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1ca0208 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | """Train or fine-tune the SEEDS conditional diffusion model."""
from __future__ import annotations
import argparse
import math
import os
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DistributedSampler
from common import SEEDS, build_model, choose_device, load_config, resolve_path, set_seed
from data_loader import SEEDSDataset, build_dataloader
def _is_distributed() -> bool:
return int(os.environ.get("WORLD_SIZE", "1")) > 1
def _setup_distributed(requested_device: str) -> tuple[torch.device, int, int, bool]:
distributed = _is_distributed()
if not distributed:
return choose_device(requested_device), 0, 1, False
if not dist.is_initialized():
backend = "nccl" if torch.cuda.is_available() else "gloo"
dist.init_process_group(backend=backend, init_method="env://")
rank = dist.get_rank()
world_size = dist.get_world_size()
local_rank = int(os.environ.get("LOCAL_RANK", rank))
if torch.cuda.is_available():
torch.cuda.set_device(local_rank)
device = torch.device("cuda", local_rank)
else:
device = torch.device("cpu")
return device, rank, world_size, True
def _denoising_loss(
model: nn.Module,
clean: torch.Tensor,
seeds: torch.Tensor,
climate: torch.Tensor,
) -> torch.Tensor:
core_model = model.module if isinstance(model, DDP) else model
diffusion_time = torch.rand(clean.shape[0], device=clean.device, dtype=clean.dtype)
noise = torch.randn_like(clean)
sigma = core_model.sigma(diffusion_time).view(-1, 1, 1, 1, 1)
noisy = clean + sigma * noise
model_input = noisy / torch.sqrt(1.0 + sigma.square())
prediction = model(model_input, seeds, climate, diffusion_time)
return ((prediction - noise) ** 2).flatten(1).mean()
def _run_validation(model: nn.Module, loader, device: torch.device, distributed: bool) -> float:
model.eval()
losses = []
cuda_devices = [device.index] if device.type == "cuda" and device.index is not None else []
with torch.random.fork_rng(devices=cuda_devices):
torch.manual_seed(12345)
with torch.no_grad():
for batch in loader:
loss = _denoising_loss(
model,
batch["targets"].to(device),
batch["seeds"].to(device),
batch["climate"].to(device),
)
losses.append(float(loss))
if not losses:
raise RuntimeError("validation loader produced no batches")
value = torch.tensor(float(np.mean(losses)), device=device)
if distributed:
dist.all_reduce(value, op=dist.ReduceOp.SUM)
value /= dist.get_world_size()
return float(value.item())
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", default="conf/config.yaml")
parser.add_argument("--max-steps", type=int, default=None)
parser.add_argument("--max-batches", type=int, default=None)
parser.add_argument("--device", default=None)
parser.add_argument("--finetune", action="store_true")
args = parser.parse_args()
config = load_config(args.config)
device, rank, world_size, distributed = _setup_distributed(args.device or config["training"]["device"])
set_seed(config["project"]["seed"] + rank)
data, paths, training = config["data"], config["paths"], config["training"]
root = args.config
train_path, val_path = resolve_path(paths["train_data"], root), resolve_path(paths["val_data"], root)
train_dataset = SEEDSDataset(train_path, len(data["variables"]), data["faces"], data["height"], data["width"], data["seed_count"])
val_dataset = SEEDSDataset(val_path, len(data["variables"]), data["faces"], data["height"], data["width"], data["seed_count"])
train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=rank, shuffle=True) if distributed else None
val_sampler = DistributedSampler(val_dataset, num_replicas=world_size, rank=rank, shuffle=False) if distributed else None
train_loader = build_dataloader(train_path, len(data["variables"]), data["faces"], data["height"], data["width"], data["seed_count"], training["batch_size"], True, training["num_workers"], args.max_batches, train_sampler, train_dataset)
val_loader = build_dataloader(val_path, len(data["variables"]), data["faces"], data["height"], data["width"], data["seed_count"], training["batch_size"], False, training["num_workers"], args.max_batches, val_sampler, val_dataset)
model = build_model(config).to(device)
checkpoint_path = resolve_path(paths["checkpoint"], root)
if args.finetune or training.get("resume"):
source = resolve_path(training.get("resume") or paths["checkpoint"], root)
if not source.exists():
raise FileNotFoundError(f"checkpoint does not exist: {source}")
state = torch.load(source, map_location=device, weights_only=False)
model.load_state_dict(state["model"] if "model" in state else state)
if distributed:
model = DDP(model, device_ids=[device.index] if device.type == "cuda" else None)
optimizer = torch.optim.AdamW(model.parameters(), lr=training["learning_rate"], weight_decay=training["weight_decay"])
train_losses, val_losses = [], []
completed_steps = 0
accumulation_steps = training.get("gradient_accumulation_steps", 1)
if accumulation_steps < 1:
raise ValueError("gradient_accumulation_steps must be positive")
loss_ema = None
for epoch in range(training["epochs"]):
if train_sampler is not None:
train_sampler.set_epoch(epoch)
model.train()
epoch_losses = []
optimizer.zero_grad(set_to_none=True)
accumulated_batches = 0
for batch in train_loader:
loss = _denoising_loss(
model,
batch["targets"].to(device),
batch["seeds"].to(device),
batch["climate"].to(device),
)
if not torch.isfinite(loss):
raise FloatingPointError(f"non-finite training loss at epoch {epoch + 1}: {loss.item()}")
(loss / accumulation_steps).backward()
epoch_losses.append(float(loss.detach()))
completed_steps += 1
accumulated_batches += 1
if accumulated_batches == accumulation_steps:
torch.nn.utils.clip_grad_norm_(model.parameters(), training["grad_clip_norm"])
optimizer.step()
optimizer.zero_grad(set_to_none=True)
accumulated_batches = 0
if args.max_steps is not None and completed_steps >= args.max_steps:
break
if accumulated_batches:
torch.nn.utils.clip_grad_norm_(model.parameters(), training["grad_clip_norm"])
optimizer.step()
optimizer.zero_grad(set_to_none=True)
if not epoch_losses:
raise RuntimeError("training loader produced no batches")
epoch_loss_tensor = torch.tensor([sum(epoch_losses), len(epoch_losses)], device=device, dtype=torch.float64)
if distributed:
dist.all_reduce(epoch_loss_tensor, op=dist.ReduceOp.SUM)
epoch_loss = float((epoch_loss_tensor[0] / epoch_loss_tensor[1]).item())
train_losses.append(epoch_loss)
loss_ema = epoch_loss if loss_ema is None else 0.9 * loss_ema + 0.1 * epoch_loss
validation = _run_validation(model, val_loader, device, distributed)
val_losses.append(validation)
if rank == 0:
print(
f"epoch={epoch + 1}/{training['epochs']} "
f"train_loss={epoch_loss:.6f} train_ema={loss_ema:.6f} val_loss={validation:.6f}"
)
if args.max_steps is not None and completed_steps >= args.max_steps:
break
checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
if rank == 0:
state_dict = model.module.state_dict() if distributed else model.state_dict()
torch.save({"model": state_dict, "config": config, "epoch": len(train_losses), "step": completed_steps}, checkpoint_path)
np.save(checkpoint_path.parent / "train_loss.npy", np.asarray(train_losses, dtype=np.float32))
np.save(checkpoint_path.parent / "val_loss.npy", np.asarray(val_losses, dtype=np.float32))
if not math.isfinite(train_losses[-1]):
raise FloatingPointError("final training loss is not finite")
if rank == 0:
print(f"saved checkpoint: {checkpoint_path}")
if distributed:
dist.destroy_process_group()
if __name__ == "__main__":
main()
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