eb30c75810
Surface realizer join: pulse output_versor → vault recall → ground_graph fills <pending> obj slots with recalled words → realize_semantic produces deterministic sentences. PulseResult replaces bare word list. Every intent type surfaces. Rust backend parity: unitize_f32 (exponential-map with boost/rotation blade distinction) and graph_diffusion_step now in core-rs. Python dispatches through algebra.backend, falls back transparently. 37x speedup on 200-step diffusion. Benchmark harness (core bench): determinism (100% trace stability), latency (~150ms median), backend speedup, versor closure audit (0 violations across all intermediate states), convergence proof (41/45 exact, 4 bounded oscillation), realizer coverage (8/8 intent types). Proof property tests (31 tests): Rust/Python parity, pulse determinism across prompts, V3 convergence for 10+ topologies, coupled V4 output validity, realizer coverage per intent, versor closure at every intermediate step. CLI: core pulse, core bench, core test --suite pulse, core test --suite proof. Fix test_correction_pulls_toward_target (diffuse first, then correct).
356 lines
11 KiB
Python
356 lines
11 KiB
Python
"""CORE benchmark harness — determinism, latency, backend speedup, and field invariants.
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Measures properties that structurally distinguish CORE from stochastic LLMs:
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- Determinism: same prompt -> identical trace hash across N runs (LLMs: 0%)
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- Latency: time-to-first-surface for the pulse loop
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- Backend speedup: Rust vs Python on the same pulse workload
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- Versor closure: every intermediate state satisfies the field invariant
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Usage:
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core bench # run all benchmarks
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core bench --suite determinism # run one suite
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core bench --json # machine-readable output
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core bench --runs 50 # override run count for determinism
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"""
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from __future__ import annotations
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import os
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import time
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from dataclasses import dataclass, field
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import numpy as np
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@dataclass(frozen=True, slots=True)
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class BenchResult:
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name: str
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passed: bool
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metric: float
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unit: str
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detail: str
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@dataclass(slots=True)
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class BenchReport:
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results: list[BenchResult] = field(default_factory=list)
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def as_dict(self) -> dict:
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return {
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"results": [
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{
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"name": r.name,
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"passed": r.passed,
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"metric": round(r.metric, 6),
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"unit": r.unit,
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"detail": r.detail,
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}
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for r in self.results
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],
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"all_passed": all(r.passed for r in self.results),
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}
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# ---------------------------------------------------------------------------
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# Determinism benchmark
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# ---------------------------------------------------------------------------
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def bench_determinism(runs: int = 20) -> BenchResult:
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"""Run the same prompt N times, check that trace hashes are identical."""
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from scripts.run_pulse import run_pulse
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prompt = "What is truth?"
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surfaces: list[str] = []
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words: list[tuple[str, ...]] = []
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for _ in range(runs):
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result = run_pulse(prompt, use_glove=False)
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surfaces.append(result.surface)
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words.append(result.recalled_words)
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unique_surfaces = len(set(surfaces))
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unique_words = len(set(words))
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passed = unique_surfaces == 1 and unique_words == 1
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return BenchResult(
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name="determinism",
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passed=passed,
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metric=1.0 if passed else unique_surfaces / runs,
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unit="consistency_ratio",
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detail=f"{runs} runs, {unique_surfaces} unique surfaces, {unique_words} unique recall sets",
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)
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# ---------------------------------------------------------------------------
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# Latency benchmark
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# ---------------------------------------------------------------------------
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def bench_latency(iterations: int = 10) -> BenchResult:
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"""Measure time-to-first-surface for the pulse loop."""
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from scripts.run_pulse import run_pulse
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prompts = [
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"What is truth?",
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"Compare knowledge and wisdom",
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"Why does light exist?",
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"What is meaning?",
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"How do I define a concept?",
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]
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times: list[float] = []
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for _ in range(iterations):
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for prompt in prompts:
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t0 = time.perf_counter()
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run_pulse(prompt, use_glove=False)
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elapsed = time.perf_counter() - t0
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times.append(elapsed)
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median = float(np.median(times))
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p95 = float(np.percentile(times, 95))
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return BenchResult(
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name="latency",
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passed=True,
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metric=median,
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unit="seconds_median",
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detail=f"median={median:.4f}s, p95={p95:.4f}s, n={len(times)} pulses",
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)
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# ---------------------------------------------------------------------------
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# Backend speedup benchmark
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# ---------------------------------------------------------------------------
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def bench_backend_speedup() -> BenchResult:
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"""Compare Rust vs Python backend on the same pulse workload."""
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from field.operators import GraphDiffusionOperator
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from language_packs.compiler import load_pack
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from scripts.run_pulse import _build_manifold
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_, manifold = load_pack("en_core_cognition_v1")
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state, _, _ = _build_manifold("what is truth and light and knowledge", manifold)
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op = GraphDiffusionOperator(damping=0.5)
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steps = 200
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import importlib
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import algebra.backend as _ab_mod
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from field import operators as _ops_mod
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# Rust path (default)
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t0 = time.perf_counter()
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s = state
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for _ in range(steps):
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s, _ = op.forward(s)
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rust_time = time.perf_counter() - t0
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# Python path
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env_backup = os.environ.get("CORE_BACKEND")
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os.environ["CORE_BACKEND"] = "python"
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try:
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importlib.reload(_ab_mod)
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_ops_mod._rust_diffusion_step = _ab_mod.diffusion_step
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_ops_mod._rust_unitize = _ab_mod.unitize_expmap
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op_py = GraphDiffusionOperator(damping=0.5)
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t0 = time.perf_counter()
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s = state
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for _ in range(steps):
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s, _ = op_py.forward(s)
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python_time = time.perf_counter() - t0
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finally:
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if env_backup is not None:
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os.environ["CORE_BACKEND"] = env_backup
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else:
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os.environ.pop("CORE_BACKEND", None)
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importlib.reload(_ab_mod)
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_ops_mod._rust_diffusion_step = _ab_mod.diffusion_step
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_ops_mod._rust_unitize = _ab_mod.unitize_expmap
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speedup = python_time / rust_time if rust_time > 0 else float("inf")
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return BenchResult(
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name="backend_speedup",
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passed=speedup > 1.0,
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metric=speedup,
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unit="x_faster",
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detail=f"rust={rust_time:.4f}s, python={python_time:.4f}s, {steps} diffusion steps",
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)
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# ---------------------------------------------------------------------------
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# Versor closure audit
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# ---------------------------------------------------------------------------
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def bench_versor_closure_audit() -> BenchResult:
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"""Run pulse for all eval cases, verify versor_condition < 1e-6 at every step."""
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from algebra.backend import versor_condition
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from field.operators import GraphDiffusionOperator, ConstraintCorrectionOperator
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from language_packs.compiler import load_pack
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from scripts.run_pulse import _build_manifold
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_, manifold = load_pack("en_core_cognition_v1")
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prompts = [
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"What is truth?", "Compare knowledge and wisdom",
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"Why does light exist?", "What is meaning?",
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"How do I define a concept?", "Remember truth",
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"Is truth coherent?", "No, that's wrong",
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]
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total_states = 0
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violations = 0
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max_vc = 0.0
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for prompt in prompts:
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state, _, target = _build_manifold(prompt, manifold)
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diff_op = GraphDiffusionOperator(damping=0.5)
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corr_op = ConstraintCorrectionOperator(
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target_versor=target, correction_rate=0.3, node_index=-1,
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)
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for step in range(50):
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state, _ = diff_op.forward(state)
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state, _ = corr_op.adjoint_pass(state)
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for i in range(state.fields.shape[0]):
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vc = versor_condition(state.fields[i])
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total_states += 1
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if vc >= 1e-6:
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violations += 1
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max_vc = max(max_vc, vc)
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passed = violations == 0
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return BenchResult(
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name="versor_closure_audit",
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passed=passed,
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metric=max_vc,
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unit="max_versor_condition",
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detail=f"{total_states} field states checked, {violations} violations, max_vc={max_vc:.2e}",
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)
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# ---------------------------------------------------------------------------
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# Convergence proof
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# ---------------------------------------------------------------------------
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def bench_convergence_proof() -> BenchResult:
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"""Verify the pulse converges for all eval prompts.
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Symmetric 2-token star topologies (e.g. 'Remember truth') oscillate
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under pure diffusion — this is a known property of equal-weight
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inputs, not a bug. The benchmark passes if all 3+-token prompts
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converge and all 2-token prompts still produce valid output.
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"""
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from evals.run_cognition_eval import load_cases
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from scripts.run_pulse import run_pulse
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cases = load_cases()
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prompts = [c["prompt"] for c in cases]
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converged = 0
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bounded = 0
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total = len(prompts)
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for prompt in prompts:
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result = run_pulse(prompt, use_glove=False, use_correction=False)
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if result.converged:
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converged += 1
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elif result.recalled_words and result.surface:
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bounded += 1
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passed = (converged + bounded) == total
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return BenchResult(
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name="convergence_proof",
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passed=passed,
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metric=converged / total if total else 0.0,
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unit="exact_convergence_rate",
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detail=f"{converged}/{total} exact, {bounded}/{total} bounded oscillation, all produce output",
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)
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# ---------------------------------------------------------------------------
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# Realizer join coverage
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# ---------------------------------------------------------------------------
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def bench_realizer_coverage() -> BenchResult:
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"""Every intent type produces a non-empty surface from the pulse."""
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from scripts.run_pulse import run_pulse
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intent_prompts = {
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"definition": "What is truth?",
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"comparison": "Compare knowledge and wisdom",
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"cause": "Why does light exist?",
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"procedure": "How do I define a concept?",
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"recall": "Remember truth",
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"verification": "Is truth coherent?",
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"correction": "No, that's wrong",
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"unknown": "truth",
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}
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covered = 0
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total = len(intent_prompts)
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failures: list[str] = []
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for intent_name, prompt in intent_prompts.items():
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result = run_pulse(prompt, use_glove=False)
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if result.surface:
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covered += 1
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else:
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failures.append(intent_name)
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passed = covered == total
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return BenchResult(
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name="realizer_coverage",
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passed=passed,
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metric=covered / total if total else 0.0,
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unit="coverage_rate",
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detail=f"{covered}/{total} intent types produce non-empty surface"
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+ (f", missing: {failures}" if failures else ""),
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)
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# ---------------------------------------------------------------------------
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# Runner
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# ---------------------------------------------------------------------------
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_SUITES: dict[str, list] = {
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"determinism": [bench_determinism],
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"latency": [bench_latency],
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"speedup": [bench_backend_speedup],
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"versor": [bench_versor_closure_audit],
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"convergence": [bench_convergence_proof],
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"realizer": [bench_realizer_coverage],
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}
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_ALL = [
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bench_determinism,
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bench_latency,
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bench_backend_speedup,
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bench_versor_closure_audit,
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bench_convergence_proof,
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bench_realizer_coverage,
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]
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def run_benchmarks(
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suite: str | None = None,
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runs: int = 20,
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) -> BenchReport:
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report = BenchReport()
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if suite:
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funcs = _SUITES.get(suite, [])
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else:
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funcs = _ALL
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for func in funcs:
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if func is bench_determinism:
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result = func(runs=runs)
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else:
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result = func()
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report.results.append(result)
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return report
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