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