core/evals/l10_continuity/runner.py

"""The L10 continuity soak runner — drives the REAL turn loop over N turns.

It runs the deterministic corpus through ``CognitiveTurnPipeline`` over a fresh
``ChatRuntime`` whose engine-state checkpoint lives in a caller-supplied
directory. Optionally it injects *reboot legs*: at a chosen turn boundary it
drops the live runtime and reconstructs a new one from the on-disk checkpoint —
exactly the lifecycle the L10 telos asks about ("resume as the same life") — and
optionally simulates a kill mid-checkpoint-write by leaving an orphan temp file
the reconstruct must ignore (ADR-0156 atomicity).

The runner is pure instrumentation: it records per-turn evidence
(``versor_condition``, canonical ``trace_hash``, vault size, peak RSS, anchor
distance, turn-to-turn field movement, and which boot segment produced the turn)
and returns it. It makes NO pass/fail judgement — that is ``predicates.py`` — and
it never repairs, normalizes, or mutates field state (it only reads what the real
pipeline produced).

What a reboot restores (today, Shape B / ADR-0146): recognizers, discovery
candidates, and ``turn_count`` — and NOTHING else. The lived field, vault,
session graph, referents, and session anchor are process-local and discarded on
exit. The ``booted_segment`` tag on each record exists precisely so the
reboot-transparency predicate (P2b) can locate where a rebooted run diverges
from an uninterrupted one.
"""

from __future__ import annotations

import resource
from dataclasses import dataclass
from pathlib import Path

import numpy as np

from chat.runtime import ChatRuntime
from core.cognition.pipeline import CognitiveTurnPipeline
from core.config import RuntimeConfig

from evals.l10_continuity.corpus import prompt_at


@dataclass(frozen=True, slots=True)
class TurnRecord:
    """Per-turn evidence captured from the real pipeline (no judgement)."""

    turn_index: int
    input_text: str
    trace_hash: str
    versor_condition: float
    surface: str
    vault_size: int
    peak_rss_raw: int
    booted_segment: int
    # P5 signals (NaN when undefined — e.g. movement on a segment's first turn,
    # or distance before an anchor exists).
    dist_to_anchor: float
    turn_movement: float


@dataclass(frozen=True, slots=True)
class SoakResult:
    """The full ordered evidence of one soak run."""

    n_turns: int
    reboot_at: tuple[int, ...]
    records: tuple[TurnRecord, ...]

    def trace_hashes(self) -> tuple[str, ...]:
        return tuple(r.trace_hash for r in self.records)

    def versor_conditions(self) -> tuple[float, ...]:
        return tuple(r.versor_condition for r in self.records)

    def post_reboot_records(self) -> tuple[TurnRecord, ...]:
        """Records produced at/after the first reboot (the recovered tail)."""
        if not self.reboot_at:
            return ()
        first = self.reboot_at[0]
        return tuple(r for r in self.records if r.turn_index >= first)


def _peak_rss_raw() -> int:
    """Process peak RSS as the OS reports it (bytes on macOS, KiB on Linux).

    The unit differs by platform, so callers must use this only for
    *ratio*/monotonic checks (P3), never as an absolute byte ceiling.
    """
    return int(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)


def _new_runtime(config: RuntimeConfig, engine_state_dir: Path) -> ChatRuntime:
    """Construct a ChatRuntime bound to the checkpoint dir.

    Reconstruction is the reboot: ``ChatRuntime.__init__`` loads the on-disk
    engine-state checkpoint when one exists (recognizers / candidates /
    turn_count), so a second instance over the same directory resumes from the
    last durable checkpoint.
    """
    return ChatRuntime(config=config, engine_state_path=engine_state_dir)


def _inject_orphan_tmp(engine_state_dir: Path) -> None:
    """Simulate a kill mid-checkpoint-write: leave an orphan temp file.

    ADR-0156 writes ``content`` to a ``.<name>.<rand>.tmp`` file, fsyncs, then
    ``os.replace``s it into place. A SIGKILL between fsync and replace leaves
    exactly such an orphan with the *real* target fully intact. The loader reads
    only the canonical filenames, so the orphan must be ignored. We write a
    deliberately-corrupt orphan to prove the loader never reads it.
    """
    engine_state_dir.mkdir(parents=True, exist_ok=True)
    orphan = engine_state_dir / ".manifest.json.deadbeef.tmp"
    orphan.write_text("{ this is a torn, half-written checkpoint <<<", encoding="utf-8")


def read_recovered_turn_count(engine_state_dir: Path) -> int | None:
    """Read ``turn_count`` from the on-disk manifest, or None if absent."""
    from engine_state import EngineStateStore

    manifest = EngineStateStore(engine_state_dir).load_manifest()
    return None if manifest is None else int(manifest.get("turn_count", 0))


def _anchor_distance(runtime: ChatRuntime) -> float:
    ctx = runtime._context
    if ctx.state is None or ctx._anchor_field is None:
        return float("nan")
    f = np.asarray(ctx.state.F, dtype=np.float64)
    anchor = np.asarray(ctx._anchor_field, dtype=np.float64)
    return float(np.linalg.norm(f - anchor))


def _current_field(runtime: ChatRuntime) -> np.ndarray | None:
    ctx = runtime._context
    return None if ctx.state is None else np.asarray(ctx.state.F, dtype=np.float64)


def run_soak(
    n_turns: int,
    *,
    engine_state_dir: Path,
    reboot_at: tuple[int, ...] = (),
    config: RuntimeConfig | None = None,
    inject_orphan_tmp_at_reboot: bool = False,
) -> SoakResult:
    """Run ``n_turns`` of the deterministic corpus, optionally rebooting.

    ``reboot_at`` is a set of turn indices at which, *before* running that turn,
    the live runtime is dropped and reconstructed from the checkpoint. A reboot
    at turn 0 is meaningless (nothing checkpointed yet) and is ignored. When
    ``inject_orphan_tmp_at_reboot`` is set, a torn-write orphan temp file is left
    in the checkpoint dir immediately before each reconstruct, so the reboot
    exercises ADR-0156 crash recovery rather than a clean restart.
    """
    if n_turns < 0:
        raise ValueError(f"n_turns must be non-negative, got {n_turns}")
    config = config or RuntimeConfig()
    reboot_set = {i for i in reboot_at if i > 0}

    runtime = _new_runtime(config, engine_state_dir)
    pipe = CognitiveTurnPipeline(runtime=runtime)
    segment = 0
    prev_field: np.ndarray | None = None
    records: list[TurnRecord] = []

    for i in range(n_turns):
        if i in reboot_set:
            if inject_orphan_tmp_at_reboot:
                _inject_orphan_tmp(engine_state_dir)
            runtime = _new_runtime(config, engine_state_dir)
            pipe = CognitiveTurnPipeline(runtime=runtime)
            segment += 1
            prev_field = None  # movement is undefined across a reboot boundary

        text = prompt_at(i)
        result = pipe.run(text)
        field = _current_field(runtime)
        movement = (
            float(np.linalg.norm(field - prev_field))
            if field is not None and prev_field is not None
            else float("nan")
        )
        records.append(
            TurnRecord(
                turn_index=i,
                input_text=text,
                trace_hash=result.trace_hash,
                versor_condition=float(result.versor_condition),
                surface=result.surface,
                vault_size=len(runtime._context.vault),
                peak_rss_raw=_peak_rss_raw(),
                booted_segment=segment,
                dist_to_anchor=_anchor_distance(runtime),
                turn_movement=movement,
            )
        )
        prev_field = field

    return SoakResult(
        n_turns=n_turns,
        reboot_at=tuple(sorted(reboot_set)),
        records=tuple(records),
    )