#!/usr/bin/env python3 """ Organ Architecture — organ_graft.py Transplant organs between models. Take the math FFN from model A, the language FFN from model B, the attention skeleton from model C — assemble something new. Build v935 """ import struct import os import sys import json import shutil import argparse from pathlib import Path def load_manifest(organ_dir): """Load manifest from extracted organs directory.""" manifest_path = Path(organ_dir) / 'manifest.json' if not manifest_path.exists(): raise FileNotFoundError(f"No manifest.json in {organ_dir}") with open(manifest_path) as f: return json.load(f) def list_organs(organ_dir, organ_type=None): """List available organs with their metadata.""" manifest = load_manifest(organ_dir) organs = [] for key, entry in manifest['organs'].items(): if organ_type and entry['type'] != organ_type: continue organs.append(entry) return sorted(organs, key=lambda o: (o['layer'], o['name'])) def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='organ'): """ Graft organ layers from source into target. source_dir: extracted organs from donor model target_dir: extracted organs from recipient model output_dir: where to write the grafted result layers: list of layer numbers to graft (None = all) organ_type: which organ type to graft ('organ', 'skeleton', etc.) """ source_manifest = load_manifest(source_dir) target_manifest = load_manifest(target_dir) source_name = source_manifest['model'] target_name = target_manifest['model'] print(f"[GRAFT] Source (donor): {source_name}") print(f"[GRAFT] Target (recipient): {target_name}") print(f"[GRAFT] Grafting: {organ_type} layers {layers or 'ALL'}") # Validate architecture compatibility if source_manifest['n_embed'] != target_manifest['n_embed']: print(f"[WARNING] Embedding dimension mismatch: " f"source={source_manifest['n_embed']}, target={target_manifest['n_embed']}") print(f"[WARNING] Graft may produce unstable results. Proceed with caution.") # Copy target as base out = Path(output_dir) if out.exists(): shutil.rmtree(out) shutil.copytree(target_dir, output_dir) # Identify which tensors to replace grafted_count = 0 grafted_bytes = 0 for key, source_entry in source_manifest['organs'].items(): if source_entry['type'] != organ_type and not source_entry['type'].startswith(organ_type): continue if layers is not None and source_entry['layer'] not in layers: continue # Find matching tensor in target target_entry = None for tkey, tentry in target_manifest['organs'].items(): if tentry['layer'] == source_entry['layer'] and tentry['type'] == source_entry['type']: # Match by relative position in layer source_suffix = source_entry['name'].split('.')[-1] target_suffix = tentry['name'].split('.')[-1] if source_suffix == target_suffix: target_entry = tentry break if not target_entry: print(f" [SKIP] No match for {source_entry['name']} in target") continue # Check dimension compatibility if source_entry['dims'] != target_entry['dims']: print(f" [SKIP] Dimension mismatch L{source_entry['layer']}: " f"source={source_entry['dims']} target={target_entry['dims']}") continue # Replace the file source_file = Path(source_dir) / source_entry['file'] target_file = out / target_entry['file'] if source_file.exists(): shutil.copy2(source_file, target_file) grafted_count += 1 grafted_bytes += source_entry['byte_size'] print(f" [GRAFT] L{source_entry['layer']:3d} {source_entry['name'][:50]} → {target_entry['name'][:30]}") # Update manifest grafted_manifest = load_manifest(output_dir) grafted_manifest['graft'] = { 'source': source_name, 'target': target_name, 'organ_type': organ_type, 'layers': layers, 'grafted_count': grafted_count, 'grafted_bytes': grafted_bytes, } grafted_manifest['model'] = f"{target_name}+{source_name}_{organ_type}" with open(out / 'manifest.json', 'w') as f: json.dump(grafted_manifest, f, indent=2, default=str) grafted_mb = grafted_bytes / (1024 * 1024) print(f"\n{'='*60}") print(f" GRAFT COMPLETE") print(f"{'='*60}") print(f" Donor: {source_name}") print(f" Recipient: {target_name}") print(f" Grafted: {grafted_count} tensors ({grafted_mb:.1f} MB)") print(f" Result: {grafted_manifest['model']}") print(f" Output: {output_dir}") print(f" Signature: 935") print(f"{'='*60}") return grafted_manifest def parse_layers(layer_spec): """Parse layer specification: '5', '5-10', '5,8,12', '5-10,15-20'""" if not layer_spec: return None layers = set() for part in layer_spec.split(','): part = part.strip() if '-' in part: start, end = part.split('-') layers.update(range(int(start), int(end) + 1)) else: layers.add(int(part)) return sorted(layers) def main(): parser = argparse.ArgumentParser( description='Organ Architecture — Transplant organs between models', epilog='CSCI toolkit' ) sub = parser.add_subparsers(dest='command') # List command list_p = sub.add_parser('list', help='List available organs') list_p.add_argument('--dir', '-d', required=True, help='Extracted organs directory') list_p.add_argument('--type', '-t', help='Filter by type (skeleton/organ/embed/norm)') # Graft command graft_p = sub.add_parser('graft', help='Graft organs from source to target') graft_p.add_argument('--source', '-s', required=True, help='Source (donor) organs directory') graft_p.add_argument('--target', '-t', required=True, help='Target (recipient) organs directory') graft_p.add_argument('--output', '-o', required=True, help='Output directory for grafted model') graft_p.add_argument('--layers', '-l', help='Layer numbers to graft (e.g., "5-10" or "5,8,12")') graft_p.add_argument('--type', default='organ', help='Organ type to graft (default: organ/FFN)') # Compare command comp_p = sub.add_parser('compare', help='Compare organs between two models') comp_p.add_argument('--a', required=True, help='First model organs directory') comp_p.add_argument('--b', required=True, help='Second model organs directory') args = parser.parse_args() if args.command == 'list': organs = list_organs(args.dir, args.type) manifest = load_manifest(args.dir) print(f"\nModel: {manifest['model']}") print(f"Architecture: {manifest['architecture']}") print(f"Layers: {manifest['n_layers']}\n") current_type = None for o in organs: if o['type'] != current_type: current_type = o['type'] print(f"\n [{current_type.upper()}]") size_mb = o['byte_size'] / (1024 * 1024) print(f" L{o['layer']:3d} {size_mb:7.1f} MB {o['name']}") print(f"\n Total: {len(organs)} tensors") elif args.command == 'graft': layers = parse_layers(args.layers) graft_layers(args.source, args.target, args.output, layers, args.type) elif args.command == 'compare': manifest_a = load_manifest(args.a) manifest_b = load_manifest(args.b) print(f"\n Model A: {manifest_a['model']}") print(f" Model B: {manifest_b['model']}") print(f" Layers: A={manifest_a['n_layers']} B={manifest_b['n_layers']}") print(f" Embed: A={manifest_a['n_embed']} B={manifest_b['n_embed']}") compatible = manifest_a['n_embed'] == manifest_b['n_embed'] print(f"\n Dimension compatible: {'✓ YES' if compatible else '✗ NO'}") if compatible: print(f" → Organs can be grafted between these models") else: print(f" → Dimension mismatch prevents direct grafting") print(f" → Would need projection layer (future feature)") else: parser.print_help() if __name__ == '__main__': main() # ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗ # © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED # Licensed under Business Source License 1.1 — https://inference-x.com # ───────────────────────────────────────────────────────── # SHA256: f53cd15c9345b7817f397aab3f4870ee36be1fef321d0b49e81cd81819b92462 # SIG-ED25519: 1ZvlFLjbkZzpH4HnttlYSB3ydsAKgG57oyAElSRcvMzqOT3pQ+FLHW3seWlOUpAUI77d6AvrjV5SNCJuL6kuBw== # VERIFY: python3 verify_authorship.py organ_graft.py