BFM-decomp/tools/build_wave_atlas.py

#!/usr/bin/env python3
"""Build a campaign wave from the FRONTIER ATLAS, optimized for gate throughput.

Why this exists (P31, 2026-08-14): the pre-baked adapt/weak card piles are the smallest,
best-seeded tail (12-42 ins). Drawing from them banks ~1,440 ins/wave against 635,744 open —
0.011pp of fleet per wave. The atlas knows where the mass actually is (cousin-multi 294k ins,
cold 183k, main-only 38k) and what lever each group needs.

TWO selection principles, both measured:
  1. GATE COST SCALES WITH (binary, TU) GROUPS, NOT DRAFTS. Each group is a whole-binary rebuild.
     Wave C was 35 drafts over 27 groups = 1.3 drafts/rebuild, ~50 min of gate for 32 banks.
     So: CONCENTRATE the wave in few binaries. This is free throughput.
  2. MASS BEATS COUNT for the instruction-weighted metric — prefer bigger functions where a seed
     exists, but keep them inside the model ladder's competence.

Selection: open stubs (derived from corpus, R32/R33), not spent in a prior wave, from atlas
groups whose lever is agent-draftable; ranked by binary concentration then instruction mass.

MUST NOT run while a gate is in flight (R35 — corpus.stubs() misreports substituted drafts).

Usage: build_wave_atlas.py <out.json> [N] [--max-bins K] [--min-ins M] [--levers a,b,c]
"""
import json, os, sys, collections, subprocess, argparse, glob
sys.path.insert(0, 'tools')
import corpus

ap = argparse.ArgumentParser()
ap.add_argument('out')
ap.add_argument('n', nargs='?', type=int, default=96)
ap.add_argument("--max-bins", type=int, default=12, help="concentrate into this many GATE GROUPS (binary,TU)")
ap.add_argument('--min-ins', type=int, default=0)
ap.add_argument('--max-ins', type=int, default=120, help='above this the bulk ladder stops being honest')
ap.add_argument('--levers', default='head-crack,seeded-crack,redraft,len-vein,integration,family-sweep,tiny-direct',
                help='agent-draftable levers; UNKNOWN/tell/jtbl/o0/cc1 need their own lanes')
ap.add_argument('--atlas', default='.run/atlas.json')
ap.add_argument('--exclude-bins', default='',
                help='comma-separated binaries to skip. NOTHING is excluded by default. '
                     '(History: main used to be excluded on a "link-resolution defect" — that '
                     'diagnosis was REFUTED 2026-08-15 by a null-draft control: the false diff '
                     'reproduces with ZERO drafts substituted. main simply cannot be gated '
                     'INCREMENTALLY, because its extract runs psyq_integrate/ld_interleave and '
                     'rewrites the .ld. Draft main like any binary; gate it with '
                     'tools/gate_main.py, never gate_lane/gate_stage.)')
ap.add_argument('--rank', choices=('groups','mass'), default='groups',
                help="'groups' (default) ranks gate groups by MEMBER COUNT -- right for overlays, "
                     "where every (binary,TU) group costs its own rebuild. 'mass' ranks purely by "
                     "instruction size across all groups -- right for MAIN, whose gate cost is per "
                     "SLATE, not per TU: with 'groups' a wide --min-ins band fills from the "
                     "biggest-by-count group, which is the SMALLEST-by-instruction one, and the "
                     "wave silently collapses to tiny functions (measured: 60 cards / 2,604 ins "
                     "avg 43, when 46 cards / 4,829 ins avg 105 were available).")
ap.add_argument('--target-ins', type=int, default=0,
                help='size the wave by INSTRUCTION MASS: keep drawing cards until this many '
                     'instructions are selected (still capped by n). The public metric is '
                     'instruction-weighted, so this is the number that matters -- 6000+ is the '
                     'P31 S52 standard (wave O: 6,266 ins in 2 gate groups, 47/49 MATCH).')
ap.add_argument('--only-bins', default='',
                help='comma-separated allow-list; if set, ONLY these binaries are eligible. '
                     'Use --only-bins main for a main wave: gate_main.py rebuilds the whole EXE '
                     'once per SLATE, so main has no per-TU gate cost and --max-bins can be large.')
a = ap.parse_args()
EXCLUDE = {b for b in a.exclude_bins.split(',') if b}
ONLY = {b for b in a.only_bins.split(',') if b}

busy = subprocess.run(['pgrep', '-f', 'tools/(gate_stage|dedup_propagate|gate_lane)'],
                      capture_output=True, text=True)
if busy.returncode == 0 and busy.stdout.strip():
    sys.exit(f"REFUSING: gate in flight (pids {busy.stdout.split()}) — corpus.stubs() would misreport (R35).")

# R32/R33: derive the already-waved set from what is ON DISK, never from a hardcoded wave-letter
# list (the literal 'a'..'l' silently missed waves m and n and would have re-issued their cards).
# Exclude OUR OWN output: the glob matches it, so re-running the selector after an aborted or
# re-tuned build marked the previous attempt's cards as 'already waved' and silently shrank the
# pool (measured: 46 candidates instead of 60 on a re-run with identical filters).
PRIORS = [p for p in sorted(glob.glob('.run/wave_*_cards.json'))
          if os.path.abspath(p) != os.path.abspath(a.out)]
taken = set()
for p in PRIORS:
    try:
        taken |= {c.get('fn') or c.get('name') for c in json.load(open(p))}
    except (FileNotFoundError, json.JSONDecodeError, TypeError):
        pass
if not PRIORS:
    print('NOTE: no prior wave card files found — nothing filtered as already-waved', file=sys.stderr)

levers = set(a.levers.split(','))
atlas = json.load(open(a.atlas))

_open = {}
def _stubmap(binary):
    if binary not in _open:
        # corpus.stubs() is addr -> Stub; the NAME lives on the record
        _open[binary] = {st.symbol: st for st in corpus.stubs(binary).values()}
    return _open[binary]

def is_open(binary, fn):
    return fn in _stubmap(binary)

def home_tu(binary, fn):
    """The stub's home .c — this is the GATE GROUP KEY (gate_lane groups by (binary, src))."""
    st = _stubmap(binary).get(fn)
    return st.path if st else None

def model_for(nins):
    if nins <= 50:  return 'haiku'
    if nins <= 120: return 'sonnet'
    return 'opus'

cands, skipped = [], collections.Counter()
for g in atlas['groups']:
    if g['lever'] not in levers:
        skipped['lever-not-in-lane'] += g['inst']; continue
    ex = g.get('exemplar') or {}
    seed = (g.get('seed') or {}).get('norm') or (g.get('seed') or {}).get('raw') or {}
    for m in g.get('members', []):
        fn, b, nins = m.get('name'), m.get('b'), m.get('nins') or 0
        if not fn or not b:                      skipped['no-name'] += 1; continue
        if b in EXCLUDE:                         skipped['excluded-binary'] += 1; continue
        if ONLY and b not in ONLY:               skipped['not-in-only-bins'] += 1; continue
        if fn in taken:                          skipped['already-waved'] += 1; continue
        if not (a.min_ins <= nins <= a.max_ins): skipped['out-of-band'] += 1; continue
        if not is_open(b, fn):                   skipped['already-banked'] += 1; continue
        sub = corpus.asm_path(b, fn)
        if not sub:                              skipped['no-asm'] += 1; continue
        cands.append({
            'tu': home_tu(b, fn),
            'fn': fn, 'binary': b, 'lane': 'mass', 'model': model_for(nins), 'nins': nins,
            'addr': m.get('a'), 'sub': __import__('os').path.dirname(sub),
            'gid': g['gid'], 'lever': g['lever'], 'confidence': g.get('confidence'),
            'lever_alts': g.get('lever_alts', []),
            'exemplar': {'binary': ex.get('b'), 'fn': ex.get('name'), 'nins': ex.get('nins')},
            'seed_sim': seed.get('sim'),
        })

# principle 1: CONCENTRATE ON GATE GROUPS. gate_lane groups by (binary, home .c) and each group
# is one whole-binary rebuild, so drafts-per-GROUP is the throughput number that matters -- not
# drafts per binary. Wave D was 42 drafts over 23 groups (1.8/group, ~40 min of gate).
by_tu = collections.defaultdict(list)
for c in cands:
    by_tu[(c['binary'], c['tu'])].append(c)
if a.rank == 'mass':
    ranked = sorted(by_tu, key=lambda k: -sum(c['nins'] for c in by_tu[k]))[:a.max_bins]
else:
    ranked = sorted(by_tu, key=lambda k: -len(by_tu[k]))[:a.max_bins]

# principle 3 (P31 S52): SIZE A WAVE BY INSTRUCTIONS, NOT BY CARDS. The public metric is
# instruction-weighted, so a wave is worth what its instructions are worth: the 12-42-ins card
# lanes produced ~1,400 ins/wave (~0.011pp) while wave O carried 6,266 ins for the same gate cost
# and the same draft rate. --target-ins keeps drawing cards until the instruction budget is met
# (still capped by n, so a wave can never spawn an unbounded fleet).
wave, tot_ins = [], 0
def _full():
    if a.target_ins:
        return tot_ins >= a.target_ins or len(wave) >= a.n
    return len(wave) >= a.n
for k in ranked:                            # principle 2: within a group, mass first
    for c in sorted(by_tu[k], key=lambda c: -c['nins']):
        if _full(): break
        wave.append(c); tot_ins += c['nins']
    if _full(): break
if a.target_ins and tot_ins < a.target_ins:
    print(f"NOTE: only {tot_ins} ins available under these filters (target {a.target_ins}) — "
          f"widen --min-ins/--max-ins/--levers or raise n ({len(wave)} of max {a.n} cards used)")

json.dump(wave, open(a.out, 'w'), indent=1)
tot = sum(c['nins'] for c in wave)
print(f"candidates {len(cands)} in {len(by_tu)} gate groups (skipped {dict(skipped)})")
ngroups = len({(c['binary'], c['tu']) for c in wave})
print(f"-> wave {len(wave)} drafts / {tot} ins across {len({c['binary'] for c in wave})} binaries "
      f"in {ngroups} GATE GROUPS = {len(wave)/max(ngroups,1):.1f} drafts per rebuild")
if wave:
    print("models:", dict(collections.Counter(c['model'] for c in wave)))
    print("levers:", dict(collections.Counter(c['lever'] for c in wave)))
    print("nins: %d-%d (avg %.0f)" % (min(c['nins'] for c in wave), max(c['nins'] for c in wave), tot/len(wave)))