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6f3c6ef | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 | """Run the self-contained virtual-data training, inference, and plotting pipeline."""
import argparse
import json
import os
import random
from pathlib import Path
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, DistributedSampler, TensorDataset
import sys
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
from model.spherical_dyffusion import SphericalDYffusion
VARIABLES = [
"PRESsfc", "surface_temperature",
*[f"air_temperature_{i}" for i in range(8)],
*[f"specific_total_water_{i}" for i in range(8)],
*[f"eastward_wind_{i}" for i in range(8)],
*[f"northward_wind_{i}" for i in range(8)],
"DSWRFtoa", "HGTsfc", "ocean_fraction",
]
def load_config(path: str) -> dict:
import yaml
with open(path, encoding="utf-8") as file:
return yaml.safe_load(file)
def resolve_device(config: dict, local_rank: int = 0) -> torch.device:
requested = str(config.get("runtime", {}).get("device", "auto")).lower()
if requested == "auto":
requested = "cuda" if torch.cuda.is_available() else "cpu"
if requested.startswith("cuda") and not torch.cuda.is_available():
raise RuntimeError(
f"runtime.device={requested!r}, but PyTorch cannot access a CUDA/ROCm device. "
"Use runtime.device=cpu or install a GPU-enabled PyTorch build."
)
device = torch.device(requested)
if device.type == "cuda":
device_index = device.index if device.index is not None else local_rank
if device_index >= torch.cuda.device_count():
raise RuntimeError(
f"LOCAL_RANK={local_rank} maps to GPU {device_index}, but only "
f"{torch.cuda.device_count()} GPU(s) are visible."
)
torch.cuda.set_device(device_index)
device = torch.device("cuda", device_index)
print(
f"device: {device} ({torch.cuda.get_device_name(device_index)}), "
f"backend={'ROCm ' + torch.version.hip if torch.version.hip else 'CUDA ' + str(torch.version.cuda)}"
)
else:
print("device: cpu")
return device
def setup_distributed(config: dict) -> tuple[int, int, torch.device]:
world_size = int(os.environ.get("WORLD_SIZE", "1"))
rank = int(os.environ.get("RANK", "0"))
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
configured_devices = config.get("runtime", {}).get("devices", "auto")
requested_device = str(config.get("runtime", {}).get("device", "auto")).lower()
if isinstance(configured_devices, int) and configured_devices > 1 and world_size == 1:
raise RuntimeError(
f"runtime.devices={configured_devices} requires a distributed launcher. Run: "
f"python -m torch.distributed.run --standalone --nproc-per-node={configured_devices} "
"scripts/train.py"
)
if isinstance(configured_devices, int) and world_size > 1 and configured_devices != world_size:
raise RuntimeError(
f"runtime.devices={configured_devices} does not match torchrun WORLD_SIZE={world_size}."
)
if world_size > 1 and requested_device.startswith("cuda:"):
raise RuntimeError(
"Do not set an explicit CUDA device index for DDP. Use runtime.device=cuda or auto; "
"each torchrun process is mapped to its LOCAL_RANK automatically."
)
device = resolve_device(config, local_rank=local_rank)
if world_size > 1:
if not dist.is_available():
raise RuntimeError("Distributed training is unavailable in this PyTorch build.")
backend = str(config.get("runtime", {}).get("distributed_backend", "auto")).lower()
if backend == "auto":
backend = "nccl" if device.type == "cuda" else "gloo"
dist.init_process_group(backend=backend, init_method="env://")
if dist.get_world_size() != world_size or dist.get_rank() != rank:
raise RuntimeError("The process group does not match the torchrun rank settings.")
return rank, world_size, device
def generate(config: dict) -> Path:
spec = config["synthetic_data"]
rng = np.random.default_rng(spec["seed"])
shape = (spec["samples"], spec["channels"], spec["latitude"], spec["longitude"])
inputs = rng.normal(0, 1, shape).astype(np.float32)
# A deterministic local dynamics rule provides a learnable target.
targets = (0.85 * inputs + 0.05 * np.roll(inputs, 1, axis=2) + 0.05 * np.roll(inputs, -1, axis=3)).astype(np.float32)
output = Path(spec["output_dir"])
output.mkdir(parents=True, exist_ok=True)
path = output / "virtual_fv3gfs.npz"
np.savez_compressed(path, inputs=inputs, targets=targets)
(output / "metadata.json").write_text(json.dumps({
"variables": VARIABLES, "shape": list(shape), "time_steps": spec["time_steps"],
"latitude": spec["latitude"], "longitude": spec["longitude"],
"dataset_type": "virtual_fv3gfs_equivalent_contract",
}, indent=2) + "\n", encoding="utf-8")
print(f"virtual data: {path}")
return path
def train(config: dict, finetune: str | None = None) -> Path:
seed = config["training"]["seed"]
random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)
rank, world_size, device = setup_distributed(config)
is_root = rank == 0
checkpoint = Path(config["training"]["checkpoint_dir"])
try:
if int(config["training"]["epochs"]) < 1:
raise ValueError("training.epochs must be at least 1.")
data_path = Path(config["synthetic_data"]["output_dir"]) / "virtual_fv3gfs.npz"
if is_root:
data_path = generate(config)
if world_size > 1:
dist.barrier()
arrays = np.load(data_path)
dataset = TensorDataset(torch.from_numpy(arrays["inputs"]), torch.from_numpy(arrays["targets"]))
global_batch_size = int(config["training"]["batch_size"])
if global_batch_size % world_size:
raise ValueError(
f"training.batch_size={global_batch_size} is the global batch size and must be "
f"divisible by WORLD_SIZE={world_size}."
)
if len(dataset) % world_size:
raise ValueError(
f"Dataset size {len(dataset)} must be divisible by WORLD_SIZE={world_size}; "
"otherwise DistributedSampler would duplicate samples and bias the epoch loss."
)
local_batch_size = global_batch_size // world_size
if local_batch_size < 1:
raise ValueError("The global batch size must be at least WORLD_SIZE.")
sampler = DistributedSampler(
dataset,
num_replicas=world_size,
rank=rank,
shuffle=True,
seed=seed,
drop_last=False,
) if world_size > 1 else None
total_workers = int(config.get("runtime", {}).get("num_workers", 0))
local_workers = max(0, total_workers // world_size)
loader = DataLoader(
dataset,
batch_size=local_batch_size,
shuffle=sampler is None,
sampler=sampler,
num_workers=local_workers,
pin_memory=device.type == "cuda",
persistent_workers=local_workers > 0,
)
model = SphericalDYffusion(config["synthetic_data"]["channels"]).to(device)
if finetune:
finetune_path = Path(finetune)
if not finetune_path.exists():
raise FileNotFoundError(f"Fine-tune checkpoint not found: {finetune_path}")
state = torch.load(finetune_path, map_location="cpu", weights_only=False)
state_dict = state.get("model", state) if isinstance(state, dict) else state
try:
model.load_state_dict(state_dict)
except RuntimeError as error:
raise RuntimeError(
"Fine-tune checkpoint is incompatible with SphericalDYffusion. "
"Use a checkpoint produced by this local pipeline or a matching model architecture."
) from error
if is_root:
print(f"fine-tuning from: {finetune_path}")
elif is_root:
print("training from scratch")
if world_size > 1:
model = DistributedDataParallel(
model,
device_ids=[device.index] if device.type == "cuda" else None,
output_device=device.index if device.type == "cuda" else None,
)
if is_root:
print(
f"distributed: DDP world_size={world_size}, global_batch_size={global_batch_size}, "
f"local_batch_size={local_batch_size}"
)
optimizer = torch.optim.Adam(model.parameters(), lr=config["training"]["learning_rate"])
loss_fn = nn.MSELoss()
best = float("inf")
if is_root:
checkpoint.mkdir(parents=True, exist_ok=True)
for epoch in range(1, config["training"]["epochs"] + 1):
if sampler is not None:
sampler.set_epoch(epoch)
model.train(); total = 0.0; sample_count = 0
for inputs, targets in loader:
inputs = inputs.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
optimizer.zero_grad(); loss = loss_fn(model(inputs), targets); loss.backward(); optimizer.step()
total += loss.item() * len(inputs); sample_count += len(inputs)
loss_stats = torch.tensor([total, sample_count], dtype=torch.float64, device=device)
if world_size > 1:
dist.all_reduce(loss_stats, op=dist.ReduceOp.SUM)
mean_loss = (loss_stats[0] / loss_stats[1]).item()
if is_root:
print(f"epoch {epoch}/{config['training']['epochs']} loss={mean_loss:.6f}")
state_dict = model.module.state_dict() if isinstance(model, DistributedDataParallel) else model.state_dict()
state = {
"model": state_dict,
"channels": config["synthetic_data"]["channels"],
"loss": mean_loss,
"world_size": world_size,
"global_batch_size": global_batch_size,
}
if mean_loss < best:
best = mean_loss
torch.save(state, checkpoint / "model_bak.pt")
if is_root:
torch.save(state, checkpoint / "last.pt")
print(f"checkpoint: {checkpoint / 'model_bak.pt'}")
if world_size > 1:
dist.barrier()
return checkpoint / "model_bak.pt"
finally:
if dist.is_available() and dist.is_initialized():
dist.destroy_process_group()
def infer(config: dict) -> Path:
arrays = np.load(Path(config["synthetic_data"]["output_dir"]) / "virtual_fv3gfs.npz")
checkpoint_path = Path(config["inference"]["checkpoint"])
if not checkpoint_path.exists():
raise FileNotFoundError(f"Inference checkpoint not found: {checkpoint_path}. Run scripts/train.py first.")
state = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
model = SphericalDYffusion(state["channels"]); model.load_state_dict(state["model"]); model.eval()
with torch.no_grad(): prediction = model(torch.from_numpy(arrays["inputs"]))
output = Path(config["inference"]["output_dir"]); output.mkdir(parents=True, exist_ok=True)
path = output / "prediction.npz"; np.savez_compressed(path, prediction=prediction.numpy(), target=arrays["targets"])
print(f"inference: {path}"); return path
def visualize(config: dict) -> Path:
import matplotlib.pyplot as plt
arrays = np.load(Path(config["inference"]["output_dir"]) / "prediction.npz")
index, channel = config["visualization"]["prediction_index"], config["visualization"]["channel"]
figure, axes = plt.subplots(1, 2, figsize=(12, 4), constrained_layout=True)
for axis, image, title in zip(axes, [arrays["target"][index, channel], arrays["prediction"][index, channel]], ["Target", "Prediction"]):
plot = axis.imshow(image, cmap="viridis"); axis.set_title(title); axis.set_xlabel("longitude"); axis.set_ylabel("latitude"); figure.colorbar(plot, ax=axis)
output = Path(config["visualization"]["output_dir"]); output.mkdir(parents=True, exist_ok=True)
path = output / "prediction_comparison.png"; figure.savefig(path, dpi=150); plt.close(figure)
print(f"visualization: {path}"); return path
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", default="conf/config.yaml")
parser.add_argument("action", choices=["generate", "train", "infer", "visualize", "all"], nargs="?", default="all")
args = parser.parse_args(); config = load_config(args.config)
if args.action == "generate":
generate(config)
elif args.action == "train":
train(config)
elif args.action == "infer":
infer(config)
elif args.action == "visualize":
visualize(config)
elif args.action == "all":
train(config)
if int(os.environ.get("RANK", "0")) == 0:
infer(config)
visualize(config)
if __name__ == "__main__":
main()
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