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"""Single-GPU training for the dense image/text HY-WorldPlay Predictor."""
from __future__ import annotations
import argparse
import json
import math
import os
import random
import time
from pathlib import Path
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from hyvideo.commons.infer_state import initialize_infer_state
from models import HYWorldPlayPredictor
from predictor_training.checkpoint import (
load_predictor_weights,
save_predictor_weights,
save_training_state,
trainable_state_dict,
)
from predictor_training.dataset import (
PredictorPairDataset,
move_pair_to_device,
predictor_collate,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--manifest", default="datasets/predictor_v1/train_eval_manifest.jsonl")
parser.add_argument(
"--teacher_checkpoint",
default="models-ms/HY-WorldPlay/ar_distilled_action_model/diffusion_pytorch_model.safetensors",
)
parser.add_argument(
"--output_dir",
default=(
"checkpoints/predictor_v1_blocks1-52_bf16_1gpu_"
"flr1e-4_blr1e-5_schedsteps2000_swarmup100_bwarmup100"
),
)
parser.add_argument("--max_steps", type=int, default=2000)
parser.add_argument("--gradient_accumulation_steps", type=int, default=4)
parser.add_argument("--warmup_blocks_steps", type=int, default=100)
parser.add_argument("--scheduler_warmup_steps", type=int, default=100)
parser.add_argument("--fusion_lr", type=float, default=1e-4)
parser.add_argument("--blocks_lr", type=float, default=1e-5)
parser.add_argument("--weight_decay", type=float, default=0.01)
parser.add_argument("--hidden_weight", type=float, default=0.1)
parser.add_argument("--velocity_weight", type=float, default=1.0)
parser.add_argument("--grad_clip", type=float, default=1.0)
parser.add_argument("--log_every", type=int, default=10)
parser.add_argument("--save_every", type=int, default=500)
parser.add_argument("--keep_snapshots", type=int, default=2)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--num_workers", type=int, default=0)
parser.add_argument("--max_records", type=int, default=None)
parser.add_argument("--gradient_checkpointing", action=argparse.BooleanOptionalAction, default=True)
parser.add_argument("--save_optimizer_state", action=argparse.BooleanOptionalAction, default=True)
parser.add_argument("--resume", default=None)
return parser.parse_args()
def initialize_runtime_state() -> None:
class Args:
sage_blocks_range = "0-53"
use_sageattn = False
enable_torch_compile = False
use_fp8_gemm = False
quant_type = "fp8-per-block"
include_patterns = "double_blocks"
use_vae_parallel = False
initialize_infer_state(Args())
def make_scheduler(optimizer: torch.optim.Optimizer, warmup: int, total: int) -> LambdaLR:
def factor(step: int) -> float:
if step < warmup:
return max(1e-8, float(step + 1) / max(1, warmup))
progress = min(1.0, float(step - warmup) / max(1, total - warmup))
return 0.5 * (1.0 + math.cos(math.pi * progress))
return LambdaLR(optimizer, factor)
def set_blocks_trainable(model: HYWorldPlayPredictor, enabled: bool) -> None:
model.double_blocks.requires_grad_(enabled)
def prune_snapshots(output_dir: Path, keep: int) -> None:
snapshots = sorted(output_dir.glob("predictor_step_*.safetensors"))
for path in snapshots[:-keep] if keep > 0 else snapshots:
path.unlink()
def main() -> None:
args = parse_args()
if not torch.cuda.is_available():
raise RuntimeError("CUDA is required")
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
initialize_runtime_state()
device = torch.device("cuda:0")
dtype = torch.bfloat16
output_dir = Path(args.output_dir).resolve()
output_dir.mkdir(parents=True, exist_ok=True)
log_path = output_dir / "train_log.jsonl"
dataset = PredictorPairDataset(args.manifest, max_records=args.max_records)
generator = torch.Generator().manual_seed(args.seed)
loader = DataLoader(
dataset,
batch_size=1,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
collate_fn=predictor_collate,
generator=generator,
)
model = HYWorldPlayPredictor()
model.load_teacher_initialization(args.teacher_checkpoint)
model.to(device=device, dtype=dtype)
model.enable_gradient_checkpointing(args.gradient_checkpointing)
model.train()
optimizer = AdamW(
[
{
"params": list(model.img_fusion.parameters())
+ list(model.txt_fusion.parameters())
+ list(model.residual_out.parameters()),
"lr": args.fusion_lr,
},
{"params": model.double_blocks.parameters(), "lr": args.blocks_lr},
],
betas=(0.9, 0.95),
weight_decay=args.weight_decay,
)
scheduler = make_scheduler(optimizer, args.scheduler_warmup_steps, args.max_steps)
global_step = 0
micro_step = 0
set_blocks_trainable(model, args.warmup_blocks_steps <= 0)
if args.resume:
state = torch.load(args.resume, map_location="cpu", weights_only=False)
model.load_state_dict(state["model"], strict=False)
optimizer.load_state_dict(state["optimizer"])
scheduler.load_state_dict(state["scheduler"])
global_step = int(state["global_step"])
micro_step = int(state.get("micro_step", global_step * args.gradient_accumulation_steps))
if "torch_rng_state" in state:
torch.set_rng_state(state["torch_rng_state"])
if "cuda_rng_state" in state:
torch.cuda.set_rng_state_all(state["cuda_rng_state"])
set_blocks_trainable(model, global_step >= args.warmup_blocks_steps)
print(
json.dumps(
{
"dataset_pairs": len(dataset),
"trainable_parameters": model.trainable_parameter_count(),
"breakdown": model.trainable_parameter_breakdown(),
"start_step": global_step,
},
indent=2,
),
flush=True,
)
optimizer.zero_grad(set_to_none=True)
running: list[dict[str, float | str]] = []
epoch = 0
while global_step < args.max_steps:
epoch += 1
for item in loader:
if global_step >= args.max_steps:
break
if global_step == args.warmup_blocks_steps:
set_blocks_trainable(model, True)
inputs, target_hidden, target_velocity = move_pair_to_device(
item, device=device, dtype=dtype
)
started = time.perf_counter()
with torch.autocast(device_type="cuda", dtype=dtype):
output = model(**inputs)
hidden_loss = F.mse_loss(
output["pred_hidden"].float(), target_hidden.float()
)
velocity_loss = F.mse_loss(
output["pred_velocity"].float(), target_velocity.float()
)
loss = (
args.hidden_weight * hidden_loss
+ args.velocity_weight * velocity_loss
)
scaled_loss = loss / args.gradient_accumulation_steps
scaled_loss.backward()
micro_step += 1
running.append(
{
"pair_id": item["pair_id"],
"loss": float(loss.detach()),
"hidden_mse": float(hidden_loss.detach()),
"velocity_mse": float(velocity_loss.detach()),
"micro_time_s": time.perf_counter() - started,
}
)
del output, loss, scaled_loss, hidden_loss, velocity_loss
if micro_step % args.gradient_accumulation_steps:
continue
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
scheduler.step()
optimizer.zero_grad(set_to_none=True)
global_step += 1
if global_step % args.log_every == 0 or global_step == 1:
torch.cuda.synchronize()
count = len(running)
record = {
"global_step": global_step,
"epoch": epoch,
"loss": sum(float(x["loss"]) for x in running) / count,
"hidden_mse": sum(float(x["hidden_mse"]) for x in running) / count,
"velocity_mse": sum(float(x["velocity_mse"]) for x in running) / count,
"grad_norm": float(grad_norm),
"lr_fusion": optimizer.param_groups[0]["lr"],
"lr_blocks": optimizer.param_groups[1]["lr"],
"peak_memory_gib": torch.cuda.max_memory_allocated() / 2**30,
"avg_micro_time_s": sum(float(x["micro_time_s"]) for x in running) / count,
}
with log_path.open("a", encoding="utf-8") as handle:
handle.write(json.dumps(record, sort_keys=True) + "\n")
print(json.dumps(record, sort_keys=True), flush=True)
running.clear()
should_save = global_step % args.save_every == 0 or global_step == args.max_steps
if should_save:
weights_path = output_dir / f"predictor_step_{global_step:05d}.safetensors"
save_predictor_weights(model, weights_path)
if args.save_optimizer_state:
latest = {
"model": trainable_state_dict(model),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"global_step": global_step,
"micro_step": micro_step,
"torch_rng_state": torch.get_rng_state(),
"cuda_rng_state": torch.cuda.get_rng_state_all(),
"args": vars(args),
"predictor_config": model.config_dict,
"weights_path": str(weights_path),
}
save_training_state(output_dir / "training_latest.pt", latest)
prune_snapshots(output_dir, args.keep_snapshots)
if __name__ == "__main__":
main()
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