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DDPM (Denoising Diffusion Probabilistic Model) for 25-dim building property vectors.
MLP-based: 25 → 256 → 256 → 256 → 25
Used by STER-GI Ideas 3 and 4.
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
DEV = 'cuda' if torch.cuda.is_available() else 'cpu'
# ============================================================
# Beta Schedule
# ============================================================
class BetaSchedule:
"""Linear beta schedule for DDPM."""
def __init__(self, T=1000, beta_start=1e-4, beta_end=0.02):
self.T = T
betas = torch.linspace(beta_start, beta_end, T)
alphas = 1.0 - betas
alpha_bars = torch.cumprod(alphas, dim=0)
self.betas = betas.to(DEV)
self.alphas = alphas.to(DEV)
self.alpha_bars = alpha_bars.to(DEV)
self.sqrt_alpha_bars = torch.sqrt(alpha_bars).to(DEV)
self.sqrt_one_minus_alpha_bars = torch.sqrt(1.0 - alpha_bars).to(DEV)
self.sqrt_recip_alphas = torch.sqrt(1.0 / alphas).to(DEV)
self.posterior_variance = betas * (1.0 - alpha_bars) / (1.0 - alpha_bars + 1e-8)
def forward_diffuse(self, x0, t, noise=None):
"""q(x_t | x_0): add noise according to schedule at step t."""
if noise is None:
noise = torch.randn_like(x0)
sqrt_ab_t = self.sqrt_alpha_bars[t].view(-1, 1)
sqrt_1mab_t = self.sqrt_one_minus_alpha_bars[t].view(-1, 1)
return sqrt_ab_t * x0 + sqrt_1mab_t * noise, noise
# ============================================================
# Denoiser Network
# ============================================================
class DenoiserMLP(nn.Module):
"""Predicts noise from noisy input x_t and timestep t."""
def __init__(self, d=25, h=256, T=1000):
super().__init__()
self.t_emb = nn.Embedding(T, h)
self.net = nn.Sequential(OrderedDict([
('in', nn.Linear(d + h, h)),
('n1', nn.LayerNorm(h)), ('a1', nn.SiLU()),
('h1', nn.Linear(h, h)),
('n2', nn.LayerNorm(h)), ('a2', nn.SiLU()),
('h2', nn.Linear(h, h)),
('n3', nn.LayerNorm(h)), ('a3', nn.SiLU()),
('out', nn.Linear(h, d)),
]))
def forward(self, x, t):
te = self.t_emb(t)
h = torch.cat([x, te], dim=-1)
return self.net(h)
# ============================================================
# DDPM Trainer
# ============================================================
class DDPM:
def __init__(self, d=25, h=256, T=1000):
self.d = d
self.h = h
self.T = T
self.schedule = BetaSchedule(T)
self.denoiser = DenoiserMLP(d, h, T).to(DEV)
self.optimizer = torch.optim.AdamW(self.denoiser.parameters(), lr=3e-4, weight_decay=1e-5)
def train_step(self, x):
"""Single DDPM training step: predict noise added to input."""
B = x.shape[0]
t = torch.randint(0, self.T, (B,), device=DEV)
x_t, noise = self.schedule.forward_diffuse(x, t)
pred = self.denoiser(x_t, t)
loss = F.mse_loss(pred, noise)
self.optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.denoiser.parameters(), 1.0)
self.optimizer.step()
return loss.item()
@torch.no_grad()
def sample(self, n=100, steps=None):
"""Generate samples from pure noise (reverse process)."""
if steps is None:
steps = self.T
self.denoiser.eval()
x = torch.randn(n, self.d, device=DEV)
step_size = self.T // steps
for t_idx in reversed(range(0, self.T, step_size)):
t = torch.full((n,), t_idx, device=DEV, dtype=torch.long)
pred_noise = self.denoiser(x, t)
alpha = self.schedule.alphas[t_idx]
alpha_bar = self.schedule.alpha_bars[t_idx]
beta = self.schedule.betas[t_idx]
if t_idx > 0:
noise = torch.randn_like(x)
sigma = torch.sqrt(beta)
x = (1.0 / torch.sqrt(alpha)) * (
x - (beta / torch.sqrt(1.0 - alpha_bar)) * pred_noise
) + sigma * noise
else:
x = (1.0 / torch.sqrt(alpha)) * (
x - (beta / torch.sqrt(1.0 - alpha_bar)) * pred_noise
)
self.denoiser.train()
return x
@torch.no_grad()
def sdedit(self, x0, t0=100, steps=None):
"""SDEdit: add noise to t0, then reverse denoise → sibling."""
if steps is None:
steps = t0
self.denoiser.eval()
n = x0.shape[0]
t0_t = torch.full((n,), t0, device=DEV, dtype=torch.long)
# Forward: add noise to t0
noise = torch.randn_like(x0)
sqrt_ab = self.schedule.sqrt_alpha_bars[t0].view(-1, 1)
sqrt_1mab = self.schedule.sqrt_one_minus_alpha_bars[t0].view(-1, 1)
x_t = sqrt_ab * x0 + sqrt_1mab * noise
# Reverse: denoise from t0 to 0
step_size = max(1, t0 // steps)
x = x_t
for t_idx in reversed(range(t0)):
if t_idx % step_size != 0 and t_idx > step_size:
continue
t = torch.full((n,), t_idx, device=DEV, dtype=torch.long)
pred_noise = self.denoiser(x, t)
alpha = self.schedule.alphas[t_idx]
alpha_bar = self.schedule.alpha_bars[t_idx]
beta = self.schedule.betas[t_idx]
if t_idx > 0:
rng_noise = torch.randn_like(x)
sigma = torch.sqrt(beta)
x = (1.0 / torch.sqrt(alpha)) * (
x - (beta / torch.sqrt(1.0 - alpha_bar)) * pred_noise
) + sigma * rng_noise
else:
x = (1.0 / torch.sqrt(alpha)) * (
x - (beta / torch.sqrt(1.0 - alpha_bar)) * pred_noise
)
self.denoiser.train()
return x
@torch.no_grad()
def score(self, x, t_vals=[50, 100, 200, 300, 400]):
"""Compute score function magnitude at x for grammar checking (Idea 4)."""
self.denoiser.eval()
scores = []
for t_idx in t_vals:
t = torch.full((x.shape[0],), t_idx, device=DEV, dtype=torch.long)
noise = torch.randn_like(x)
sqrt_ab = self.schedule.sqrt_alpha_bars[t_idx]
sqrt_1mab = self.schedule.sqrt_one_minus_alpha_bars[t_idx]
x_t = sqrt_ab * x + sqrt_1mab * noise
pred_noise = self.denoiser(x_t, t)
# Score ≈ -pred_noise / sqrt(1-alpha_bar)
score_val = -pred_noise / sqrt_1mab
scores.append(torch.norm(score_val, dim=-1))
self.denoiser.train()
return torch.stack(scores, dim=-1) # (B, len(t_vals))
def save(self, path):
torch.save({'denoiser': self.denoiser.state_dict(),
'd': self.d, 'h': self.h, 'T': self.T}, path)
@classmethod
def load(cls, path):
state = torch.load(path, map_location=DEV)
model = cls(d=state['d'], h=state['h'], T=state['T'])
model.denoiser.load_state_dict(state['denoiser'])
model.denoiser.to(DEV)
return model
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