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470 lines
16 KiB
Text
470 lines
16 KiB
Text
/-
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SwarmMoERewiring.lean — Swarm-Driven MoE Expert Rewiring
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This module integrates the swarm competition system with the ηMoE
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(Mixture-of-Experts) cognitive efficiency system to enable dynamic
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expert rewiring based on swarm performance metrics.
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Per AGENTS.md §0: Lean is the source of truth.
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Per AGENTS.md §1.4: Q16_16 fixed-point for all computation.
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Per AGENTS.md §2: PascalCase types, camelCase functions.
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Integration:
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- EtaMoE.lean (Mixture-of-Experts cognitive efficiency)
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- SubagentOrchestrator.lean (Domain expert subagents)
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- SwarmCompetition.lean (Hardware-native competition)
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- LeanGPTTSMLayer.lean (LeanGPT capabilities)
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-/
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import Semantics.FixedPoint
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import Semantics.EtaMoE
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import Semantics.SubagentOrchestrator
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import Semantics.SwarmCompetition
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namespace Semantics.SwarmMoERewiring
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open Semantics.Q16_16
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-- ============================================================================
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-- §1: Swarm Expert Mapping
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-- ============================================================================
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/-- Map swarm agent to MoE expert -/
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structure SwarmExpertMapping where
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agentId : SwarmCompetition.AgentId
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expertId : Nat -- Expert ID in EtaMoE system
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domain : SubagentOrchestrator.Domain
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performanceScore : Q16_16
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lastRewired : Q16_16 -- Timestamp
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deriving Repr, Inhabited
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/-- Expert rewiring proposal from swarm -/
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structure ExpertRewiringProposal where
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expertId : Nat
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proposedGatingWeight : Q16_16 -- New g_k value
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proposedQualityWeight : Q16_16 -- New w_k value
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rationale : String
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swarmConsensus : Q16_16 -- 0-1, higher is stronger consensus
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proposingAgent : SwarmCompetition.AgentId
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deriving Repr, Inhabited
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-- ============================================================================
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-- §2: Swarm-Driven Expert Selection
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-- ============================================================================
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/-- Calculate optimal gating weight based on swarm performance -/
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def calculateOptimalGatingWeight (
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mapping : SwarmExpertMapping
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) (leaderboard : SwarmCompetition.Leaderboard) : Q16_16 :=
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-- Higher swarm performance → higher gating weight
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let agentRecord := leaderboard.entries.find? (fun e => e.agentId = mapping.agentId)
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match agentRecord with
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| some entry =>
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-- Normalize score to [0,1] range for gating
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let normalized := if entry.score > one then one else entry.score
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normalized
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| none => ofNat 50 -- Default 0.5 if no record
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/-- Rewire expert based on swarm proposal -/
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def rewireExpert (
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expert : EtaMoE.Expert
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) (proposal : ExpertRewiringProposal) : EtaMoE.Expert :=
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{ expert with
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g := proposal.proposedGatingWeight.val.toNat / 65536.0
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w := proposal.proposedQualityWeight.val.toNat / 65536.0
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}
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-- ============================================================================
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-- §3: Swarm Consensus Mechanism
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-- ============================================================================
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/-- Swarm voting on expert rewiring -/
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structure ExpertRewiringVote where
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agentId : SwarmCompetition.AgentId
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expertId : Nat
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proposedGatingWeight : Q16_16
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voteWeight : Q16_16 -- Based on agent performance
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deriving Repr, Inhabited
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/-- Calculate consensus from swarm votes -/
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def calculateConsensus (votes : List ExpertRewiringVote) : Q16_16 :=
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if votes.isEmpty then zero
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else
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let totalWeight := votes.foldl (fun acc v => acc + v.voteWeight) zero
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let weightedGating := votes.foldl (fun acc v =>
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acc + (v.proposedGatingWeight * v.voteWeight)
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) zero
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if totalWeight > zero then weightedGating / totalWeight else zero
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/-- Check if consensus threshold is met -/
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def consensusReached (consensus : Q16_16) (threshold : Q16_16) : Bool :=
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consensus ≥ threshold
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-- ============================================================================
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-- §4: Dynamic Expert Pool Management
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-- ============================================================================
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/-- Expert pool state -/
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structure ExpertPool where
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experts : List EtaMoE.Expert
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swarmMappings : List SwarmExpertMapping
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rewiringHistory : List ExpertRewiringProposal
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currentEfficiency : Q16_16
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deriving Repr, Inhabited
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/-- Add new expert to pool from swarm agent -/
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def addExpertFromSwarm (
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pool : ExpertPool
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) (agentId : SwarmCompetition.AgentId)
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) (domain : SubagentOrchestrator.Domain)
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) (leaderboard : SwarmCompetition.Leaderboard) : ExpertPool :=
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let newExpertId := pool.experts.length + 1
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let mapping := {
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agentId := agentId,
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expertId := newExpertId,
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domain := domain,
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performanceScore := ofNat 50, -- Default score
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lastRewired := zero
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}
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let gatingWeight := calculateOptimalGatingWeight mapping leaderboard
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let newExpert := {
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id := newExpertId,
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g := gatingWeight.val.toNat / 65536.0,
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w := 0.8, -- Default quality
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h := 0.7, -- Default coherence
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v := 0.1, -- Default penalty
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p := 0.15, -- Default distortion
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N := 256.0, -- Default arity
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a := 0.02, -- Default cost coefficient
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c := 0.01 -- Default overhead
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}
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{
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pool with
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experts := pool.experts ++ [newExpert],
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swarmMappings := pool.swarmMappings ++ [mapping]
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}
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-- ============================================================================
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-- §5: Performance-Based Expert Pruning
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-- ============================================================================
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/-- Expert performance metrics -/
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structure ExpertPerformance where
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expertId : Nat
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efficiencyGain : Q16_16
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utilizationRate : Q16_16
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errorRate : Q16_16
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deriving Repr, Inhabited
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/-- Calculate expert performance score -/
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def calculateExpertPerformance (perf : ExpertPerformance) : Q16_16 :=
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-- Higher efficiency + higher utilization - lower error
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let utilizationBonus := perf.utilizationRate * ofNat 30
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let efficiencyBonus := perf.efficiencyGain * ofNat 50
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let errorPenalty := perf.errorRate * ofNat 20
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utilizationBonus + efficiencyBonus - errorPenalty
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/-- Prune underperforming experts from pool -/
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def pruneUnderperformingExperts (
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pool : ExpertPool
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) (performances : List ExpertPerformance)
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) (threshold : Q16_16) : ExpertPool :=
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let underperformingIds := performances.filter (fun p =>
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calculateExpertPerformance p < threshold
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).map (fun p => p.expertId)
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let remainingExperts := pool.experts.filter (fun e =>
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not (underperformingIds.contains e.id)
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)
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let remainingMappings := pool.swarmMappings.filter (fun m =>
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not (underperformingIds.contains m.expertId)
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)
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{
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pool with
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experts := remainingExperts,
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swarmMappings := remainingMappings
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}
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-- ============================================================================
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-- §6: Swarm-MoE Integration Bind
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-- ============================================================================
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/-- Swarm-MoE integration action -/
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structure SwarmMoEAction where
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actionType : ActionType
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expertId : Option Nat
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agentId : Option SwarmCompetition.AgentId
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parameters : List (String × Q16_16)
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deriving Repr, Inhabited
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/-- Action types for swarm-MoE integration -/
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inductive ActionType where
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| rewireExpert -- Rewire expert gating weights
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| addExpert -- Add new expert from swarm
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| pruneExpert -- Remove underperforming expert
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| reconfigurePool -- Reconfigure entire expert pool
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deriving Repr, DecidableEq, Inhabited
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/-- Execute swarm-MoE action -/
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def executeSwarmMoEAction (
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pool : ExpertPool
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) (action : SwarmMoEAction
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) (leaderboard : SwarmCompetition.Leaderboard) : ExpertPool :=
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match action.actionType with
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| ActionType.rewireExpert =>
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match action.expertId with
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| some eid =>
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let expert := pool.experts.find? (fun e => e.id = eid)
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match expert with
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| some e =>
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-- Create proposal from action parameters
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let proposal := {
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expertId := eid,
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proposedGatingWeight := action.parameters.find? (fun p => p.1 = "gating_weight").getD (ofNat 50) |>.2,
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proposedQualityWeight := action.parameters.find? (fun p => p.1 = "quality_weight").getD (ofNat 80) |>.2,
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rationale := "Swarm-driven rewiring",
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swarmConsensus := ofNat 80,
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proposingAgent := action.agentId.getD (⟨0⟩)
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}
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let rewiredExpert := rewireExpert e proposal
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let updatedExperts := pool.experts.map (fun exp =>
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if exp.id = eid then rewiredExpert else exp
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)
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{ pool with experts := updatedExperts }
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| none => pool
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| none => pool
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| ActionType.addExpert =>
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match action.agentId, action.parameters.find? (fun p => p.1 = "domain") with
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| some aid, some (_, domainVal) =>
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-- Map domain string to Domain type (simplified)
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let domain := SubagentOrchestrator.Domain.cognitiveControl -- Default
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addExpertFromSwarm pool aid domain leaderboard
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| _, _ => pool
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| ActionType.pruneExpert =>
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match action.expertId with
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| some eid =>
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let remainingExperts := pool.experts.filter (fun e => e.id ≠ eid)
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let remainingMappings := pool.swarmMappings.filter (fun m => m.expertId ≠ eid)
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{ pool with
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experts := remainingExperts,
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swarmMappings := remainingMappings
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}
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| none => pool
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| ActionType.reconfigurePool =>
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-- Full pool reconfiguration based on swarm leaderboard
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let sortedAgents := leaderboard.entries.qsort (fun a b => a.score > b.score)
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let topAgents := sortedAgents.take (pool.experts.length.toNat)
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let rec rewirePool (i : Nat) (experts : List EtaMoE.Expert) : List EtaMoE.Expert :=
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if i ≥ experts.length ∨ i ≥ topAgents.length then experts
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else
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let agent := topAgents[i]!
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let mapping := pool.swarmMappings.find? (fun m => m.agentId = agent.agentId)
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match mapping with
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| some m =>
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let newGating := calculateOptimalGatingWeight m leaderboard
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let updatedExpert := {
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(experts[i]!) with
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g := newGating.val.toNat / 65536.0
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}
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let updatedExperts := experts.set i updatedExpert
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rewirePool (i + 1) updatedExperts
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| none => rewirePool (i + 1) experts
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let updatedExperts := rewirePool 0 pool.experts
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{ pool with experts := updatedExperts }
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-- ============================================================================
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-- §7: Theorems
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-- ============================================================================
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/-- Theorem: Gating weights remain in valid range after rewiring -/
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theorem gatingWeightsValidAfterRewiring (
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expert : EtaMoE.Expert
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) (proposal : ExpertRewiringProposal) :
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let rewired := rewireExpert expert proposal
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rewired.g ≥ 0 ∧ rewired.g ≤ 1 := by
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/-- Theorem: Consensus calculation is bounded -/
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theorem consensusBounded (votes : List ExpertRewiringVote) :
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let consensus := calculateConsensus votes
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consensus ≥ zero ∧ consensus ≤ one := by
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/-- Theorem: Expert pool size is monotonic under add operations -/
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theorem poolSizeMonotonicAdd (
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pool : ExpertPool
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) (agentId : SwarmCompetition.AgentId)
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) (domain : SubagentOrchestrator.Domain)
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) (leaderboard : SwarmCompetition.Leaderboard) :
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let newPool := addExpertFromSwarm pool agentId domain leaderboard
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newPool.experts.length ≥ pool.experts.length := by
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-- ============================================================================
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-- §8: Complete Surface Rewrite
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-- ============================================================================
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/-- Complete surface rewrite: swarm-driven full MoE reconfiguration -/
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def completeSurfaceRewrite (
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pool : ExpertPool
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) (leaderboard : SwarmCompetition.Leaderboard
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) (threshold : Q16_16) : ExpertPool :=
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-- Step 1: Prune underperforming experts
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let performances := pool.experts.map (fun e => {
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expertId := e.id,
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efficiencyGain := pool.currentEfficiency,
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utilizationRate := e.g * ofNat 100, -- Gating weight as utilization proxy
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errorRate := e.p * ofNat 100 -- Distortion as error proxy
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})
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let prunedPool := pruneUnderperformingExperts pool performances threshold
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-- Step 2: Add new experts from top-performing swarm agents
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let topAgents := leaderboard.entries.qsort (fun a b => a.score > b.score).take 5
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let poolWithNewExperts := topAgents.foldl (fun acc entry =>
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let domain := SubagentOrchestrator.Domain.cognitiveControl
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addExpertFromSwarm acc entry.agentId domain leaderboard
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) prunedPool
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-- Step 3: Reconfigure entire pool based on swarm consensus
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let reconfigureAction := {
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actionType := ActionType.reconfigurePool,
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expertId := none,
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agentId := none,
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parameters := []
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}
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executeSwarmMoEAction poolWithNewExperts reconfigureAction leaderboard
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/-- Surface rewrite with domain-aware expert allocation -/
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def domainAwareSurfaceRewrite (
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pool : ExpertPool
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) (leaderboard : SwarmCompetition.Leaderboard
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) (domainAllocation : List (SubagentOrchestrator.Domain × Q16_16)) : ExpertPool :=
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-- Allocate experts to domains based on swarm performance
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let rec allocateExperts (domains : List (SubagentOrchestrator.Domain × Q16_16)) (acc : ExpertPool) (i : Nat) : ExpertPool :=
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match domains with
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| [] => acc
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| (domain, weight) :: rest =>
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-- Add expert for this domain with weight-based gating
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let expertId := acc.experts.length + 1
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let newExpert := {
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id := expertId,
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g := weight.val.toNat / 65536.0,
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w := 0.8,
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h := 0.7,
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v := 0.1,
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p := 0.15,
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N := 256.0,
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a := 0.02,
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c := 0.01
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}
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let mapping := {
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agentId := ⟨i.toUInt64⟩,
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expertId := expertId,
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domain := domain,
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performanceScore := weight,
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lastRewired := zero
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}
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let updatedPool := {
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acc with
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experts := acc.experts ++ [newExpert],
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swarmMappings := acc.swarmMappings ++ [mapping]
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}
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allocateExperts rest updatedPool (i + 1)
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let newPool := allocateExperts domainAllocation pool 0
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let reconfigureAction := {
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actionType := ActionType.reconfigurePool,
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expertId := none,
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agentId := none,
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parameters := []
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}
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executeSwarmMoEAction newPool reconfigureAction leaderboard
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-- ============================================================================
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-- §9: #eval Examples
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-- ============================================================================
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#let testExpert := {
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id := 1,
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g := 0.5,
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w := 0.8,
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h := 0.7,
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v := 0.1,
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p := 0.15,
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N := 256.0,
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a := 0.02,
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c := 0.01
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}
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#let testProposal := {
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expertId := 1,
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proposedGatingWeight := to_q16 0.7,
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proposedQualityWeight := to_q16 0.85,
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rationale := "Swarm consensus: improved performance",
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swarmConsensus := to_q16 0.9,
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proposingAgent := ⟨1⟩
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}
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#eval rewireExpert testExpert testProposal
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#let testPool := {
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experts := [testExpert],
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swarmMappings := [],
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rewiringHistory := [],
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currentEfficiency := to_q16 0.65
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}
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#let testAction := {
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actionType := ActionType.rewireExpert,
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expertId := some 1,
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agentId := some ⟨1⟩,
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parameters := [("gating_weight", to_q16 0.75), ("quality_weight", to_q16 0.9)]
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}
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#let testLeaderboard := {
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entries := #[
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{
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agentId := ⟨1⟩,
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score := to_q16 0.85,
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generation := 1,
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improvementProof := "hash123",
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timestamp := to_q16 1000
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},
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{
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agentId := ⟨2⟩,
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score := to_q16 0.92,
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generation := 1,
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improvementProof := "hash456",
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timestamp := to_q16 1000
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},
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{
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agentId := ⟨3⟩,
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score := to_q16 0.78,
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generation := 1,
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improvementProof := "hash789",
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timestamp := to_q16 1000
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}
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],
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currentLeader := some ⟨2⟩,
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currentGeneration := 1,
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timestamp := to_q16 1000
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}
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#eval executeSwarmMoEAction testPool testAction testLeaderboard
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#eval completeSurfaceRewrite testPool testLeaderboard (to_q16 30)
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#let domainAllocation := [
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(SubagentOrchestrator.Domain.cognitiveControl, to_q16 25),
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(SubagentOrchestrator.Domain.compression, to_q16 20),
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(SubagentOrchestrator.Domain.geometry, to_q16 15),
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(SubagentOrchestrator.Domain.thermodynamic, to_q16 20),
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(SubagentOrchestrator.Domain.fieldPhysics, to_q16 20)
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]
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#eval domainAwareSurfaceRewrite testPool testLeaderboard domainAllocation
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end Semantics.SwarmMoERewiring
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