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Running on Zero
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import json
import os, torch
from tqdm import tqdm
from accelerate import Accelerator
from .training_module import DiffusionTrainingModule
from .logger import ModelLogger
RESUME_META_FILE = "resume_meta.json"
def _checkpoint_dir(output_path, step):
return os.path.join(output_path, f"training_state-step-{step}")
def _find_latest_checkpoint_dir(output_path):
if not os.path.isdir(output_path):
return None
candidates = []
for name in os.listdir(output_path):
if not name.startswith("training_state-step-"):
continue
try:
step = int(name.rsplit("-", 1)[-1])
except ValueError:
continue
path = os.path.join(output_path, name)
if os.path.isdir(path):
candidates.append((step, path))
if not candidates:
return None
candidates.sort(key=lambda item: item[0])
return candidates[-1][1]
def _save_resume_state(accelerator, output_path, global_step, epoch_id, step_in_epoch):
accelerator.wait_for_everyone()
checkpoint_path = _checkpoint_dir(output_path, global_step)
accelerator.save_state(checkpoint_path)
accelerator.wait_for_everyone()
if accelerator.is_main_process:
meta = {
"global_step": int(global_step),
"epoch_id": int(epoch_id),
"step_in_epoch": int(step_in_epoch),
}
with open(os.path.join(checkpoint_path, RESUME_META_FILE), "w", encoding="utf-8") as handle:
json.dump(meta, handle)
def _load_resume_state(accelerator, resume_from, output_path):
if resume_from is None:
return None
if resume_from == "latest":
checkpoint_path = _find_latest_checkpoint_dir(output_path)
if checkpoint_path is None:
return None
else:
checkpoint_path = os.path.abspath(resume_from)
if not os.path.isdir(checkpoint_path):
raise FileNotFoundError(f"Resume checkpoint folder does not exist: {checkpoint_path}")
accelerator.load_state(checkpoint_path)
meta_path = os.path.join(checkpoint_path, RESUME_META_FILE)
if os.path.isfile(meta_path):
with open(meta_path, "r", encoding="utf-8") as handle:
meta = json.load(handle)
else:
meta = {"global_step": 0, "epoch_id": 0, "step_in_epoch": 0}
meta["checkpoint_path"] = checkpoint_path
return meta
def _module_resume_fingerprint(unwrapped_model, module_attr="dit", max_tensors=8, sample_size=2048):
pipe = getattr(unwrapped_model, "pipe", None)
module = getattr(pipe, module_attr, None) if pipe is not None else None
if module is None:
return None
entries = []
for name, tensor in sorted(module.state_dict().items()):
if not torch.is_tensor(tensor):
continue
if not tensor.is_floating_point():
continue
if tensor.numel() == 0:
continue
flat = tensor.detach().float().view(-1)
n = min(sample_size, flat.numel())
sample = flat[:n].cpu()
sample_bytes = sample.numpy().tobytes()
entries.append({
"name": name,
"shape": tuple(tensor.shape),
"hash": hashlib.sha1(sample_bytes).hexdigest()[:12],
"mean": float(sample.mean().item()),
"std": float(sample.std(unbiased=False).item()) if n > 1 else 0.0,
"norm": float(sample.norm().item()),
})
if len(entries) >= max_tensors:
break
return {"module": f"pipe.{module_attr}", "entries": entries}
def _compare_fingerprints(before_fp, after_fp):
if before_fp is None or after_fp is None:
return None
before = {entry["name"]: entry["hash"] for entry in before_fp.get("entries", [])}
after = {entry["name"]: entry["hash"] for entry in after_fp.get("entries", [])}
common = sorted(set(before.keys()) & set(after.keys()))
changed = [name for name in common if before[name] != after[name]]
return {
"total_common": len(common),
"changed": changed,
}
def launch_training_task(
accelerator: Accelerator,
dataset: torch.utils.data.Dataset,
model: DiffusionTrainingModule,
model_logger: ModelLogger,
learning_rate: float = 1e-5,
weight_decay: float = 1e-2,
num_workers: int = 1,
save_steps: int = None,
num_epochs: int = 1,
args = None,
):
if args is not None:
learning_rate = args.learning_rate
weight_decay = args.weight_decay
num_workers = args.dataset_num_workers
save_steps = args.save_steps
num_epochs = args.num_epochs
optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate, weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
resume_from = getattr(args, "resume_from", None) if args is not None else None
pre_resume_fp = None
if resume_from is not None:
unwrapped_model = accelerator.unwrap_model(model)
pre_resume_fp = _module_resume_fingerprint(unwrapped_model)
if accelerator.is_main_process and pre_resume_fp is not None:
print(
f"[ResumeCheck] Pre-resume fingerprint on {pre_resume_fp['module']} "
f"(sample_tensors={len(pre_resume_fp['entries'])})"
)
resume_meta = _load_resume_state(
accelerator=accelerator,
resume_from=resume_from,
output_path=getattr(args, "output_path", "./models") if args is not None else "./models",
)
start_epoch = 0
start_step_in_epoch = 0
global_step = 0
if resume_meta is not None:
global_step = int(resume_meta.get("global_step", 0))
start_epoch = int(resume_meta.get("epoch_id", 0))
start_step_in_epoch = int(resume_meta.get("step_in_epoch", 0))
model_logger.num_steps = global_step
unwrapped_model = accelerator.unwrap_model(model)
if hasattr(unwrapped_model, "_debug_forward_step"):
unwrapped_model._debug_forward_step = global_step
if hasattr(unwrapped_model, "pipe"):
unwrapped_model.pipe._debug_forward_step = global_step
if accelerator.is_main_process:
print(f"[Resume] Loaded state from {resume_meta.get('checkpoint_path')}")
print(f"[Resume] global_step={global_step}, epoch={start_epoch}, step_in_epoch={start_step_in_epoch}")
post_resume_fp = _module_resume_fingerprint(unwrapped_model)
fp_cmp = _compare_fingerprints(pre_resume_fp, post_resume_fp)
if fp_cmp is not None:
print(
f"[ResumeCheck] Fingerprint changed tensors: "
f"{len(fp_cmp['changed'])}/{fp_cmp['total_common']}"
)
if len(fp_cmp["changed"]) == 0:
print("[ResumeCheck][WARN] No sampled tensor changed after resume load.")
else:
print(f"[ResumeCheck] Changed (first 5): {fp_cmp['changed'][:5]}")
elif resume_from is not None and accelerator.is_main_process:
print(f"[Resume] No checkpoint found for resume_from={resume_from}. Starting from scratch.")
for epoch_id in range(start_epoch, num_epochs):
progress_bar = tqdm(
dataloader,
desc=f"Epoch {epoch_id + 1}/{num_epochs}",
disable=not accelerator.is_main_process,
leave=True
)
for step_in_epoch, data in enumerate(progress_bar):
if epoch_id == start_epoch and step_in_epoch < start_step_in_epoch:
continue
# Skip empty or invalid samples.
if data is None:
continue
# Extra guard: skip if the parsed sample has no video payload.
if data.get("video") is None:
continue
with accelerator.accumulate(model):
optimizer.zero_grad()
if dataset.load_from_cache:
loss = model({}, inputs=data)
else:
loss = model(data)
accelerator.backward(loss)
optimizer.step()
global_step += 1
model_logger.on_step_end(accelerator, model, save_steps)
scheduler.step()
if save_steps is not None and global_step % save_steps == 0:
_save_resume_state(
accelerator=accelerator,
output_path=model_logger.output_path,
global_step=global_step,
epoch_id=epoch_id,
step_in_epoch=step_in_epoch + 1,
)
if accelerator.is_main_process:
# Read current learning rate.
current_lr = 0.0
if hasattr(optimizer, "param_groups"):
current_lr = optimizer.param_groups[0]['lr']
# Refresh progress bar display.
progress_bar.set_postfix(
loss=f"{loss.item():.4f}",
lr=f"{current_lr:.1e}"
)
start_step_in_epoch = 0
if save_steps is None:
model_logger.on_epoch_end(accelerator, model, epoch_id)
_save_resume_state(
accelerator=accelerator,
output_path=model_logger.output_path,
global_step=global_step,
epoch_id=epoch_id + 1,
step_in_epoch=0,
)
model_logger.on_training_end(accelerator, model, save_steps)
def launch_data_process_task(
accelerator: Accelerator,
dataset: torch.utils.data.Dataset,
model: DiffusionTrainingModule,
model_logger: ModelLogger,
num_workers: int = 8,
args = None,
):
if args is not None:
num_workers = args.dataset_num_workers
dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], num_workers=num_workers)
model, dataloader = accelerator.prepare(model, dataloader)
for data_id, data in enumerate(tqdm(dataloader)):
with accelerator.accumulate(model):
with torch.no_grad():
folder = os.path.join(model_logger.output_path, str(accelerator.process_index))
os.makedirs(folder, exist_ok=True)
save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth")
data = model(data)
torch.save(data, save_path)
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