"""Validate Vortex Exact Attention's retained reference and design models.""" from __future__ import annotations import hashlib import json import math from pathlib import Path PROJECT = Path(__file__).resolve().parents[1] ROOT = PROJECT.parents[1] def load_models() -> tuple[dict, dict]: inventory = json.loads((PROJECT / "benchmarks/reference_inventory.json").read_text()) model = json.loads((PROJECT / "benchmarks/architecture_model.json").read_text()) return inventory, model def validate_documents(inventory: dict, model: dict, root: Path = ROOT) -> None: if len(inventory["artifacts"]) != 10: raise RuntimeError("the imported reference inventory must contain ten artifacts") for artifact in inventory["artifacts"]: path = root / artifact["path"] if path.stat().st_size != artifact["size_bytes"]: raise RuntimeError(f"size mismatch: {path}") if hashlib.sha256(path.read_bytes()).hexdigest() != artifact["sha256"]: raise RuntimeError(f"SHA-256 mismatch: {path}") if model["status"] != "projected_not_measured": raise RuntimeError("design estimates must not be labeled as measurements") if len(model["candidates"]) < 3: raise RuntimeError("at least three state-ownership designs are required") preparation_components = model["model_basis"]["exact_preparation_components_ms"] preparation_range = model["model_basis"]["exact_preparation_ms"] if preparation_range[0] != math.ceil(sum(preparation_components.values())): raise RuntimeError("exact-preparation lower bound is inconsistent") candidate_ids = [candidate["id"] for candidate in model["candidates"]] if set(candidate_ids) != {"VEA-A", "VEA-B", "VEA-C"} or len(candidate_ids) != 3: raise RuntimeError("the three reviewed architecture IDs must be unique and retained") for candidate in model["candidates"]: ownership = candidate["ownership"] if ownership["qk_fragments"] == ownership["output_accumulation"]: raise RuntimeError(f"{candidate['id']} does not separate QK and RO ownership") if ownership["m_d"] == ownership["output_accumulation"]: raise RuntimeError(f"{candidate['id']} does not separate m/d and RO ownership") if candidate["handoff"]["same_warp"]: raise RuntimeError(f"{candidate['id']} retains QK and PV state in one warp") if not candidate["handoff"]["bounded_tile_local"]: raise RuntimeError(f"{candidate['id']} uses a material handoff") if candidate["handoff"]["consumer_order"] != "strict_epoch_order": raise RuntimeError(f"{candidate['id']} does not preserve tile consumption order") if candidate["mainloop_ms_projected"][0] >= 220: raise RuntimeError(f"{candidate['id']} cannot plausibly beat the model gate") recovery = candidate["recovered_no_eligible_fraction"] synchronization = candidate["synchronization_ms_projected"] baseline = model["baseline"]["mainloop_p50_ms"] no_eligible = model["baseline"]["no_eligible_cycles_percent"] / 100.0 expected_mainloop = [ round(baseline * (1.0 - no_eligible * recovery[1]) + synchronization[0]), round(baseline * (1.0 - no_eligible * recovery[0]) + synchronization[1]), ] if candidate["mainloop_ms_projected"] != expected_mainloop: raise RuntimeError(f"{candidate['id']} screening arithmetic is inconsistent") expected_complete = [value + margin for value, margin in zip( candidate["mainloop_ms_projected"], preparation_range, strict=True, )] if candidate["complete_attention_ms_projected"] != expected_complete: raise RuntimeError(f"{candidate['id']} complete-attention arithmetic is inconsistent") if not candidate["capability_dependencies"]: raise RuntimeError(f"{candidate['id']} omits unresolved capability dependencies") by_id = {candidate["id"]: candidate for candidate in model["candidates"]} a_shared = by_id["VEA-A"]["shared_kib"] if a_shared["total"] != [ a_shared["q"] + a_shared["k_double"] + a_shared["v_double"] + a_shared["score_double"] + value for value in a_shared["metadata"] ]: raise RuntimeError("VEA-A shared-memory arithmetic is inconsistent") b_shared = by_id["VEA-B"]["shared_kib"] if b_shared["total"] != [value - b_shared["alias_saving"] for value in b_shared["unaliased"]]: raise RuntimeError("VEA-B shared-memory alias arithmetic is inconsistent") c_shared = by_id["VEA-C"]["shared_kib"] if c_shared["cluster_total"] != c_shared["producer"] + c_shared["consumer"]: raise RuntimeError("VEA-C shared-memory arithmetic is inconsistent") phase2a = model["phase2a_capability_measurements"] if phase2a["status"] != "passed_for_isolated_aligned_prototype": raise RuntimeError("Phase 2A status must remain scoped to an isolated prototype") if phase2a["selected_handoff"] != "cuda_block_scope_mbarrier": raise RuntimeError("Phase 2A must retain the sanitizer-clean handoff") if max(phase2a["registers_per_thread"].values()) > 200: raise RuntimeError("Phase 2A measured register limit is exceeded") if phase2a["local_bytes_per_thread"] or phase2a["ptxas_spill_loads"] or phase2a["ptxas_spill_stores"]: raise RuntimeError("Phase 2A must retain zero local memory and spills") if phase2a["resident_ctas_per_sm"] < 1: raise RuntimeError("Phase 2A must retain one resident ten-warp CTA") if phase2a["timing_ms"]["mbarrier_only_p95"] >= phase2a["timing_ms"]["budget"]: raise RuntimeError("Phase 2A handoff exceeds its synchronization budget") sanitizer = phase2a["sanitizer"] if any(sanitizer.values()): raise RuntimeError("Phase 2A selected probes must remain sanitizer-clean") fixture = phase2a["canonical_fixture"] if not fixture["reload_verified"] or fixture["output_sha256"] != "4c666c20f5f8f651158a2ced33ccff08f3bada07665c595b99008d171db30574": raise RuntimeError("Phase 2A canonical fixture is not locked to Sage2") if phase2a["attention_kernel_implemented"] or phase2a["attention_latency_measured"]: raise RuntimeError("Phase 2A capability evidence must not claim attention results") decision = model["decision"] if decision["kernel_implementation_started"] or decision["production_dispatch_changed"]: raise RuntimeError("Vortex attention must remain unimplemented and isolated") if not decision["phase2a_capability_passed"] or not decision["isolated_aligned_prototype_authorized"]: raise RuntimeError("Phase 2A decision is inconsistent with retained measurements") def validate() -> None: validate_documents(*load_models()) if __name__ == "__main__": validate() print("Vortex exact reference and architecture models are valid")