import torch
import torch.nn.functional as F
import pytorch_lightning as pl
import time
import os
from models.transformer import Denoiser
from diffusion.noise_schedule import PredefinedNoiseScheduleDiscrete, MarginalTransition
from diffusion import diffusion_utils
from metrics.train_loss import TrainLossDiscrete
from metrics.abstract_metrics import SumExceptBatchMetric, SumExceptBatchKL, NLL
import utils
class Graph_DiT(pl.LightningModule):
# def __init__(self, cfg, dataset_infos, train_metrics, sampling_metrics, visualization_tools):
def __init__(self, cfg, dataset_infos, visualization_tools):
super().__init__()
# self.save_hyperparameters(ignore=['train_metrics', 'sampling_metrics'])
self.test_only = cfg.general.test_only
self.guidance_target = getattr(cfg.dataset, 'guidance_target', None)
input_dims = dataset_infos.input_dims
output_dims = dataset_infos.output_dims
nodes_dist = dataset_infos.nodes_dist
active_index = dataset_infos.active_index
self.cfg = cfg
self.name = cfg.general.name
self.T = cfg.model.diffusion_steps
self.guide_scale = cfg.model.guide_scale
self.Xdim = input_dims['X']
self.Edim = input_dims['E']
self.ydim = input_dims['y']
self.Xdim_output = output_dims['X']
self.Edim_output = output_dims['E']
self.ydim_output = output_dims['y']
self.node_dist = nodes_dist
self.active_index = active_index
self.dataset_info = dataset_infos
self.train_loss = TrainLossDiscrete(self.cfg.model.lambda_train)
self.val_nll = NLL()
self.val_X_kl = SumExceptBatchKL()
self.val_E_kl = SumExceptBatchKL()
self.val_X_logp = SumExceptBatchMetric()
self.val_E_logp = SumExceptBatchMetric()
self.val_y_collection = []
self.test_nll = NLL()
self.test_X_kl = SumExceptBatchKL()
self.test_E_kl = SumExceptBatchKL()
self.test_X_logp = SumExceptBatchMetric()
self.test_E_logp = SumExceptBatchMetric()
self.test_y_collection = []
# self.train_metrics = train_metrics
# self.sampling_metrics = sampling_metrics
self.visualization_tools = visualization_tools
self.max_n_nodes = dataset_infos.max_n_nodes
self.model = Denoiser(max_n_nodes=self.max_n_nodes,
hidden_size=cfg.model.hidden_size,
depth=cfg.model.depth,
num_heads=cfg.model.num_heads,
mlp_ratio=cfg.model.mlp_ratio,
drop_condition=cfg.model.drop_condition,
Xdim=self.Xdim,
Edim=self.Edim,
ydim=self.ydim,
task_type=dataset_infos.task_type)
self.noise_schedule = PredefinedNoiseScheduleDiscrete(cfg.model.diffusion_noise_schedule,
timesteps=cfg.model.diffusion_steps)
print("__init__")
print("dataset_info.node_types", self.dataset_info.node_types)
# dataset_info.node_types tensor([7.4826e-01, 2.6870e-02, 9.3930e-02, 4.4959e-02, 5.2982e-03, 7.5689e-04, 5.3739e-03, 1.5138e-03, 7.5689e-05, 4.3143e-03, 6.8650e-02])
x_marginals = self.dataset_info.node_types.float() / torch.sum(self.dataset_info.node_types.float())
e_marginals = self.dataset_info.edge_types.float() / torch.sum(self.dataset_info.edge_types.float())
x_marginals = x_marginals / (x_marginals ).sum()
e_marginals = e_marginals / (e_marginals ).sum()
# transition e is the probability of transitioning from x1 to x2 with e
xe_conditions = self.dataset_info.transition_E.float()
xe_conditions = xe_conditions[self.active_index][:, self.active_index]
xe_conditions = xe_conditions.sum(dim=1)
ex_conditions = xe_conditions.t()
xe_conditions = xe_conditions / xe_conditions.sum(dim=-1, keepdim=True)
ex_conditions = ex_conditions / ex_conditions.sum(dim=-1, keepdim=True)
self.transition_model = MarginalTransition(x_marginals=x_marginals,
e_marginals=e_marginals,
xe_conditions=xe_conditions,
ex_conditions=ex_conditions,
y_classes=self.ydim_output,
n_nodes=self.max_n_nodes)
self.limit_dist = utils.PlaceHolder(X=x_marginals, E=e_marginals, y=None)
self.start_epoch_time = None
self.train_iterations = None
self.val_iterations = None
self.log_every_steps = cfg.general.log_every_steps
self.number_chain_steps = cfg.general.number_chain_steps
self.best_val_nll = 1e8
self.val_counter = 0
self.batch_size = self.cfg.train.batch_size
def forward(self, noisy_data, unconditioned=False):
x, e, y = noisy_data['X_t'].float(), noisy_data['E_t'].float(), noisy_data['y_t'].float().clone()
node_mask, t = noisy_data['node_mask'], noisy_data['t']
pred = self.model(x, e, node_mask, y=y, t=t, unconditioned=unconditioned)
return pred
def training_step(self, data, i):
data_x = F.one_hot(data.x, num_classes=118).float()[:, self.active_index]
data_edge_attr = F.one_hot(data.edge_attr, num_classes=5).float()
dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
dense_data = dense_data.mask(node_mask)
X, E = dense_data.X, dense_data.E
noisy_data = self.apply_noise(X, E, data.y, node_mask)
pred = self.forward(noisy_data)
loss = self.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
log=i % self.log_every_steps == 0)
self.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
log=i % self.log_every_steps == 0)
self.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
return {'loss': loss}
def configure_optimizers(self):
params = self.parameters()
optimizer = torch.optim.AdamW(params, lr=self.cfg.train.lr, amsgrad=True,
weight_decay=self.cfg.train.weight_decay)
return optimizer
def on_fit_start(self) -> None:
self.train_iterations = self.trainer.datamodule.training_iterations
print('on fit train iteration:', self.train_iterations)
print("Size of the input features Xdim {}, Edim {}, ydim {}".format(self.Xdim, self.Edim, self.ydim))
def on_train_epoch_start(self) -> None:
if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
print("Starting train epoch {}/{}...".format(self.current_epoch, self.trainer.max_epochs))
self.start_epoch_time = time.time()
self.train_loss.reset()
self.train_metrics.reset()
def on_train_epoch_end(self) -> None:
if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
log = True
else:
log = False
self.train_loss.log_epoch_metrics(self.current_epoch, self.start_epoch_time, log)
self.train_metrics.log_epoch_metrics(self.current_epoch, log)
def on_validation_epoch_start(self) -> None:
self.val_nll.reset()
self.val_X_kl.reset()
self.val_E_kl.reset()
self.val_X_logp.reset()
self.val_E_logp.reset()
# self.sampling_metrics.reset()
self.val_y_collection = []
@torch.no_grad()
def validation_step(self, data, i):
data_x = F.one_hot(data.x, num_classes=118).float()[:, self.active_index]
data_edge_attr = F.one_hot(data.edge_attr, num_classes=5).float()
dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
dense_data = dense_data.mask(node_mask)
noisy_data = self.apply_noise(dense_data.X, dense_data.E, data.y, node_mask)
pred = self.forward(noisy_data)
nll = self.compute_val_loss(pred, noisy_data, dense_data.X, dense_data.E, data.y, node_mask, test=False)
self.val_y_collection.append(data.y)
self.log(f'valid_nll', nll, batch_size=data.x.size(0), sync_dist=True)
return {'loss': nll}
def on_validation_epoch_end(self) -> None:
metrics = [self.val_nll.compute(), self.val_X_kl.compute() * self.T, self.val_E_kl.compute() * self.T,
self.val_X_logp.compute(), self.val_E_logp.compute()]
if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
print(f"Epoch {self.current_epoch}: Val NLL {metrics[0] :.2f} -- Val Atom type KL {metrics[1] :.2f} -- ",
f"Val Edge type KL: {metrics[2] :.2f}", 'Val loss: %.2f \t Best : %.2f\n' % (metrics[0], self.best_val_nll))
# Log val nll with default Lightning logger, so it can be monitored by checkpoint callback
self.log("val/NLL", metrics[0], sync_dist=True)
if metrics[0] < self.best_val_nll:
self.best_val_nll = metrics[0]
self.val_counter += 1
if self.val_counter % self.cfg.general.sample_every_val == 0 and self.val_counter > 1:
start = time.time()
samples_left_to_generate = self.cfg.general.samples_to_generate
samples_left_to_save = self.cfg.general.samples_to_save
chains_left_to_save = self.cfg.general.chains_to_save
samples, all_ys, ident = [], [], 0
self.val_y_collection = torch.cat(self.val_y_collection, dim=0)
num_examples = self.val_y_collection.size(0)
start_index = 0
while samples_left_to_generate > 0:
bs = 1 * self.cfg.train.batch_size
to_generate = min(samples_left_to_generate, bs)
to_save = min(samples_left_to_save, bs)
chains_save = min(chains_left_to_save, bs)
if start_index + to_generate > num_examples:
start_index = 0
if to_generate > num_examples:
ratio = to_generate // num_examples
self.val_y_collection = self.val_y_collection.repeat(ratio+1, 1)
num_examples = self.val_y_collection.size(0)
batch_y = self.val_y_collection[start_index:start_index + to_generate]
all_ys.append(batch_y)
samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
save_final=to_save,
keep_chain=chains_save,
number_chain_steps=self.number_chain_steps))
ident += to_generate
start_index += to_generate
samples_left_to_save -= to_save
samples_left_to_generate -= to_generate
chains_left_to_save -= chains_save
# print(f"Computing sampling metrics", ' ...')
# valid_smiles = self.sampling_metrics(samples, all_ys, self.name, self.current_epoch, val_counter=-1, test=False)
# print(f'Done. Sampling took {time.time() - start:.2f} seconds\n')
current_path = os.getcwd()
result_path = os.path.join(current_path,
f'graphs/{self.name}/epoch{self.current_epoch}_b0/')
# self.visualization_tools.visualize_by_smiles(result_path, valid_smiles, self.cfg.general.samples_to_save)
# self.sampling_metrics.reset()
def on_test_epoch_start(self) -> None:
print("Starting test...")
self.test_nll.reset()
self.test_X_kl.reset()
self.test_E_kl.reset()
self.test_X_logp.reset()
self.test_E_logp.reset()
self.test_y_collection = []
@torch.no_grad()
def test_step(self, data, i):
data_x = F.one_hot(data.x, num_classes=118).float()[:, self.active_index]
data_edge_attr = F.one_hot(data.edge_attr, num_classes=5).float()
dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
dense_data = dense_data.mask(node_mask)
noisy_data = self.apply_noise(dense_data.X, dense_data.E, data.y, node_mask)
pred = self.forward(noisy_data)
nll = self.compute_val_loss(pred, noisy_data, dense_data.X, dense_data.E, data.y, node_mask, test=True)
self.test_y_collection.append(data.y)
return {'loss': nll}
def on_test_epoch_end(self) -> None:
""" Measure likelihood on a test set and compute stability metrics. """
metrics = [self.test_nll.compute(), self.test_X_kl.compute(), self.test_E_kl.compute(),
self.test_X_logp.compute(), self.test_E_logp.compute()]
print(f"Epoch {self.current_epoch}: Test NLL {metrics[0] :.2f} -- Test Atom type KL {metrics[1] :.2f} -- ",
f"Test Edge type KL: {metrics[2] :.2f}")
## final epcoh
samples_left_to_generate = self.cfg.general.final_model_samples_to_generate
samples_left_to_save = self.cfg.general.final_model_samples_to_save
chains_left_to_save = self.cfg.general.final_model_chains_to_save
samples, all_ys, batch_id = [], [], 0
test_y_collection = torch.cat(self.test_y_collection, dim=0)
num_examples = test_y_collection.size(0)
if self.cfg.general.final_model_samples_to_generate > num_examples:
ratio = self.cfg.general.final_model_samples_to_generate // num_examples
test_y_collection = test_y_collection.repeat(ratio+1, 1)
num_examples = test_y_collection.size(0)
while samples_left_to_generate > 0:
print(f'samples left to generate: {samples_left_to_generate}/'
f'{self.cfg.general.final_model_samples_to_generate}', end='', flush=True)
bs = 1 * self.cfg.train.batch_size
to_generate = min(samples_left_to_generate, bs)
to_save = min(samples_left_to_save, bs)
chains_save = min(chains_left_to_save, bs)
batch_y = test_y_collection[batch_id : batch_id + to_generate]
cur_sample = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
keep_chain=chains_save, number_chain_steps=self.number_chain_steps)
samples = samples + cur_sample
all_ys.append(batch_y)
batch_id += to_generate
samples_left_to_save -= to_save
samples_left_to_generate -= to_generate
chains_left_to_save -= chains_save
print(f"final Computing sampling metrics...")
self.sampling_metrics.reset()
self.sampling_metrics(samples, all_ys, self.name, self.current_epoch, self.val_counter, test=True)
self.sampling_metrics.reset()
print(f"Done.")
def kl_prior(self, X, E, node_mask):
"""Computes the KL between q(z1 | x) and the prior p(z1) = Normal(0, 1).
This is essentially a lot of work for something that is in practice negligible in the loss. However, you
compute it so that you see it when you've made a mistake in your noise schedule.
"""
# Compute the last alpha value, alpha_T.
ones = torch.ones((X.size(0), 1), device=X.device)
Ts = self.T * ones
alpha_t_bar = self.noise_schedule.get_alpha_bar(t_int=Ts) # (bs, 1)
Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, self.device)
bs, n, d = X.shape
X_all = torch.cat([X, E.reshape(bs, n, -1)], dim=-1)
prob_all = X_all @ Qtb.X
probX = prob_all[:, :, :self.Xdim_output]
probE = prob_all[:, :, self.Xdim_output:].reshape((bs, n, n, -1))
assert probX.shape == X.shape
limit_X = self.limit_dist.X[None, None, :].expand(bs, n, -1).type_as(probX)
limit_E = self.limit_dist.E[None, None, None, :].expand(bs, n, n, -1).type_as(probE)
# Make sure that masked rows do not contribute to the loss
limit_dist_X, limit_dist_E, probX, probE = diffusion_utils.mask_distributions(true_X=limit_X.clone(),
true_E=limit_E.clone(),
pred_X=probX,
pred_E=probE,
node_mask=node_mask)
kl_distance_X = F.kl_div(input=probX.log(), target=limit_dist_X, reduction='none')
kl_distance_E = F.kl_div(input=probE.log(), target=limit_dist_E, reduction='none')
return diffusion_utils.sum_except_batch(kl_distance_X) + \
diffusion_utils.sum_except_batch(kl_distance_E)
def compute_Lt(self, X, E, y, pred, noisy_data, node_mask, test):
pred_probs_X = F.softmax(pred.X, dim=-1)
pred_probs_E = F.softmax(pred.E, dim=-1)
Qtb = self.transition_model.get_Qt_bar(noisy_data['alpha_t_bar'], self.device)
Qsb = self.transition_model.get_Qt_bar(noisy_data['alpha_s_bar'], self.device)
Qt = self.transition_model.get_Qt(noisy_data['beta_t'], self.device)
# Compute distributions to compare with KL
bs, n, d = X.shape
X_all = torch.cat([X, E.reshape(bs, n, -1)], dim=-1).float()
Xt_all = torch.cat([noisy_data['X_t'], noisy_data['E_t'].reshape(bs, n, -1)], dim=-1).float()
pred_probs_all = torch.cat([pred_probs_X, pred_probs_E.reshape(bs, n, -1)], dim=-1).float()
prob_true = diffusion_utils.posterior_distributions(X=X_all, X_t=Xt_all, Qt=Qt, Qsb=Qsb, Qtb=Qtb, X_dim=self.Xdim_output)
prob_true.E = prob_true.E.reshape((bs, n, n, -1))
prob_pred = diffusion_utils.posterior_distributions(X=pred_probs_all, X_t=Xt_all, Qt=Qt, Qsb=Qsb, Qtb=Qtb, X_dim=self.Xdim_output)
prob_pred.E = prob_pred.E.reshape((bs, n, n, -1))
# Reshape and filter masked rows
prob_true_X, prob_true_E, prob_pred.X, prob_pred.E = diffusion_utils.mask_distributions(true_X=prob_true.X,
true_E=prob_true.E,
pred_X=prob_pred.X,
pred_E=prob_pred.E,
node_mask=node_mask)
kl_x = (self.test_X_kl if test else self.val_X_kl)(prob_true.X, torch.log(prob_pred.X))
kl_e = (self.test_E_kl if test else self.val_E_kl)(prob_true.E, torch.log(prob_pred.E))
return self.T * (kl_x + kl_e)
def reconstruction_logp(self, t, X, E, y, node_mask):
# Compute noise values for t = 0.
t_zeros = torch.zeros_like(t)
beta_0 = self.noise_schedule(t_zeros)
Q0 = self.transition_model.get_Qt(beta_0, self.device)
bs, n, d = X.shape
X_all = torch.cat([X, E.reshape(bs, n, -1)], dim=-1)
prob_all = X_all @ Q0.X
probX0 = prob_all[:, :, :self.Xdim_output]
probE0 = prob_all[:, :, self.Xdim_output:].reshape((bs, n, n, -1))
sampled0 = diffusion_utils.sample_discrete_features(probX=probX0, probE=probE0, node_mask=node_mask)
X0 = F.one_hot(sampled0.X, num_classes=self.Xdim_output).float()
E0 = F.one_hot(sampled0.E, num_classes=self.Edim_output).float()
assert (X.shape == X0.shape) and (E.shape == E0.shape)
sampled_0 = utils.PlaceHolder(X=X0, E=E0, y=y).mask(node_mask)
# Predictions
noisy_data = {'X_t': sampled_0.X, 'E_t': sampled_0.E, 'y_t': sampled_0.y, 'node_mask': node_mask,
't': torch.zeros(X0.shape[0], 1).type_as(y)}
pred0 = self.forward(noisy_data)
# Normalize predictions
probX0 = F.softmax(pred0.X, dim=-1)
probE0 = F.softmax(pred0.E, dim=-1)
proby0 = None
# Set masked rows to arbitrary values that don't contribute to loss
probX0[~node_mask] = torch.ones(self.Xdim_output).type_as(probX0)
probE0[~(node_mask.unsqueeze(1) * node_mask.unsqueeze(2))] = torch.ones(self.Edim_output).type_as(probE0)
diag_mask = torch.eye(probE0.size(1)).type_as(probE0).bool()
diag_mask = diag_mask.unsqueeze(0).expand(probE0.size(0), -1, -1)
probE0[diag_mask] = torch.ones(self.Edim_output).type_as(probE0)
return utils.PlaceHolder(X=probX0, E=probE0, y=proby0)
def apply_noise(self, X, E, y, node_mask):
""" Sample noise and apply it to the data. """
# Sample a timestep t.
# When evaluating, the loss for t=0 is computed separately
lowest_t = 0 if self.training else 1
t_int = torch.randint(lowest_t, self.T + 1, size=(X.size(0), 1), device=X.device).float() # (bs, 1)
s_int = t_int - 1
t_float = t_int / self.T
s_float = s_int / self.T
# beta_t and alpha_s_bar are used for denoising/loss computation
beta_t = self.noise_schedule(t_normalized=t_float) # (bs, 1)
alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s_float) # (bs, 1)
alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t_float) # (bs, 1)
Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, self.device) # (bs, dx_in, dx_out), (bs, de_in, de_out)
bs, n, d = X.shape
X_all = torch.cat([X, E.reshape(bs, n, -1)], dim=-1)
prob_all = X_all @ Qtb.X
probX = prob_all[:, :, :self.Xdim_output]
probE = prob_all[:, :, self.Xdim_output:].reshape(bs, n, n, -1)
sampled_t = diffusion_utils.sample_discrete_features(probX=probX, probE=probE, node_mask=node_mask)
X_t = F.one_hot(sampled_t.X, num_classes=self.Xdim_output)
E_t = F.one_hot(sampled_t.E, num_classes=self.Edim_output)
assert (X.shape == X_t.shape) and (E.shape == E_t.shape)
y_t = y
z_t = utils.PlaceHolder(X=X_t, E=E_t, y=y_t).type_as(X_t).mask(node_mask)
noisy_data = {'t_int': t_int, 't': t_float, 'beta_t': beta_t, 'alpha_s_bar': alpha_s_bar,
'alpha_t_bar': alpha_t_bar, 'X_t': z_t.X, 'E_t': z_t.E, 'y_t': z_t.y, 'node_mask': node_mask}
return noisy_data
def compute_val_loss(self, pred, noisy_data, X, E, y, node_mask, test=False):
"""Computes an estimator for the variational lower bound.
pred: (batch_size, n, total_features)
noisy_data: dict
X, E, y : (bs, n, dx), (bs, n, n, de), (bs, dy)
node_mask : (bs, n)
Output: nll (size 1)
"""
t = noisy_data['t']
# 1.
N = node_mask.sum(1).long()
log_pN = self.node_dist.log_prob(N)
# 2. The KL between q(z_T | x) and p(z_T) = Uniform(1/num_classes). Should be close to zero.
kl_prior = self.kl_prior(X, E, node_mask)
# 3. Diffusion loss
loss_all_t = self.compute_Lt(X, E, y, pred, noisy_data, node_mask, test)
# 4. Reconstruction loss
# Compute L0 term : -log p (X, E, y | z_0) = reconstruction loss
prob0 = self.reconstruction_logp(t, X, E, y, node_mask)
eps = 1e-8
loss_term_0 = self.val_X_logp(X * (prob0.X+eps).log()) + self.val_E_logp(E * (prob0.E+eps).log())
# Combine terms
nlls = - log_pN + kl_prior + loss_all_t - loss_term_0
assert len(nlls.shape) == 1, f'{nlls.shape} has more than only batch dim.'
# Update NLL metric object and return batch nll
nll = (self.test_nll if test else self.val_nll)(nlls) # Average over the batch
return nll
@torch.no_grad()
def sample_batch(self, batch_id, batch_size, y, keep_chain, number_chain_steps, save_final, num_nodes=None):
"""
:param batch_id: int
:param batch_size: int
:param num_nodes: int, <int>tensor (batch_size) (optional) for specifying number of nodes
:param save_final: int: number of predictions to save to file
:param keep_chain: int: number of chains to save to file (disabled)
:param keep_chain_steps: number of timesteps to save for each chain (disabled)
:return: molecule_list. Each element of this list is a tuple (atom_types, charges, positions)
"""
if num_nodes is None:
n_nodes = self.node_dist.sample_n(batch_size, self.device)
elif type(num_nodes) == int:
n_nodes = num_nodes * torch.ones(batch_size, device=self.device, dtype=torch.int)
else:
assert isinstance(num_nodes, torch.Tensor)
n_nodes = num_nodes
n_max = self.max_n_nodes
arange = torch.arange(n_max, device=self.device).unsqueeze(0).expand(batch_size, -1)
node_mask = arange < n_nodes.unsqueeze(1)
z_T = diffusion_utils.sample_discrete_feature_noise(limit_dist=self.limit_dist, node_mask=node_mask)
X, E = z_T.X, z_T.E
assert (E == torch.transpose(E, 1, 2)).all()
# Iteratively sample p(z_s | z_t) for t = 1, ..., T, with s = t - 1.
for s_int in reversed(range(0, self.T)):
s_array = s_int * torch.ones((batch_size, 1)).type_as(y)
t_array = s_array + 1
s_norm = s_array / self.T
t_norm = t_array / self.T
# Sample z_s
sampled_s, discrete_sampled_s = self.sample_p_zs_given_zt(s_norm, t_norm, X, E, y, node_mask)
X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
# Sample
sampled_s = sampled_s.mask(node_mask, collapse=True)
X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
molecule_list = []
for i in range(batch_size):
n = n_nodes[i]
atom_types = X[i, :n].cpu()
edge_types = E[i, :n, :n].cpu()
molecule_list.append([atom_types, edge_types])
return molecule_list
def sample_p_zs_given_zt(self, s, t, X_t, E_t, y_t, node_mask):
"""Samples from zs ~ p(zs | zt). Only used during sampling.
if last_step, return the graph prediction as well"""
bs, n, dxs = X_t.shape
beta_t = self.noise_schedule(t_normalized=t) # (bs, 1)
alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s)
alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t)
# Neural net predictions
noisy_data = {'X_t': X_t, 'E_t': E_t, 'y_t': y_t, 't': t, 'node_mask': node_mask}
def get_prob(noisy_data, unconditioned=False):
pred = self.forward(noisy_data, unconditioned=unconditioned)
# Normalize predictions
pred_X = F.softmax(pred.X, dim=-1) # bs, n, d0
pred_E = F.softmax(pred.E, dim=-1) # bs, n, n, d0
# Retrieve transitions matrix
Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, self.device)
Qsb = self.transition_model.get_Qt_bar(alpha_s_bar, self.device)
Qt = self.transition_model.get_Qt(beta_t, self.device)
Xt_all = torch.cat([X_t, E_t.reshape(bs, n, -1)], dim=-1)
p_s_and_t_given_0 = diffusion_utils.compute_batched_over0_posterior_distribution(X_t=Xt_all,
Qt=Qt.X,
Qsb=Qsb.X,
Qtb=Qtb.X)
predX_all = torch.cat([pred_X, pred_E.reshape(bs, n, -1)], dim=-1)
weightedX_all = predX_all.unsqueeze(-1) * p_s_and_t_given_0
unnormalized_probX_all = weightedX_all.sum(dim=2) # bs, n, d_t-1
unnormalized_prob_X = unnormalized_probX_all[:, :, :self.Xdim_output]
unnormalized_prob_E = unnormalized_probX_all[:, :, self.Xdim_output:].reshape(bs, n*n, -1)
unnormalized_prob_X[torch.sum(unnormalized_prob_X, dim=-1) == 0] = 1e-5
unnormalized_prob_E[torch.sum(unnormalized_prob_E, dim=-1) == 0] = 1e-5
prob_X = unnormalized_prob_X / torch.sum(unnormalized_prob_X, dim=-1, keepdim=True) # bs, n, d_t-1
prob_E = unnormalized_prob_E / torch.sum(unnormalized_prob_E, dim=-1, keepdim=True) # bs, n, d_t-1
prob_E = prob_E.reshape(bs, n, n, pred_E.shape[-1])
return prob_X, prob_E
prob_X, prob_E = get_prob(noisy_data)
### Guidance
if self.guidance_target is not None and self.guide_scale is not None and self.guide_scale != 1:
uncon_prob_X, uncon_prob_E = get_prob(noisy_data, unconditioned=True)
prob_X = uncon_prob_X * (prob_X / uncon_prob_X.clamp_min(1e-10)) ** self.guide_scale
prob_E = uncon_prob_E * (prob_E / uncon_prob_E.clamp_min(1e-10)) ** self.guide_scale
prob_X = prob_X / prob_X.sum(dim=-1, keepdim=True).clamp_min(1e-10)
prob_E = prob_E / prob_E.sum(dim=-1, keepdim=True).clamp_min(1e-10)
assert ((prob_X.sum(dim=-1) - 1).abs() < 1e-4).all()
assert ((prob_E.sum(dim=-1) - 1).abs() < 1e-4).all()
sampled_s = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
X_s = F.one_hot(sampled_s.X, num_classes=self.Xdim_output).float()
E_s = F.one_hot(sampled_s.E, num_classes=self.Edim_output).float()
assert (E_s == torch.transpose(E_s, 1, 2)).all()
assert (X_t.shape == X_s.shape) and (E_t.shape == E_s.shape)
out_one_hot = utils.PlaceHolder(X=X_s, E=E_s, y=y_t)
out_discrete = utils.PlaceHolder(X=X_s, E=E_s, y=y_t)
return out_one_hot.mask(node_mask).type_as(y_t), out_discrete.mask(node_mask, collapse=True).type_as(y_t)