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Running on Zero
| # Torch | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import lightning.pytorch as pl | |
| # General | |
| import time | |
| import os | |
| import sys | |
| sys.path.append('..') | |
| import warnings | |
| # Local modules | |
| #Model | |
| from models.transformer_model import GraphTransformer | |
| #Diffusion | |
| from diffusion.noise_schedule import PredefinedNoiseScheduleDiscrete,\ | |
| MarginalUniformTransition | |
| #Metrics | |
| from metrics.train_metrics_mixed import TrainLoss_Single | |
| from metrics.abstract_metrics import SumExceptBatchMetric, SumExceptBatchKL, NLL | |
| from torchmetrics import MeanSquaredError | |
| #Utils | |
| from utils import utils, diffusion_utils | |
| from utils.visualization import MolecularVisualization | |
| # Filter error | |
| warnings.filterwarnings("ignore", "You are using `torch.load` with `weights_only=False`*.") | |
| class DiscDenDiff(pl.LightningModule): | |
| def __init__(self,name, | |
| dataset_infos, | |
| nn_params, | |
| train_params, | |
| diffusion_params, | |
| train_metrics, | |
| sampling_metrics, | |
| val_param, | |
| test_param, | |
| guidance=False, | |
| guidance_params=None,): | |
| ''' | |
| Discrete Denoise Diffusion Model with guidance. | |
| This only operates over the dg_wo_cg per each molecule. | |
| Note: This is an adaptation of digress that is a code for discrete | |
| diffusion models. The original code is available at | |
| https://github.com/cvignac/digress | |
| ''' | |
| super().__init__() | |
| # General parameters | |
| self.name = name | |
| self.model_dtype = torch.float32 | |
| self.train_lr = train_params.train_lr | |
| self.train_weight_decay = train_params.train_weight_decay | |
| #Diffusion parameters | |
| self.T = diffusion_params.diffusion_steps | |
| # Reading dataset information | |
| self.dataset_info = dataset_infos | |
| # Copy rather than alias: dataset_infos (and its input_dims dict) is | |
| # cached and reused across repeated model construction (e.g. gen_mols' | |
| # _ensure_loaded()/initialize_model()), and this constructor mutates | |
| # input_dims['y'] below. Mutating the shared dict would make the | |
| # inferred y-dimension grow on every subsequent call. | |
| self.input_dims = dict(self.dataset_info.input_dims) | |
| self.output_dims = dict(self.dataset_info.output_dims) | |
| self.nodes_dist = self.dataset_info.nodes_dist | |
| self.atom_decoder = self.dataset_info.atom_decoder | |
| # Dimensions of input of edges (X), nodes (E) and y (latent space) | |
| self.Xdim = self.input_dims['X'] | |
| self.Edim = self.input_dims['E'] | |
| self.ydim = self.input_dims['y'] + 1 | |
| # Rewrote the parameter of input: | |
| self.input_dims['y'] = self.ydim | |
| # Dimensions of input of edges (X), nodes (E) and y | |
| self.Xdim_output = self.output_dims['X'] | |
| self.Edim_output = self.output_dims['E'] | |
| self.ydim_output = self.output_dims['y'] | |
| # Guidance | |
| self.guidance = guidance | |
| self.guidance_params = guidance_params | |
| if self.guidance and self.guidance_params is not None: | |
| self.trainable_cf = self.guidance_params.trainable_cf | |
| #self.p_uncond = p_uncond | |
| self.dropout_fix = self.guidance_params.dropout_fix | |
| if not self.dropout_fix: | |
| assert isinstance(self.guidance_params.p_dropout, | |
| (list)), "The p_dropout parameter must be a list of [p_min,p_max, scale]" | |
| self.p_uncond_min = float(self.guidance_params.p_dropout[0]) | |
| self.p_uncond_max = float(self.guidance_params.p_dropout[1]) | |
| self.p_uncond_alpha = float(self.guidance_params.p_dropout[2]) | |
| else: | |
| assert isinstance(self.guidance_params.p_dropout,float), ("The p_dropout parameter is fix." | |
| "Therefore it must be float") | |
| self.p_dropout = self.guidance_params.p_dropout | |
| # Probability for unconditional generation | |
| self.s = self.guidance_params.s # Scaling for the guidance vector | |
| self.guidance_in = self.guidance_params.guidance_in # This determines if the guidance is the value y or in the edges and nodes. Default is y | |
| self.ydim = self.input_dims['guidance'] + 1 | |
| self.ydim_output = self.input_dims['guidance'] | |
| # Guidance | |
| if (self.input_dims['guidance'] != None): | |
| # self.gdim = self.input_dims['guidance'] | |
| self.gdim = self.input_dims['guidance'] # Dimension of the hidden representation | |
| else: | |
| self.gdim = 0 | |
| # Null token required to calculate cases without guidance | |
| if (self.guidance_params.trainable_cf == True): | |
| self.cf_null_token = torch.nn.parameter.Parameter(torch.randn(size=(1, self.gdim))) | |
| else: | |
| self.cf_null_token = torch.zeros(size=(1, self.gdim)) | |
| elif self.guidance and self.guidance_params is None: | |
| print('Guidance is activated but not parameters were passed') | |
| sys.exit('Add parameters for guidance') | |
| elif not self.guidance and self.guidance_params is not None: | |
| print('Guidance is deactivated but parameters were passed.') | |
| sys.exit('Activate guidance if you want to run a guided train') | |
| else: | |
| print('You are running a training without guidance. \n' | |
| 'If this is not what you want, restart and check \n' | |
| 'your parameters.') | |
| self.ydim = None | |
| self.ydim_output = None | |
| self.train_loss =TrainLoss_Single(train_params.lambda_train,train_guidance=self.guidance) | |
| # Metrics Validation | |
| 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_dgwo = MeanSquaredError() | |
| # Metrics Test | |
| self.test_nll = NLL() | |
| self.test_X_kl = SumExceptBatchKL() | |
| self.test_E_kl = SumExceptBatchKL() | |
| self.test_X_logp = SumExceptBatchMetric() | |
| self.test_E_logp = SumExceptBatchMetric() | |
| # Metrics Train | |
| self.train_metrics = train_metrics | |
| # Metrics Sampling | |
| self.sampling_metrics = sampling_metrics | |
| #Parameters for validation and testing | |
| self.val_param = val_param | |
| self.test_param = test_param | |
| # Validation of the generated molecules | |
| self.cond_val = NLL() | |
| #Visualization | |
| self.visualization = False | |
| if diffusion_params.visualization: | |
| self.visualization = True | |
| self.visualization_tools = MolecularVisualization(remove_h=False, | |
| dataset_infos=self.dataset_info) | |
| # Model: Graph Transformer | |
| self.model = GraphTransformer(n_layers=nn_params.n_layers, | |
| input_dims=self.input_dims, | |
| hidden_mlp_dims=nn_params.hidden_mlp_dims, | |
| hidden_dims=nn_params.hidden_dims, | |
| output_dims=self.output_dims, | |
| act_fn_in=nn.ReLU(), | |
| act_fn_out=nn.ReLU()) | |
| # Noise schedule for the diffusion | |
| self.noise_schedule = PredefinedNoiseScheduleDiscrete(diffusion_params.diffusion_noise_schedule, | |
| timesteps=self.T) | |
| # Noise transition. The matrices to compute the probability of jumping between states | |
| # Note: We use the marginal transition. | |
| node_types = self.dataset_info.node_types.float() | |
| x_marginals = node_types / torch.sum(node_types) | |
| edge_types = self.dataset_info.edge_types.float() | |
| e_marginals = edge_types / torch.sum(edge_types) | |
| print(f"Marginal distribution of the classes: {x_marginals} for nodes, {e_marginals} for edges") | |
| self.transition_model = MarginalUniformTransition(x_marginals=x_marginals, e_marginals=e_marginals, | |
| y_classes=self.ydim_output) | |
| self.limit_dist = utils.PlaceHolder(X=x_marginals, E=e_marginals, | |
| y=torch.ones(self.ydim_output) / self.ydim_output) | |
| self.start_epoch_time = None | |
| self.train_iterations = None | |
| self.val_iterations = None | |
| self.log_every_steps = train_params.log_every_steps | |
| self.number_chain_steps = diffusion_params.number_chain_steps | |
| self.best_val_nll = 1e8 | |
| self.val_counter = 0 | |
| # self.save_hyperparameters(ignore=['train_metrics', 'sampling_metrics']) | |
| ################################################### | |
| # Optimizer and forward pass | |
| def configure_optimizers(self): | |
| # Optimizer | |
| return torch.optim.AdamW(self.model.parameters(), lr=self.train_lr, amsgrad=True, | |
| weight_decay=self.train_weight_decay) | |
| # lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min') | |
| # return {'optimizer':optimizer, 'lr_scheduler':lr_scheduler} | |
| def get_new_XEy(self,noisy_data,extra_data,guidance=None): | |
| '''Get the new X, E and y data for the model''' | |
| # First clone the noise values | |
| # Following is a note from the original code. | |
| # X = transformer output which uses guidance (if any) | |
| # X_null = transformer output which uses the null token | |
| X = noisy_data['X_t'].clone().float() | |
| E = noisy_data['E_t'].clone().float() | |
| y = noisy_data['y_t'].clone().float() | |
| if (guidance != None): | |
| # We add the guidance vector to the X and E data. | |
| bs = extra_data.X.size(0) | |
| n = extra_data.X.size(1) | |
| if self.guidance_in in ['y','all']: | |
| if len(guidance.shape) == 1: | |
| g_y = guidance.unsqueeze(-1) | |
| else: | |
| g_y = guidance | |
| y = torch.hstack((y, g_y)).float() | |
| elif self.guidance_in in ['XE','all']: | |
| #TODO: Check if this is the correct way to spread the guidance. | |
| g_X = torch.reshape(guidance, shape=(bs, 1, -1)).repeat((1, n, 1)) | |
| g_E = torch.reshape(guidance, shape=(bs, 1, 1, -1)).repeat((1, n, n, 1)) | |
| #g_X = guidance.reshape(bs,n,self.ydim_output) | |
| X = torch.hstack((X, g_X)).float() | |
| #g_E = guidance.reshape(bs,n,n,self.ydim_output) | |
| E = torch.hstack((E, g_E)).float() | |
| # spreads the guidance on all nodes | |
| # g_X must have size (bs, n, features) | |
| # g_E must have size (bs, n, n, features) | |
| # Concatenate the guidance to the X and E data | |
| X = torch.cat((X, g_X), dim=-1) | |
| E = torch.cat((E, g_E), dim=-1) | |
| else: | |
| pass | |
| y = torch.hstack((y, extra_data.y)).float() | |
| return X, E, y | |
| def p_drop_from_label_id(self,counts,type='mean'): | |
| """ | |
| Takes the tensor of counts for the samples and returns probability | |
| """ | |
| # counts_ = np.array(counts) | |
| counts_t = counts.to(self.device).float() | |
| if type == 'mean': | |
| ref = torch.mean(counts_t) | |
| elif type == 'median': | |
| ref = torch.median(counts_t) | |
| elif type == 'max': | |
| ref = torch.max(counts_t) | |
| else: | |
| print('No option selected. Defaulting to mean. Available options: mean, median, max') | |
| ref = torch.mean(counts_t) | |
| # Tail gets larger dropout protection: smaller counts -> larger ratio -> larger p_drop | |
| ratio = ref / counts_t | |
| p = self.p_uncond_max * (ratio ** self.p_uncond_alpha) | |
| return p.clamp(self.p_uncond_min, self.p_uncond_max) | |
| def forward_guidance(self, noisy_data,extra_data,node_mask,guidance=None,train_step=False): | |
| ''' | |
| This function is used when you have guidance activated | |
| ''' | |
| bs = extra_data.X.size(0) # Batch size | |
| device = extra_data.X.device | |
| cf_null_token = self.cf_null_token.expand(bs, 1).to(device) | |
| # Resolve guidance encoding | |
| guidance_enc = cf_null_token if guidance is None else guidance | |
| if train_step: | |
| if self.dropout_fix: | |
| drop_mask = ( | |
| torch.rand(bs,1, device=device) < self.p_dropout | |
| ).expand(bs, self.ydim_output) | |
| else: | |
| p_drop = self.p_drop_from_label_id(guidance.bead_freq, type="mean") | |
| drop_mask = ( | |
| torch.rand(bs, device=device) < p_drop | |
| )[:, None].expand(bs, self.ydim_output) | |
| guidance_enc = torch.where(drop_mask, cf_null_token, guidance_enc.unsqueeze(-1)) | |
| else: | |
| # Repeat guidance to match batch size when sampling multiple molecules | |
| if bs > guidance_enc.size(0): | |
| guidance_enc = guidance_enc.expand(bs, -1) | |
| X, E, y = self.get_new_XEy(noisy_data, extra_data, guidance=guidance_enc) | |
| out = self.model(X, E, y, node_mask) | |
| # Apply CFG scale at inference time | |
| if not train_step and self.s > 0: | |
| X_null, E_null, y_null = self.get_new_XEy(noisy_data, extra_data, guidance=cf_null_token) | |
| out_null = self.model(X_null, E_null, y_null, node_mask) | |
| out.X = out_null.X + self.s * (out.X - out_null.X) | |
| out.E = out_null.E + self.s * (out.E - out_null.E) | |
| return out | |
| def forward(self, noisy_data,extra_data,node_mask,guidance=None,train_step=False): | |
| ''' | |
| Forward pass of the model. | |
| ''' | |
| if self.guidance: | |
| out = self.forward_guidance(noisy_data, extra_data, node_mask, guidance=guidance, train_step=train_step) | |
| pred_form = utils.PlaceHolder(X=out.X,E=out.E,y=out.y).mask(node_mask) | |
| return pred_form | |
| else: | |
| X = noisy_data['X_t'].float() | |
| E = noisy_data['E_t'].float() | |
| y = torch.hstack((noisy_data['y_t'].float(), extra_data.y.float())) | |
| out = self.model(X, E, y, node_mask) | |
| return utils.PlaceHolder(X=out.X,E=out.E,y=out.y).mask(node_mask) | |
| ################################################### | |
| # Training and evaluation steps | |
| def training_step(self, data, i): | |
| ''' | |
| Training step of the model | |
| :param data: | |
| :param i: | |
| :return: | |
| ''' | |
| if data.edge_index.numel() == 0: | |
| self.print("Found a batch with no edges. Skipping.") | |
| return | |
| #Convert data of edges and nodes to dense matrix. | |
| dense_data, node_mask = utils.to_dense(data.x, data.edge_index, data.edge_attr, data.batch) | |
| # Mask the data | |
| dense_data = dense_data.mask(node_mask) | |
| # Split in nodes (X) and edges (E) | |
| X, E = dense_data.X, dense_data.E | |
| # Apply noise to the data. Data.y = 0 | |
| noisy_data = self.apply_noise(X, E, data.y, node_mask) | |
| # Compute extra data (Add the time) | |
| extra_data = self.compute_extra_data(noisy_data) | |
| # Forward pass Prediction by the model | |
| pred = self.forward(noisy_data, extra_data, node_mask, | |
| guidance=data.guidance,train_step=True) | |
| # # Compute the loss | |
| vals_log, loss = self.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, | |
| true_X=X, true_E=E,pred_dgwo=pred.y,true_dgwo=data.guidance, | |
| log=i % self.log_every_steps == 0) | |
| # Molecular metrics: | |
| to_log_mol_metrics = 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) | |
| # First write the metrics for the molecular properties. | |
| if to_log_mol_metrics is not None: | |
| for val in to_log_mol_metrics.keys(): | |
| self.log(val, to_log_mol_metrics[val]) | |
| # All other values. Metrics for nodes, edges, etc... | |
| if vals_log is not None: | |
| for val in vals_log.keys(): | |
| self.log(val, vals_log[val]) | |
| self.log("train/loss", loss) | |
| return {'loss': loss} | |
| def validation_step(self, data, i): | |
| #Convert data of edges and nodes to dense matrix. | |
| dense_data, node_mask = utils.to_dense(data.x, data.edge_index, data.edge_attr, data.batch) | |
| # Mask the data | |
| dense_data = dense_data.mask(node_mask) | |
| # Split in nodes (X) and edges (E) | |
| X, E = dense_data.X, dense_data.E | |
| # Apply noise to the data. Data.y = 0 | |
| noisy_data = self.apply_noise(X, E, data.y, node_mask) | |
| # Compute extra data (Add the time) | |
| extra_data = self.compute_extra_data(noisy_data) | |
| # Forward pass Prediction by the model | |
| pred = self.forward(noisy_data, extra_data, node_mask, guidance=data.guidance, train_step=False) | |
| nll = self.compute_val_loss(pred, noisy_data, X, E,data.y,guidance=data.guidance, | |
| node_mask=node_mask,test=False) | |
| return {'loss': nll} | |
| def compute_val_loss(self, pred, noisy_data, X, E,y, node_mask, guidance,test=False): | |
| # Validation loss function | |
| """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) | |
| guidance: True data of beads | |
| bead_pred: (batch_size, n, total_features) Prediction of beads | |
| Output: nll (size 1) | |
| """ | |
| t = noisy_data['t'] | |
| # 1. | |
| N = node_mask.sum(1).long() | |
| log_pN = self.nodes_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, node_mask,guidance) | |
| log_pX = prob0.X.log() | |
| log_pE = prob0.E.log() | |
| term_x = X*log_pX | |
| term_E = E*log_pE | |
| # print('nodes',self.val_X_logp(term_x)) | |
| # print('edges',self.val_E_logp(term_E)) | |
| loss_term_0 = self.val_X_logp(term_x) + self.val_E_logp(term_E) | |
| # Combine terms | |
| nlls = - log_pN + kl_prior + loss_all_t - loss_term_0 | |
| if torch.isnan(nlls).any(): | |
| print('loss value is nan') | |
| nlls = torch.zeros(nlls.shape,device=self.device) | |
| assert len(nlls.shape) == 1, f'{nlls.shape} has more than only batch dim.' | |
| # Compute the MSE of prediction of dgwo | |
| if self.guidance: | |
| # Guidance is the value of reference, y is the prediction | |
| val_mse_dgwo = self.val_dgwo(guidance,pred.y.squeeze()) | |
| else: | |
| val_mse_dgwo = None | |
| # Update NLL metric object and return batch nll | |
| nll = (self.test_nll if test else self.val_nll)(nlls) # Average over the batch | |
| self.log("val/kl prior",kl_prior.mean(),batch_size=X.shape[0],on_epoch=False,on_step=True) | |
| self.log("val/log_pn",log_pN.mean(),batch_size=X.shape[0],on_epoch=False,on_step=True) | |
| self.log("val/loss_all_t",loss_all_t.mean(),batch_size=X.shape[0],on_epoch=False,on_step=True) | |
| self.log("val/loss_term_0",loss_term_0.mean(),batch_size=X.shape[0],on_epoch=False,on_step=True) | |
| self.log("val/mse_dgwo",val_mse_dgwo,batch_size= X.shape[0],on_epoch=False,on_step=True) | |
| if test: | |
| self.log("test/batch_test_nll",nll,batch_size=X.shape[0],on_epoch=False,on_step=True) | |
| return nll | |
| def test_step(self, data, i): | |
| #Convert data of edges and nodes to dense matrix. | |
| dense_data, node_mask = utils.to_dense(data.x, data.edge_index, data.edge_attr, data.batch) | |
| # Mask the data | |
| dense_data = dense_data.mask(node_mask) | |
| # Split in nodes (X) and edges (E) | |
| X, E = dense_data.X, dense_data.E | |
| # Apply noise to the data. Data.y = 0 | |
| noisy_data = self.apply_noise(X, E, data.y, node_mask) | |
| # Compute extra data (Add the time) | |
| extra_data = self.compute_extra_data(noisy_data) | |
| # Forward pass Prediction by the model | |
| pred = self.forward(noisy_data, extra_data, node_mask, guidance=data.guidance, train_step=False) | |
| nll = self.compute_val_loss(pred, noisy_data, X, E,data.y,guidance=data.guidance, | |
| node_mask=node_mask,test=True) | |
| # Generate samples | |
| samples_left_to_generate = self.test_param.samples_to_generate | |
| samples_left_to_save = self.test_param.samples_to_save | |
| chains_left_to_save = self.test_param.chains_to_save | |
| samples = [] | |
| id = 0 | |
| while samples_left_to_generate > 0: | |
| self.print(f'Samples left to generate: {samples_left_to_generate}/' | |
| f'{self.test_param.samples_to_generate}', end='') | |
| bs = 2 * self.dataset_info.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) | |
| samples.extend(self.sample_batch(to_generate, | |
| num_nodes=None, | |
| keep_chain=chains_save, | |
| number_chain_steps=self.test_param.number_chain_steps, | |
| guidance=data.guidance)) | |
| id += to_generate | |
| samples_left_to_save -= to_save | |
| samples_left_to_generate -= to_generate | |
| chains_left_to_save -= chains_save | |
| self.print("Saving the generated graphs") | |
| filename = f'generated_samples_1.txt' | |
| for i in range(2, 10): | |
| if os.path.exists(filename): | |
| filename = f'generated_samples_{i}.txt' | |
| else: | |
| break | |
| with open(filename, 'w') as f: | |
| for item in samples: | |
| f.write(f"N={item[0].shape[0]}\n") | |
| atoms = item[0].tolist() | |
| f.write("X: \n") | |
| for at in atoms: | |
| f.write(f"{at} ") | |
| f.write("\n") | |
| f.write("E: \n") | |
| for bond_list in item[1]: | |
| for bond in bond_list: | |
| f.write(f"{bond} ") | |
| f.write("\n") | |
| f.write("\n") | |
| to_log, others = self.sampling_metrics(samples, self.name, self.current_epoch, self.val_counter, test=True, | |
| local_rank=self.local_rank) | |
| if to_log is not None: | |
| for val in to_log.keys(): | |
| self.log(val, to_log[val]) | |
| for val1 in others: | |
| self.log(val1, others[val1]) | |
| self.log('test/loss',nll) | |
| return {'loss': nll} | |
| def accuracy_test(self,samples,input_properties): | |
| '''Compute the accuracy of the generated samples''' | |
| nll_assigment = self.cond_val(samples.y, input_properties) | |
| print(f'Accuracy of the generated samples: {nll_assigment}') | |
| return nll_assigment | |
| ################################################### | |
| # Noise! Noise! Noise! | |
| 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, device=self.device) # (bs, dx_in, dx_out), (bs, de_in, de_out) | |
| assert (abs(Qtb.X.sum(dim=2) - 1.) < 1e-4).all(), Qtb.X.sum(dim=2) - 1 | |
| assert (abs(Qtb.E.sum(dim=2) - 1.) < 1e-4).all() | |
| # Compute transition probabilities | |
| probX = X @ Qtb.X # (bs, n, dx_out) | |
| probE = E @ Qtb.E.unsqueeze(1) # (bs, n, n, de_out) | |
| 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) | |
| z_t = utils.PlaceHolder(X=X_t, E=E_t, y=y).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 | |
| ########################################### | |
| # Here start functions for the training and evaluation of the model work as callbacks | |
| def on_fit_start(self) -> None: | |
| self.print("Starting training...") | |
| self.train_iterations = len(self.trainer.datamodule.train_dataloader()) | |
| self.print("Size of the input features", self.Xdim, self.Edim, self.ydim) | |
| def on_train_epoch_start(self) -> None: | |
| if self.current_epoch % self.log_every_steps == 0: | |
| self.print("Starting train epoch...") | |
| self.start_epoch_time = time.time() | |
| self.train_loss.reset() | |
| self.train_metrics.reset() | |
| def on_train_epoch_end(self) -> None: | |
| to_log = self.train_loss.log_epoch_metrics() | |
| for val in to_log: | |
| self.log(val, to_log[val]) | |
| if self.start_epoch_time is None: | |
| self.start_epoch_time = time.time() | |
| self.print(f"Epoch {self.current_epoch}: X_CE: {to_log['train_epoch/X_CE'] :.3f}" | |
| f" -- E_CE: {to_log['train_epoch/E_CE'] :.3f} " | |
| # f" -- loss_RAE: {to_log['train_epoch/rae'] :.3f}" | |
| f" -- {time.time() - self.start_epoch_time:.1f}s ") | |
| # epoch_at_metrics, epoch_bond_metrics = self.train_metrics.log_epoch_metrics() | |
| # epoch_bond_metrics = self.train_metrics.log_epoch_metrics() | |
| # self.print(f"Epoch {self.current_epoch}: ") | |
| # # self.print(f"{k}: {v}" for k, v in epoch_at_metrics.items()) | |
| # for k in epoch_bond_metrics.keys(): | |
| # self.print(f"{k}: {epoch_bond_metrics[k]}") | |
| def on_validation_epoch_start(self) -> None: | |
| print('Starting validation...') | |
| 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_dgwo.reset() | |
| 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()] | |
| self.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}") | |
| #Loggin of data | |
| self.log("val/X_kl_epoch", metrics[1],on_epoch=True,on_step=False) | |
| self.log("val/E_kl_epoch", metrics[2],on_epoch=True,on_step=False) | |
| self.log("val/X_logp_epoch", metrics[3],on_epoch=True,on_step=False) | |
| self.log("val/E_logp_epoch", metrics[4],on_epoch=True,on_step=False) | |
| # Log val nll with default Lightning logger, so it can be monitored by checkpoint callback | |
| val_nll = metrics[0] | |
| self.log("val/epoch_NLL", val_nll, sync_dist=True,on_epoch=True,on_step=False) | |
| if val_nll < self.best_val_nll: | |
| self.best_val_nll = val_nll | |
| self.print('Val loss: %.4f \t Best val loss: %.4f\n' % (val_nll, self.best_val_nll)) | |
| def on_test_epoch_start(self) -> None: | |
| self.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.val_dgwo.reset() | |
| 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()] | |
| self.log("test/epoch_NLL", metrics[0], sync_dist=True,on_epoch=True,on_step=False) | |
| self.log("test/epoch_X_kl", metrics[1], sync_dist=True,on_epoch=True,on_step=False) | |
| self.log("test/epoch_E_kl", metrics[2], sync_dist=True,on_epoch=True,on_step=False) | |
| self.log("test/epoch_X_logp", metrics[3], sync_dist=True,on_epoch=True,on_step=False) | |
| self.log("test/epoch_E_logp", metrics[4], sync_dist=True,on_epoch=True,on_step=False) | |
| self.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}") | |
| test_nll = metrics[0] | |
| self.print(f'Test loss: {test_nll :.4f}') | |
| self.print("Generated graphs Saved. Computing sampling metrics...") | |
| self.print("Done testing.") | |
| ################################################### | |
| # Here start other functions | |
| 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) | |
| # Compute transition probabilities | |
| probX = X @ Qtb.X # (bs, n, dx_out) | |
| probE = E @ Qtb.E.unsqueeze(1) # (bs, n, n, de_out) | |
| assert probX.shape == X.shape | |
| bs, n, _ = probX.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) | |
| #Avoid numerical inestability | |
| probX[probX == .0] = 1e-6 | |
| probE[probE == .0] = 1e-6 | |
| limit_dist_X[limit_dist_X == .0] = 1e-6 | |
| limit_dist_E[limit_dist_E == .0] = 1e-6 | |
| 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) | |
| pred_probs_y = F.softmax(pred.y, 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 | |
| prob_true = diffusion_utils.posterior_distributions(X=X, E=E, y=y, X_t=noisy_data['X_t'], E_t=noisy_data['E_t'], | |
| y_t=noisy_data['y_t'], Qt=Qt, Qsb=Qsb, Qtb=Qtb) | |
| prob_true.E = prob_true.E.reshape((bs, n, n, -1)) | |
| prob_pred = diffusion_utils.posterior_distributions(X=pred_probs_X, E=pred_probs_E, y=pred_probs_y, | |
| X_t=noisy_data['X_t'], E_t=noisy_data['E_t'], | |
| y_t=noisy_data['y_t'], Qt=Qt, Qsb=Qsb, Qtb=Qtb) | |
| 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) | |
| #Avoid numerical inestability | |
| prob_true.X[prob_true.X == 0.0] = 1e-6 | |
| prob_pred.X[prob_pred.X == 0.0] = 1e-6 | |
| prob_true.E[prob_true.E == 0.0] = 1e-6 | |
| prob_pred.E[prob_pred.E == 0.0] = 1e-6 | |
| 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, node_mask,guidance=None): | |
| # 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_t=beta_0, device=self.device) | |
| probX0 = X @ Q0.X # (bs, n, dx_out) | |
| probE0 = E @ Q0.E.unsqueeze(1) # (bs, n, n, de_out) | |
| 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() | |
| y0 = sampled0.y | |
| assert (X.shape == X0.shape) and (E.shape == E0.shape) | |
| sampled_0 = utils.PlaceHolder(X=X0, E=E0, y=y0).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(y0)} | |
| extra_data = self.compute_extra_data(noisy_data) | |
| pred0 = self.forward(noisy_data, extra_data, node_mask,guidance=guidance) | |
| # Normalize predictions | |
| # Adding a regularization term to avoid nan in validation. | |
| probX0 = F.softmax(pred0.X, dim=-1) + 1e-8 | |
| probE0 = F.softmax(pred0.E, dim=-1) + 1e-8 | |
| proby0 = F.softmax(pred0.y,dim=-1) + 1e-8 | |
| # 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) | |
| ####################################################### | |
| # Sampling! | |
| def sample_batch(self, batch_size: int, | |
| keep_chain: int, | |
| number_chain_steps: int, | |
| num_nodes=None, | |
| guidance=None): | |
| """ | |
| :param batch_id: int #Maybe I add this parameter later | |
| :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 #Also for later | |
| :param keep_chain: int: number of chains to save to file | |
| :param keep_chain_steps: number of timesteps to save for each chain | |
| :return: molecule_list. Each element of this list is a tuple (atom_types, charges, positions) | |
| """ | |
| # model.eval() | |
| if num_nodes is None: | |
| n_nodes = self.nodes_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 = torch.max(n_nodes).item() | |
| # Build the masks | |
| arange = torch.arange(n_max, device=self.device).unsqueeze(0).expand(batch_size, -1) | |
| node_mask = arange < n_nodes.unsqueeze(1) | |
| # Sample noise -- z has size (n_samples, n_nodes, n_features) | |
| z_T = diffusion_utils.sample_discrete_feature_noise(limit_dist=self.limit_dist, node_mask=node_mask) | |
| X, E, y = z_T.X, z_T.E, z_T.y | |
| if (self.guidance) and (guidance is None): | |
| guidance = self.cf_null_token.repeat((batch_size, 1)) | |
| assert (E == torch.transpose(E, 1, 2)).all() | |
| assert number_chain_steps < self.T | |
| chain_X_size = torch.Size((number_chain_steps, keep_chain, X.size(1))) | |
| chain_E_size = torch.Size((number_chain_steps, keep_chain, E.size(1), E.size(2))) | |
| chain_X = torch.zeros(chain_X_size) | |
| chain_E = torch.zeros(chain_E_size) | |
| # 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,g_T=guidance) | |
| X, E, y = sampled_s.X, sampled_s.E, sampled_s.y | |
| # Save the first keep_chain graphs | |
| write_index = (s_int * number_chain_steps) // self.T | |
| chain_X[write_index] = discrete_sampled_s.X[:keep_chain] | |
| chain_E[write_index] = discrete_sampled_s.E[:keep_chain] | |
| # Sample | |
| sampled_s = sampled_s.mask(node_mask, collapse=True) | |
| X, E, y = sampled_s.X, sampled_s.E, sampled_s.y | |
| # Prepare the chain for saving | |
| if keep_chain > 0: | |
| final_X_chain = X[:keep_chain] | |
| final_E_chain = E[:keep_chain] | |
| chain_X[0] = final_X_chain # Overwrite last frame with the resulting X, E | |
| chain_E[0] = final_E_chain | |
| chain_X = diffusion_utils.reverse_tensor(chain_X) | |
| chain_E = diffusion_utils.reverse_tensor(chain_E) | |
| # Repeat last frame to see final sample better | |
| chain_X = torch.cat([chain_X, chain_X[-1:].repeat(5, 1, 1)], dim=0) | |
| chain_E = torch.cat([chain_E, chain_E[-1:].repeat(5, 1, 1, 1)], dim=0) | |
| assert chain_X.size(0) == (number_chain_steps + 5) | |
| molecule_list = [] | |
| for i in range(batch_size): | |
| n = n_nodes[i] | |
| atom_types = X[i, :n].to('cpu', non_blocking=True) | |
| edge_types = E[i, :n, :n].to('cpu', non_blocking=True) | |
| molecule_list.append([atom_types, edge_types]) | |
| # Visualize chains | |
| if self.visualization: | |
| self.print('Visualizing chains...') | |
| current_path = os.getcwd() | |
| num_molecules = chain_X.size(1) # number of molecules | |
| for i in range(num_molecules): | |
| result_path = os.path.join(current_path, f'chains/{self.name}/' | |
| f'epoch{self.current_epoch}/' | |
| f'chains/molecule_{i}') | |
| if not os.path.exists(result_path): | |
| os.makedirs(result_path) | |
| _ = self.visualization_tools.visualize_chain(result_path, | |
| chain_X[:, i, :].numpy(), | |
| chain_E[:, i, :].numpy()) | |
| self.print('\r{}/{} complete'.format(i+1, num_molecules), end='') | |
| self.print('\nVisualizing molecules...') | |
| current_path = os.getcwd() | |
| result_path = os.path.join(current_path, | |
| f'graphs/{self.name}/') | |
| self.visualization_tools.visualize(result_path, molecule_list,num_molecules_to_visualize=5) | |
| self.print("Done.") | |
| return molecule_list | |
| def sample_p_zs_given_zt(self, s, t, X_t, E_t, y_t, node_mask,g_T=None): | |
| """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) | |
| # 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) | |
| # Neural net predictions | |
| noisy_data = {'X_t': X_t, 'E_t': E_t, 'y_t': y_t, 't': t, 'node_mask': node_mask} | |
| extra_data = self.compute_extra_data(noisy_data) | |
| pred = self.forward(noisy_data, extra_data, node_mask,guidance=g_T) | |
| # 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 | |
| p_s_and_t_given_0_X = diffusion_utils.compute_batched_over0_posterior_distribution(X_t=X_t, | |
| Qt=Qt.X, | |
| Qsb=Qsb.X, | |
| Qtb=Qtb.X) | |
| p_s_and_t_given_0_E = diffusion_utils.compute_batched_over0_posterior_distribution(X_t=E_t, | |
| Qt=Qt.E, | |
| Qsb=Qsb.E, | |
| Qtb=Qtb.E) | |
| # Dim of these two tensors: bs, N, d0, d_t-1 | |
| weighted_X = pred_X.unsqueeze(-1) * p_s_and_t_given_0_X # bs, n, d0, d_t-1 | |
| unnormalized_prob_X = weighted_X.sum(dim=2) # bs, n, d_t-1 | |
| unnormalized_prob_X[torch.sum(unnormalized_prob_X, dim=-1) == 0] = 1e-5 | |
| prob_X = unnormalized_prob_X / torch.sum(unnormalized_prob_X, dim=-1, keepdim=True) # bs, n, d_t-1 | |
| pred_E = pred_E.reshape((bs, -1, pred_E.shape[-1])) | |
| weighted_E = pred_E.unsqueeze(-1) * p_s_and_t_given_0_E # bs, N, d0, d_t-1 | |
| unnormalized_prob_E = weighted_E.sum(dim=-2) | |
| unnormalized_prob_E[torch.sum(unnormalized_prob_E, dim=-1) == 0] = 1e-5 | |
| prob_E = unnormalized_prob_E / torch.sum(unnormalized_prob_E, dim=-1, keepdim=True) | |
| prob_E = prob_E.reshape(bs, n, n, pred_E.shape[-1]) | |
| 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) | |
| 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=torch.zeros(y_t.shape[0], 0)) | |
| out_discrete = utils.PlaceHolder(X=X_s, E=E_s, y=torch.zeros(y_t.shape[0], 0)) | |
| return out_one_hot.mask(node_mask).type_as(y_t), out_discrete.mask(node_mask, collapse=True).type_as(y_t) | |
| def compute_extra_data(self, noisy_data): | |
| """ At every training step (after adding noise) and step in sampling, compute extra information and append to | |
| the network input. """ | |
| t = noisy_data['t'] | |
| extra_y = torch.cat((noisy_data['y_t'], t), dim=1) | |
| return utils.PlaceHolder(X=noisy_data['X_t'], E=noisy_data['E_t'], y=extra_y) | |