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import random
from pathlib import Path
import numpy as np
import torch
import yaml
from torch import nn
LEVELS_HPA = (1000, 850, 700, 500, 300, 200, 100, 50)
CHANNELS = (
[f"temperature_{p}" for p in LEVELS_HPA]
+ [f"specific_humidity_{p}" for p in LEVELS_HPA]
+ [f"u_wind_{p}" for p in LEVELS_HPA]
+ [f"v_wind_{p}" for p in LEVELS_HPA]
+ [f"geopotential_{p}" for p in LEVELS_HPA]
+ [
"surface_pressure",
"air_temperature_2m",
"specific_humidity_2m",
"eastward_wind_10m",
"northward_wind_10m",
"sea_surface_temperature",
"total_precipitation_6h",
"surface_downward_shortwave",
"surface_downward_longwave",
"toa_outgoing_longwave",
]
)
assert len(CHANNELS) == 50
Q_INDICES = tuple(range(8, 16)) + (42,)
SURFACE_PRESSURE = 40
PRECIPITATION = 46
RADIATION_INDICES = (47, 48, 49)
class SpectralConv2d(nn.Module):
def __init__(self, width, modes_lat, modes_lon):
super().__init__()
self.modes_lat, self.modes_lon = modes_lat, modes_lon
scale = 1.0 / width
self.weight = nn.Parameter(
scale * torch.randn(width, width, modes_lat, modes_lon, dtype=torch.cfloat)
)
def forward(self, x):
spectrum = torch.fft.rfft2(x, norm="ortho")
out = torch.zeros_like(spectrum)
ml = min(self.modes_lat, spectrum.shape[-2])
mn = min(self.modes_lon, spectrum.shape[-1])
out[:, :, :ml, :mn] = torch.einsum(
"bixy,ioxy->boxy", spectrum[:, :, :ml, :mn], self.weight[:, :, :ml, :mn]
)
return torch.fft.irfft2(out, s=x.shape[-2:], norm="ortho")
class SFNOBlock(nn.Module):
def __init__(self, width, modes_lat, modes_lon):
super().__init__()
self.spectral = SpectralConv2d(width, modes_lat, modes_lon)
self.mlp = nn.Sequential(
nn.Conv2d(width, width * 2, 1), nn.GELU(), nn.Conv2d(width * 2, width, 1)
)
self.norm = nn.GroupNorm(1, width)
def forward(self, x):
return x + self.mlp(self.norm(self.spectral(x)))
class CompactSFNO(nn.Module):
def __init__(self, channels=50, forcing_channels=4, width=4, depth=1,
modes_lat=4, modes_lon=4):
super().__init__()
self.lift = nn.Conv2d(channels + forcing_channels, width, 1)
self.blocks = nn.Sequential(
*[SFNOBlock(width, modes_lat, modes_lon) for _ in range(depth)]
)
self.project = nn.Sequential(nn.GELU(), nn.Conv2d(width, channels, 1))
def forward(self, state, forcing):
features = self.blocks(self.lift(torch.cat((state, forcing), dim=1)))
return state + self.project(features)
def area_weights(height, device, dtype):
lat = torch.linspace(-89.5, 89.5, height, device=device, dtype=dtype)
return torch.cos(torch.deg2rad(lat)).view(1, 1, height, 1)
def weighted_mean(x, weights):
return (x * weights).sum(dim=(-2, -1), keepdim=True) / (
weights.sum(dim=(-2, -1), keepdim=True) * x.shape[-1]
)
def hard_correct(previous, predicted):
"""Apply differentiable positivity, dry-mass, and global-water constraints."""
out = predicted.clone()
positive = list(Q_INDICES) + [PRECIPITATION] + list(RADIATION_INDICES)
out[:, positive] = torch.clamp_min(out[:, positive], 0.0)
weights = area_weights(out.shape[-2], out.device, out.dtype)
q_prev = previous[:, Q_INDICES].sum(dim=1, keepdim=True)
water_target = weighted_mean(q_prev, weights)
precip = weighted_mean(out[:, PRECIPITATION:PRECIPITATION + 1], weights)
precip_scale = torch.clamp(
0.5 * water_target / torch.clamp_min(precip, 1e-8), max=1.0
)
out[:, PRECIPITATION:PRECIPITATION + 1] *= precip_scale
precip = weighted_mean(out[:, PRECIPITATION:PRECIPITATION + 1], weights)
q_target = torch.clamp_min(water_target - precip, 0.0)
q_now = weighted_mean(out[:, Q_INDICES].sum(dim=1, keepdim=True), weights)
out[:, Q_INDICES] *= q_target / torch.clamp_min(q_now, 1e-8)
q_new = out[:, Q_INDICES].sum(dim=1, keepdim=True)
dry_target = weighted_mean(
previous[:, SURFACE_PRESSURE:SURFACE_PRESSURE + 1] - q_prev, weights
)
dry_now = weighted_mean(
out[:, SURFACE_PRESSURE:SURFACE_PRESSURE + 1] - q_new, weights
)
out[:, SURFACE_PRESSURE:SURFACE_PRESSURE + 1] += dry_target - dry_now
return out
def load_config(root=None):
root = Path(root) if root is not None else Path(__file__).resolve().parents[1]
with (root / "conf" / "config.yaml").open(encoding="utf-8") as handle:
return yaml.safe_load(handle)
def seed_all(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def forcing_for_hours(hours, height=180, width=360):
hours = np.asarray(hours, dtype=np.float32)
phase = 2 * np.pi * hours / (365.25 * 24)
lat = np.deg2rad(np.linspace(-89.5, 89.5, height, dtype=np.float32))
lon = np.deg2rad(np.linspace(0.5, 359.5, width, dtype=np.float32))
solar = np.maximum(
0,
np.cos(lat)[None, :, None]
* np.cos(lon[None, None, :] + phase[:, None, None]),
)
fields = np.empty((len(hours), 4, height, width), dtype=np.float32)
fields[:, 0] = np.sin(phase)[:, None, None]
fields[:, 1] = np.cos(phase)[:, None, None]
fields[:, 2] = (400.0 + 0.01 * hours)[:, None, None] / 500.0
fields[:, 3] = solar
return fields
def init_distributed():
distributed = int(os.environ.get("WORLD_SIZE", "1")) > 1
world_size = int(os.environ.get("WORLD_SIZE", "1"))
use_cuda = torch.cuda.is_available() and torch.cuda.device_count() >= world_size
if distributed:
backend = "nccl" if use_cuda else "gloo"
torch.distributed.init_process_group(backend=backend)
rank = torch.distributed.get_rank()
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
else:
rank = local_rank = 0
device = torch.device(
f"cuda:{local_rank}" if use_cuda else "cpu"
)
if device.type == "cuda":
torch.cuda.set_device(device)
return distributed, rank, device
def build_model(config):
return CompactSFNO(channels=config["data"]["channels"], **config["model"])
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