Spaces:
Paused
Paused
File size: 10,145 Bytes
830a558 |
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 |
import os
import hydra
from omegaconf import OmegaConf
import torch
from omegaconf import DictConfig
from physicsnemo.distributed import DistributedManager
from physicsnemo.launch.logging import LaunchLogger, PythonLogger
from physicsnemo.launch.utils import load_checkpoint, save_checkpoint
from physicsnemo.sym.hydra import to_absolute_path
from torch.nn.parallel import DistributedDataParallel
from torch.optim import AdamW
from dataloaders import Dedalus2DDataset, MHDDataloaderVecPot
from losses import LossMHDVecPot_PhysicsNeMo
from tfno import TFNO
from utils.plot_utils import plot_predictions_mhd, plot_predictions_mhd_plotly
dtype = torch.float
torch.set_default_dtype(dtype)
@hydra.main(
version_base="1.3", config_path="config", config_name="train_mhd_vec_pot_tfno.yaml"
)
def main(cfg: DictConfig) -> None:
DistributedManager.initialize() # Only call this once in the entire script!
dist = DistributedManager() # call if required elsewhere
cfg = OmegaConf.to_container(cfg, resolve=True)
# initialize monitoring
log = PythonLogger(name="mhd_pino")
log.file_logging()
log_params = cfg["log_params"]
# Load config file parameters
model_params = cfg["model_params"]
dataset_params = cfg["dataset_params"]
train_loader_params = cfg["train_loader_params"]
val_loader_params = cfg["val_loader_params"]
loss_params = cfg["loss_params"]
optimizer_params = cfg["optimizer_params"]
train_params = cfg["train_params"]
load_ckpt = cfg["load_ckpt"]
output_dir = cfg["output_dir"]
output_dir = to_absolute_path(output_dir)
os.makedirs(output_dir, exist_ok=True)
data_dir = dataset_params["data_dir"]
ckpt_path = train_params["ckpt_path"]
# Construct dataloaders
dataset_train = Dedalus2DDataset(
dataset_params["data_dir"],
output_names=dataset_params["output_names"],
field_names=dataset_params["field_names"],
num_train=dataset_params["num_train"],
num_test=dataset_params["num_test"],
num=dataset_params["num"],
use_train=True,
)
dataset_val = Dedalus2DDataset(
data_dir,
output_names=dataset_params["output_names"],
field_names=dataset_params["field_names"],
num_train=dataset_params["num_train"],
num_test=dataset_params["num_test"],
num=dataset_params["num"],
use_train=False,
)
mhd_dataloader_train = MHDDataloaderVecPot(
dataset_train,
sub_x=dataset_params["sub_x"],
sub_t=dataset_params["sub_t"],
ind_x=dataset_params["ind_x"],
ind_t=dataset_params["ind_t"],
)
mhd_dataloader_val = MHDDataloaderVecPot(
dataset_val,
sub_x=dataset_params["sub_x"],
sub_t=dataset_params["sub_t"],
ind_x=dataset_params["ind_x"],
ind_t=dataset_params["ind_t"],
)
dataloader_train, sampler_train = mhd_dataloader_train.create_dataloader(
batch_size=train_loader_params["batch_size"],
shuffle=train_loader_params["shuffle"],
num_workers=train_loader_params["num_workers"],
pin_memory=train_loader_params["pin_memory"],
distributed=dist.distributed,
)
dataloader_val, sampler_val = mhd_dataloader_val.create_dataloader(
batch_size=val_loader_params["batch_size"],
shuffle=val_loader_params["shuffle"],
num_workers=val_loader_params["num_workers"],
pin_memory=val_loader_params["pin_memory"],
distributed=dist.distributed,
)
# define FNO model
model = TFNO(
in_channels=model_params["in_dim"],
out_channels=model_params["out_dim"],
decoder_layers=model_params["decoder_layers"],
decoder_layer_size=model_params["fc_dim"],
dimension=model_params["dimension"],
latent_channels=model_params["layers"],
num_fno_layers=model_params["num_fno_layers"],
num_fno_modes=model_params["modes"],
padding=[model_params["pad_z"], model_params["pad_y"], model_params["pad_x"]],
rank=model_params["rank"],
factorization=model_params["factorization"],
fixed_rank_modes=model_params["fixed_rank_modes"],
decomposition_kwargs=model_params["decomposition_kwargs"],
).to(dist.device)
# Set up DistributedDataParallel if using more than a single process.
# The `distributed` property of DistributedManager can be used to
# check this.
if dist.distributed:
ddps = torch.cuda.Stream()
with torch.cuda.stream(ddps):
model = DistributedDataParallel(
model,
device_ids=[dist.local_rank], # Set the device_id to be
# the local rank of this process on
# this node
output_device=dist.device,
broadcast_buffers=dist.broadcast_buffers,
find_unused_parameters=dist.find_unused_parameters,
)
torch.cuda.current_stream().wait_stream(ddps)
# Construct optimizer and scheduler
optimizer = AdamW(
model.parameters(),
betas=optimizer_params["betas"],
lr=optimizer_params["lr"],
weight_decay=0.1,
)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=optimizer_params["milestones"],
gamma=optimizer_params["gamma"],
)
# Construct Loss class
mhd_loss = LossMHDVecPot_PhysicsNeMo(**loss_params)
# Load model from checkpoint (if exists)
loaded_epoch = 0
if load_ckpt:
loaded_epoch = load_checkpoint(
ckpt_path, model, optimizer, scheduler, device=dist.device
)
# Training Loop
epochs = train_params["epochs"]
ckpt_freq = train_params["ckpt_freq"]
names = dataset_params["fields"]
input_norm = torch.tensor(model_params["input_norm"]).to(dist.device)
output_norm = torch.tensor(model_params["output_norm"]).to(dist.device)
for epoch in range(max(1, loaded_epoch + 1), epochs + 1):
with LaunchLogger(
"train",
epoch=epoch,
num_mini_batch=len(dataloader_train),
epoch_alert_freq=1,
) as log:
if dist.distributed:
sampler_train.set_epoch(epoch)
# Train Loop
model.train()
for i, (inputs, outputs) in enumerate(dataloader_train):
inputs = inputs.type(torch.FloatTensor).to(dist.device)
outputs = outputs.type(torch.FloatTensor).to(dist.device)
# Zero Gradients
optimizer.zero_grad()
# Compute Predictions
pred = (
model((inputs / input_norm).permute(0, 4, 1, 2, 3)).permute(
0, 2, 3, 4, 1
)
* output_norm
)
# Compute Loss
loss, loss_dict = mhd_loss(pred, outputs, inputs, return_loss_dict=True)
# Compute Gradients for Back Propagation
loss.backward()
# Update Weights
optimizer.step()
log.log_minibatch(loss_dict)
log.log_epoch({"Learning Rate": optimizer.param_groups[0]["lr"]})
scheduler.step()
with LaunchLogger("valid", epoch=epoch) as log:
# Val loop
model.eval()
plot_count = 0
with torch.no_grad():
for i, (inputs, outputs) in enumerate(dataloader_val):
inputs = inputs.type(dtype).to(dist.device)
outputs = outputs.type(dtype).to(dist.device)
# Compute Predictions
pred = (
model((inputs / input_norm).permute(0, 4, 1, 2, 3)).permute(
0, 2, 3, 4, 1
)
* output_norm
)
# Compute Loss
loss, loss_dict = mhd_loss(
pred, outputs, inputs, return_loss_dict=True
)
log.log_minibatch(loss_dict)
# Get prediction plots to log
# Do for number of batches specified in the config file
if (i < log_params["log_num_plots"]) and (
epoch % log_params["log_plot_freq"] == 0
):
# Add all predictions in batch
for j, _ in enumerate(pred):
# Make plots for each field
for index, name in enumerate(names):
# Generate figure
_ = plot_predictions_mhd_plotly(
pred[j].cpu(),
outputs[j].cpu(),
inputs[j].cpu(),
index=index,
name=name,
)
plot_count += 1
# Get prediction plots and save images locally
if (i < 2) and (epoch % log_params["log_plot_freq"] == 0):
# Add all predictions in batch
for j, _ in enumerate(pred):
# Generate figure
plot_predictions_mhd(
pred[j].cpu(),
outputs[j].cpu(),
inputs[j].cpu(),
names=names,
save_path=os.path.join(
output_dir,
"MHD_physicsnemo" + "_" + str(dist.rank),
),
save_suffix=i,
)
if epoch % ckpt_freq == 0 and dist.rank == 0:
save_checkpoint(ckpt_path, model, optimizer, scheduler, epoch=epoch)
if __name__ == "__main__":
main()
|