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929e312 | 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 | """Single-device and torchrun-based distributed training for Samudra v1."""
try:
from ._bootstrap import ROOT
except ImportError:
from _bootstrap import ROOT
import argparse
import os
import random
from pathlib import Path
import numpy as np
import torch
import yaml
from torch import nn
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset, DistributedSampler
from model.samudra import build_model
STATE_CHANNELS = 77
BOUNDARY_CHANNELS = 4
def load_data(path: str | Path) -> tuple[np.ndarray, np.ndarray]:
"""Load and validate native Samudra time-major arrays."""
with np.load(path) as data:
prognostic = np.asarray(data["prognostic"], dtype=np.float32)
boundary = np.asarray(data["boundary"], dtype=np.float32)
if prognostic.ndim != 4 or prognostic.shape[1] != STATE_CHANNELS:
raise ValueError("prognostic must have shape [time, 77, lat, lon]")
if boundary.ndim != 4 or boundary.shape[1] != BOUNDARY_CHANNELS:
raise ValueError("boundary must have shape [time, 4, lat, lon]")
if prognostic.shape[0] != boundary.shape[0] or prognostic.shape[2:] != boundary.shape[2:]:
raise ValueError("prognostic and boundary time/grid dimensions must match")
return prognostic, boundary
class SamudraDataset(Dataset):
"""Build recurrent training windows from native Samudra arrays."""
def __init__(self, path: str | Path, recurrent_passes: int):
self.prognostic, self.boundary = load_data(path)
self.recurrent_passes = recurrent_passes
self.end = self.prognostic.shape[0] - 2 * recurrent_passes
if self.end <= 1:
raise ValueError("dataset does not contain enough samples for recurrent training")
def __len__(self) -> int:
return self.end - 1
def __getitem__(self, index: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
t = index + 1
history = np.stack((self.prognostic[t - 1], self.prognostic[t]))
forcing = self.boundary[t : t + self.recurrent_passes]
labels = np.stack(
[
np.concatenate(
(self.prognostic[t + 2 * step + 1], self.prognostic[t + 2 * step + 2])
)
for step in range(self.recurrent_passes)
]
)
return torch.from_numpy(history), torch.from_numpy(forcing), torch.from_numpy(labels)
def set_seed(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def load_config(path: str) -> dict:
with open(path, encoding="utf-8") as handle:
return yaml.safe_load(handle)
def distributed_setup(device: torch.device) -> tuple[int, int, bool]:
world_size = int(os.environ.get("WORLD_SIZE", "1"))
rank = int(os.environ.get("RANK", "0"))
distributed = world_size > 1
if distributed:
backend = "nccl" if device.type == "cuda" else "gloo"
torch.distributed.init_process_group(backend=backend)
return rank, world_size, distributed
def save_checkpoint(path: Path, model: nn.Module, optimizer: torch.optim.Optimizer, scheduler, epoch: int, loss: float) -> None:
state = model.module.state_dict() if isinstance(model, DistributedDataParallel) else model.state_dict()
torch.save({"epoch": epoch, "loss": loss, "model": state, "optimizer": optimizer.state_dict(), "scheduler": scheduler.state_dict()}, path)
def freeze_batch_norm_stats(module: nn.Module) -> None:
"""Prevent recurrent forwards from mutating BatchNorm buffers in one graph."""
for child in module.modules():
if isinstance(child, nn.modules.batchnorm._BatchNorm):
child.eval()
def train(
config_path: str,
data_path: str,
device_name: str | None = None,
epochs_override: int | None = None,
output_dir_override: str | None = None,
) -> None:
config = load_config(config_path)
rank = int(os.environ.get("RANK", "0"))
local_rank = int(os.environ.get("LOCAL_RANK", rank))
if device_name:
device = torch.device(device_name)
elif torch.cuda.is_available():
device = torch.device(f"cuda:{local_rank}")
else:
device = torch.device("cpu")
rank, world_size, distributed = distributed_setup(device)
set_seed(int(config["project"].get("seed", 1)) + rank)
if device.type == "cuda":
torch.cuda.set_device(device)
recurrent_passes = int(config["data"].get("recurrent_passes", 1))
dataset = SamudraDataset(data_path, recurrent_passes)
sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=True) if distributed else None
loader = DataLoader(dataset, batch_size=config["training"]["batch_size"], shuffle=sampler is None, sampler=sampler, num_workers=config["training"].get("num_workers", 0), pin_memory=device.type == "cuda")
model = build_model(config).to(device)
if distributed:
model = DistributedDataParallel(
model,
device_ids=[device.index] if device.type == "cuda" else None,
broadcast_buffers=False,
)
optimizer = torch.optim.Adam(model.parameters(), lr=config["training"]["learning_rate"], weight_decay=config["training"].get("weight_decay", 0.0))
epochs = epochs_override if epochs_override is not None else int(config["training"]["epochs"])
if epochs < 1:
raise ValueError("epochs must be positive")
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
resume = config["training"].get("resume_checkpoint")
start_epoch = 0
if resume:
try:
checkpoint = torch.load(resume, map_location=device, weights_only=True)
except TypeError:
checkpoint = torch.load(resume, map_location=device)
target = model.module if isinstance(model, DistributedDataParallel) else model
state = {key: value for key, value in checkpoint["model"].items() if not key.endswith(".cap")}
target.load_state_dict(state)
optimizer.load_state_dict(checkpoint["optimizer"])
scheduler.load_state_dict(checkpoint["scheduler"])
start_epoch = int(checkpoint["epoch"]) + 1
output_dir = Path(output_dir_override or config["training"]["output_dir"])
output_dir.mkdir(parents=True, exist_ok=True)
for epoch in range(start_epoch, epochs):
if sampler is not None:
sampler.set_epoch(epoch)
model.train()
freeze_batch_norm_stats(model)
total_loss = 0.0
for batch in loader:
optimizer.zero_grad(set_to_none=True)
history, forcing, labels = (item.to(device, non_blocking=True) for item in batch)
previous, current = history[:, 0], history[:, 1]
losses = []
for step in range(recurrent_passes):
prediction = model(torch.cat((previous, current, forcing[:, step]), dim=1))
losses.append(nn.functional.mse_loss(prediction, labels[:, step]))
previous, current = prediction[:, :STATE_CHANNELS], prediction[:, STATE_CHANNELS:]
loss = torch.stack(losses).mean()
loss.backward()
optimizer.step()
total_loss += float(loss.detach())
scheduler.step()
mean_loss = total_loss / max(1, len(loader))
if rank == 0:
print(f"epoch={epoch + 1} loss={mean_loss:.6e} lr={scheduler.get_last_lr()[0]:.6e}")
frequency = int(config["training"].get("save_frequency", 5))
if (epoch + 1) % frequency == 0 or epoch + 1 == epochs:
save_checkpoint(output_dir / f"epoch_{epoch + 1:04d}.pt", model, optimizer, scheduler, epoch, mean_loss)
save_checkpoint(output_dir / "model_bak.pth", model, optimizer, scheduler, epoch, mean_loss)
if distributed:
torch.distributed.destroy_process_group()
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", default="./conf/config.yaml")
parser.add_argument("--data", default="./data/train.npz")
parser.add_argument("--device", default=None)
parser.add_argument("--epochs", type=int, default=None)
parser.add_argument("--output-dir", default=None)
args = parser.parse_args()
train(args.config, args.data, args.device, args.epochs, args.output_dir)
if __name__ == "__main__":
main()
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