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1d2de8a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 | """Public document-state API and four-state answer semantics."""
import copy
import json
import math
from pathlib import Path
from ._vendor.contract import _state_parts, decision, parse_questions, present
from ._vendor.prompts import boolean_block, label_block, listwise_block
from ._vendor.semantics import listed_probs, p_yes
from .artifacts import digest, sha256
TASKS = ("boolean", "single", "ordered", "multilabel", "entity")
def branches(spec):
task, req = spec["task"], spec["request"]
if task == "boolean":
return [(boolean_block(req["question"]), 4, "boolean/state4")]
if task in ("single", "ordered"):
block, width = listwise_block(
req["question"], req["options"], ordered=task == "ordered", reserved=task == "single"
)
return [(block, width, task + ("/choiceR" if task == "single" else "/choiceS"))]
if task == "entity":
return [
(boolean_block(req["template"].replace("{entity}", c)), 4, "entity/state4")
for c in req["entities"]
]
return [(*label_block(req["question"], c), "multilabel/state4") for c in req["labels"]]
def distributions(spec, rows, temperature):
if spec["task"] in ("boolean", "entity", "multilabel"):
values = [p_yes(r["letter_logits"], temperature) for r in rows]
return [[p, 1 - p] for p in values]
return [listed_probs(rows[0]["letter_logits"], len(spec["texts"]), temperature).tolist()]
def ordering_score(values):
"""Product of per-unit top probabilities; not a calibrated joint probability."""
if not values:
raise ValueError("At least one answer unit is required")
for p in values:
if len(p) < 2 or not all(math.isfinite(v) and v >= 0 for v in p) or abs(sum(p) - 1) > 1e-6:
raise ValueError("Invalid answer distribution")
return math.prod(max(p) for p in values)
class DocumentState:
def __init__(self, owner, data):
self._owner, self._data, self.closed = owner, data, False
self.image_hashes = {p["image"]: sha256(p["image"]) for p in data["parts"] if "image" in p}
@property
def prefix_tokens(self):
self._check()
return len(self._data["prefix_ids"])
def _check(self):
if self.closed:
raise ValueError("Document state is closed")
if any(sha256(path) != value for path, value in self.image_hashes.items()):
raise ValueError("Document image changed after prefill")
def save(self, path):
"""Save a source-bound replay recipe, never pickle executable cache objects."""
self._check()
body = {
"format": "solomon-mlx-replay-v1",
"runtime": self._owner.identity["fingerprint"],
"parts": self._data["parts"],
"image_hashes": self.image_hashes,
"prefix_ids_sha256": digest(self._data["prefix_ids"]),
}
Path(path).write_text(json.dumps({**body, "sha256": digest(body)}, indent=2))
def close(self):
with self._owner.engine.lock:
self._data.clear()
self.closed = True
def __enter__(self):
self._check()
return self
def __exit__(self, *args):
self.close()
class Solomon:
@classmethod
def load(
cls,
model_dir,
profile="quality",
*,
chunk_size=2048,
max_tokens=40960,
page_selector=None,
calibration=None,
):
if profile != "quality":
raise ValueError("Only full BF16 quality is implemented; quantization is secondary")
from .engine import Engine
return cls(
Engine(model_dir, chunk_size=chunk_size, max_tokens=max_tokens),
page_selector=page_selector,
calibration=calibration,
)
def __init__(self, engine, *, page_selector=None, calibration=None):
self.engine, self.identity, self.page_selector = engine, engine.identity, page_selector
self.temperatures = dict.fromkeys(TASKS, 1.0)
self.calibration_status = "uncalibrated"
if calibration is not None:
artifact = json.loads(Path(calibration).read_text())
payload = {k: v for k, v in artifact.items() if k != "sha256"}
if (
artifact.get("sha256") != digest(payload)
or artifact["runtime"] != self.identity["fingerprint"]
):
raise ValueError("Calibration checksum or MLX runtime identity mismatch")
temps = artifact["temperatures"]
if set(temps) != set(TASKS) or any(
isinstance(v, bool)
or not isinstance(v, (int, float))
or not math.isfinite(v)
or not 0 < v <= 20
for v in temps.values()
):
raise ValueError("Invalid temperatures")
self.temperatures, self.calibration_status = temps, "profile_fitted"
def prefill(self, document):
parts = copy.deepcopy(_state_parts(document))
if not parts:
parts = [{"text": ""}]
for p in parts:
if not isinstance(p, dict) or set(p) not in ({"text"}, {"image"}):
raise ValueError("Each document part must contain only text or image")
if "text" in p and not isinstance(p["text"], str):
raise ValueError("Text parts must be strings")
if "image" in p:
p["image"] = str(Path(p["image"]).resolve(strict=True))
hashes = {p["image"]: sha256(p["image"]) for p in parts if "image" in p}
state = DocumentState(self, self.engine.prefill(parts))
if state.image_hashes != hashes:
state.close()
raise ValueError("Image changed while document was being prefilled")
return state
def replay(self, path):
body = json.loads(Path(path).read_text())
expected = body.pop("sha256")
if (
digest(body) != expected
or body["format"] != "solomon-mlx-replay-v1"
or body["runtime"] != self.identity["fingerprint"]
):
raise ValueError("Replay checksum or runtime mismatch")
if any(sha256(p) != h for p, h in body["image_hashes"].items()):
raise ValueError("Replay image changed")
state = self.prefill(body["parts"])
if digest(state._data["prefix_ids"]) != body["prefix_ids_sha256"]:
state.close()
raise ValueError("Replay tokenization differs")
return state
def _answer(self, state, spec, execution="cached"):
rows = [self.engine.ask(state._data, b, n, h, execution=execution) for b, n, h in branches(spec)]
dists = distributions(spec, rows, self.temperatures[spec["task"]])
return {
**present(spec, dists),
"ordering_score": ordering_score(dists),
"temperature": self.temperatures[spec["task"]],
}, rows
def decide(
self,
*,
state,
questions,
evidence="support",
evidence_max_calls=64,
execution="cached",
diagnostics=False,
):
if not isinstance(state, DocumentState) or state._owner is not self:
raise ValueError("State belongs to a different model instance")
if evidence not in ("none", "support", "sufficiency", "removal"):
raise ValueError("Invalid evidence level")
if type(evidence_max_calls) is not int or not 0 <= evidence_max_calls <= 512:
raise ValueError("Invalid evidence call budget")
specs = parse_questions(questions)
with self.engine.lock:
state._check()
answers, usage = {}, {"branches": 0, "input_tokens": 0, "evidence_calls": 0}
for spec in specs:
answer, rows = self._answer(state, spec, execution)
body = self._evidence(state, spec, answer, evidence, evidence_max_calls)
answer.update(
evidence=body["references"], evidence_status=body["status"], evidence_detail=body
)
if diagnostics:
answer["branches"] = rows
answers[spec["id"]] = answer
usage["branches"] += len(rows)
usage["input_tokens"] += sum(
r["branch_tokens"] if execution == "cached" else r["prompt_tokens"] for r in rows
)
usage["evidence_calls"] += body.get("calls", 0)
return {
"answers": answers,
"usage": usage,
"runtime": self.identity,
"calibration_status": self.calibration_status,
"answer_policy": "always_answers",
}
def _fresh(self, document, spec):
with self.prefill(document) as state:
answer, _ = self._answer(state, spec)
return decision(spec, answer)
def _evidence(self, state, spec, answer, level, budget):
from ._vendor import evidence_v3 as v3
from ._vendor.evidence import image_pages, validate_pages, validate_spans
from ._vendor.retrieval import lexical_select, remove
body = {
"references": [],
"status": "not_requested",
"calls": 0,
"verification": "none",
"faithfulness_established": False,
}
if level == "none":
return body
parts, req = state._data["parts"], spec["request"]
task = spec["task"]
if task == "entity":
questions = [req["template"].replace("{entity}", c) for c in req["entities"]]
elif task == "multilabel":
questions = [req["question"] + " Label: " + c for c in req["labels"]]
else:
questions = [req["question"] + (" " + " ".join(req["options"]) if "options" in req else "")]
images = [p["image"] for p in parts if "image" in p]
needed = (1 if images else len(questions)) if level in ("sufficiency", "removal") else 0
needed += int(level == "removal")
if needed > budget:
return {**body, "status": "budget_exhausted", "required_calls": needed}
baseline = decision(spec, answer)
if images:
if self.page_selector is None:
return {**body, "status": "unsupported_page_selector", "pages_available": len(images)}
pages = image_pages(images)
selector = self.page_selector
plan = None
if hasattr(selector, "plan"):
plan = selector.plan(
pages, questions, **({"task": task} if getattr(selector, "task_aware", False) else {})
)
if type(plan.get("calls")) is not int or plan["calls"] < 0:
raise ValueError("Invalid page selector call estimate")
if needed + plan["calls"] > budget:
return {**body, "status": "budget_exhausted", "required_calls": needed + plan["calls"]}
selection = (
selector.execute(plan) if plan is not None else selector(copy.deepcopy(pages), questions)
)
calls = selection.get("cost", {}).get("calls", 0)
if calls != (plan["calls"] if plan is not None else 0):
raise ValueError("Page selector exceeded its declared call budget")
refs = validate_pages(pages, selection["evidence"])
body["calls"] = calls
selected = {r["page"] for r in refs}
page, remainder = 0, []
for part in parts:
if "image" in part:
page += 1
if page in selected:
continue
remainder.append(part)
subsets = [([{"image": r["path"]} for r in refs], spec)]
else:
text = "".join(p["text"] for p in parts)
selection = lexical_select(text, questions)
refs = validate_spans(text, selection["evidence"])
structure = v3.Structure(text)
packages, subsets = [], []
for i, q in enumerate(questions):
subject = req["entities"][i] if task == "entity" else None
package = v3.build(text, q, refs, subject=subject, structure=structure)
unit = copy.deepcopy(spec)
if "candidates" in spec:
candidate = spec["candidates"][i]
unit["candidates"] = [candidate]
unit["request"]["entities" if task == "entity" else "labels"] = [candidate]
subsets.append((package["text"], unit))
packages.append({k: v for k, v in package.items() if k != "text"})
body["packages"] = packages
remainder = remove(text, refs)
body.update(
references=refs, status="found" if refs else "no_support_found", verification="retrieval_only"
)
if level in ("sufficiency", "removal"):
predictions = [self._fresh(doc, unit) for doc, unit in subsets]
assembled = (
{k: v for d in predictions for k, v in d.items()}
if "candidates" in spec and not images
else predictions[0]
)
body["evidence_only"] = {"prediction": assembled, "agrees_with_full": assembled == baseline}
body["calls"] += len(subsets)
body["verification"] = "fresh_source_reencoding"
if level == "removal":
removed = self._fresh(remainder, spec)
body["evidence_removed"] = {"prediction": removed, "agrees_with_full": removed == baseline}
body["calls"] += 1
return body
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