Upload CIDM Foundation A1-R1-R2 FIX1 experimental results and data (part 2)

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"""
Training script for Navier-Stokes equation using neural operators.
This script trains a neural operator on the 2D Navier-Stokes equations
for fluid dynamics simulation. The model learns to predict velocity fields
from initial conditions, supporting multiple resolutions and distributed training.
"""

import sys

from pathlib import Path
import torch
from torch.utils.data import DataLoader, DistributedSampler
import torch.distributed as dist
import wandb

from neuralop import H1Loss, LpLoss, Trainer, get_model
from neuralop.data.datasets.navier_stokes import load_navier_stokes_pt
from neuralop.data.transforms.data_processors import MGPatchingDataProcessor
from neuralop.utils import get_wandb_api_key, count_model_params
from neuralop.mpu.comm import get_local_rank
from neuralop.training import setup, AdamW

# Configuration setup
config_name = "default"
from zencfg import make_config_from_cli
import sys

sys.path.insert(0, "../")
from config.navier_stokes_config import Default

config = make_config_from_cli(Default)
config = config.to_dict()

# Distributed training setup, if enabled
device, is_logger = setup(config)

# WandB logging configuration
wandb_init_args = None
if config.wandb.log and is_logger:
    print(config.wandb.log)
    print(config)
    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_name,
                config.model.n_layers,
                config.model.n_modes,
                config.model.hidden_channels,
                config.model.factorization,
                config.model.rank,
                config.patching.levels,
                config.patching.padding,
            ]
        )
    wandb_init_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_init_args)

# Make sure we only print information when needed
config.verbose = config.verbose and is_logger

# Print configuration details
if config.verbose:
    print(f"##### CONFIG #####\n")
    print(config)

# Data directory setup
data_dir = Path(config.data.folder).expanduser()

# Load the Navier-Stokes dataset
train_loader, test_loaders, data_processor = load_navier_stokes_pt(
    data_root=data_dir,
    train_resolution=config.data.train_resolution,
    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=config.data.encode_input,
    encode_output=config.data.encode_output,
)

# Model initialization
model = get_model(config)
model = model.to(device)
# 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,
    )
data_processor = data_processor.to(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}")


# Loss function configuration
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:
    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,
    data_processor=data_processor,
    device=device,
    mixed_precision=config.opt.mixed_precision,
    eval_interval=config.opt.eval_interval,
    log_output=config.wandb.log_output,
    use_distributed=config.distributed.use_distributed,
    verbose=config.verbose,
    wandb_log=config.wandb.log,
)

# 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,
    test_loaders,
    optimizer,
    scheduler,
    regularizer=False,
    training_loss=train_loss,
    eval_losses=eval_losses,
)

# Finalize WandB logging
if config.wandb.log and is_logger:
    wandb.finish()

# Clean up distributed training
if dist.is_initialized():
    dist.destroy_process_group()

	"""
	Training script for Navier-Stokes equation using neural operators.

	This script trains a neural operator on the 2D Navier-Stokes equations
	for fluid dynamics simulation. The model learns to predict velocity fields
	from initial conditions, supporting multiple resolutions and distributed training.
	"""

	import sys

	from pathlib import Path
	import torch
	from torch.utils.data import DataLoader, DistributedSampler
	import torch.distributed as dist
	import wandb

	from neuralop import H1Loss, LpLoss, Trainer, get_model
	from neuralop.data.datasets.navier_stokes import load_navier_stokes_pt
	from neuralop.data.transforms.data_processors import MGPatchingDataProcessor
	from neuralop.utils import get_wandb_api_key, count_model_params
	from neuralop.mpu.comm import get_local_rank
	from neuralop.training import setup, AdamW

	# Configuration setup
	config_name = "default"
	from zencfg import make_config_from_cli
	import sys

	sys.path.insert(0, "../")
	from config.navier_stokes_config import Default

	config = make_config_from_cli(Default)
	config = config.to_dict()

	# Distributed training setup, if enabled
	device, is_logger = setup(config)

	# WandB logging configuration
	wandb_init_args = None
	if config.wandb.log and is_logger:
	print(config.wandb.log)
	print(config)
	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_name,
	config.model.n_layers,
	config.model.n_modes,
	config.model.hidden_channels,
	config.model.factorization,
	config.model.rank,
	config.patching.levels,
	config.patching.padding,
	]
	)
	wandb_init_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_init_args)

	# Make sure we only print information when needed
	config.verbose = config.verbose and is_logger

	# Print configuration details
	if config.verbose:
	print(f"##### CONFIG #####\n")
	print(config)

	# Data directory setup
	data_dir = Path(config.data.folder).expanduser()

	# Load the Navier-Stokes dataset
	train_loader, test_loaders, data_processor = load_navier_stokes_pt(
	data_root=data_dir,
	train_resolution=config.data.train_resolution,
	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=config.data.encode_input,
	encode_output=config.data.encode_output,
	)

	# Model initialization
	model = get_model(config)
	model = model.to(device)
	# 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,
	)
	data_processor = data_processor.to(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}")


	# Loss function configuration
	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:
	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,
	data_processor=data_processor,
	device=device,
	mixed_precision=config.opt.mixed_precision,
	eval_interval=config.opt.eval_interval,
	log_output=config.wandb.log_output,
	use_distributed=config.distributed.use_distributed,
	verbose=config.verbose,
	wandb_log=config.wandb.log,
	)

	# 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,
	test_loaders,
	optimizer,
	scheduler,
	regularizer=False,
	training_loss=train_loss,
	eval_losses=eval_losses,
	)

	# Finalize WandB logging
	if config.wandb.log and is_logger:
	wandb.finish()

	# Clean up distributed training
	if dist.is_initialized():
	dist.destroy_process_group()