import torch
import torch.nn.functional as F
import pytorch_lightning as pl
import time
import os
from naswot.score_networks import get_nasbench201_nodes_score
from naswot import nasspace
from naswot import datasets
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
class Args:
pass
self.args = Args()
self.args.trainval = True
self.args.augtype = 'none'
self.args.repeat = 1
self.args.score = 'hook_logdet'
self.args.sigma = 0.05
self.args.nasspace = 'nasbench201'
self.args.batch_size = 128
self.args.GPU = '0'
self.args.dataset = 'cifar10-valid'
self.args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
self.args.data_loc = '../cifardata/'
self.args.seed = 777
self.args.init = ''
self.args.save_loc = 'results'
self.args.save_string = 'naswot'
self.args.dropout = False
self.args.maxofn = 1
self.args.n_samples = 100
self.args.n_runs = 500
self.args.stem_out_channels = 16
self.args.num_stacks = 3
self.args.num_modules_per_stack = 3
self.args.num_labels = 1
if 'valid' in self.args.dataset:
self.args.dataset = self.args.dataset.replace('-valid', '')
print('graph_dit starts to get searchspace of nasbench201')
self.searchspace = nasspace.get_search_space(self.args)
print('searchspace of nasbench201 is obtained')
print('graphdit starts to get train_loader')
self.train_loader = datasets.get_data(self.args.dataset, self.args.data_loc, self.args.trainval, self.args.batch_size, self.args.augtype, self.args.repeat, self.args)
print('train_loader is obtained')
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=8).float()[:, self.active_index]
data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).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)
# print(f'training loss: {loss}, epoch: {self.current_epoch}, batch: {i}\n, pred type: {type(pred)}, pred.X shape: {type(pred.X)}, {pred.X.shape}, pred.E shape: {type(pred.E)}, {pred.E.shape}')
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)
print(f"training loss: {loss}")
with open("training-loss.csv", "a") as f:
f.write(f"{loss}, {i}\n")
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
log = True
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=8).float()[:, self.active_index]
data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).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, collapse=False)
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)
print(f'validation loss: {nll}, epoch: {self.current_epoch}')
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))
with open("validation-metrics.csv", "a") as f:
# save the metrics as csv file
f.write(f"{self.current_epoch}, {metrics[0]}, {metrics[1]}, {metrics[2]}, {metrics[3]}, {metrics[4]}\n")
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=8).float()[:, self.active_index]
data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).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()]
with open("test-metrics.csv", "a") as f:
f.write(f"{self.current_epoch}, {metrics[0]}, {metrics[1]}, {metrics[2]}, {metrics[3]}, {metrics[4]}\n")
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
# print(f"apply_noise X shape: {X.shape}, E shape: {E.shape}, y shape: {y.shape}, node_mask shape: {node_mask.shape}")
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)
# print(f"alpha_s_bar: {alpha_s_bar.shape}, alpha_t_bar: {alpha_t_bar.shape}")
Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, self.device) # (bs, dx_in, dx_out), (bs, de_in, de_out)
# print(f"X shape: {X.shape}, E shape: {E.shape}, node_mask shape: {node_mask.shape}")
# print(f"Qtb shape: {Qtb.X.shape}")
"""
X shape: torch.Size([1200, 8]),
E shape: torch.Size([1200, 8, 8]),
y shape: torch.Size([1200, 1]),
node_mask shape: torch.Size([1200, 8])
alpha_s_bar: torch.Size([1200, 1]), alpha_t_bar: torch.Size([1200, 1])
"""
# print(X.shape)
bs, n, d = X.shape
E = E[..., :2]
# bs, n = 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)
# print(f"X.shape: {X.shape}, X_t shape: {X_t.shape}, E.shape: {E.shape}, E_t shape: {E_t.shape}")
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
# gradient
# 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(x_0|x_t)
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, pred
# diffusion nag: P_t(G_{t-1} |G_t, C) = P_t(G_{t-1} |G_t) + P_t(C | G_{t-1}, G_t)
# with condition = P_t(G_{t-1} |G_t, C)
# with condition = P_t(A_{t-1} |A_t, y)
prob_X, prob_E, pred = 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, pred = 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())
# sample multiple times and get the best score arch...
num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
op_type = {
'input': 0,
'nor_conv_1x1': 1,
'nor_conv_3x3': 2,
'avg_pool_3x3': 3,
'skip_connect': 4,
'none': 5,
'output': 6,
}
def check_valid_graph(nodes, edges):
nodes = [num_to_op[i] for i in nodes]
if len(nodes) != edges.shape[0] or len(nodes) != edges.shape[1]:
return False
if nodes[0] != 'input' or nodes[-1] != 'output':
return False
for i in range(0, len(nodes)):
if edges[i][i] == 1:
return False
for i in range(1, len(nodes) - 1):
if nodes[i] not in op_type or nodes[i] == 'input' or nodes[i] == 'output':
return False
for i in range(0, len(nodes)):
for j in range(i, len(nodes)):
if edges[i, j] == 1 and nodes[j] == 'input':
return False
for i in range(0, len(nodes)):
for j in range(i, len(nodes)):
if edges[i, j] == 1 and nodes[i] == 'output':
return False
flag = 0
for i in range(0,len(nodes)):
if edges[i,-1] == 1:
flag = 1
break
if flag == 0: return False
return True
class Args:
pass
def get_score(sampled_s):
x_list = sampled_s.X.unbind(dim=0)
e_list = sampled_s.E.unbind(dim=0)
valid_rlt = [check_valid_graph(x_list[i].cpu().numpy(), e_list[i].cpu().numpy()) for i in range(len(x_list))]
from graph_dit.naswot.naswot.score_networks import get_nasbench201_nodes_score
score = []
for i in range(len(x_list)):
if valid_rlt[i]:
nodes = [num_to_op[j] for j in x_list[i].cpu().numpy()]
# edges = e_list[i].cpu().numpy()
score.append(get_nasbench201_nodes_score(nodes,train_loader=self.train_loader,searchspace=self.searchspace,device=sampled_s.X.device , args=self.args))
else:
score.append(-1)
# return torch.tensor(score, dtype=torch.float32, requires_grad=True).to(x_list[0].device)
target_score = torch.ones(100, dtype=torch.float32, device=sampled_s.X.device, requires_grad=True) * 2000.0
# target_score_list = [2000 for i in range(100)]
# return torch.tensor(score, device=sampled_s.X.device ,dtype=torch.float32, requires_grad=True), torch.tensor(target_score_list, device=sampled_s.X.device, dtype=torch.float32, requires_grad=True)
return torch.tensor(score, device=sampled_s.X.device ,dtype=torch.float32, requires_grad=True), target_score
sample_num = 10
best_arch = None
best_score_int = -1e8
score = torch.ones(100, dtype=torch.float32, requires_grad=True) * -1e8
print(f'score.requires_grad: {score.requires_grad}')
for i in range(sample_num):
sampled_s = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
score, target_score = get_score(sampled_s)
print(f'score: {score}')
print(f'score.shape: {score.shape}')
print(f'torch.sum(score): {torch.sum(score)}')
sum_score = torch.sum(score)
print(f'sum_score: {sum_score}')
if sum_score > best_score_int:
best_score_int = sum_score
best_score = score
best_arch = sampled_s
# print(f'prob_X: {prob_X.shape}, prob_E: {prob_E.shape}')
# best_arch = 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()
print(f'best_arch.X: {best_arch.X.shape}, best_arch.E: {best_arch.E.shape}') # 100 8 8, bs n n, 100 8 8 2, bs n n 2
print(f'best_arch.X: {best_arch.X}, best_arch.E: {best_arch.E}')
X_s = F.one_hot(best_arch.X, num_classes=self.Xdim_output).float()
E_s = F.one_hot(best_arch.E, num_classes=self.Edim_output).float()
print(f'X_s: {X_s}, E_s: {E_s}')
# NASWOT score
# target_score = torch.ones(100, requires_grad=True, device=X_s.device) * 2000.0
# target_score = torch.ones(100, requires_grad=True) * 2000.0
print(f'best_score: {best_score.shape}, target_score: {target_score.shape}')
print(f'best_score.requires_grad: {best_score.requires_grad}, target_score.requires_grad: {target_score.requires_grad}')
print(f'best_score.device: {best_score.device}, target_score.device: {target_score.device}')
# target_score = target_score.to(X_s.device)
# print(f'best_score: {best_score.shape}, target_score: {target_score.shape}')
# print(f'best_score.requires_grad: {best_score.requires_grad}, target_score.requires_grad: {target_score.requires_grad}')
# compute loss mse(cur_score - target_score)
mse_loss = torch.nn.MSELoss()
loss = mse_loss(target_score, best_score)
print(f'loss: {loss.requires_grad}')
loss.backward(retain_graph=True)
# loss backward = gradient
# get prob.X, prob_E gradient
x_grad = pred.X.grad
e_grad = pred.E.grad
beta_ratio = 0.5
# x_current = pred.X - beta_ratio * x_grad
# e_current = pred.E - beta_ratio * e_grad
X_s = pred.X - beta_ratio * x_grad
E_s = pred.E - beta_ratio * e_grad
# update prob.X prob_E with using gradient
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)