append(err.item())
copy_errs.append(copy_err.item())
model.train()
return float(np.mean(errs)), float(np.mean(copy_errs)), float(np.mean(losses))
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--out", default="runs/dualrate_v1")
ap.add_argument("--smoke", action="store_true", help="2k scenes / 2k steps")
ap.add_argument("--max-scenes", type=int, default=None)
ap.add_argument("--steps", type=int, default=None)
ap.add_argument("--batch-size", type=int, default=None)
ap.add_argument("--lr", type=float, default=None)
ap.add_argument("--tick-k", type=int, default=None)
ap.add_argument("--denoise-steps", type=int, default=None)
ap.add_argument("--workers", type=int, default=8)
ap.add_argument("--no-wandb", action="store_true")
ap.add_argument("--val-every", type=int, default=500)
ap.add_argument("--save-every", type=int, default=5000)
ap.add_argument("--resume", default=None)
args = ap.parse_args()
cfg = DualRateConfig()
if args.steps:
cfg.total_steps = args.steps
if args.batch_size:
cfg.batch_size = args.batch_size
if args.lr:
cfg.lr = args.lr
if args.tick_k:
cfg.tick_k = args.tick_k
if args.denoise_steps:
cfg.denoise_steps = args.denoise_steps
max_scenes = args.max_scenes
if args.smoke:
cfg.total_steps = args.steps or 2000
cfg.warmup_steps = 100
max_scenes = max_scenes or 2000
out_dir = Path(args.out)
out_dir.mkdir(parents=True, exist_ok=True)
device = "cuda"
torch.manual_seed(cfg.seed)
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)
norm = StateNormalizer.from_config(cfg)
train_ds = DualRateDataset("data_dualrate/cache", "train", tokenizer, cfg, norm,
max_scenes=max_scenes, seed=cfg.seed)
val_ds = DualRateDataset("data_dualrate/cache", "val", tokenizer, cfg, norm,
max_scenes=256, seed=cfg.seed)
val_ds.deterministic = True
collate = partial(collate_dualrate, pad_token_id=tokenizer.pad_token_id)
train_dl = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True,
num_workers=args.workers, collate_fn=collate,
pin_memory=True, drop_last=True, persistent_workers=args.workers > 0)
val_batches = list(DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False,
num_workers=2, collate_fn=collate))
print(f"train scenes {len(train_ds)}, val scenes {len(val_ds)}, "
f"steps {cfg.total_steps}, batch {cfg.batch_size}x{cfg.grad_accum}")
model = DualRatePhysicsModel(cfg, readout_token_id=tokenizer.pad_token_id).to(device)
trainable = model.trainable_parameters()
n_train = sum(p.numel() for p in trainable)
print(f"trainable params: {n_train / 1e6:.1f}M")
opt = torch.optim.AdamW(trainable, lr=cfg.lr, betas=cfg.betas,
weight_decay=cfg.weight_decay)
sched = torch.optim.lr_scheduler.LambdaLR(opt, partial(lr_lambda, cfg=cfg))
start_step = 0
if args.resume:
ck = torch.load(args.resume, map_location=device, weights_only=False)
model.load_state_dict(ck["model"], strict=False)
opt.load_state_dict(ck["opt"])
sched.load_state_dict(ck["sched"])
start_step = ck["step"]
print(f"resumed from {args.resume} @ step {start_step}")
use_wandb = not args.no_wandb
if use_wandb:
try:
import wandb
wandb.init(project="physics-dualrate", name=out_dir.name, mode="offline",
config={**cfg.to_dict(), "max_scenes": max_scenes})
except Exception as e: # noqa: BLE001
print(f"wandb disabled: {e}")
use_wandb = False
def save(step, tag):
state = {k: v for k, v in model.state_dict().items()
if not k.startswith("semantic.lm.")}
torch.save({"model": state, "opt": opt.state_dict(), "sched": sched.state_dict(),
"step": step, "config": cfg.to_dict(),
"normalizer": norm.state_dict()}, out_dir / f"ckpt_{tag}.pt")
(out_dir / "config.json").write_text(json.dumps(cfg.to_dict(), indent=2))
model.train()
step = start_step
t0 = time.time()
loss_acc, log_every = [], 50
data_iter = iter(train_dl)
while step < cfg.total_steps:
opt.zero_grad(set_to_none=True)
for _ in range(cfg.grad_accum):
try:
batch = next(data_iter)
except StopIteration:
data_iter = iter(train_dl)
batch = next(data_iter)
b = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
latent = model.compute_latent(b["input_ids"], b["attention_mask"])
loss = model.flow_loss(latent, b["cur_state"], b["static"], b["obj_mask"],
b["target_delta"], b["gravity_dt"])
(loss / cfg.grad_accum).backward()
loss_acc.append(loss.item())
torch.nn.utils.clip_grad_norm_(trainable, cfg.grad_clip)
opt.step()
sched.step()
step += 1
if step % log_every == 0:
l = float(np.mean(loss_acc[-log_every * cfg.grad_accum:]))
sps = log_every / (time.time() - t0)
t0 = time.time()
print(f"step {step}/{cfg.total_steps} loss {l:.4f} "
f"lr {sched.get_last_lr()[0]:.2e} {sps:.2f} it/s", flush=True)
if use_wandb:
wandb.log({"train/flow_loss": l, "train/lr": sched.get_last_lr()[0],
"train/steps_per_sec": sps}, step=step)
if step % args.val_every == 0 or step == cfg.total_steps:
pos_err, copy_err, vloss = validate(model, val_batches, norm, cfg, device)
print(f" VAL step {step}: dpos_err {pos_err:.3f}px (copy-frame {copy_err:.3f}px) "
f"flow_loss {vloss:.4f}", flush=True)
if use_wandb:
wandb.log({"val/dpos_err_px": pos_err, "val/copy_frame_err_px": copy_err,
"val/flow_loss": vloss}, step=step)
if step % args.save_every == 0:
save(step, f"step{step}")
save(step, "final")
print(f"done, saved to {out_dir}")
if use_wandb:
wandb.finish()
if __name__ == "__main__":
main()
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