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Training script for Darcy flow equation using neural operators.
This script trains a neural operator on the 2D Darcy flow equation,
which models fluid flow through porous media. The script supports
distributed training and multi-grid patching for high-resolution data.
"""
from pathlib import Path
import sys
import torch
from torch.utils.data import DataLoader, DistributedSampler
import wandb
from neuralop import H1Loss, LpLoss, Trainer, get_model
from neuralop.data.datasets import load_darcy_flow_small
from neuralop.data.transforms.data_processors import MGPatchingDataProcessor
from neuralop.training import setup, AdamW
from neuralop.mpu.comm import get_local_rank
from neuralop.utils import get_wandb_api_key, count_model_params
# Read the configuration
from zencfg import make_config_from_cli
import sys
sys.path.insert(0, "../")
from config.darcy_config import Default
config = make_config_from_cli(Default)
config = config.to_dict()
# Distributed training setup, if enabled
device, is_logger = setup(config)
# Set up WandB logging
wandb_args = None
if config.wandb.log and is_logger:
wandb.login(key=get_wandb_api_key())
if config.wandb.name:
wandb_name = config.wandb.name
else:
wandb_name = "_".join(
f"{var}"
for var in [
config.model.model_arch,
config.model.n_layers,
config.model.n_modes,
config.model.hidden_channels,
]
)
wandb_args = dict(
config=config,
name=wandb_name,
group=config.wandb.group,
project=config.wandb.project,
entity=config.wandb.entity,
)
if config.wandb.sweep:
for key in wandb.config.keys():
config.params[key] = wandb.config[key]
wandb.init(**wandb_args)
# Make sure we only print information when needed
config.verbose = config.verbose and is_logger
# Print configuration details
if config.verbose and is_logger:
print(f"##### CONFIG #####\n")
print(config)
sys.stdout.flush()
# Load the Darcy flow dataset
data_root = Path(config.data.folder).expanduser()
train_loader, test_loaders, data_processor = load_darcy_flow_small(
data_root=data_root,
n_train=config.data.n_train,
batch_size=config.data.batch_size,
test_resolutions=config.data.test_resolutions,
n_tests=config.data.n_tests,
test_batch_sizes=config.data.test_batch_sizes,
encode_input=False,
encode_output=False,
)
# Model initialization
model = get_model(config)
# convert dataprocessor to an MGPatchingDataProcessor if patching levels > 0
if config.patching.levels > 0:
data_processor = MGPatchingDataProcessor(
model=model,
in_normalizer=data_processor.in_normalizer,
out_normalizer=data_processor.out_normalizer,
padding_fraction=config.patching.padding,
stitching=config.patching.stitching,
levels=config.patching.levels,
use_distributed=config.distributed.use_distributed,
device=device,
)
# Distributed data parallel setup
# Reconfigure DataLoaders to use a DistributedSampler if in distributed mode
if config.distributed.use_distributed:
train_db = train_loader.dataset
train_sampler = DistributedSampler(train_db, rank=get_local_rank())
train_loader = DataLoader(
dataset=train_db, batch_size=config.data.batch_size, sampler=train_sampler
)
for (res, loader), batch_size in zip(
test_loaders.items(), config.data.test_batch_sizes
):
test_db = loader.dataset
test_sampler = DistributedSampler(test_db, rank=get_local_rank())
test_loaders[res] = DataLoader(
dataset=test_db, batch_size=batch_size, shuffle=False, sampler=test_sampler
)
# Create the optimizer
optimizer = AdamW(
model.parameters(),
lr=config.opt.learning_rate,
weight_decay=config.opt.weight_decay,
)
if config.opt.scheduler == "ReduceLROnPlateau":
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=config.opt.gamma,
patience=config.opt.scheduler_patience,
mode="min",
)
elif config.opt.scheduler == "CosineAnnealingLR":
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=config.opt.scheduler_T_max
)
elif config.opt.scheduler == "StepLR":
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer, step_size=config.opt.step_size, gamma=config.opt.gamma
)
else:
raise ValueError(f"Got scheduler={config.opt.scheduler}")
# Create the losses functions
l2loss = LpLoss(d=2, p=2)
h1loss = H1Loss(d=2)
if config.opt.training_loss == "l2":
train_loss = l2loss
elif config.opt.training_loss == "h1":
train_loss = h1loss
else:
raise ValueError(
f"Got training_loss={config.opt.training_loss} "
f'but expected one of ["l2", "h1"]'
)
eval_losses = {"h1": h1loss, "l2": l2loss}
if config.verbose and is_logger:
print("\n### MODEL ###\n", model)
print("\n### OPTIMIZER ###\n", optimizer)
print("\n### SCHEDULER ###\n", scheduler)
print("\n### LOSSES ###")
print(f"\n * Train: {train_loss}")
print(f"\n * Test: {eval_losses}")
print(f"\n### Beginning Training...\n")
sys.stdout.flush()
trainer = Trainer(
model=model,
n_epochs=config.opt.n_epochs,
device=device,
data_processor=data_processor,
mixed_precision=config.opt.mixed_precision,
wandb_log=config.wandb.log,
eval_interval=config.opt.eval_interval,
log_output=config.wandb.log_output,
use_distributed=config.distributed.use_distributed,
verbose=config.verbose and is_logger,
)
# Log model parameter count
if is_logger:
n_params = count_model_params(model)
if config.verbose:
print(f"\nn_params: {n_params}")
sys.stdout.flush()
if config.wandb.log:
to_log = {"n_params": n_params}
if config.n_params_baseline is not None:
to_log["n_params_baseline"] = (config.n_params_baseline,)
to_log["compression_ratio"] = (config.n_params_baseline / n_params,)
to_log["space_savings"] = 1 - (n_params / config.n_params_baseline)
wandb.log(to_log, commit=False)
wandb.watch(model)
# Start training process
trainer.train(
train_loader=train_loader,
test_loaders=test_loaders,
optimizer=optimizer,
scheduler=scheduler,
regularizer=False,
training_loss=train_loss,
eval_losses=eval_losses,
)
# Finalize WandB logging
if config.wandb.log and is_logger:
wandb.finish()
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