import numpy as np import torch import utils from diffusion import diffusion_utils class PredefinedNoiseSchedule(torch.nn.Module): """ Predefined noise schedule. Essentially creates a lookup array for predefined (non-learned) noise schedules. """ def __init__(self, noise_schedule, timesteps): super(PredefinedNoiseSchedule, self).__init__() self.timesteps = timesteps if noise_schedule == 'cosine': alphas2 = diffusion_utils.cosine_beta_schedule(timesteps) elif noise_schedule == 'custom': raise NotImplementedError() else: raise ValueError(noise_schedule) # print('alphas2', alphas2) sigmas2 = 1 - alphas2 log_alphas2 = np.log(alphas2) log_sigmas2 = np.log(sigmas2) log_alphas2_to_sigmas2 = log_alphas2 - log_sigmas2 # (timesteps + 1, ) # print('gamma', -log_alphas2_to_sigmas2) self.gamma = torch.nn.Parameter( torch.from_numpy(-log_alphas2_to_sigmas2).float(), requires_grad=False) def forward(self, t): t_int = torch.round(t * self.timesteps).long() return self.gamma[t_int] class PredefinedNoiseScheduleDiscrete(torch.nn.Module): """ Predefined noise schedule. Essentially creates a lookup array for predefined (non-learned) noise schedules. """ 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()) self.alphas = 1 - torch.clamp(self.betas, min=0, max=0.9999) log_alpha = torch.log(self.alphas) log_alpha_bar = torch.cumsum(log_alpha, dim=0) self.alphas_bar = torch.exp(log_alpha_bar) # print(f"[Noise schedule: {noise_schedule}] alpha_bar:", self.alphas_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) return self.alphas_bar.to(t_int.device)[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): """ 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): """ 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 MarginalUniformTransition: def __init__(self, x_marginals, e_marginals, y_classes): self.X_classes = len(x_marginals) self.E_classes = len(e_marginals) self.y_classes = y_classes self.x_marginals = x_marginals self.e_marginals = e_marginals self.u_x = x_marginals.unsqueeze(0).expand(self.X_classes, -1).unsqueeze(0) self.u_e = e_marginals.unsqueeze(0).expand(self.E_classes, -1).unsqueeze(0) 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): """ 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): """ 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 AbsorbingStateTransition: def __init__(self, abs_state: int, 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.zeros(1, self.X_classes, self.X_classes) self.u_x[:, :, abs_state] = 1 self.u_e = torch.zeros(1, self.E_classes, self.E_classes) self.u_e[:, :, abs_state] = 1 self.u_y = torch.zeros(1, self.y_classes, self.y_classes) self.u_e[:, :, abs_state] = 1 def get_Qt(self, beta_t): """ Returns two transition matrix for X and E""" beta_t = beta_t.unsqueeze(1) q_x = beta_t * self.u_x + (1 - beta_t) * torch.eye(self.X_classes).unsqueeze(0) q_e = beta_t * self.u_e + (1 - beta_t) * torch.eye(self.E_classes).unsqueeze(0) q_y = beta_t * self.u_y + (1 - beta_t) * torch.eye(self.y_classes).unsqueeze(0) return q_x, q_e, q_y def get_Qt_bar(self, alpha_bar_t): """ beta_t: (bs) Returns transition matrices for X and E""" alpha_bar_t = alpha_bar_t.unsqueeze(1) q_x = alpha_bar_t * torch.eye(self.X_classes).unsqueeze(0) + (1 - alpha_bar_t) * self.u_x q_e = alpha_bar_t * torch.eye(self.E_classes).unsqueeze(0) + (1 - alpha_bar_t) * self.u_e q_y = alpha_bar_t * torch.eye(self.y_classes).unsqueeze(0) + (1 - alpha_bar_t) * self.u_y return q_x, q_e, q_y