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b222eb5 02bdb20 b222eb5 02bdb20 b222eb5 02bdb20 b222eb5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | """Coherent Contextual Decoding (CCD) and CCD-DS for Dream diffusion language models.
Independent reimplementation of:
"Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Models"
(arXiv 2512.02044 / OpenReview b0O96emqNj), Section 3.
No official code was released; this follows the equations in the paper.
Equation map
------------
Eq. (1) baseline single-step sampling (Dream `alg="entropy"`)
Eq. (6) p_bar(x_i|s) = (1/(T-t+1)) sum_k p_theta(x_i | c_{T-k,i}, s) -- marginalized target
Eq. (16) H_t : sliding-window historical buffer, last d iters, top-V per iter,
I_t = intersection of the top-V position sets over those d iters
Eq. (17) I^c_t = (current top-V set) intersect (positions present in H_t)
Eq. (18) CCD sampling: p_{t,i} = p_bar over the d+1 buffer entries; J_t = top-b_t by -H(p_bar)
Eq. (20) CCD-DS adaptive budget:
|I^c_t| <= b_t -> J_t = I^c_t
|I^c_t| > b_t -> J_t = top-b_t U {i : H(p_bar_i) < eps}
with the paper's Sec. 4.2 stability heuristic standing in for eps:
argmax token index identical across all d+1 buffer entries.
Implementation decisions where the paper is silent (documented in the logbook):
* Buffer warm-up: at the first iteration no history exists (Eq. 16 gives
j in {1..min(d, T-t)} = empty), so I^c_t is unconstrained and p_bar is the
single-step distribution (Monte-Carlo count 1). The buffer fills over the
first d iterations.
* Empty-intersection fallback: if I^c_t is empty the step would decode nothing
and stall forever. We fall back to the baseline rule (Eq. 1): decode the
top-b_t positions by single-step confidence. This is the natural reading of
the paper's claim that CCD "degrades to the existing single-context method",
and it guarantees CCD-DS never needs more steps than the baseline.
"""
import torch
import torch.nn.functional as F
import torch.distributions as dists
# ---------------------------------------------------------------- distributions
def _apply_filters(logits, temperature=0.0, top_p=None, top_k=None):
"""Dream's logit preprocessing (verbatim from Dream generation_utils.sample_tokens)."""
if temperature > 0:
logits = logits / temperature
if top_p is not None and top_p < 1:
logits = _top_p_logits(logits, top_p)
if top_k is not None:
logits = _top_k_logits(logits, top_k)
return torch.softmax(logits.float(), dim=-1)
def _top_p_logits(logits, top_p=None):
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
mask = torch.zeros_like(logits, dtype=torch.bool, device=logits.device)
mask = mask.scatter_(-1, sorted_indices, sorted_indices_to_remove)
return logits.masked_fill(mask, torch.finfo(logits.dtype).min)
def _top_k_logits(logits, top_k=None):
top_k = min(top_k, logits.size(-1))
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
return logits.masked_fill(indices_to_remove, torch.finfo(logits.dtype).min)
def _neg_entropy(probs):
"""Dream's confidence surrogate for alg='entropy': -H(p) (higher = more confident)."""
return torch.sum(probs * torch.log(probs + 1e-10), dim=-1)
def _pick_token(probs, temperature):
"""Token value from a distribution: argmax at temp 0, categorical sample otherwise.
Mirrors Dream's sample_tokens so CCD and the baseline differ only in *which*
distribution is used, never in how a token is drawn from it.
"""
if temperature > 0:
try:
return dists.Categorical(probs=probs).sample()
except Exception:
return probs.argmax(dim=-1)
return probs.argmax(dim=-1)
# ---------------------------------------------------------------- the decoder
@torch.no_grad()
def generate(
model,
input_ids,
attention_mask=None,
max_new_tokens=256,
steps=256,
temperature=0.0,
top_p=None,
top_k=None,
mask_token_id=151666,
eps=1e-3,
method="baseline", # 'baseline' | 'ccd' | 'ccd_ds'
buffer_V=4, # top-V confident tokens retained per iteration
history_d=3, # history length d (Dream default in the paper)
eos_token_id=None,
):
"""Decode one sequence (batch size 1) and return (sequence, stats).
stats: {'steps': forward passes actually run, 'budgets': tokens decoded per step,
'fallbacks': steps that used the empty-intersection fallback}
"""
assert input_ids.shape[0] == 1, "batch size 1 (per-sequence buffer bookkeeping)"
device = input_ids.device
prompt_len = input_ids.shape[1]
max_length = prompt_len + max_new_tokens
x = F.pad(input_ids, (0, max_new_tokens), value=mask_token_id)
if attention_mask is not None and torch.any(attention_mask == 0.0):
attention_mask = F.pad(attention_mask, (0, max_new_tokens), value=1.0)
tok_idx = attention_mask.long().cumsum(-1) - 1
tok_idx.masked_fill_(attention_mask == 0, 1)
attention_mask = torch.logical_and(
attention_mask.unsqueeze(1).unsqueeze(-2),
attention_mask.unsqueeze(1).unsqueeze(-1),
)
else:
tok_idx = None
attention_mask = "full"
timesteps = torch.linspace(1, eps, steps + 1, device=device)
# sliding-window historical buffer: list of (positions[V], probs[V, |X|]),
# most recent last, at most `history_d` entries.
buffer = []
stats = {"steps": 0, "budgets": [], "fallbacks": 0,
# diagnostics: what actually gates the adaptive budget (Eq. 20)
"ic_sizes": [], "n_stable": []}
for i in range(steps):
mask_index = (x == mask_token_id)
if not mask_index.any():
break # early stop: every position decoded (CCD-DS)
logits = model(x, attention_mask, tok_idx).logits
logits = torch.cat([logits[:, :1], logits[:, :-1]], dim=1) # Dream's shift
stats["steps"] += 1
mask_pos = mask_index[0].nonzero(as_tuple=True)[0] # [M]
probs_cur = _apply_filters(logits[0, mask_pos], temperature, top_p, top_k) # [M, |X|]
conf_cur = _neg_entropy(probs_cur) # [M]
# baseline uniform budget b_t (Dream's schedule)
num_mask = mask_pos.numel()
t, s = timesteps[i], timesteps[i + 1]
b_t = int(num_mask * (1 - s / t)) if i < steps - 1 else num_mask
b_t = max(b_t, 1)
if method == "baseline":
k = min(b_t, num_mask)
sel = torch.topk(conf_cur, k).indices
x[0, mask_pos[sel]] = _pick_token(probs_cur[sel], temperature)
stats["budgets"].append(int(k))
continue
# ---- Eq. (16): current top-V confident positions
V = min(buffer_V, num_mask)
topv_local = torch.topk(conf_cur, V).indices
topv_pos = mask_pos[topv_local]
# ---- Eq. (16)/(17): I_t = intersection of buffered top-V position sets,
# I^c_t = current top-V intersect I_t
cur_set = set(topv_pos.tolist())
inter = cur_set
for pos_b, _ in buffer:
inter = inter & set(pos_b.tolist())
ic_t = sorted(inter)
if len(ic_t) == 0:
# fallback -> baseline rule (Eq. 1)
stats["fallbacks"] += 1
stats["ic_sizes"].append(0)
stats["n_stable"].append(0)
k = min(b_t, num_mask)
sel = torch.topk(conf_cur, k).indices
x[0, mask_pos[sel]] = _pick_token(probs_cur[sel], temperature)
stats["budgets"].append(int(k))
else:
# ---- Eq. (6): p_bar = mean over the d+1 buffer entries (history + current)
pos_index_cur = {int(p): j for j, p in enumerate(mask_pos.tolist())}
p_bars, stable = [], []
for pos in ic_t:
dists_i = [probs_cur[pos_index_cur[pos]]]
for pos_b, prob_b in buffer:
j = (pos_b == pos).nonzero(as_tuple=True)[0]
if j.numel() > 0:
dists_i.append(prob_b[j[0]])
stacked = torch.stack(dists_i, dim=0) # [d+1, |X|]
p_bars.append(stacked.mean(dim=0))
# Sec. 4.2 stability heuristic standing in for H(p_bar) < eps
argmaxes = stacked.argmax(dim=-1)
stable.append(bool((argmaxes == argmaxes[0]).all()) and stacked.shape[0] > 1)
stats["ic_sizes"].append(len(ic_t))
stats["n_stable"].append(int(sum(stable)))
p_bar = torch.stack(p_bars, dim=0) # [|I^c_t|, |X|]
marg_conf = _neg_entropy(p_bar) # -H(p_bar)
ic_pos = torch.tensor(ic_t, device=device)
# ---- Eq. (18) / Eq. (20): choose J_t
if method == "ccd":
k = min(b_t, len(ic_t))
sel = torch.topk(marg_conf, k).indices
elif method == "ccd_ds":
if len(ic_t) <= b_t:
sel = torch.arange(len(ic_t), device=device)
else:
sel = torch.topk(marg_conf, b_t).indices
extra = [j for j in range(len(ic_t))
if stable[j] and j not in set(sel.tolist())]
if extra:
sel = torch.cat([sel, torch.tensor(extra, device=device)])
else:
raise ValueError(f"unknown method: {method}")
x[0, ic_pos[sel]] = _pick_token(p_bar[sel], temperature)
stats["budgets"].append(int(sel.numel()))
# ---- update the sliding window (store only still-masked top-V positions)
still_masked = (x[0, topv_pos] == mask_token_id)
keep_pos = topv_pos[still_masked]
if keep_pos.numel() > 0:
keep_local = topv_local[still_masked]
buffer.append((keep_pos.clone(), probs_cur[keep_local].clone()))
else:
buffer.append((torch.empty(0, dtype=torch.long, device=device),
torch.empty(0, probs_cur.shape[-1], device=device)))
if len(buffer) > history_d:
buffer.pop(0)
return x, stats
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