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Add an interface to schnetpack #60

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Add an interface to schnetpack #60

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83cb3a6
Start implementation of SchNet learner
WardLT Nov 13, 2024
3def019
Implement training using PL
WardLT Nov 15, 2024
6315230
Add training script for SchNet
WardLT Nov 22, 2024
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187 changes: 187 additions & 0 deletions 1_ml-potential/spk/0_train-spk.py
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"""Training script where we vary different parameters of SchNet"""
import json
import logging
import sys
from argparse import ArgumentParser
from datetime import datetime
from hashlib import sha256
from pathlib import Path
from platform import node
from time import perf_counter

import numpy as np
import torch
from ase import Atoms
from ase.io import read, iread
import schnetpack as spk

from cascade.learning.spk import SchnetPackInterface

if __name__ == "__main__":
# Make the argument parser
parser = ArgumentParser()

group = parser.add_argument_group(title='Training Data', description='Setting which control how the training data are generated')
group.add_argument('--train-files', nargs='+', help='Trajectories used for training')
group.add_argument('--test-files', nargs='+', help='Trajectories used for testing')
group.add_argument('--val-frac', default=0.1, help='What fraction of the trajectory to use for validation. '
'We use the last frames of the trajectory for validation')

group = parser.add_argument_group(title='Optimization Setting', description='Parameters of the optimization algorithm')
group.add_argument('--num-epochs', default=32, help='Number of training epochs', type=int)
group.add_argument('--batch-size', default=64, help='Number of records per batch', type=int)
group.add_argument('--learning-rate', default=1e-4, help='Learning rate for optimizer', type=float)
group.add_argument('--loss-weights', default=(1, 1, 0.1), help='Weights for the energy, forces, and stress', nargs=3, type=float)

group = parser.add_argument_group(title='Model Architecture', description='Parameters describing the CHGNet model')
group.add_argument('--representation', choices=['schnet', 'painn'], help='Which representation to use')
group.add_argument('--num-atom-basis', help='Number of features per atom', default=128, type=int)
group.add_argument('--num-interactions', help='Number of message passing layers', default=3, type=int)
group.add_argument('--num-radial-basis', help='Number of radial basis functions', default=20, type=int)
group.add_argument('--cutoff', help='Cutoff distance for atomic interactions', default=6, type=float)
group.add_argument('--output-layers', help='Number of layers in output network', default=1, type=int)
args = parser.parse_args()

# Load the training data
train_atoms: list[Atoms] = []
valid_atoms: list[Atoms] = []
train_hasher = sha256()
rng = np.random.RandomState(1)
for file in args.train_files:
my_atoms = read(file, slice(None))

# Shuffle the data if they are not from a traj file
if not file.endswith('.traj'):
rng.shuffle(my_atoms)

# Hash dataset for reproducibility
for atoms in my_atoms:
train_hasher.update(atoms.positions.tobytes())
train_hasher.update(atoms.cell.tobytes())

# Split off the validation set
valid_start = int(len(my_atoms) * args.val_frac)
valid_atoms.extend(my_atoms[-valid_start:])
train_atoms.extend(my_atoms[:-valid_start])
train_hash = train_hasher.hexdigest()[-8:]

# Load the test data
test_hasher = sha256()
test_atoms: list[Atoms] = []
test_source: list[int] = []
for i, file in enumerate(args.test_files):
for atoms in iread(file):
test_hasher.update(atoms.positions.tobytes())
test_hasher.update(atoms.cell.tobytes())
test_atoms.append(atoms)
test_source.append(i)
test_hash = test_hasher.hexdigest()[-8:]

# Make a run directory
params = args.__dict__.copy()
params.update({
'train_hash': train_hash,
'train_size': len(train_atoms),
'valid_size': len(valid_atoms),
'test_hash': test_hash,
'test_size': len(test_atoms),
'cuda_version': torch.version.cuda,
'device_name': torch.cuda.get_device_name(0),
'host': node()
})

run_hash = sha256(json.dumps(params).encode()).hexdigest()[-8:]
start_time = datetime.now().isoformat(timespec="seconds").replace(":", "").replace("-", "")
run_dir = Path('runs') / f'train-{train_hash}' / f'{start_time}-{run_hash}'
run_dir.mkdir(parents=True)

# Make a logger
logger = logging.getLogger('main')
handlers = [logging.StreamHandler(sys.stdout), logging.FileHandler(filename=run_dir / 'run.log')]
for handler in handlers:
handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
for my_logger in [logger, logging.getLogger('cascade')]:
my_logger.addHandler(handler)
my_logger.setLevel(logging.INFO)
logger.info(f'Started. Running in {run_dir}')
with open(run_dir / 'params.json', 'w') as fp:
json.dump(params, fp, indent=2)

# Assemble the model
rep = {
'schnet': spk.representation.SchNet,
'painn': spk.representation.PaiNN
}[args.represnetation]

pred_energy = spk.atomistic.Atomwise(n_in=args.num_atom_basis, output_key='energy')
pred_forces = spk.atomistic.Forces(calc_stress=True)
model = spk.model.NeuralNetworkPotential(
representation=rep(
n_atom_basis=args.num_atom_basis,
n_interactions=args.num_interactions,
radial_basis=spk.nn.GaussianRBF(args.num_radial_basis, args.cutoff),
cutoff_fn=spk.nn.CosineCutoff(cutoff=args.cutoff)
),
input_modules=[
spk.atomistic.Strain(), spk.atomistic.PairwiseDistances(),
],
postprocessors=[
spk.transform.AddOffsets("energy", add_mean=True, add_atomrefs=False),
]
)
logger.info('Loaded model and moved it to RAM for now')

# Train using the wrapper function from Cascade
ani = SchnetPackInterface()
loss_e, loss_f, loss_s = args.loss_weights
train_time = perf_counter()
model_msg, train_log = ani.train(
model,
train_data=train_atoms,
valid_data=valid_atoms,
num_epochs=args.num_epochs,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
force_weight=loss_f / loss_e,
stress_weight=loss_s / loss_e,
reset_weights=args.reset_weights,
device='cpu'
)
train_time = perf_counter() - train_time
logger.info('Training complete. Running inference')

# Run inference on the validation dataset
performance = {'train_time': train_time / args.num_epochs}
for name, atoms in [('train', train_atoms), ('valid', valid_atoms), ('test', test_atoms)]:
eval_time = perf_counter()
pred_e, pred_f, pred_s = ani.evaluate(model_msg, atoms, batch_size=args.batch_size, device='cuda')
eval_time = perf_counter() - eval_time
logger.info(f'Computing performance on {name} set')

true_n = np.array([len(a) for a in atoms])
true_e = np.array([a.get_potential_energy() for a in atoms])
true_f = [a.get_forces() for a in atoms]
true_s = np.array([a.get_stress(False) for a in atoms])

pred_e_pa = pred_e / true_n
true_e_pa = true_e / true_n

performance[name] = {
'size': len(atoms),
'eval_time': eval_time / len(atoms),
'energy_mae': float(np.abs(pred_e_pa - true_e_pa).mean()),
'force_mean_error': float(np.mean([np.linalg.norm(t - p, axis=1).mean() for t, p in zip(true_f, pred_f)])),
'force_max_error': float(np.mean([np.linalg.norm(t - p, axis=1).max() for t, p in zip(true_f, pred_f)])),
'stress_mae': float(np.abs(true_s.flatten() - pred_s.flatten()).mean())
}
np.savez_compressed(run_dir / f'{name}_true.npz', source=test_source, count=true_n, energy=true_e, forces=np.concatenate(true_f, axis=0), stress=true_s)
np.savez_compressed(run_dir / f'{name}_pred.npz', source=test_source, count=true_n, energy=pred_e, forces=np.concatenate(pred_f, axis=0), stress=pred_s)
for key, value in train_log.iloc[-1].to_dict().items():
performance[f'last_epoch.{key}'] = value

# Store the model, predictions, and ground truth
logger.info('Saving model and results')
(run_dir / 'model.pt').write_bytes(model_msg)
train_log.to_csv(run_dir / 'log.csv', index=False)
with open(run_dir / 'performance.json', 'w') as fp:
json.dump(performance, fp, indent=2)
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