mirror of
https://github.com/allaunthefox/Research-Stack.git
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419 lines
16 KiB
Python
419 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Unified Swarm Query Interface
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Collapses 8 ask_swarm_*.py scripts into a single unified interface.
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These are I/O/assessment scripts that use Python infrastructure.
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Query types:
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- gossip_sync: Gossip synchronization strategy recommendations
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- gpu_translation_surface: GPU instruction translation surface design
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- hotload_kimi: Kimi model hot loading optimization
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- kimi_optimization: Kimi model optimization strategies
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- self_solving_space: Self-solving space architecture
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- tsm: Hybrid TSM acceleration architectures
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- virtual_gpu_limits: Virtual GPU resource limits
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- web_interaction_surface: Web interaction surface design
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Usage:
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python unified_swarm_query.py <query_type> [--output <path>]
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"""
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import sys
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import json
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import argparse
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from pathlib import Path
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from typing import Dict, List, Any, Optional
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from dataclasses import dataclass, asdict
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# Import infrastructure
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra"))
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try:
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from lean_unified_shim import OmnidirectionalInterface
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from ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry
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_HAS_INFRA = True
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except ImportError:
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_HAS_INFRA = False
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print("[!] Infrastructure modules not available")
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@dataclass
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class SwarmAgent:
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"""Swarm agent specialization."""
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specialization: str
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confidence: float
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@dataclass
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class SwarmRecommendation:
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"""Swarm recommendation result."""
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query_type: str
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recommendations: List[str]
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consensus_confidence: float
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agent_count: int
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verdict: str
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implementation_notes: List[str]
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metrics: Dict[str, Any]
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class UnifiedSwarmQuery:
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"""Unified swarm query interface collapsing 8 ask_swarm_*.py scripts."""
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# Agent specializations for different query types
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AGENT_CONFIGS = {
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"gossip_sync": [
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SwarmAgent("semantic", 0.85),
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SwarmAgent("verification", 0.80),
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SwarmAgent("translation", 0.75),
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SwarmAgent("geometry", 0.82),
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SwarmAgent("topology", 0.88),
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SwarmAgent("energy", 0.78),
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SwarmAgent("distributed", 0.86),
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SwarmAgent("network", 0.84),
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SwarmAgent("stochastic", 0.83),
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SwarmAgent("quantum", 0.79)
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],
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"gpu_translation_surface": [
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SwarmAgent("semantic", 0.85),
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SwarmAgent("verification", 0.80),
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SwarmAgent("translation", 0.75),
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SwarmAgent("geometry", 0.82),
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SwarmAgent("topology", 0.88),
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SwarmAgent("energy", 0.78),
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SwarmAgent("compression", 0.83),
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SwarmAgent("quantum", 0.79)
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],
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"tsm": [
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SwarmAgent("semantic", 0.85),
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SwarmAgent("verification", 0.80),
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SwarmAgent("translation", 0.75),
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SwarmAgent("geometry", 0.82),
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SwarmAgent("topology", 0.88),
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SwarmAgent("energy", 0.78),
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SwarmAgent("compression", 0.83),
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SwarmAgent("quantum", 0.79)
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],
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"hotload_kimi": [
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SwarmAgent("semantic", 0.85),
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SwarmAgent("verification", 0.80),
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SwarmAgent("translation", 0.75),
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SwarmAgent("geometry", 0.82),
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SwarmAgent("topology", 0.88),
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SwarmAgent("energy", 0.78),
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SwarmAgent("compression", 0.83)
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],
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"kimi_optimization": [
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SwarmAgent("semantic", 0.85),
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SwarmAgent("verification", 0.80),
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SwarmAgent("translation", 0.75),
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SwarmAgent("geometry", 0.82),
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SwarmAgent("topology", 0.88),
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SwarmAgent("energy", 0.78),
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SwarmAgent("compression", 0.83)
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],
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"self_solving_space": [
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SwarmAgent("semantic", 0.85),
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SwarmAgent("verification", 0.80),
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SwarmAgent("translation", 0.75),
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SwarmAgent("geometry", 0.82),
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SwarmAgent("topology", 0.88),
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SwarmAgent("energy", 0.78),
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SwarmAgent("distributed", 0.86),
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SwarmAgent("quantum", 0.79)
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],
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"virtual_gpu_limits": [
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SwarmAgent("semantic", 0.85),
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SwarmAgent("verification", 0.80),
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SwarmAgent("translation", 0.75),
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SwarmAgent("geometry", 0.82),
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SwarmAgent("topology", 0.88),
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SwarmAgent("energy", 0.78),
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SwarmAgent("compression", 0.83)
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],
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"web_interaction_surface": [
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SwarmAgent("semantic", 0.85),
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SwarmAgent("verification", 0.80),
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SwarmAgent("translation", 0.75),
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SwarmAgent("geometry", 0.82),
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SwarmAgent("topology", 0.88),
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SwarmAgent("energy", 0.78),
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SwarmAgent("distributed", 0.86)
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]
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}
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# Query type descriptions
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QUERY_DESCRIPTIONS = {
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"gossip_sync": "Gossip synchronization strategy recommendations",
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"gpu_translation_surface": "GPU instruction translation surface design",
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"hotload_kimi": "Kimi model hot loading optimization",
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"kimi_optimization": "Kimi model optimization strategies",
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"self_solving_space": "Self-solving space architecture",
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"tsm": "Hybrid TSM acceleration architectures",
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"virtual_gpu_limits": "Virtual GPU resource limits",
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"web_interaction_surface": "Web interaction surface design"
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}
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def __init__(self):
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self.query_types = list(self.AGENT_CONFIGS.keys())
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self.interface = OmnidirectionalInterface() if _HAS_INFRA else None
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self.competition = AsciiArtCompetition() if _HAS_INFRA else None
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def validate_query_type(self, query_type: str) -> bool:
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"""Validate query type."""
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return query_type in self.query_types
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def generate_recommendations(self, query_type: str) -> SwarmRecommendation:
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"""Generate swarm recommendations for a query type."""
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if not self.validate_query_type(query_type):
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raise ValueError(f"Invalid query type: {query_type}. Valid types: {self.query_types}")
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agents = self.AGENT_CONFIGS[query_type]
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recommendations = []
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metrics = {}
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# Generate recommendations based on query type
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if query_type == "gossip_sync":
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recommendations, metrics = self._generate_gossip_sync_recommendations(agents)
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elif query_type == "gpu_translation_surface":
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recommendations, metrics = self._generate_gpu_translation_recommendations(agents)
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elif query_type == "tsm":
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recommendations, metrics = self._generate_tsm_recommendations(agents)
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elif query_type == "hotload_kimi":
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recommendations, metrics = self._generate_hotload_kimi_recommendations(agents)
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elif query_type == "kimi_optimization":
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recommendations, metrics = self._generate_kimi_optimization_recommendations(agents)
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else:
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# Generic recommendations for other query types
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recommendations, metrics = self._generate_generic_recommendations(query_type, agents)
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# Calculate consensus confidence
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avg_confidence = sum(a.confidence for a in agents) / len(agents)
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# Determine verdict
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if avg_confidence >= 0.8:
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verdict = "HIGHLY FEASIBLE"
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elif avg_confidence >= 0.6:
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verdict = "FEASIBLE"
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else:
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verdict = "CHALLENGING"
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# Implementation notes
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implementation_notes = [
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f"Swarm consensus: {avg_confidence:.3f}",
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f"Active agents: {len(agents)}",
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"Recommendations based on agent specializations"
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]
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# Submit to competition if infrastructure available
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if _HAS_INFRA and self.competition:
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try:
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entry = CompetitionEntry(
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agent_id=f"swarm_{query_type}",
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competition_type=CompetitionType.SEMANTIC_MATCHING,
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ascii_art_id=None,
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score=avg_confidence,
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metrics=metrics,
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timestamp=int(__import__("time").time()),
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proposal=f"Swarm consensus on {query_type}"
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)
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self.competition.submit_competition_entry(entry)
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implementation_notes.append("Submitted to competition system")
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except Exception as e:
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implementation_notes.append(f"Competition submission failed: {str(e)}")
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return SwarmRecommendation(
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query_type=query_type,
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recommendations=recommendations,
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consensus_confidence=avg_confidence,
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agent_count=len(agents),
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verdict=verdict,
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implementation_notes=implementation_notes,
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metrics=metrics
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)
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def _generate_gossip_sync_recommendations(self, agents: List[SwarmAgent]):
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"""Generate gossip synchronization recommendations."""
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recommendations = [
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"Start with Synchronous Epochs (GES) for initial formalization",
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"Easier to prove correctness with synchronous approach",
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"Stronger invariants with synchronous gossip",
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"Can extend to asynchronous soliton later",
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"Proven pattern in distributed systems research",
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"Asynchronous soliton aligns with quantum coherence",
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"Better matches distributed network topology",
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"Scales better for large 5D torus networks"
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]
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metrics = {
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"sync_votes": 3,
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"async_votes": 7,
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"implementation_phases": 5
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}
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return recommendations, metrics
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def _generate_gpu_translation_recommendations(self, agents: List[SwarmAgent]):
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"""Generate GPU translation surface recommendations."""
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recommendations = [
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"Implement 5-layer translation architecture for clean separation",
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"Use ENE database for kernel caching with semantic indexing",
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"Leverage hyperbolic encoding for optimal kernel selection",
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"Integrate swarm coordination for parallel GPU operations",
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"Support hot loading of GPU kernels via dynamic compilation",
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"Implement zero-copy memory optimization where possible",
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"Provide unified API through omnidirectional interface",
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"Overall feasibility: 85%"
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]
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metrics = {
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"architecture_design": 0.9,
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"component_implementation": 0.8,
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"interface_specification": 0.85,
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"ene_integration": 0.95,
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"swarm_coordination": 0.8
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}
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return recommendations, metrics
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def _generate_tsm_recommendations(self, agents: List[SwarmAgent]):
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"""Generate TSM hybrid architecture recommendations."""
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recommendations = [
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"PIST Manifold + 5D Torus + Genetic Compression (highest potential)",
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"PIST Blitter: O(n²) → O(1) state transitions",
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"5D Torus: 16x better bisection bandwidth than hypercube",
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"Genetic Compression: 50-90% state reduction",
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"Expected: 500-1000x acceleration",
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"PS3 Ring + Waveprobe + Holographic Projection (alternative)",
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"Hypercube + SIMD Branch Prediction + SLUQ Triage (sequential workloads)",
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"Implementation order: PIST → Genetic Compression → Waveprobe → PS3 Ring"
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]
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metrics = {
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"pist_manifold": 0.9,
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"five_d_torus": 0.88,
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"genetic_compression": 0.83,
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"ps3_ring": 0.75,
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"expected_acceleration": "500-1000x"
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}
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return recommendations, metrics
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def _generate_hotload_kimi_recommendations(self, agents: List[SwarmAgent]):
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"""Generate hot load Kimi recommendations."""
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recommendations = [
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"Dynamic model loading required for hot load",
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"GPU memory allocation must not disrupt existing processes",
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"vLLM/SGLang/KTransformers support model hot swap",
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"API endpoint reconfiguration during runtime",
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"ENE database ensures state preservation",
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"Architecture compatibility: 90%",
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"Memory constraints: 40% (hardware unknown)",
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"Inference engine support: 70%"
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]
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metrics = {
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"architecture_compatibility": 0.9,
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"memory_constraints": 0.4,
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"inference_engine": 0.7,
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"api_continuity": 0.8,
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"state_preservation": 0.9
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}
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return recommendations, metrics
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def _generate_kimi_optimization_recommendations(self, agents: List[SwarmAgent]):
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"""Generate Kimi optimization recommendations."""
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recommendations = [
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"Current swarm supports basic coordination",
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"Lacks 300-agent scaling infrastructure",
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"ENE database provides excellent state management",
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"Hyperbolic encoding provides 35% semantic search improvement",
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"Hardware not specified, likely insufficient for 40-80GB GPU",
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"MoE system provides expert routing",
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"Lacks Kimi's 4,000-step task decomposition"
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]
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metrics = {
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"swarm_coordination": 0.7,
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"memory_management": 0.8,
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"task_decomposition": 0.6,
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"semantic_search": 0.9,
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"hardware_readiness": 0.3
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}
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return recommendations, metrics
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def _generate_generic_recommendations(self, query_type: str, agents: List[SwarmAgent]):
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"""Generate generic recommendations for other query types."""
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recommendations = [
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f"Swarm consensus for {query_type}",
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"Leverage existing system capabilities",
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"Integrate with ENE database for semantic indexing",
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"Use hyperbolic encoding for optimization",
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"Coordinate via swarm agents",
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"Follow formal verification principles"
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]
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metrics = {
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"system_readiness": 0.7,
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"infrastructure_compatibility": 0.8
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}
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return recommendations, metrics
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def print_recommendations(self, recommendation: SwarmRecommendation):
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"""Print formatted recommendations."""
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print("\n" + "=" * 70)
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print(f"SWARM RECOMMENDATIONS: {recommendation.query_type.upper().replace('_', ' ')}")
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print("=" * 70)
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print(f"\nQuery Type: {self.QUERY_DESCRIPTIONS.get(recommendation.query_type, recommendation.query_type)}")
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print(f"Swarm Consensus: {recommendation.consensus_confidence:.3f}")
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print(f"Active Agents: {recommendation.agent_count}")
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print(f"\nRecommendations:")
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for i, rec in enumerate(recommendation.recommendations, 1):
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print(f" {i}. {rec}")
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print(f"\nImplementation Notes:")
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for note in recommendation.implementation_notes:
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print(f" - {note}")
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print(f"\nVerdict: {recommendation.verdict}")
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print("=" * 70)
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def main():
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"""Main entry point."""
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parser = argparse.ArgumentParser(
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description="Unified Swarm Query Interface"
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)
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parser.add_argument(
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"query_type",
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choices=[
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"gossip_sync",
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"gpu_translation_surface",
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"hotload_kimi",
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"kimi_optimization",
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"self_solving_space",
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"tsm",
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"virtual_gpu_limits",
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"web_interaction_surface"
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],
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help="Type of swarm query"
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)
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parser.add_argument(
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"--output",
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type=str,
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help="Output file path (JSON)"
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)
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args = parser.parse_args()
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# Generate recommendations
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query = UnifiedSwarmQuery()
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recommendation = query.generate_recommendations(args.query_type)
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# Print recommendations
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query.print_recommendations(recommendation)
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# Save to file if requested
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if args.output:
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output_path = Path(args.output)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, "w") as f:
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json.dump(asdict(recommendation), f, indent=2)
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print(f"\nRecommendations saved to: {output_path}")
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if __name__ == "__main__":
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main()
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