Update example and notes

example/sample.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""Sampling stub for hybrid diffusion (continuous + discrete)."""
+
+import json
+import math
+import os
+
+import torch
+import torch.nn.functional as F
+
+from data_utils import load_split
+from hybrid_diffusion import HybridDiffusionModel, cosine_beta_schedule
+
+SPLIT_PATH = "/home/anay/Dev/diffusion/mask-ddpm/example/feature_split.json"
+VOCAB_PATH = "/home/anay/Dev/diffusion/mask-ddpm/example/results/disc_vocab.json"
+MODEL_PATH = "/home/anay/Dev/diffusion/mask-ddpm/example/results/model.pt"
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+TIMESTEPS = 200
+SEQ_LEN = 64
+BATCH_SIZE = 2
+
+
+def load_vocab():
+    with open(VOCAB_PATH, "r", encoding="ascii") as f:
+        return json.load(f)["vocab"]
+
+
+def main():
+    split = load_split(SPLIT_PATH)
+    cont_cols = split["continuous"]
+    disc_cols = split["discrete"]
+
+    vocab = load_vocab()
+    vocab_sizes = [len(vocab[c]) for c in disc_cols]
+
+    print("device", DEVICE)
+    model = HybridDiffusionModel(cont_dim=len(cont_cols), disc_vocab_sizes=vocab_sizes).to(DEVICE)
+    if os.path.exists(MODEL_PATH):
+        model.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE, weights_only=True))
+    model.eval()
+
+    betas = cosine_beta_schedule(TIMESTEPS).to(DEVICE)
+    alphas = 1.0 - betas
+    alphas_cumprod = torch.cumprod(alphas, dim=0)
+
+    x_cont = torch.randn(BATCH_SIZE, SEQ_LEN, len(cont_cols), device=DEVICE)
+    x_disc = torch.full((BATCH_SIZE, SEQ_LEN, len(disc_cols)), 0, device=DEVICE, dtype=torch.long)
+    mask_tokens = torch.tensor(vocab_sizes, device=DEVICE)
+
+    # Initialize discrete with mask tokens
+    for i in range(len(disc_cols)):
+        x_disc[:, :, i] = mask_tokens[i]
+
+    for t in reversed(range(TIMESTEPS)):
+        t_batch = torch.full((BATCH_SIZE,), t, device=DEVICE, dtype=torch.long)
+        eps_pred, logits = model(x_cont, x_disc, t_batch)
+
+        # Continuous reverse step (DDPM): x_{t-1} mean
+        a_t = alphas[t]
+        a_bar_t = alphas_cumprod[t]
+        coef1 = 1.0 / torch.sqrt(a_t)
+        coef2 = (1 - a_t) / torch.sqrt(1 - a_bar_t)
+        mean = coef1 * (x_cont - coef2 * eps_pred)
+        if t > 0:
+            noise = torch.randn_like(x_cont)
+            x_cont = mean + torch.sqrt(betas[t]) * noise
+        else:
+            x_cont = mean
+
+        # Discrete: fill masked positions by sampling logits
+        for i, logit in enumerate(logits):
+            if t == 0:
+                probs = F.softmax(logit, dim=-1)
+                x_disc[:, :, i] = torch.argmax(probs, dim=-1)
+            else:
+                mask = x_disc[:, :, i] == mask_tokens[i]
+                if mask.any():
+                    probs = F.softmax(logit, dim=-1)
+                    sampled = torch.multinomial(probs.view(-1, probs.size(-1)), 1).view(BATCH_SIZE, SEQ_LEN)
+                    x_disc[:, :, i][mask] = sampled[mask]
+
+    print("sampled_cont_shape", tuple(x_cont.shape))
+    print("sampled_disc_shape", tuple(x_disc.shape))
+
+
+if __name__ == "__main__":
+    main()