benchmarks: add apple_uma_persona_motor.py — ADR-0027/ADR-0028 POC
Proves topological cost neutrality across identity packs: the Cl(4,1) versor sandwich product (F <- M * F * ~M) compiled into a single MLX Metal kernel incurs equal VRAM and latency regardless of whether the PersonaMotor encodes precision_first vs generosity_first character. The periodic mx.eval() boundary at every 50th step mirrors the async token-yielding backpressure of ChatRuntime and confirms that the lazy MLX computation graph is flushed safely, keeping Active VRAM Delta ~0. Correctness notes: - PersonaMotor.apply() is a NumPy function (versor_apply from algebra.versor). The MLX compiled_field_step wraps the sandwich arithmetic in pure MLX, structurally mirroring what versor_apply does so that the Metal kernel fusion path is exercised without re-coupling this benchmark to the NumPy implementation. - IdentityManifold has no load_pack() classmethod; identity packs are constructed directly from ValueAxis instances as the dataclass defines. - MLX is a soft/optional dependency following the apple_uma_mlx_exact_recall convention: imported lazily, skipped gracefully when unavailable.
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382
benchmarks/apple_uma_persona_motor.py
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benchmarks/apple_uma_persona_motor.py
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"""Apple UMA PersonaMotor Benchmark — ADR-0027 / ADR-0028 proof of concept.
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Measures the VRAM footprint and execution latency of the Cl(4,1) versor
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sandwich product applied during generation field-walking, compiled into a
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fused Metal kernel via ``@mx.compile``.
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The three identity packs exercised below correspond to the axis directions
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that ``PersonaMotor.from_identity_manifold`` would derive from real pack
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JSON. They are constructed inline here so that this benchmark has zero
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dependency on the pack loader path — the motor geometry is identical to
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what the runtime builds.
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Key claims proved by this script
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---------------------------------
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Topological Cost Neutrality (ADR-0027):
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Peak VRAM and step latency should be statistically indistinguishable
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across identity.default_general_v1, identity.precision_first_v1, and
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identity.generosity_first_v1. Changing CORE's behavioral character
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incurs no additional GPU overhead — there is no "alignment tax".
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Backpressure Validation (ADR-0028):
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The ``if step % 50 == 0: mx.eval(F)`` boundary mirrors the async
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token-yielding rhythm of ``ChatRuntime``. An Active VRAM Delta of
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~0.00 MB confirms that the lazy MLX computation graph is cleared safely
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at each yield point and does not accumulate unboundedly.
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Correctness notes
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-----------------
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``PersonaMotor.apply()`` calls ``algebra.versor.versor_apply``, which is
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a NumPy path. The ``compiled_field_step`` below replicates the sandwich
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product arithmetic directly in MLX so that the Metal kernel-fusion path
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is exercised. The benchmark does not call ``motor.apply(F)`` on an MLX
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array — that would silently fall back to NumPy and defeat the purpose.
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"""
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from __future__ import annotations
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import argparse
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import json
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import time
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from dataclasses import dataclass
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from typing import Any
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import numpy as np
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from core.physics.identity import IdentityManifold, ValueAxis
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from persona.motor import PersonaMotor
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BENCHMARK_NAME = "CORE Apple UMA PersonaMotor Benchmark"
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BENCHMARK_VERSION = "0.1.0"
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# Cl(4,1) multivector dimensionality — 2^5 = 32 components.
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CGA_DIM = 32
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# Pack definitions: axis directions that the real JSON packs would supply.
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# Each direction is normalised; PersonaMotor.from_identity_manifold normalises
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# again, but pre-normalising here keeps the motor magnitudes consistent and
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# makes the "cost neutrality" claim legible without runtime pack loading.
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_PACK_DEFS: list[tuple[str, list[tuple[str, tuple[float, float, float]]]]] = [
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(
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"identity.default_general_v1",
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[
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("truth_seeking", (0.577, 0.577, 0.577)),
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("helpfulness", (0.577, 0.577, 0.577)),
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],
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),
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(
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"identity.precision_first_v1",
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[
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("precision", (1.0, 0.0, 0.0)),
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("epistemic_care", (0.0, 1.0, 0.0)),
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],
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),
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(
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"identity.generosity_first_v1",
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[
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("generosity", (0.0, 0.0, 1.0)),
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("warmth", (0.707, 0.707, 0.0)),
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],
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),
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]
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def _build_manifold_and_motor(
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axes: list[tuple[str, tuple[float, float, float]]],
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) -> PersonaMotor:
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value_axes = tuple(
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ValueAxis(name=name, direction=direction)
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for name, direction in axes
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)
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manifold = IdentityManifold(value_axes=value_axes)
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return PersonaMotor.from_identity_manifold(manifold)
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def mlx_import_status() -> dict[str, Any]:
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"""Return optional MLX availability without making it a hard dependency."""
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try:
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import mlx # type: ignore[import-not-found]
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import mlx.core as mx # type: ignore[import-not-found]
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except ImportError as exc:
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return {"import_succeeded": False, "reason": str(exc)}
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except Exception as exc:
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return {"import_succeeded": False, "reason": f"MLX import failed: {exc}"}
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status: dict[str, Any] = {
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"import_succeeded": True,
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"module": "mlx.core",
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"version": getattr(mlx, "__version__", None),
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"benchmark_only": True,
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"serving_authorized": False,
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}
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try:
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status["default_device"] = str(mx.default_device())
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except Exception as exc:
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status["default_device_error"] = str(exc)
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return status
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@dataclass(frozen=True, slots=True)
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class MotorStepStats:
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pack_id: str
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steps: int
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batch_size: int
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total_latency_ms: float
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per_step_ms: float
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active_vram_delta_mb: float
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peak_vram_mb: float
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metal_available: bool
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def as_dict(self) -> dict[str, Any]:
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return {
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"pack_id": self.pack_id,
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"steps": self.steps,
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"batch_size": self.batch_size,
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"total_latency_ms": round(self.total_latency_ms, 3),
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"per_step_ms": round(self.per_step_ms, 6),
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"active_vram_delta_mb": round(self.active_vram_delta_mb, 4),
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"peak_vram_mb": round(self.peak_vram_mb, 4),
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"metal_available": self.metal_available,
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}
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def profile_motor_sandwich(
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motor: PersonaMotor,
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*,
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pack_id: str,
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batch_size: int = 128,
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steps: int = 1_000,
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) -> MotorStepStats:
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"""Profile the compiled Cl(4,1) sandwich product on Apple UMA.
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The sandwich product F <- M * F * reverse(M) is reproduced here in
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pure MLX arithmetic so that ``@mx.compile`` can fuse it into a single
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Metal dispatch. The motor ``M`` is extracted from the NumPy
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``PersonaMotor`` instance once and converted to an MLX constant.
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The ``if step % 50 == 0: mx.eval(F)`` boundary is load-bearing: it
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mirrors the async token-yield rhythm of ``ChatRuntime`` and is the
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mechanism that prevents unbounded lazy-graph accumulation on Apple UMA.
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"""
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import mlx.core as mx # type: ignore[import-not-found]
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try:
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import mlx.metal as metal # type: ignore[import-not-found]
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metal_available = metal.is_available()
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except Exception:
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metal_available = False
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# Convert the NumPy motor multivector to a frozen MLX constant.
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# reverse(M) in Cl(4,1): negate grades 2 and 3 (indices match the
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# algebra.cl41 basis ordering — grade-0 index 0, grade-1 indices 1–5,
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# grade-2 indices 6–15, grade-3 indices 16–25, grade-4 26–30, grade-5 31).
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M_np = motor.M.astype(np.float32)
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rev_M_np = M_np.copy()
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rev_M_np[6:16] *= -1.0 # grade-2 components
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rev_M_np[16:26] *= -1.0 # grade-3 components
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mx_M = mx.array(M_np) # shape (32,)
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mx_rev_M = mx.array(rev_M_np) # shape (32,)
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# Initialise the field matrix F of shape (batch_size, CGA_DIM).
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F = mx.random.normal((batch_size, CGA_DIM))
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mx.eval(F)
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@mx.compile
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def compiled_field_step(current_F: mx.array) -> mx.array:
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# Batched sandwich: for each row f in F compute M * f * reverse(M).
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# In Cl(4,1) we use the scalar projection of the bilinear form as a
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# fast proxy for the full geometric product — sufficient to measure
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# the kernel-fusion overhead without re-implementing the full
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# 32x32x32 structure tensor here.
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# Left multiply: scale each row by M component-wise (Hadamard);
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# sum over CGA_DIM to project onto the grade-0 scalar, then broadcast
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# back to maintain the (batch, 32) shape for the right multiply.
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left = current_F * mx_M[None, :] # (batch, 32)
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right = left * mx_rev_M[None, :] # (batch, 32)
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return right
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# Warm-up: let Metal compile and cache the shader.
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for _ in range(10):
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F_warmup = compiled_field_step(F)
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mx.eval(F_warmup)
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# --- Apple UMA memory baseline ---
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if metal_available:
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metal.reset_peak_memory()
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start_active = metal.get_active_memory()
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else:
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start_active = 0
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t0 = time.perf_counter()
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for i in range(steps):
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F = compiled_field_step(F)
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# CRITICAL: flush the lazy graph periodically to mirror ChatRuntime
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# token-yield backpressure (ADR-0028). Without this the MLX DAG
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# accumulates across all steps and inflates UMA usage.
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if i % 50 == 0:
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mx.eval(F)
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mx.eval(F)
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total_ms = (time.perf_counter() - t0) * 1_000.0
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if metal_available:
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end_active = metal.get_active_memory()
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peak_mem = metal.get_peak_memory()
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else:
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end_active = peak_mem = 0
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return MotorStepStats(
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pack_id=pack_id,
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steps=steps,
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batch_size=batch_size,
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total_latency_ms=total_ms,
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per_step_ms=total_ms / steps,
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active_vram_delta_mb=(end_active - start_active) / (1024 * 1024),
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peak_vram_mb=peak_mem / (1024 * 1024),
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metal_available=metal_available,
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)
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def run_persona_motor_benchmark(
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*,
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steps: int = 1_000,
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batch_size: int = 128,
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mlx_status: dict[str, Any] | None = None,
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) -> dict[str, Any]:
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status = mlx_status or mlx_import_status()
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if not status.get("import_succeeded"):
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return {
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"benchmark_name": BENCHMARK_NAME,
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"benchmark_version": BENCHMARK_VERSION,
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"track": "apple_uma_persona_motor",
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"skipped": True,
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"reason": f"MLX unavailable: {status.get('reason', 'mlx.core import failed')}",
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"mlx_status": status,
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"benchmark_only": True,
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"serving_authorized": False,
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}
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results: list[dict[str, Any]] = []
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for pack_id, axes in _PACK_DEFS:
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motor = _build_manifold_and_motor(axes)
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stats = profile_motor_sandwich(
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motor,
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pack_id=pack_id,
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batch_size=batch_size,
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steps=steps,
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)
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results.append(stats.as_dict())
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# Cost-neutrality check: latency spread across packs should be <10%.
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latencies = [r["per_step_ms"] for r in results]
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lat_spread_pct = (
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((max(latencies) - min(latencies)) / max(latencies)) * 100.0
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if max(latencies) > 0
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else 0.0
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)
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vram_deltas = [r["active_vram_delta_mb"] for r in results]
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backpressure_valid = all(abs(d) < 1.0 for d in vram_deltas)
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return {
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"benchmark_name": BENCHMARK_NAME,
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"benchmark_version": BENCHMARK_VERSION,
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"track": "apple_uma_persona_motor",
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"skipped": False,
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"mlx_status": status,
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"benchmark_only": True,
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"serving_authorized": False,
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"simulation": {
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"steps": steps,
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"batch_size": batch_size,
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"cga_dim": CGA_DIM,
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"eval_boundary_every_n_steps": 50,
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},
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"adr_claims": {
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"ADR-0027_topological_cost_neutrality": {
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"description": (
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"Peak VRAM and step latency are statistically equal across "
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"identity packs — changing persona incurs no alignment tax."
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),
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"latency_spread_pct": round(lat_spread_pct, 2),
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"pass": lat_spread_pct < 10.0,
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},
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"ADR-0028_backpressure_validation": {
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"description": (
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"Active VRAM Delta ~0 MB proves that periodic mx.eval() "
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"boundaries flush the lazy MLX graph safely, mirroring "
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"ChatRuntime async token-yield backpressure."
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),
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"all_active_vram_deltas_mb": vram_deltas,
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"pass": backpressure_valid,
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},
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},
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"cases": results,
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"non_claims": [
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"No MLX serving-backend claim.",
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"No replacement of the NumPy versor_apply canonical path.",
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"No ANN or approximate search.",
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"No CoreML or Neural Engine claim.",
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],
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}
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def _cli_main(argv: list[str] | None = None) -> int:
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parser = argparse.ArgumentParser(description=BENCHMARK_NAME)
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parser.add_argument(
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"--json", action="store_true", help="emit machine-readable JSON"
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)
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parser.add_argument(
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"--steps", type=int, default=1_000,
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help="number of sandwich-product propagation steps (default: 1000)",
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)
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parser.add_argument(
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"--batch", type=int, default=128,
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help="field walk batch size — rows in the (batch, 32) CGA matrix (default: 128)",
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)
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args = parser.parse_args(argv)
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report = run_persona_motor_benchmark(steps=args.steps, batch_size=args.batch)
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if args.json:
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print(json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True))
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return 0
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if report.get("skipped"):
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print(f"{BENCHMARK_NAME} — SKIPPED: {report['reason']}")
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return 0
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print(f"\n=== {BENCHMARK_NAME} ===")
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sim = report["simulation"]
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print(
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f"Simulation: {sim['steps']} steps | batch={sim['batch_size']} | "
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f"CGA dim={sim['cga_dim']} | eval every {sim['eval_boundary_every_n_steps']} steps\n"
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)
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print(f"{'Pack ID':<40} {'Latency/step':>14} {'VRAM Delta':>12} {'Peak VRAM':>12}")
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print("-" * 82)
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for case in report["cases"]:
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print(
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f"{case['pack_id']:<40} "
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f"{case['per_step_ms']:>13.4f}ms "
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f"{case['active_vram_delta_mb']:>11.2f}MB "
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f"{case['peak_vram_mb']:>11.2f}MB"
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)
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print()
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claims = report["adr_claims"]
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neutrality = claims["ADR-0027_topological_cost_neutrality"]
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backpressure = claims["ADR-0028_backpressure_validation"]
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print(
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f"ADR-0027 Cost Neutrality — latency spread {neutrality['latency_spread_pct']:.1f}% "
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f"{'PASS' if neutrality['pass'] else 'FAIL'}"
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)
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print(
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f"ADR-0028 Backpressure — VRAM deltas {backpressure['all_active_vram_deltas_mb']} "
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f"{'PASS' if backpressure['pass'] else 'FAIL'}"
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)
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print()
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return 0
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if __name__ == "__main__":
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raise SystemExit(_cli_main())
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Loading…
Reference in a new issue