import enum import math import numpy as np import torch as th ########################################################################################## # DIFFUSION CODE BASE FOR PROTEIN SEQUENCE DIFFUSION WAS ADAPTED FROM LM-DIFFUSION # # (https://github.com/XiangLi1999/Diffusion-LM) # ########################################################################################## class GaussianDiffusion_SEQDIFF: """ T = number of timesteps to set up diffuser with schedule = type of noise schedule to use linear, cosine, gaussian noise = type of ditribution to sample from; DEFAULT - normal_gaussian """ def __init__(self, T=1000, schedule='sqrt', sample_distribution='normal', sample_distribution_gmm_means=[-1.0, 1.0], sample_distribution_gmm_variances=[1.0, 1.0], F=1, ): # Use float64 for accuracy. betas = np.array(get_named_beta_schedule(schedule, T), dtype=np.float64) self.betas = betas assert len(betas.shape) == 1, "betas must be 1-D" assert (betas > 0).all() and (betas <= 1).all() self.num_timesteps = int(betas.shape[0]) self.F = F alphas = 1.0 - betas self.alphas_cumprod = np.cumprod(alphas, axis=0) self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1]) self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0) assert self.alphas_cumprod_prev.shape == (self.num_timesteps,) # calculations for posterior q(x_{t-1} | x_t, x_0) self.posterior_variance = (betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)) # log calculation clipped because the posterior variance is 0 at the # beginning of the diffusion chain. self.posterior_log_variance_clipped = np.log(np.append(self.posterior_variance[1], self.posterior_variance[1:])) self.posterior_mean_coef1 = (betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)) self.posterior_mean_coef2 = ((1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod)) # calculations for diffusion q(x_t | x_{t-1}) and others self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod) self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod) self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod) self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod) # sample_distribution_params self.sample_distribution = sample_distribution self.sample_distribution_gmm_means = [float(mean) for mean in sample_distribution_gmm_means] self.sample_distribution_gmm_variances = [float(variance) for variance in sample_distribution_gmm_variances] if self.sample_distribution == 'normal': self.noise_function = th.randn_like else: self.noise_function = self.randnmixture_like def q_mean_variance(self, x_start, t): """ Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diffusion steps (minus 1). Here, 0 means one step. :return: A tuple (mean, variance, log_variance), all of x_start's shape. """ mean = ( _extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start ) variance = _extract(1.0 - self.alphas_cumprod, t, x_start.shape) log_variance = _extract( self.log_one_minus_alphas_cumprod, t, x_start.shape ) return mean, variance, log_variance def q_sample(self, x_start, t, mask=None, DEVICE=None): """ Diffuse the data for a given number of diffusion steps. In other words, sample from q(x_t | x_0). :param x_start: the initial data batch. :param t: the number of diffusion steps (minus 1). Here, 0 means one step. :param noise: if specified, the split-out normal noise. :return: A noisy version of x_start. """ # noise_function is determined in init depending on type of noise specified noise = self.noise_function(x_start)*(self.F**2) if DEVICE != None: noise = noise.to(DEVICE) assert noise.shape == x_start.shape x_sample = ( _extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + _extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) if mask is not None: x_sample[mask]=x_start[mask] return x_sample def q_posterior_mean_variance(self, x_start, x_t, t): """ Compute the mean and variance of the diffusion posterior: q(x_{t-1} | x_t, x_0) """ assert x_start.shape == x_t.shape posterior_mean = (_extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + _extract(self.posterior_mean_coef2, t, x_t.shape) * x_t) posterior_variance = _extract(self.posterior_variance, t, x_t.shape) posterior_log_variance_clipped = _extract(self.posterior_log_variance_clipped, t, x_t.shape) assert ( posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] == x_start.shape[0] ) return posterior_mean, posterior_variance, posterior_log_variance_clipped def randnmixture_like(self, tensor_like, number_normal=3, weights_normal=None): if self.sample_distribution_gmm_means and self.sample_distribution_gmm_variances: assert len(self.sample_distribution_gmm_means) == len(self.sample_distribution_gmm_variances) if not weights_normal: mix = th.distributions.Categorical(th.ones(len(self.sample_distribution_gmm_means))) #number_normal else: assert len(weights_normal) == number_normal mix = th.distributions.Categorical(weights_normal) #comp = torch.distributions.Normal(torch.randn(number_normal), torch.rand(number_normal)) comp = th.distributions.Normal(th.tensor(self.sample_distribution_gmm_means), th.tensor(self.sample_distribution_gmm_variances)) #comp = torch.distributions.Normal([-3, 3], [1, 1]) #comp = torch.distributions.Normal([-3, 0, 3], [1, 1, 1]) #comp = torch.distributions.Normal([-3, 0, 3], [1, 1, 1]) gmm = th.distributions.mixture_same_family.MixtureSameFamily(mix, comp) return th.tensor([gmm.sample() for _ in range(np.prod(tensor_like.shape))]).reshape(tensor_like.shape) def get_named_beta_schedule(schedule_name, num_diffusion_timesteps): """ Get a pre-defined beta schedule for the given name. The beta schedule library consists of beta schedules which remain similar in the limit of num_diffusion_timesteps. Beta schedules may be added, but should not be removed or changed once they are committed to maintain backwards compatibility. """ if schedule_name == "linear": # Linear schedule from Ho et al, extended to work for any number of # diffusion steps. scale = 1000 / num_diffusion_timesteps beta_start = scale * 0.0001 beta_end = scale * 0.02 return np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64) elif schedule_name == "cosine": return betas_for_alpha_bar(num_diffusion_timesteps, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,) elif schedule_name == 'sqrt': return betas_for_alpha_bar(num_diffusion_timesteps, lambda t: 1-np.sqrt(t + 0.0001),) else: raise NotImplementedError(f"unknown beta schedule: {schedule_name}") def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): """ Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t from 0 to 1 and produces the cumulative product of (1-beta) up to that part of the diffusion process. :param max_beta: the maximum beta to use; use values lower than 1 to prevent singularities. """ betas = [] for i in range(num_diffusion_timesteps): t1 = i / num_diffusion_timesteps t2 = (i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) return np.array(betas) def _extract(arr, timesteps, broadcast_shape): """ Extract values from a 1-D numpy array for a batch of indices. :param arr: the 1-D numpy array. :param timesteps: a tensor of indices into the array to extract. :param broadcast_shape: a larger shape of K dimensions with the batch dimension equal to the length of timesteps. :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims. """ res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float() while len(res.shape) < len(broadcast_shape): res = res[..., None] return res.expand(broadcast_shape)