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import pytorch_lightning as pl
from .eqV2 import EquiformerV2_NMR
class MagNET_Lightning(pl.LightningModule):
def __init__(self, params, precision = 32, filter_solvent_edges = None, solvent_edge_radius = None):
super().__init__()
self.save_hyperparameters()
self.precision = precision
# if filter_solvent_edges and solvent_edge_radius are not provided as arguments, read from params file
# this is convenient, because we don't need to constantly provide filter_solvent_edges and solvent_edge_radius
# as arguments if they're already specified in params, while still allowing us to override their previous
# values if we need to (e.g., when loading in a pretrained checkpoint)
if filter_solvent_edges is None:
assert solvent_edge_radius is None
filter_solvent_edges = False
solvent_edge_radius = params['max_radius']
if 'filter_solvent_edges' in params:
filter_solvent_edges = params['filter_solvent_edges']
if 'solvent_edge_radius' in params:
solvent_edge_radius = params['solvent_edge_radius']
self.filter_solvent_edges = filter_solvent_edges
self.solvent_edge_radius = solvent_edge_radius
self.lr = params['lr']
if 'min_lr' in params:
self.min_lr = params['min_lr']
else:
self.min_lr = self.lr
# number of exponential decay steps (= # batches) from self.lr to self.min_lr
if 'lr_steps' in params:
self.lr_steps = params['lr_steps']
else:
self.lr_steps = 300000
self.model_type = params['model_type'] # in ['EquiformerV2']
evidential_regression = False
if 'evidential_regression' in params:
evidential_regression = params['evidential_regression']
self.evidential_regression = evidential_regression
if self.model_type == 'EquiformerV2':
assert self.precision == 32
self.model = EquiformerV2_NMR(
None, None, None,
use_pbc=False, regress_forces=False,
otf_graph = True,
max_neighbors = params['max_neighbors'],
max_radius = params['max_radius'],
max_num_elements = params['max_num_elements'],
num_layers = params['num_layers'],
sphere_channels = params['sphere_channels'],
attn_hidden_channels = params['attn_hidden_channels'],
num_heads = params['num_heads'],
attn_alpha_channels = params['attn_alpha_channels'],
attn_value_channels = params['attn_value_channels'],
ffn_hidden_channels = params['ffn_hidden_channels'],
norm_type='layer_norm_sh',
lmax_list = params['lmax_list'],
mmax_list = params['mmax_list'],
grid_resolution = params['grid_resolution'],
num_sphere_samples=128,
edge_channels=128,
use_atom_edge_embedding=True,
share_atom_edge_embedding=False,
use_m_share_rad=False,
distance_function="gaussian",
num_distance_basis=512, # not used; hard-coded by Equiformer-V2 to 600
attn_activation='silu',
use_s2_act_attn=False,
use_attn_renorm=True,
ffn_activation='silu',
use_gate_act=False,
use_grid_mlp=True,
use_sep_s2_act=True,
alpha_drop=0.0,
drop_path_rate=0.0,
proj_drop=0.0,
weight_init = params['weight_init'],
evidential_regression = self.evidential_regression,
filter_solvent_edges = self.filter_solvent_edges,
solvent_edge_radius = self.solvent_edge_radius,
)
def forward(self, data):
if 'atomic_numbers' not in data.keys():
data.atomic_numbers = data.x
if 'natoms' not in data.keys():
data.natoms = torch.unique_consecutive(data.batch, return_counts = True)[1]
if self.model_type == 'EquiformerV2':
if self.evidential_regression:
mu,v,alpha,beta = self.model(data).squeeze(1).chunk(4, dim = 1)
v = torch.nn.functional.softplus(v) + 1e-8
alpha = torch.nn.functional.softplus(alpha) + 1. + 1e-8
beta = torch.nn.functional.softplus(beta) + 1e-8
return torch.cat([mu,v,alpha,beta], dim = 1)
else:
return self.model(data).squeeze(1)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr = self.lr)
# exponential lr decay from self.lr to self.min_lr in self.lr_steps steps
gamma = (self.min_lr / self.lr) ** (1.0 / self.lr_steps)
func = lambda step: max(gamma**(step), self.min_lr / self.lr)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda = func)
lr_scheduler_config = {
"scheduler": scheduler,
"interval": "step",
"frequency": 1,
"strict": False,
"name": None,
}
return {"optimizer": optimizer, "lr_scheduler": lr_scheduler_config}
def training_step(self, train_batch, batch_idx):
data = train_batch
data.atomic_numbers = data.x
data.natoms = torch.unique_consecutive(data.batch, return_counts = True)[1]
out = self.forward(data)
if self.precision == 64:
y = data.shieldings.double()
else:
y = data.shieldings
if self.evidential_regression:
lam = 0.2
mu,v,alpha,beta = out.chunk(4, dim = 1)
mu = mu.squeeze(dim=1)[data.atom_type_mask]
v = v.squeeze(dim=1)[data.atom_type_mask]
alpha = alpha.squeeze(dim=1)[data.atom_type_mask]
beta = beta.squeeze(dim=1)[data.atom_type_mask]
loss = 0.5 * (torch.log(torch.pi / v)) \
- alpha * torch.log(2.*beta*(1. + v)) \
+ (alpha + 0.5) * torch.log(v*(y[data.atom_type_mask] - mu)**2. + 2.*beta*(1. + v)) \
+ torch.lgamma(alpha) - torch.lgamma(alpha + 0.5) \
+ lam * (torch.abs(y[data.atom_type_mask] - mu) * (2. * v + alpha) - 1e-4)
loss = loss.mean()
else:
loss = torch.mean(((out.squeeze(1) - y)[data.atom_type_mask])**2.0)
self.log('train_loss', loss, batch_size = data.atom_type_mask.sum().item())
# does not account for symmetric atoms
return loss
def validation_step(self, val_batch, batch_idx):
data = val_batch
data.atomic_numbers = data.x
data.natoms = torch.unique_consecutive(data.batch, return_counts = True)[1]
out = self.forward(data)
if self.precision == 64:
y = data.shieldings.double()
else:
y = data.shieldings
if self.evidential_regression:
mu,v,alpha,beta = out.chunk(4, dim = 1)
mu = mu.squeeze(dim=1)[data.atom_type_mask]
v = v.squeeze(dim=1)[data.atom_type_mask]
alpha = alpha.squeeze(dim=1)[data.atom_type_mask]
beta = beta.squeeze(dim=1)[data.atom_type_mask]
lam = 0.2
loss = 0.5 * (torch.log(torch.pi / v)) \
- alpha * torch.log(2.*beta*(1. + v)) \
+ (alpha + 0.5) * torch.log(v*(y[data.atom_type_mask] - mu)**2. + 2.*beta*(1. + v)) \
+ torch.lgamma(alpha) - torch.lgamma(alpha + 0.5) \
+ lam * (torch.abs(y[data.atom_type_mask] - mu) * (2. * v + alpha) - 1e-4)
loss = loss.mean()
mae = torch.mean(torch.abs(mu - y[data.atom_type_mask]))
else:
loss = torch.mean(((out.squeeze(1) - y)[data.atom_type_mask])**2.0)
mae = torch.mean(torch.abs(((out.squeeze(1) - y)[data.atom_type_mask])))
self.log_dict({"val_loss": loss, "val_mae": mae}, batch_size = data.atom_type_mask.sum().item())
return loss
def test_step(self, test_batch, batch_idx):
data = test_batch
data.atomic_numbers = data.x
data.natoms = torch.unique_consecutive(data.batch, return_counts = True)[1]
out = self.forward(data)
if self.precision == 64:
y = data.shieldings.double()
else:
y = data.shieldings
if self.evidential_regression:
lam = 0.2
mu,v,alpha,beta = out.chunk(4, dim = 1)
mu = mu.squeeze(dim=1)[data.atom_type_mask]
v = v.squeeze(dim=1)[data.atom_type_mask]
alpha = alpha.squeeze(dim=1)[data.atom_type_mask]
beta = beta.squeeze(dim=1)[data.atom_type_mask]
loss = 0.5 * (torch.log(torch.pi / v)) \
- alpha * torch.log(2.*beta*(1. + v)) \
+ (alpha + 0.5) * torch.log(v*(y[data.atom_type_mask] - mu)**2. + 2.*beta*(1. + v)) \
+ torch.lgamma(alpha) - torch.lgamma(alpha + 0.5) \
+ lam * (torch.abs(y[data.atom_type_mask] - mu) * (2. * v + alpha) - 1e-4)
loss = loss.mean()
mae = torch.mean(torch.abs(mu - y[data.atom_type_mask]))
else:
loss = torch.mean(((out.squeeze(1) - y)[data.atom_type_mask])**2.0)
mae = torch.mean(torch.abs(((out.squeeze(1) - y)[data.atom_type_mask])))
self.log_dict({"test_loss": loss, "test_mae": mae}, batch_size = data.atom_type_mask.sum().item())
return loss
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