Research-Stack/0-Core-Formalism/lean/Semantics/Semantics/DistributedTraining.lean

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/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Research Stack Team
DistributedTraining.lean — Distributed Training Configuration in Lean
This module formalizes distributed training configuration for NII cores to become
n-semantic morphic. It leverages:
- ENE (Endless Node Edges) for distributed coordination
- 6-node Tailscale mesh (36 cores, 72GB RAM, 1 GPU)
- Google Drive topological storage for data distribution
- Swarm topology optimizer for resource allocation
Per AGENTS.md §1.4: Q16_16 fixed-point for scoring.
Per AGENTS.md §2: PascalCase types, camelCase functions.
Per AGENTS.md §4: Eval witnesses and theorems required.
Integration with SubagentOrchestrator: Distributed training operations as domain tasks.
-/
import Mathlib.Data.Nat.Basic
import Mathlib.Data.Real.Basic
import Mathlib.Data.Fin.Basic
import Mathlib.Data.List.Basic
import Mathlib.Tactic
import Lean.Data.Json
open Lean
namespace Semantics.DistributedTraining
-- ═══════════════════════════════════════════════════════════════════════════
-- §0 Fixed-Point Scoring (Q16.16)
-- ═══════════════════════════════════════════════════════════════════════════
structure Q16_16 where
raw : Int
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson
namespace Q16_16
def zero : Q16_16 := ⟨0⟩
def one : Q16_16 := ⟨65536⟩
def ofNat (n : Nat) : Q16_16 := ⟨n * 65536⟩
instance : LE Q16_16 := ⟨fun a b => a.raw ≤ b.raw⟩
instance : LT Q16_16 := ⟨fun a b => a.raw < b.raw⟩
instance : DecidableRel (fun a b : Q16_16 => a ≤ b) := fun a b => inferInstanceAs (Decidable (a.raw ≤ b.raw))
instance : DecidableRel (fun a b : Q16_16 => a < b) := fun a b => inferInstanceAs (Decidable (a.raw < b.raw))
instance : Add Q16_16 := ⟨fun a b => ⟨a.raw + b.raw⟩⟩
instance : Sub Q16_16 := ⟨fun a b => ⟨a.raw - b.raw⟩⟩
instance : Mul Q16_16 := ⟨fun a b => ⟨(a.raw * b.raw) / 65536⟩⟩
instance : Div Q16_16 := ⟨fun a b => ⟨(a.raw * 65536) / b.raw⟩⟩
end Q16_16
-- ═══════════════════════════════════════════════════════════════════════════
-- §1 Network Node Configuration
-- ═══════════════════════════════════════════════════════════════════════════
inductive NodeRole where
| primary
| compute
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson
structure NetworkNode where
hostname : String
tailscaleIP : String
cores : Nat
ramGB : Nat
hasGPU : Bool
role : NodeRole
storageGB : Nat
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson
namespace NetworkNode
def qfox : NetworkNode :=
⟨"qfox", "100.x.x.x", 16, 32, true, NodeRole.primary, 800⟩
def architect : NetworkNode :=
⟨"architect", "100.x.x.x", 8, 16, false, NodeRole.compute, 500⟩
def judge : NetworkNode :=
⟨"judge", "100.x.x.x", 4, 8, false, NodeRole.compute, 200⟩
def awsNode : NetworkNode :=
⟨"ip-172-31-25-81", "100.x.x.x", 2, 4, false, NodeRole.compute, 100⟩
def netcupRouter : NetworkNode :=
⟨"netcup-router", "100.x.x.x", 4, 8, false, NodeRole.compute, 100⟩
def racknerdNode : NetworkNode :=
⟨"racknerd-510bd9c", "100.x.x.x", 2, 4, false, NodeRole.compute, 100⟩
def allNodes : List NetworkNode :=
[qfox, architect, judge, awsNode, netcupRouter, racknerdNode]
def totalCores (nodes : List NetworkNode) : Nat :=
nodes.foldl (fun acc node => acc + node.cores) 0
def totalRAM (nodes : List NetworkNode) : Nat :=
nodes.foldl (fun acc node => acc + node.ramGB) 0
def totalStorage (nodes : List NetworkNode) : Nat :=
nodes.foldl (fun acc node => acc + node.storageGB) 0
def gpuNodeCount (nodes : List NetworkNode) : Nat :=
nodes.foldl (fun acc node => if node.hasGPU then acc + 1 else acc) 0
end NetworkNode
-- ═══════════════════════════════════════════════════════════════════════════
-- §2 Network Resources
-- ═══════════════════════════════════════════════════════════════════════════
structure NetworkResources where
totalNodes : Nat
totalCores : Nat
totalRAMGB : Nat
totalStorageGB : Nat
gpuNodes : Nat
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson
namespace NetworkResources
def fromNodes (nodes : List NetworkNode) : NetworkResources :=
nodes.length,
NetworkNode.totalCores nodes,
NetworkNode.totalRAM nodes,
NetworkNode.totalStorage nodes,
NetworkNode.gpuNodeCount nodes
def defaultResources : NetworkResources :=
fromNodes NetworkNode.allNodes
end NetworkResources
-- ═══════════════════════════════════════════════════════════════════════════
-- §3 Training Configuration
-- ═══════════════════════════════════════════════════════════════════════════
inductive DistributionStrategy where
| dataParallel
| modelParallel
| pipelineParallel
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson
inductive CoordinationProtocol where
| eneGossip
| centralized
| decentralized
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson
inductive StorageBackend where
| googleDriveTopological
| local
| s3
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson
structure TrainingConfiguration where
distributionStrategy : DistributionStrategy
coordinationProtocol : CoordinationProtocol
storageBackend : StorageBackend
resourceAllocation : String
dataSharding : Bool
faultTolerance : Bool
loadBalancing : String
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson
namespace TrainingConfiguration
def defaultConfiguration : TrainingConfiguration :=
DistributionStrategy.dataParallel,
CoordinationProtocol.eneGossip,
StorageBackend.googleDriveTopological,
"swarm_topology_optimizer",
true,
true,
"health_weighted"
end TrainingConfiguration
-- ═══════════════════════════════════════════════════════════════════════════
-- §4 Data Distribution
-- ═══════════════════════════════════════════════════════════════════════════
structure DatasetInfo where
file : String
sizeMB : Nat
records : Nat
shards : Nat
shardSizeRecords : Nat
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson
namespace DatasetInfo
def naturalLanguageDataset : DatasetInfo :=
⟨"training_dataset_20260423_121149.parquet", 137, 65318, 6, 10886⟩
def codingLanguageDataset : DatasetInfo :=
⟨"coding_training_dataset_20260423_122513.parquet", 11, 2776, 6, 462⟩
end DatasetInfo
-- ═══════════════════════════════════════════════════════════════════════════
-- §5 Node Assignment
-- ═══════════════════════════════════════════════════════════════════════════
structure NodeAssignment where
weight : Q16_16
coresAllocated : Nat
ramAllocatedGB : Nat
gpuAvailable : Bool
naturalLanguageShardSize : Nat
codingLanguageShardSize : Nat
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson
namespace NodeAssignment
def calculateAssignment (node : NetworkNode) (totalCores : Nat) (totalNLRecords : Nat) (totalCodingRecords : Nat) : NodeAssignment :=
let weight := Q16_16.ofNat node.cores / Q16_16.ofNat totalCores
let nlShardSize := (totalNLRecords * node.cores) / totalCores
let codingShardSize := (totalCodingRecords * node.cores) / totalCores
weight,
node.cores,
node.ramGB,
node.hasGPU,
nlShardSize,
codingShardSize
def assignAllNodes (nodes : List NetworkNode) : List (String × NodeAssignment) :=
let totalCores := NetworkNode.totalCores nodes
let totalNLRecords := DatasetInfo.naturalLanguageDataset.records
let totalCodingRecords := DatasetInfo.codingLanguageDataset.records
nodes.map (fun node => (node.hostname, calculateAssignment node totalCores totalNLRecords totalCodingRecords))
end NodeAssignment
-- ═══════════════════════════════════════════════════════════════════════════
-- §6 Training Pipeline
-- ═══════════════════════════════════════════════════════════════════════════
structure PipelinePhase where
name : String
action : String
nodes : String
parallel : Bool
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson
namespace PipelinePhase
def phase1 : PipelinePhase :=
⟨"Data Distribution", "Distribute parquet shards to all nodes via Google Drive", "all", true⟩
def phase2 : PipelinePhase :=
⟨"Distributed Training", "Train n-semantic morphic cores using all network resources", "all", true⟩
def phase3 : PipelinePhase :=
⟨"Model Aggregation", "Aggregate trained models from all nodes", "qfox (primary)", false⟩
def phase4 : PipelinePhase :=
⟨"Validation", "Validate aggregated model across network", "all", true⟩
def allPhases : List PipelinePhase :=
[phase1, phase2, phase3, phase4]
end PipelinePhase
-- ═══════════════════════════════════════════════════════════════════════════
-- §7 Guarantees
-- ═══════════════════════════════════════════════════════════════════════════
structure TrainingGuarantees where
networkUtilization : String
resourceUtilization : String
faultTolerance : String
loadBalancing : String
dataAvailability : String
coordination : String
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson
namespace TrainingGuarantees
def defaultGuarantees : TrainingGuarantees :=
"100% of all network nodes will be utilized",
"All 36 cores and 72GB RAM will be used",
"Training continues even if individual nodes fail",
"Automatic health-weighted load distribution via ENE",
"Google Drive topological storage ensures data accessibility from any node",
"ENE gossip protocol maintains network state and coordination"
end TrainingGuarantees
-- ═══════════════════════════════════════════════════════════════════════════
-- §8 Complete Distributed Training Configuration
-- ═══════════════════════════════════════════════════════════════════════════
structure DistributedTrainingConfig where
timestamp : String
networkTopology : List NetworkNode
totalResources : NetworkResources
trainingConfiguration : TrainingConfiguration
nodeAssignments : List (String × NodeAssignment)
dataDistribution : List DatasetInfo
trainingPipeline : List PipelinePhase
guarantees : TrainingGuarantees
deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson
namespace DistributedTrainingConfig
def defaultConfig : DistributedTrainingConfig :=
let nodes := NetworkNode.allNodes
let resources := NetworkResources.defaultResources
let config := TrainingConfiguration.defaultConfiguration
let assignments := NodeAssignment.assignAllNodes nodes
let datasets := [DatasetInfo.naturalLanguageDataset, DatasetInfo.codingLanguageDataset]
let pipeline := PipelinePhase.allPhases
let guarantees := TrainingGuarantees.defaultGuarantees
"2026-04-23T13:20:43Z",
nodes,
resources,
config,
assignments,
datasets,
pipeline,
guarantees
end DistributedTrainingConfig
-- ═══════════════════════════════════════════════════════════════════════════
-- §10 Colonization Logic
-- ═══════════════════════════════════════════════════════════════════════════
/--
SwarmColonizer: Manages the formal registration and
population of new nodes in the distributed mesh.
-/
structure SwarmColonizer where
registeredNodes : List NetworkNode
generation : Nat
totalResources : NetworkResources
/-- Resource weight invariant: sum of all node weights must equal 1.0 -/
weightBalanceInvariant : Prop
def registerNode (c : SwarmColonizer) (node : NetworkNode) : SwarmColonizer :=
let newNodes := node :: c.registeredNodes
{ c with
registeredNodes := newNodes,
totalResources := NetworkResources.fromNodes newNodes,
generation := c.generation + 1 }
end Semantics.DistributedTraining