File size: 6,417 Bytes
d3e46b7 | 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 | """Train FuXi-Ocean on globally indexed tiles, with optional torchrun DDP."""
import json
import os
from pathlib import Path
import random
import sys
import numpy as np
import torch
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.distributed import DistributedSampler
import yaml
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.fuxi_ocean import FORMAT_VERSION, FuXiOcean, channel_mask, latitude_weighted_charbonnier
class TileDataset(Dataset):
def __init__(self, data, indices):
self.data, self.indices = data, list(indices)
def __len__(self):
return len(self.indices)
def __getitem__(self, item):
index = self.indices[item]
lat, lon = self.data["latitude_deg"][index], self.data["longitude_deg"][index]
coordinates = np.stack(np.meshgrid(lon / 180 - 1, lat / 90, indexing="xy"))
names = ("ocean", "atmosphere", "bathymetry_m", "depth_mask", "time_features", "targets")
values = [self.data[name][index] for name in names]
values[2] = values[2] / 5000
return tuple(torch.as_tensor(value, dtype=torch.float32) for value in (*values[:2], coordinates, *values[2:], lat))
def validate_data_contract(data, config):
expected = config["data"]
checks = {"format_version": str(data["format_version"]) == config["data"]["format_version"],
"input_shape": data["input_shape"].tolist() == expected["input_shape"],
"atmosphere_shape": data["atmosphere_shape"].tolist() == expected["atmosphere_shape"],
"output_shape": data["output_shape"].tolist() == expected["output_shape"]}
if not all(checks.values()):
raise ValueError(f"data contract mismatch: {checks}")
def main():
config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
torch.set_num_threads(config["runtime"]["num_threads"])
random.seed(config["seed"]); np.random.seed(config["seed"]); torch.manual_seed(config["seed"])
world = int(os.environ.get("WORLD_SIZE", "1")); distributed = world > 1
if distributed:
torch.distributed.init_process_group(config["runtime"]["ddp_backend"])
rank = torch.distributed.get_rank() if distributed else 0
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
available_devices = torch.cuda.device_count() if torch.cuda.is_available() else 0
requested_auto_gpu = config["runtime"]["device"] != "cpu" and available_devices >= world
use_cuda = requested_auto_gpu and local_rank < available_devices
device = torch.device(f"cuda:{local_rank}" if use_cuda else "cpu")
data = np.load(ROOT / config["data"]["path"])
validate_data_contract(data, config)
if config["data"]["format_version"] != FORMAT_VERSION:
raise ValueError("configuration/model format version mismatch")
model_args = {key: value for key, value in config["model"].items() if key != "epsilon"}
model = FuXiOcean(**model_args).to(device)
if distributed:
model = DistributedDataParallel(model, device_ids=[local_rank] if use_cuda else None)
optimizer = torch.optim.AdamW(model.parameters(), lr=config["training"]["learning_rate"],
weight_decay=config["training"]["weight_decay"])
losses = []
train_count = int(data["train_count"])
dataset = TileDataset(data, range(train_count))
sampler = DistributedSampler(dataset, num_replicas=world, rank=rank, shuffle=True, seed=config["seed"]) if distributed else None
loader = DataLoader(dataset, batch_size=config["training"]["batch_size"], sampler=sampler,
shuffle=sampler is None, drop_last=False)
for epoch in range(config["training"]["epochs"]):
if sampler is not None:
sampler.set_epoch(epoch)
for batch in loader:
ocean, atmosphere, coordinates, bathymetry, mask, time_info, target, latitude = [value.to(device) for value in batch]
history = ocean
step_losses = []
for step in range(config["training"]["multistep_rollout"]):
time_info[:, 2] = step
prediction = model(history, atmosphere, coordinates, bathymetry, mask, time_info)
step_target = target + step * 0.005
step_losses.append(latitude_weighted_charbonnier(prediction, step_target, latitude,
channel_mask(mask), config["model"]["epsilon"]))
history = torch.cat((history[:, 1:], prediction[:, None]), dim=1)
loss = torch.stack(step_losses).mean()
optimizer.zero_grad(); loss.backward(); optimizer.step()
losses.append(float(loss.detach()))
local = torch.tensor([sum(losses), len(losses)], dtype=torch.float64, device=device)
if distributed:
torch.distributed.all_reduce(local)
if rank == 0:
raw_model = model.module if distributed else model
checkpoint_path = ROOT / config["paths"]["checkpoint"]
checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
model_config = {"architecture": model_args, "data_format_version": FORMAT_VERSION,
"input_shape": data["input_shape"].tolist(), "atmosphere_shape": data["atmosphere_shape"].tolist(),
"output_shape": data["output_shape"].tolist()}
torch.save({"model": raw_model.state_dict(), "model_config": model_config, "format_version": FORMAT_VERSION,
"optimizer": optimizer.state_dict()}, checkpoint_path)
metrics_path = ROOT / config["paths"]["training_metrics"]
metrics_path.parent.mkdir(parents=True, exist_ok=True)
metrics_path.write_text(json.dumps({"mean_loss": local[0].item() / local[1].item(), "world_size": world,
"backward_pass": True, "global_loss_all_reduce": distributed,
"batch_size": config["training"]["batch_size"],
"input_shape": data["input_shape"].tolist(), "synthetic": True}, indent=2) + "\n")
print(f"checkpoint={checkpoint_path.relative_to(ROOT)} loss={local[0].item() / local[1].item():.6f}")
if distributed:
torch.distributed.destroy_process_group()
if __name__ == "__main__":
main()
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