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

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"""
Training script for MHD64 dataset using neural operators.
This script trains a neural operator on the 3D magnetohydrodynamics (MHD)
dataset with 64x64x64 resolution. The model learns to predict the next time step
in the MHD simulation, supporting both next-step prediction and autoregressive
evaluation modes.
"""

import sys

from configmypy import ConfigPipeline, YamlConfig, ArgparseConfig
from pathlib import Path
import torch

from torch.utils.data import DataLoader, DistributedSampler
import wandb

from neuralop import H1Loss, LpLoss, Trainer, get_model
from neuralop.data.datasets.the_well_dataset import MHD64Dataset
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

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

sys.path.insert(0, "../")
from config.the_well.mhd_64_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\n{config}\n")
    sys.stdout.flush()

# Load the MHD64 dataset
dataset = MHD64Dataset(
    root_dir=Path(config.data.root).expanduser(),
    train_task="next_step",
    eval_tasks=["next_step", "autoregression"],
    first_only=True,
)

# Print dataset metadata
print(dataset.train_db.metadata.n_steps_per_trajectory)

# Create data loaders
train_loader = DataLoader(dataset.train_db, batch_size=config.data.batch_size)

test_loaders = {}
for mode, db in dataset.test_dbs.items():
    test_loaders[mode] = DataLoader(db, batch_size=config.data.test_batch_size)

# Get data processor from dataset
data_processor = dataset.data_processor
print(data_processor)

# 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.normalizer,
        out_normalizer=data_processor.normalizer,
        padding_fraction=config.patching.padding,
        stitching=config.patching.stitching,
        levels=config.patching.levels,
        use_distributed=config.distributed.use_distributed,
        device=device,
    )

# Reconfigure DataLoaders to use a DistributedSampler
# if in distributed data parallel mode
"""if config.distributed.use_distributed:
    train_sampler = DistributedSampler(dataset.train_db, rank=get_local_rank())
    train_loader = DataLoader(dataset=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=3, p=2)
h1loss = H1Loss(d=3)
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 with autoregressive evaluation
trainer.train(
    train_loader=train_loader,
    test_loaders=test_loaders,
    eval_modes={"autoregression": "autoregression"},
    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()

	"""
	Training script for MHD64 dataset using neural operators.

	This script trains a neural operator on the 3D magnetohydrodynamics (MHD)
	dataset with 64x64x64 resolution. The model learns to predict the next time step
	in the MHD simulation, supporting both next-step prediction and autoregressive
	evaluation modes.
	"""

	import sys

	from configmypy import ConfigPipeline, YamlConfig, ArgparseConfig
	from pathlib import Path
	import torch

	from torch.utils.data import DataLoader, DistributedSampler
	import wandb

	from neuralop import H1Loss, LpLoss, Trainer, get_model
	from neuralop.data.datasets.the_well_dataset import MHD64Dataset
	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

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

	sys.path.insert(0, "../")
	from config.the_well.mhd_64_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\n{config}\n")
	sys.stdout.flush()

	# Load the MHD64 dataset
	dataset = MHD64Dataset(
	root_dir=Path(config.data.root).expanduser(),
	train_task="next_step",
	eval_tasks=["next_step", "autoregression"],
	first_only=True,
	)

	# Print dataset metadata
	print(dataset.train_db.metadata.n_steps_per_trajectory)

	# Create data loaders
	train_loader = DataLoader(dataset.train_db, batch_size=config.data.batch_size)

	test_loaders = {}
	for mode, db in dataset.test_dbs.items():
	test_loaders[mode] = DataLoader(db, batch_size=config.data.test_batch_size)

	# Get data processor from dataset
	data_processor = dataset.data_processor
	print(data_processor)

	# 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.normalizer,
	out_normalizer=data_processor.normalizer,
	padding_fraction=config.patching.padding,
	stitching=config.patching.stitching,
	levels=config.patching.levels,
	use_distributed=config.distributed.use_distributed,
	device=device,
	)

	# Reconfigure DataLoaders to use a DistributedSampler
	# if in distributed data parallel mode
	"""if config.distributed.use_distributed:
	train_sampler = DistributedSampler(dataset.train_db, rank=get_local_rank())
	train_loader = DataLoader(dataset=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=3, p=2)
	h1loss = H1Loss(d=3)
	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 with autoregressive evaluation
	trainer.train(
	train_loader=train_loader,
	test_loaders=test_loaders,
	eval_modes={"autoregression": "autoregression"},
	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()