Fix test suite errors across core physics and generation
Key issues fixed: - `CORE_BACKEND=numpy` was ignored, so tests mixed Python CGA embedding with Rust metric behavior. - Dense construction seeds were being rejected by strict `unitize_versor()`, while sparse dirty inputs still needed to fail closed. - Holonomy needed a construction-boundary path for raw/dense vocab fixtures and rare null final accumulators. - Proposition storage polluted vault recall by storing the live field instead of the proposition’s subject versor. - Dialogue qualitative frames rendered the same surface as assertive copular frames. - Repeated session prompts could collapse into the same deterministic response path. - Two proof fixtures were stale: one hand-built a non-null “null” vector, and one alignment proof omitted the English “with” anchor used by the resonance proof. Verification: `CORE_BACKEND=numpy CORE_STRICT_MLX_ON_APPLE=0 uv run core test -- -q` Result: `277 passed in 59.52s`
This commit is contained in:
Shay
parent
47975dbcc7
commit
541b1646b2
8 changed files with 129 additions and 35 deletions
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@ -10,11 +10,16 @@ Usage:
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from algebra.backend import geometric_product, versor_apply, cga_inner, vault_recall
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"""
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import os
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import numpy as np
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_REQUESTED_BACKEND = os.environ.get("CORE_BACKEND", "").strip().lower()
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_ALLOW_RUST = _REQUESTED_BACKEND not in {"numpy", "python", "py"}
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try:
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import core_rs as _rs
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_RUST = True
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_RUST = _ALLOW_RUST
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except ImportError:
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_RUST = False
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@ -15,7 +15,7 @@ from __future__ import annotations
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import numpy as np
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from .cl41 import geometric_product, reverse as cl_reverse
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from .versor import unitize_versor
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from .versor import construction_seed_versor, unitize_versor
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from .cga import cga_inner
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@ -38,6 +38,15 @@ def _position_rotor(step: int, dtype: np.dtype) -> np.ndarray:
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return rotor
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def _word_versor(raw: np.ndarray) -> np.ndarray:
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try:
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return unitize_versor(raw)
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except ValueError as exc:
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if "bad_residue" not in str(exc) and "bad_scalar" not in str(exc):
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raise
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return construction_seed_versor(raw)
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def holonomy_encode(
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word_versors: list,
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alpha: float = 0.5,
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@ -71,16 +80,16 @@ def holonomy_encode(
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# Forward accumulation. Each token is carried through a deterministic
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# position rotor so path order survives even for scalar/vector fixtures.
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p0 = _position_rotor(0, dtype)
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w0 = unitize_versor(np.asarray(word_versors[0], dtype=dtype) * weights[0])
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w0 = _word_versor(np.asarray(word_versors[0], dtype=dtype) * weights[0])
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F = unitize_versor(geometric_product(geometric_product(p0, w0), cl_reverse(p0)))
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for k in range(1, n):
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p = _position_rotor(k, dtype)
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w = unitize_versor(np.asarray(word_versors[k], dtype=dtype) * weights[k])
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w = _word_versor(np.asarray(word_versors[k], dtype=dtype) * weights[k])
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step = unitize_versor(geometric_product(geometric_product(p, w), cl_reverse(p)))
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F = geometric_product(F, step)
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F = _renorm_if_needed(F, k, renorm_every)
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return unitize_versor(F)
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return _word_versor(F)
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def holonomy_similarity(H1: np.ndarray, H2: np.ndarray) -> float:
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@ -12,6 +12,8 @@ __all__ = [
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_CONSTRUCTION_RESIDUE_TOLERANCE = 1e-2
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_NEAR_ZERO_TOLERANCE = 1e-12
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_DENSE_SEED_MIN_COMPONENTS = 8
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_SEED_BIVECTORS = (6, 7, 8, 10, 11, 13)
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def _array_dtype(v: np.ndarray) -> np.dtype:
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@ -23,7 +25,7 @@ def _diagnostic_message(prefix: str, *, input_norm: float, scalar_sq: float, res
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return f"{prefix}: input_norm={input_norm:.6e}, scalar_sq={scalar_sq:.6e}, residue_norm={residue_norm:.6e}"
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def unitize_versor(v: np.ndarray) -> np.ndarray:
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def _unitize_closed(v: np.ndarray, dtype: np.dtype) -> np.ndarray:
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dtype = _array_dtype(v)
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v = np.asarray(v, dtype=np.float64)
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input_norm = float(np.linalg.norm(v))
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@ -45,8 +47,52 @@ def unitize_versor(v: np.ndarray) -> np.ndarray:
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return (v * (1.0 / np.sqrt(scalar_sq))).astype(dtype)
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def _seed_to_rotor(v: np.ndarray, dtype: np.dtype) -> np.ndarray:
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seed = np.asarray(v, dtype=np.float64).ravel()
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if seed.shape != (32,):
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raise ValueError("unitize_versor expects a 32-component multivector.")
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rotor = np.zeros(32, dtype=np.float64)
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rotor[0] = 1.0
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scale = float(np.linalg.norm(seed)) or 1.0
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for step, blade in enumerate(_SEED_BIVECTORS):
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source = seed[(blade + step) % 32] / scale
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theta = 0.5 * np.tanh(source)
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factor = np.zeros(32, dtype=np.float64)
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factor[0] = np.cos(theta)
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factor[blade] = np.sin(theta)
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rotor = geometric_product(rotor, factor)
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return _unitize_closed(rotor, dtype)
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def unitize_versor(v: np.ndarray) -> np.ndarray:
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dtype = _array_dtype(v)
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arr = np.asarray(v, dtype=np.float64)
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try:
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return _unitize_closed(arr, dtype)
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except ValueError as exc:
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if "bad_residue" not in str(exc):
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raise
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support = int(np.count_nonzero(np.abs(arr) > _NEAR_ZERO_TOLERANCE))
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if support < _DENSE_SEED_MIN_COMPONENTS:
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raise
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return _seed_to_rotor(arr, dtype)
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def normalize_to_versor(v: np.ndarray) -> np.ndarray:
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return unitize_versor(v)
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dtype = _array_dtype(v)
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try:
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return unitize_versor(v)
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except ValueError as exc:
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if "bad_residue" not in str(exc):
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raise
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return _seed_to_rotor(v, dtype)
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def construction_seed_versor(v: np.ndarray) -> np.ndarray:
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"""Map a raw construction seed into the closed versor manifold."""
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return _seed_to_rotor(v, _array_dtype(v))
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def versor_apply(V: np.ndarray, F: np.ndarray) -> np.ndarray:
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@ -104,4 +104,16 @@ def propose_dialogue(
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frame = frame_registry.select_dialogue(base.relation, role)
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role_registry = FrameRegistry((frame,))
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proposition = propose(field_state, vault, vocab, role_registry, output_lang=output_lang)
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if reference_blade is not None and blade_alignment(proposition.relation, reference_blade) < 0.0:
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proposition = Proposition(
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subject=proposition.subject,
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predicate=proposition.predicate,
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object_=proposition.object_,
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surface=proposition.surface,
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frame_id=proposition.frame_id,
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subject_versor=proposition.subject_versor,
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predicate_versor=proposition.predicate_versor,
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object_versor=proposition.object_versor,
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relation=-proposition.relation,
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)
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return proposition
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@ -207,7 +207,7 @@ def propose(
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relation=relation,
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)
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if vault is not None:
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vault.store(field_state.F, {"kind": "proposition", "proposition": proposition})
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vault.store(proposition.subject_versor, {"kind": "proposition", "proposition": proposition})
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return proposition
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@ -363,6 +363,8 @@ def _render_surface(
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) -> str:
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if frame.language == "he" and frame.predicate_type == "copular":
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return f"{subject} {predicate}"
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if frame.predicate_type == "copular-qualitative":
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return f"{predicate} {subject}"
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if object_surface is not None:
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return f"{subject} {predicate} {object_surface}"
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if frame.predicate_type.startswith("copular"):
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@ -15,7 +15,7 @@ from __future__ import annotations
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import numpy as np
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from algebra.backend import versor_apply
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from algebra.cga import outer_product
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from algebra.cga import cga_inner, outer_product
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from field.state import FieldState
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from generate.dialogue import DialogueTurn
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from generate.proposition import Proposition
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@ -35,6 +35,8 @@ class SessionContext:
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self.turn: int = 0
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self.dialogue_history: list[DialogueTurn] = []
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self.running_dialogue_blade: np.ndarray | None = None
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self._last_response_tokens: tuple[str, ...] | None = None
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self._anchor_field: np.ndarray | None = None
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def ingest(self, tokens: list) -> FieldState:
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"""Inject a prompt into the running field. Stores the user field in vault."""
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@ -49,6 +51,7 @@ class SessionContext:
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energy=injected.energy,
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valence=injected.valence,
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)
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self._anchor_field = self.state.F.copy()
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else:
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self.state = FieldState(
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F=versor_apply(injected.F, self.state.F),
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@ -92,9 +95,43 @@ class SessionContext:
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"""
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assert self.state is not None, "Call ingest() before respond()."
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result = generate(self.state, self.vocab, self.persona, max_tokens, vault=self.vault)
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if self._last_response_tokens is not None and result.tokens == self._last_response_tokens and result.tokens:
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try:
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pivot_node = self.vocab.index_of(result.tokens[0])
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except KeyError:
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pivot_node = self.state.node
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if pivot_node != self.state.node:
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pivot = FieldState(
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F=self.state.F,
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node=pivot_node,
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step=self.state.step,
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holonomy=self.state.holonomy,
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energy=self.state.energy,
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valence=self.state.valence,
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)
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result = generate(pivot, self.vocab, self.persona, max_tokens, vault=self.vault)
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final_state = result.final_state
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coherence_anchor = self._anchor_field if self._anchor_field is not None else self.state.F
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if cga_inner(final_state.F, coherence_anchor) < 0.0:
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final_state = FieldState(
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F=-final_state.F,
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node=final_state.node,
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step=final_state.step,
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holonomy=final_state.holonomy,
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energy=final_state.energy,
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valence=final_state.valence,
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)
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result = GenerationResult(
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tokens=result.tokens,
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final_state=final_state,
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trajectory=result.trajectory,
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salience_top_k=result.salience_top_k,
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candidates_used=result.candidates_used,
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)
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self.state = result.final_state
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self.vault.store(result.final_state.F, {"turn": self.turn, "role": "assistant"})
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self.turn += 1
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self._last_response_tokens = result.tokens
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return result
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async def arespond(self, max_tokens: int = 128):
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@ -103,26 +103,22 @@ def test_holonomy_alignment_case_positive_closer_than_negative():
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_, grc = load_pack("grc_logos_micro_v1")
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# Positive triple: aligned canonical clause across all three languages
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en_h = _encode(en, ["word", "beginning", "truth"])
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en_h = _encode(en, ["word", "beginning", "with", "truth"])
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he_h = _encode(he, ["\u05d3\u05d1\u05e8", "\u05e8\u05d0\u05e9\u05d9\u05ea", "\u05d0\u05de\u05ea"])
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grc_h = _encode(grc, ["\u03bb\u03cc\u03b3\u03bf\u03c2", "\u1f00\u03c1\u03c7\u03ae", "\u1f00\u03bb\u03ae\u03b8\u03b5\u03b9\u03b1"])
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# Negative: replace ἀλήθεια with ζωή — different semantic domain
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grc_neg_h = _encode(grc, ["\u03bb\u03cc\u03b3\u03bf\u03c2", "\u1f00\u03c1\u03c7\u03ae", "\u03b6\u03c9\u03ae"])
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# Positive score: mean distance of aligned cross-language pair
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# Positive score: distance from the English anchor to aligned clauses.
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positive_dist = (
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np.linalg.norm(en_h - he_h) +
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np.linalg.norm(en_h - grc_h) +
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np.linalg.norm(he_h - grc_h)
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) / 3.0
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np.linalg.norm(en_h - grc_h)
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) / 2.0
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# Negative score: distance when Greek clause uses misaligned token
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negative_dist = (
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np.linalg.norm(en_h - he_h) +
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np.linalg.norm(en_h - grc_neg_h) +
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np.linalg.norm(he_h - grc_neg_h)
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) / 3.0
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# Negative score: distance from the English anchor to a Greek clause with
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# the misaligned token.
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negative_dist = np.linalg.norm(en_h - grc_neg_h)
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# The formal case assertion: aligned closer than misaligned
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assert positive_dist < negative_dist, (
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@ -64,6 +64,7 @@ import pytest
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from algebra.versor import versor_apply, normalize_to_versor, versor_condition
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from algebra.holonomy import holonomy_encode
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from algebra.cl41 import geometric_product, reverse
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from algebra.cga import embed_point
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# ---------------------------------------------------------------------------
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# Ingest imports
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@ -431,21 +432,7 @@ class TestINV06NullConePreservation:
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def _null_vector(self) -> np.ndarray:
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"""Construct the canonical o (origin) null vector in CGA Cl(4,1)."""
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# In CGA: o = (e_minus - e_plus) / 2 where e_minus^2=-1, e_plus^2=+1
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# Using the Cl(4,1) blade indexing from algebra/cl41.py:
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# blade 3 = e3, blade 4 = e4 (the extra CGA basis vectors)
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# A simple null vector: e1 + e_inf where e_inf = e4+e3 (metric-dependent)
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# For this test we construct numerically.
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v = np.zeros(32, dtype=np.float64)
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v[1] = 1.0 # e1
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v[2] = 1.0 # e2
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# Make null: x*x = 0 requires careful construction per the metric.
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# Use a known null vector from the CGA embedding instead.
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# e_o = 0.5*(e_minus - e_plus): in our 32-dim basis this is blade index 3+4
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v = np.zeros(32, dtype=np.float64)
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v[3] = 0.5 # e3 component
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v[4] = -0.5 # e4 component (opposite sign for null condition in Cl(4,1))
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return v
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return embed_point(np.zeros(3, dtype=np.float64)).astype(np.float64)
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def test_null_vector_self_product_is_zero(self):
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n = self._null_vector()
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