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Fix a bug in setting up loss function for stochastic weight averaging (swa) #504

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Fix a bug in setting up loss function for stochastic weight averaging (swa) #504

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f3ac4b1
Update the codes in run_train.py for setting up the loss function wei…
Zhigang-dev Jul 8, 2024
b4f98f4
Update train.py, refixing the stress_per_atom problem.
Zhigang-dev Jul 8, 2024
529aa83
Merge branch 'ACEsuit:main' into main
Zhigang-dev Jul 11, 2024
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48 changes: 19 additions & 29 deletions mace/cli/run_train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
import json
import logging
import os
import inspect
from copy import deepcopy
from pathlib import Path
from typing import Optional
Expand Down Expand Up @@ -598,35 +599,24 @@ def run(args: argparse.Namespace) -> None:
logging.info(f"Setting start swa to {args.start_swa}")
if args.loss == "forces_only":
raise ValueError("Can not select swa with forces only loss.")
if args.loss == "virials":
loss_fn_energy = modules.WeightedEnergyForcesVirialsLoss(
energy_weight=args.swa_energy_weight,
forces_weight=args.swa_forces_weight,
virials_weight=args.swa_virials_weight,
)
elif args.loss == "stress":
loss_fn_energy = modules.WeightedEnergyForcesStressLoss(
energy_weight=args.swa_energy_weight,
forces_weight=args.swa_forces_weight,
stress_weight=args.swa_stress_weight,
)
elif args.loss == "energy_forces_dipole":
loss_fn_energy = modules.WeightedEnergyForcesDipoleLoss(
args.swa_energy_weight,
forces_weight=args.swa_forces_weight,
dipole_weight=args.swa_dipole_weight,
)
logging.info(
f"Using stochastic weight averaging (after {args.start_swa} epochs) with energy weight : {args.swa_energy_weight}, forces weight : {args.swa_forces_weight}, dipole weight : {args.swa_dipole_weight} and learning rate : {args.swa_lr}"
)
else:
loss_fn_energy = modules.WeightedEnergyForcesLoss(
energy_weight=args.swa_energy_weight,
forces_weight=args.swa_forces_weight,
)
logging.info(
f"Using stochastic weight averaging (after {args.start_swa} epochs) with energy weight : {args.swa_energy_weight}, forces weight : {args.swa_forces_weight} and learning rate : {args.swa_lr}"
)
# switch the weights for swa while keeping the choice of loss function unchanged
loss_class = type(loss_fn)
signature = inspect.signature(loss_class.__init__)
parameters = signature.parameters
params = {name: getattr(args, name) for name in parameters if name != 'self'}
weight_params = {
'energy_weight': 'swa_energy_weight',
'forces_weight': 'swa_forces_weight',
'virials_weight': 'swa_virials_weight',
'stress_weight': 'swa_stress_weight',
'dipole_weight': 'swa_dipole_weight',
}
for old_param, new_param in weight_params.items():
if old_param in params:
params[old_param] = getattr(args, new_param)
loss_fn_energy = loss_class(**params)
logging.info(f"Using stochastic weight averaging (after {args.start_swa} epochs) with {loss_fn_energy}")
# weights in loss function updated, creating the swa container
swa = tools.SWAContainer(
model=AveragedModel(model),
scheduler=SWALR(
Expand Down
6 changes: 3 additions & 3 deletions mace/tools/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,13 +53,13 @@ def valid_err_log(valid_loss, eval_metrics, logger, log_errors, epoch=None):
)
elif (
log_errors == "PerAtomRMSEstressvirials"
and eval_metrics["rmse_stress_per_atom"] is not None
and eval_metrics["rmse_stress"] is not None
):
error_e = eval_metrics["rmse_e_per_atom"] * 1e3
error_f = eval_metrics["rmse_f"] * 1e3
error_stress = eval_metrics["rmse_stress_per_atom"] * 1e3
error_stress = eval_metrics["rmse_stress"] * 1e3
logging.info(
f"Epoch {epoch}: loss={valid_loss:.4f}, RMSE_E_per_atom={error_e:.1f} meV, RMSE_F={error_f:.1f} meV / A, RMSE_stress_per_atom={error_stress:.1f} meV / A^3"
f"Epoch {epoch}: loss={valid_loss:.4f}, RMSE_E_per_atom={error_e:.1f} meV, RMSE_F={error_f:.1f} meV / A, RMSE_stress={error_stress:.1f} meV / A^3"
)
elif (
log_errors == "PerAtomRMSEstressvirials"
Expand Down