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b30553b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 | """Observe the pinned native decoder without replacing its sampling/edit logic."""
from __future__ import annotations
from dataclasses import dataclass
from html import escape
from queue import Empty, Full, Queue
from threading import Event, Lock, Thread
import time
import torch
MODEL_ID = "inclusionAI/LLaDA2.2-mini"
MODEL_REVISION = "e4f77d25ab5ca86f2223c6cdc6a72b887e20c8a5"
MASK, DELETE, INSERT, EOS = 156895, 156930, 156931, 156892
@dataclass(frozen=True)
class Mark:
probability: float = 0.0
iteration: int = 0
@dataclass
class Frame:
tokens: list[int]
marks: list[Mark]
iteration: int
deletes: int = 0
inserts: int = 0
done: bool = False
def move_marks(written, old, marks, probabilities, iteration, length):
"""Mirror native DELETE / [MASK, old token] INSERT and fixed-block resizing."""
result = []
for i, token in enumerate(written):
if token == DELETE:
continue
if token == INSERT:
result.extend([Mark(), marks[i]])
else:
result.append(marks[i] if token == old[i] else Mark(probabilities[i], iteration))
return (result + [Mark()] * length)[:length]
class NativeStreamer:
def __init__(self, model):
self.model = model
self.lock = Lock()
def stream(self, input_ids, *, timeout=240, selector=None, **options):
"""Run unchanged model.generate; send CPU snapshots after native edit operations.
A bounded queue applies backpressure. Cancellation and errors restore instance methods
before another request can enter; no patched global classes or retained GPU frames.
"""
queue = Queue(maxsize=2)
cancel = Event()
def send(item):
while not cancel.is_set():
try:
queue.put(item, timeout=.1)
return
except Full:
pass
def work():
try:
with self.lock, torch.inference_mode():
if cancel.is_set():
return
self._run(input_ids, options, send, cancel, timeout, selector)
except BaseException as exc:
send(exc)
finally:
send(None)
thread = Thread(target=work, daemon=True)
thread.start()
try:
while True:
try:
item = queue.get(timeout=.1)
except Empty:
if not thread.is_alive():
break
continue
if item is None:
break
if isinstance(item, BaseException):
raise item
yield item
finally:
cancel.set()
thread.join()
def _run(self, inputs, options, send, cancel, timeout, selector):
model = self.model
start = time.monotonic()
prompt_length = inputs.shape[1]
state = dict(iteration=0, deletes=0, inserts=0, marks=[], logits=None)
# Save instance attributes separately: restoring a bound method onto an instance
# would shadow its original class descriptor indefinitely.
names = ("_joint_decode_block", "_apply_edit_operations_with_tracking", "_diffusion_sample")
saved = {name: model.__dict__.get(name) for name in names}
present = {name: name in model.__dict__ for name in names}
joint = model._joint_decode_block
edit = model._apply_edit_operations_with_tracking
sample = model._diffusion_sample
def guided_sample(logits, temperature=1.0, top_k=None, top_p=None):
tokens, probabilities = sample(logits, temperature, top_k, top_p)
state["sample_calls"] += 1
initial_calls = 2 if options["temperature"] != 0 else 1
if selector is None or state["sample_calls"] > initial_calls:
return tokens, probabilities
old = state["x"][0, state["block_start"]:state["block_end"]]
# Edit decisions remain native. Guide only contiguous ordinary-token
# predictions, without scoring any edge across a structural operation.
allowed = (tokens != DELETE) & (tokens != INSERT) & (tokens != MASK)
positions = torch.arange(len(tokens), device=tokens.device) + state["block_start"]
allowed &= positions >= prompt_length
begin = 0
while begin < len(tokens):
if not allowed[begin]:
begin += 1
continue
end = begin + 1
while end < len(tokens) and allowed[end]:
end += 1
scores = logits[begin:end].clone()
scores[:, [MASK, DELETE, INSERT]] = -torch.inf
absolute = state["block_start"] + begin
left_id = None
# Only completed blocks or immutable prompt tokens supply a
# boundary. Never bridge an edit barrier inside the active block.
if absolute > 0 and (begin == 0 or absolute == prompt_length):
previous = state["x"][0, absolute - 1].item()
if previous not in (MASK, DELETE, INSERT):
left_id = previous
selected, _ = selector.select_block(
scores, old[begin:end], torch.ones(end-begin, dtype=torch.bool, device=tokens.device),
top_k=top_k or 16, temperature=temperature, left_id=left_id,
)
tokens[begin:end] = selected
begin = end
# Confidence gates always use the full, unmodified model distribution.
probabilities = logits.float().softmax(-1).gather(-1, tokens[:, None]).squeeze(-1)
return tokens, probabilities
def before_forward(module, args, kwargs):
if cancel.is_set():
raise RuntimeError("Generation cancelled")
if time.monotonic() - start > timeout:
raise RuntimeError("GPU time budget reached; generation is incomplete. Try fewer tokens or refinement steps.")
def after_forward(module, args, output):
state["logits"] = output.logits
state["sample_calls"] = 0
def observe_block(x, block_start, block_end, *args, **kwargs):
state.update(x=x, block_start=block_start, block_end=block_end)
if not state["marks"]:
state["marks"] = [Mark()] * x.shape[1]
return joint(x, block_start, block_end, *args, **kwargs)
def observe_edit(written, old, tracking, mask_id, block_length, delete_token_id, split_token_id):
result, new_tracking = edit(written, old, tracking, mask_id, block_length,
delete_token_id, split_token_id)
state["iteration"] += 1
lo, hi = state["block_start"], state["block_end"]
logits = state["logits"][0, lo:hi].float()
ids = torch.tensor(written, device=logits.device).unsqueeze(-1)
probabilities = logits.softmax(-1).gather(-1, ids).squeeze(-1).tolist()
state["logits"] = None
state["marks"][lo:hi] = move_marks(written, old, state["marks"][lo:hi],
probabilities, state["iteration"], block_length)
state["deletes"] += written.count(DELETE)
state["inserts"] += written.count(INSERT)
tokens = state["x"][0].tolist()
tokens[lo:hi] = result
end = prompt_length + options["gen_length"]
send(Frame(tokens[prompt_length:end], list(state["marks"][prompt_length:end]),
state["iteration"], state["deletes"], state["inserts"]))
return result, new_tracking
try:
model._joint_decode_block = observe_block
model._apply_edit_operations_with_tracking = observe_edit
model._diffusion_sample = guided_sample
# Native decoder calls self.forward directly, bypassing Module.__call__ hooks.
original_forward = model.forward
forward_present = "forward" in model.__dict__
saved_forward = model.__dict__.get("forward")
def observed_forward(*args, **kwargs):
before_forward(model, args, kwargs)
output = original_forward(*args, **kwargs)
after_forward(model, args, output)
return output
model.forward = observed_forward
try:
output = model.generate(inputs=inputs, **options)
finally:
if forward_present:
model.forward = saved_forward
else:
del model.forward
tokens = output[0].tolist()
if any(token in (MASK, DELETE, INSERT) for token in tokens):
raise RuntimeError("Native decoder reached its iteration cap with unresolved tokens. Increase the cap or reduce output length.")
send(Frame(tokens, list(state["marks"][prompt_length:prompt_length + len(tokens)]),
state["iteration"], state["deletes"], state["inserts"], done=True))
finally:
for name in names:
if present[name]:
setattr(model, name, saved[name])
else:
delattr(model, name)
def render(frame, tokenizer, color_mode="Prediction iteration"):
tokens = frame.tokens
if EOS in tokens:
tokens = tokens[:tokens.index(EOS)]
pieces = []
for i, token in enumerate(tokens):
mark = frame.marks[i] if i < len(frame.marks) else Mark()
text = "mask" if token == MASK else tokenizer.decode([token], skip_special_tokens=False)
if token == MASK:
style = "background:#d1d5db;color:#4b5563;border-radius:4px;padding:0 4px;margin:0 1px"
elif color_mode == "Prediction probability":
style = f"color:hsl({int(mark.probability * 120)},90%,30%)"
elif color_mode == "Prediction iteration":
lightness = 72 - round(42 * mark.iteration / max(frame.iteration, 1))
style = f"color:hsl(210,90%,{lightness}%)"
else:
style = "color:inherit"
title = f"position {i} 路 predicted at iteration {mark.iteration} 路 model probability {mark.probability:.1%}"
pieces.append(f'<span style="{style}" title="{title}">{escape(text)}</span>')
# Decode contiguous spans, preserving whitespace and multi-byte token sequences.
spans, pending = [], []
for token in tokens + [MASK]:
if token == MASK:
spans.append(tokenizer.decode(pending, skip_special_tokens=True, clean_up_tokenization_spaces=False))
spans.append("[MASK]")
pending = []
else:
pending.append(token)
plain = "".join(spans[:-1])
return ("<div style='font-family:system-ui;padding:14px;border:1px solid #d1d5db;"
"border-radius:9px;background:#fafafa'>"
f"<div style='font-weight:700;color:#2563eb'>Denoising iteration {frame.iteration} 路 "
f"{len(tokens)} output positions 路 {frame.inserts} insertions 路 {frame.deletes} deletions</div>"
f"<div style='line-height:2;font-size:15px;white-space:pre-wrap'>{''.join(pieces)}</div>"
"<div style='font-size:11px;color:#6b7280'>Gray chips are masks. Hover for prediction details. "
"Inserted and surviving tokens carry their prediction history as positions shift.</div>"
"<details style='margin-top:10px'><summary>Plain text / copy code</summary>"
"<textarea readonly aria-label='Plain text output' spellcheck='false' "
"style='width:100%;min-height:9em;white-space:pre;font-family:monospace'>"
f"{escape(plain)}</textarea></details></div>")
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