| """Exercise packaged wheel code and checkpoint with real GPU text/image inference.""" |
|
|
| import argparse |
| import hashlib |
| import json |
| import math |
| import sys |
| from pathlib import Path |
|
|
|
|
| def digest(path): |
| return hashlib.sha256(path.read_bytes()).hexdigest() |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--package", type=Path, required=True) |
| parser.add_argument("--records", type=Path, required=True) |
| parser.add_argument("--output", type=Path, required=True) |
| args = parser.parse_args() |
| if not sys.flags.isolated: |
| raise RuntimeError("run Python with -I to isolate the package from the source directory") |
| if args.output.exists(): |
| raise FileExistsError("use a new verification report") |
| folder = args.package.resolve() |
| checksums = json.loads((folder / "checksums.json").read_text(encoding="utf-8")) |
| for name, expected in checksums.items(): |
| path = (folder / name).resolve() |
| if not path.is_relative_to(folder) or digest(path) != expected: |
| raise ValueError("package checksum mismatch: " + name) |
| wheels = list((folder / "runtime").glob("*.whl")) |
| if len(wheels) != 1: |
| raise ValueError("expected exactly one runtime wheel") |
| wheel = wheels[0] |
| sys.path.insert(0, str(wheel)) |
| import torch |
|
|
| import veyra |
| from veyra.option_model import OptionModel |
| from veyra.schema import DecisionRequest |
|
|
| if not str(veyra.__file__).startswith(str(wheel)): |
| raise RuntimeError("Veyra was imported from outside the bundled wheel") |
| records = [ |
| json.loads(line) |
| for line in args.records.read_text(encoding="utf-8").splitlines() |
| if line.strip() |
| ] |
| photographs = sorted( |
| (row for row in records if row["split"] == "test" and "domain:image_waste" in row["tags"]), |
| key=lambda row: row["id"], |
| ) |
| first = photographs[0] |
| views = [r for r in photographs if r["group_id"] == first["group_id"]] |
| |
| questions = {} |
| for row in views: |
| for question in row["request"]["questions"].values(): |
| questions.setdefault(question["type"], question) |
| if set(questions) != {"choice", "score", "noul"}: |
| raise ValueError("photograph fixture must exercise all three types") |
| requests = { |
| "text": DecisionRequest.from_json((folder / "sample_request.json").read_text("utf-8")), |
| "photograph_three_questions": DecisionRequest.model_validate( |
| {"state": first["request"]["state"], "questions": questions} |
| ), |
| } |
| torch.set_num_threads(4) |
| model = OptionModel.load(folder, local_files_only=True, merge=True) |
| forwards = [] |
| hook = model.encoder.model.register_forward_hook(lambda *_: forwards.append(1)) |
| results = {} |
| try: |
| for name, request in requests.items(): |
| before = len(forwards) |
| answer = model.predict(request, args.records.parent.resolve()) |
| count = len(forwards) - before |
| if count != 1 or answer["usage"]["output_tokens"] != 0: |
| raise ValueError("single-forward, zero-generated-token contract failed") |
| for question in answer["answers"].values(): |
| values = list(question["probabilities"].values()) |
| if not all(math.isfinite(x) and 0 <= x <= 1 for x in values): |
| raise ValueError("invalid probabilities") |
| if abs(sum(values) - 1) > 1e-5: |
| raise ValueError("probabilities do not sum to one") |
| results[name] = {"backbone_forwards": count, "response": answer} |
| finally: |
| hook.remove() |
| modules = { |
| name: str(getattr(module, "__file__", "")) |
| for name, module in sys.modules.items() |
| if name == "veyra" or name.startswith("veyra.") |
| } |
| if not all(path.startswith(str(wheel)) for path in modules.values()): |
| raise RuntimeError("source-directory modules leaked into wheel verification") |
| result = { |
| "passed": True, |
| "published_to_huggingface": False, |
| "source_sha256": digest(Path(__file__)), |
| "weights_sha256": digest(folder / "head.safetensors"), |
| "manifest_sha256": digest(folder / "manifest.json"), |
| "checksums_sha256": digest(folder / "checksums.json"), |
| "verified_files": len(checksums), |
| "wheel_sha256": digest(wheel), |
| "veyra_version": veyra.__version__, |
| "python_isolated": True, |
| "modules_from_bundled_wheel": modules, |
| "dependencies": "Existing pinned Python 3.12 environment; not a clean dependency install", |
| "base_model": "Pinned locally cached Qwen base; no fresh base-weight download", |
| "gpu": torch.cuda.get_device_name(), |
| "photo_record_id": first["id"], |
| "results": results, |
| } |
| args.output.parent.mkdir(parents=True, exist_ok=True) |
| args.output.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8", newline="\n") |
| print(json.dumps({"passed": True, "files": len(checksums), "requests": len(results)})) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
