6cb28566ec
agenerate() skipped _recall_state() entirely, meaning async streaming responses were disconnected from session memory. This patch brings agenerate() to full parity with the synchronous path: - Accepts vault and recall_top_k parameters (default 3, matching generate()) - Calls _recall_state(_voiced_state(current, persona), vault, recall_top_k) at each step before nearest-node selection - Does not add stop_nodes or salience (those remain sync-only for now; the core correctness gap is vault recall) The async return value is still token-by-token via yield. Callers that want final_state should use the synchronous path or wrap in a collector.
347 lines
11 KiB
Python
347 lines
11 KiB
Python
"""
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Generation loop — token streaming from the versor manifold.
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Every token: nearest non-current word to current F via CGA inner product.
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Every step: F <- versor_apply(V, F) where V = word_transition_rotor(A, B).
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Architectural boundaries enforced here:
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- VocabManifold owns manifold points only (get_versor_at, nearest).
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- algebra.rotor.word_transition_rotor constructs the transition operator.
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- Generation returns GenerationResult carrying final_state, not list[str].
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- No normalization inside this loop. FieldState invariant is maintained
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structurally by versor_apply() and the closed algebra.
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No confidence gates. No IDK fallback. No attractor clamping.
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F is always on the manifold. nearest() is exact.
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"""
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from __future__ import annotations
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from collections import deque
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import numpy as np
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from field.state import FieldState
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from field.propagate import propagate_step
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from algebra.rotor import word_transition_rotor
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from generate.attention import AttentionOperator
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from generate.result import GenerationResult
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from generate.salience import SalienceOperator
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_RECENT_WINDOW = 3
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_STOP_TOKENS = frozenset({"it", "to", "word"})
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def _articulate(vocab, word: str) -> str:
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"""
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Recover the emitted surface through MorphologyEntry when available.
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The manifold walk selects a vocabulary point. Articulation then returns
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the structured surface carried by that point, preserving script and
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inflection without introducing a corrective pass.
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"""
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morphology_for_word = getattr(vocab, "morphology_for_word", None)
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if morphology_for_word is None:
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return word
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morphology = morphology_for_word(word)
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return morphology.surface if morphology is not None else word
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def _nearest_next(
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vocab,
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F_voiced,
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current_node: int,
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recent_nodes: tuple[int, ...] = (),
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stop_nodes: frozenset[int] = frozenset(),
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candidate_indices: np.ndarray | None = None,
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) -> tuple[str, int]:
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"""
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Select the nearest vocabulary point while avoiding short loops.
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Allowing the current node to win makes V = transition(A, A), which is an
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identity-like transition and can stall generation forever on one token.
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Recent-node exclusion reduces two- and three-token attractor cycles.
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Stop-node exclusion keeps function-word wells from dominating when more
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informative neighbors are available.
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If attention/language filtering leaves only the current node available,
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the final fallback deliberately permits that singleton candidate instead
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of crashing. That keeps inhibition fail-closed to the attended region.
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"""
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if len(vocab) <= 1:
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return vocab.nearest(F_voiced, candidate_indices=candidate_indices)
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recent = set(recent_nodes)
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stop = set(stop_nodes)
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fallback_orders = (
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recent | stop,
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stop,
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recent,
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set(),
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)
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for extra in fallback_orders:
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try:
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return _nearest_with_optional_candidates(
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vocab,
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F_voiced,
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current_node,
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extra,
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candidate_indices,
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)
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except ValueError:
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continue
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return _nearest_with_optional_candidates(
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vocab,
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F_voiced,
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-1,
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set(),
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candidate_indices,
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)
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def _nearest_with_optional_candidates(
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vocab,
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F_voiced,
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current_node: int,
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exclude_indices: set[int],
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candidate_indices: np.ndarray | None,
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) -> tuple[str, int]:
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try:
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return vocab.nearest(
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F_voiced,
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exclude_idx=current_node,
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exclude_indices=exclude_indices,
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candidate_indices=candidate_indices,
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)
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except TypeError:
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if candidate_indices is not None:
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raise
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return vocab.nearest(
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F_voiced,
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exclude_idx=current_node,
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exclude_indices=exclude_indices,
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)
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def _voiced_state(state: FieldState, persona) -> FieldState:
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"""Compose the session persona motor into the live field path."""
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return FieldState(
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F=persona.apply(state.F),
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node=state.node,
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step=state.step,
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holonomy=state.holonomy,
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energy=state.energy,
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valence=state.valence,
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)
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def _recall_state(state: FieldState, vault, top_k: int) -> FieldState:
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"""
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Feed exact vault recall back into the field as sequential operators.
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Recall returns stored versors ranked by the vault's exact metric. Each hit
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is treated as an additional operator in the propagation path.
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"""
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if vault is None or top_k <= 0:
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return state
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current = state
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for hit in vault.recall(current.F, top_k=top_k):
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recalled_F = hit["versor"]
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V = word_transition_rotor(current.F, recalled_F)
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current = propagate_step(current, V)
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current = FieldState(
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F=current.F,
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node=state.node,
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step=current.step,
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holonomy=state.holonomy,
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energy=state.energy,
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valence=state.valence,
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)
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return current
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def _candidate_indices_for_language(vocab, output_lang: str | None) -> np.ndarray | None:
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if output_lang is None:
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return None
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indices_for_language = getattr(vocab, "indices_for_language", None)
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if indices_for_language is None:
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return None
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indices = indices_for_language(output_lang)
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if len(indices) == 0:
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raise ValueError(f"No generation candidates for output language {output_lang!r}.")
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return indices
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def _intersect_candidates(a: np.ndarray | None, b: np.ndarray | None) -> np.ndarray | None:
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if a is None:
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return b
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if b is None:
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return a
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if len(a) == 0 or len(b) == 0:
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return np.asarray([], dtype=np.int64)
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b_set = {int(idx) for idx in b}
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return np.asarray([int(idx) for idx in a if int(idx) in b_set], dtype=np.int64)
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def _attention_candidates(
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state: FieldState,
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vocab,
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use_salience: bool,
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salience_top_k: int,
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inhibition_threshold: float,
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) -> tuple[np.ndarray | None, int | None, int | None]:
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if not use_salience:
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return None, None, None
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salience = SalienceOperator().compute(state, vocab, top_k=salience_top_k)
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attention = AttentionOperator(inhibition_threshold).plan(salience, vocab)
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return attention.allowed_indices, salience.budget, len(attention.allowed_indices)
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def generate(
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state: FieldState,
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vocab,
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persona,
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max_tokens: int = 128,
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record_trajectory: bool = False,
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vault=None,
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recall_top_k: int = 3,
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output_lang: str | None = None,
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allow_cross_language_generation: bool = True,
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use_salience: bool = False,
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salience_top_k: int = 16,
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inhibition_threshold: float = 0.3,
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) -> GenerationResult:
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"""
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Generate a token sequence from an initial FieldState.
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Loop:
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1. Compose the persistent persona motor into the current field
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2. Propagate exact vault recall hits into the current field
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3. Find nearest non-current vocab node via CGA inner product
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4. Emit token
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5. Build transition rotor: V = word_transition_rotor(A, B)
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where A = versor at current node, B = versor at nearest node
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6. Propagate: F <- versor_apply(V, F)
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7. Advance node pointer
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Returns:
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GenerationResult with tokens, final_state, optional trajectory,
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and salience telemetry when attention is enabled.
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"""
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tokens = []
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trajectory = [] if record_trajectory else None
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current = state
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recent_nodes = deque([state.node], maxlen=_RECENT_WINDOW)
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language_candidates = None if allow_cross_language_generation else _candidate_indices_for_language(vocab, output_lang)
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salience_candidates, salience_budget, candidates_used = _attention_candidates(
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state,
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vocab,
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use_salience=use_salience,
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salience_top_k=salience_top_k,
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inhibition_threshold=inhibition_threshold,
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)
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candidate_indices = _intersect_candidates(language_candidates, salience_candidates)
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if candidate_indices is not None and len(candidate_indices) == 0:
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candidate_indices = salience_candidates if salience_candidates is not None else language_candidates
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candidates_used = None if candidate_indices is None else len(candidate_indices)
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stop_nodes = frozenset(
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vocab.index_of(token)
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for token in _STOP_TOKENS
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if token in {vocab.get_word_at(i) for i in range(len(vocab))}
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)
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token_budget = min(max_tokens, int(candidates_used)) if candidates_used is not None else max_tokens
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for _ in range(token_budget):
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current = _recall_state(_voiced_state(current, persona), vault, recall_top_k)
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word, word_idx = _nearest_next(
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vocab,
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current.F,
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current.node,
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recent_nodes=tuple(recent_nodes),
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stop_nodes=stop_nodes,
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candidate_indices=candidate_indices,
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)
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tokens.append(_articulate(vocab, word))
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if record_trajectory:
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trajectory.append(current)
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A = vocab.get_versor_at(current.node)
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B = vocab.get_versor_at(word_idx)
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V = word_transition_rotor(A, B)
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current = propagate_step(current, V)
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current = FieldState(
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F=current.F,
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node=word_idx,
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step=current.step,
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holonomy=current.holonomy,
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energy=current.energy,
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valence=current.valence,
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)
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recent_nodes.append(word_idx)
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return GenerationResult(
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tokens=tokens,
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final_state=current,
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trajectory=trajectory,
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salience_top_k=salience_budget,
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candidates_used=candidates_used,
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)
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async def agenerate(
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state: FieldState,
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vocab,
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persona,
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max_tokens: int = 128,
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vault=None,
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recall_top_k: int = 3,
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):
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"""
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Async streaming version — yields one token at a time.
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Maintains parity with the synchronous generate() path:
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- Persona motor applied via _voiced_state() every step
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- Vault recall fed back into field via _recall_state() every step
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- Recent-node and stop-node exclusion applied
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The caller receives tokens as they are emitted. For the full
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GenerationResult (final_state, trajectory), use the synchronous
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generate() path or wrap this generator in an async collector.
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Yields: str (one token per iteration)
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"""
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current = state
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recent_nodes = deque([state.node], maxlen=_RECENT_WINDOW)
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stop_nodes = frozenset(
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vocab.index_of(token)
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for token in _STOP_TOKENS
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if token in {vocab.get_word_at(i) for i in range(len(vocab))}
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)
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for _ in range(max_tokens):
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current = _recall_state(_voiced_state(current, persona), vault, recall_top_k)
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word, word_idx = _nearest_next(
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vocab,
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current.F,
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current.node,
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recent_nodes=tuple(recent_nodes),
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stop_nodes=stop_nodes,
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)
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yield _articulate(vocab, word)
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A = vocab.get_versor_at(current.node)
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B = vocab.get_versor_at(word_idx)
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V = word_transition_rotor(A, B)
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current = propagate_step(current, V)
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current = FieldState(
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F=current.F,
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node=word_idx,
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step=current.step,
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holonomy=current.holonomy,
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energy=current.energy,
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valence=current.valence,
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)
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recent_nodes.append(word_idx)
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