import torch import utils from diffusion import diffusion_utils class PredefinedNoiseScheduleDiscrete(torch.nn.Module): def __init__(self, noise_schedule, timesteps): super(PredefinedNoiseScheduleDiscrete, self).__init__() self.timesteps = timesteps if noise_schedule == 'cosine': betas = diffusion_utils.cosine_beta_schedule_discrete(timesteps) elif noise_schedule == 'custom': betas = diffusion_utils.custom_beta_schedule_discrete(timesteps) else: raise NotImplementedError(noise_schedule) self.register_buffer('betas', torch.from_numpy(betas).float()) # 0.9999 self.alphas = 1 - torch.clamp(self.betas, min=0, max=1) log_alpha = torch.log(self.alphas) log_alpha_bar = torch.cumsum(log_alpha, dim=0) self.alphas_bar = torch.exp(log_alpha_bar) def forward(self, t_normalized=None, t_int=None): assert int(t_normalized is None) + int(t_int is None) == 1 if t_int is None: t_int = torch.round(t_normalized * self.timesteps) return self.betas[t_int.long()] def get_alpha_bar(self, t_normalized=None, t_int=None): assert int(t_normalized is None) + int(t_int is None) == 1 if t_int is None: t_int = torch.round(t_normalized * self.timesteps) ### new self.alphas_bar = self.alphas_bar.to(t_int.device) return self.alphas_bar[t_int.long()] class DiscreteUniformTransition: def __init__(self, x_classes: int, e_classes: int, y_classes: int): self.X_classes = x_classes self.E_classes = e_classes self.y_classes = y_classes self.u_x = torch.ones(1, self.X_classes, self.X_classes) if self.X_classes > 0: self.u_x = self.u_x / self.X_classes self.u_e = torch.ones(1, self.E_classes, self.E_classes) if self.E_classes > 0: self.u_e = self.u_e / self.E_classes self.u_y = torch.ones(1, self.y_classes, self.y_classes) if self.y_classes > 0: self.u_y = self.u_y / self.y_classes def get_Qt(self, beta_t, device, X=None, flatten_e=None): """ Returns one-step transition matrices for X and E, from step t - 1 to step t. Qt = (1 - beta_t) * I + beta_t / K beta_t: (bs) noise level between 0 and 1 returns: qx (bs, dx, dx), qe (bs, de, de), qy (bs, dy, dy). """ beta_t = beta_t.unsqueeze(1) beta_t = beta_t.to(device) self.u_x = self.u_x.to(device) self.u_e = self.u_e.to(device) self.u_y = self.u_y.to(device) q_x = beta_t * self.u_x + (1 - beta_t) * torch.eye(self.X_classes, device=device).unsqueeze(0) q_e = beta_t * self.u_e + (1 - beta_t) * torch.eye(self.E_classes, device=device).unsqueeze(0) q_y = beta_t * self.u_y + (1 - beta_t) * torch.eye(self.y_classes, device=device).unsqueeze(0) return utils.PlaceHolder(X=q_x, E=q_e, y=q_y) def get_Qt_bar(self, alpha_bar_t, device, X=None, flatten_e=None): """ Returns t-step transition matrices for X and E, from step 0 to step t. Qt = prod(1 - beta_t) * I + (1 - prod(1 - beta_t)) / K alpha_bar_t: (bs) Product of the (1 - beta_t) for each time step from 0 to t. returns: qx (bs, dx, dx), qe (bs, de, de), qy (bs, dy, dy). """ alpha_bar_t = alpha_bar_t.unsqueeze(1) alpha_bar_t = alpha_bar_t.to(device) self.u_x = self.u_x.to(device) self.u_e = self.u_e.to(device) self.u_y = self.u_y.to(device) q_x = alpha_bar_t * torch.eye(self.X_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_x q_e = alpha_bar_t * torch.eye(self.E_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_e q_y = alpha_bar_t * torch.eye(self.y_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_y return utils.PlaceHolder(X=q_x, E=q_e, y=q_y) class MarginalTransition: def __init__(self, x_marginals, e_marginals, xe_conditions, ex_conditions, y_classes, n_nodes): self.X_classes = len(x_marginals) self.E_classes = len(e_marginals) self.y_classes = y_classes self.x_marginals = x_marginals # Dx self.e_marginals = e_marginals # Dx, De self.xe_conditions = xe_conditions self.u_x = x_marginals.unsqueeze(0).expand(self.X_classes, -1).unsqueeze(0) # 1, Dx, Dx self.u_e = e_marginals.unsqueeze(0).expand(self.E_classes, -1).unsqueeze(0) # 1, De, De self.u_xe = xe_conditions.unsqueeze(0) # 1, Dx, De self.u_ex = ex_conditions.unsqueeze(0) # 1, De, Dx self.u = self.get_union_transition(self.u_x, self.u_e, self.u_xe, self.u_ex, n_nodes) # 1, Dx + n*De, Dx + n*De def get_union_transition(self, u_x, u_e, u_xe, u_ex, n_nodes): u_e = u_e.repeat(1, n_nodes, n_nodes) # (1, n*de, n*de) u_xe = u_xe.repeat(1, 1, n_nodes) # (1, dx, n*de) u_ex = u_ex.repeat(1, n_nodes, 1) # (1, n*de, dx) u0 = torch.cat([u_x, u_xe], dim=2) # (1, dx, dx + n*de) u1 = torch.cat([u_ex, u_e], dim=2) # (1, n*de, dx + n*de) u = torch.cat([u0, u1], dim=1) # (1, dx + n*de, dx + n*de) return u def index_edge_margin(self, X, q_e, n_bond=5): # q_e: (bs, dx, de) --> (bs, n, de) bs, n, n_atom = X.shape node_indices = X.argmax(-1) # (bs, n) ind = node_indices[ :, :, None].expand(bs, n, n_bond) q_e = torch.gather(q_e, 1, ind) return q_e def get_Qt(self, beta_t, device): """ Returns one-step transition matrices for X and E, from step t - 1 to step t. Qt = (1 - beta_t) * I + beta_t / K beta_t: (bs) returns: q (bs, d0, d0) """ bs = beta_t.size(0) d0 = self.u.size(-1) self.u = self.u.to(device) u = self.u.expand(bs, d0, d0) beta_t = beta_t.to(device) beta_t = beta_t.view(bs, 1, 1) q = beta_t * u + (1 - beta_t) * torch.eye(d0, device=device).unsqueeze(0) return utils.PlaceHolder(X=q, E=None, y=None) def get_Qt_bar(self, alpha_bar_t, device): """ Returns t-step transition matrices for X and E, from step 0 to step t. Qt = prod(1 - beta_t) * I + (1 - prod(1 - beta_t)) * K alpha_bar_t: (bs, 1) roduct of the (1 - beta_t) for each time step from 0 to t. returns: q (bs, d0, d0) """ bs = alpha_bar_t.size(0) d0 = self.u.size(-1) alpha_bar_t = alpha_bar_t.to(device) alpha_bar_t = alpha_bar_t.view(bs, 1, 1) self.u = self.u.to(device) q = alpha_bar_t * torch.eye(d0, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u return utils.PlaceHolder(X=q, E=None, y=None)