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Lean: update Semantics modules, add new numerics/physics data files Hardware: update FPGA bitstreams (tangnano9k_uart_loopback) Infra: k3s-flake tests, netcup-vps configuration, VCN compute substrate Docs: ARCHITECTURE, specs, citation updates
686 lines
31 KiB
Text
686 lines
31 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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SSMS.lean — Scalar-Spawning Manifold State Machine
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Full Lean 4 formalization covering:
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§1 Q16.16 fixed-point arithmetic
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§2 Ternary weights and dot product
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§3 BitLinear activation scaling
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§4 MLGRU recurrent state
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§5 Scalar node state machine
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§6 SUBLEQ core and step semantics
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§7 N-gossip protocol
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§7.5 Phantom coupling (J_phantom cost)
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§8 Directed simplicial complex
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§9 Betti Swoosh Hamiltonian H_M(t) = −Δ_M + V_M
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§10 Anti-Collision Identity (ACI) and preservation theorem
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§11 SRAM banking layout and conflict-free theorem
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Per AGENTS.md §1.4: All new hot-path code uses Q16_16 fixed-point.
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Per AGENTS.md §2: All code uses PascalCase for types, camelCase for functions.
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-/
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import Std
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import Mathlib.Tactic.NormNum
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import Semantics.Timing
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import Semantics.FixedPoint
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import Semantics.Tactics
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namespace Semantics.SSMS
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open Semantics
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-- ════════════════════════════════════════════════════════════
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-- §2 Ternary Weights
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-- w̃ ∈ {−1, 0, +1} stored as 2-bit codes, 16 per 32-bit word.
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-- Dot product: only ADD/SUB, no MUL co-processor.
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-- ════════════════════════════════════════════════════════════
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inductive TernaryWeight where
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| Pos : TernaryWeight -- code 01 → +1
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| Zero : TernaryWeight -- code 00 → 0
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| Neg : TernaryWeight -- code 10 → −1
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deriving Repr, DecidableEq, Inhabited
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def TernaryWeight.toQ : TernaryWeight → Q16_16
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| .Pos => Q16_16.one
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| .Zero => Q16_16.zero
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| .Neg => Q16_16.negOne
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/-- Number of 32-bit words needed to store d ternary weights (2 bits each). -/
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def wordsNeeded (d : Nat) : Nat := (d + 15) / 16
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/-- Ternary weight slice for one scalar: d weights as two Boolean arrays.
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Disjoint invariant: no weight can be simultaneously +1 and −1. -/
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structure TernarySlice (d : Nat) where
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wPos : Array Bool -- wPos[j] = true ↔ w̃ⱼ = +1
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wNeg : Array Bool -- wNeg[j] = true ↔ w̃ⱼ = −1
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sizePos : wPos.size = d
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sizeNeg : wNeg.size = d
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disjoint : ∀ j : Fin d,
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¬ (wPos[j]'(sizePos ▸ j.isLt) ∧ wNeg[j]'(sizeNeg ▸ j.isLt))
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/-- Ternary dot product: Σⱼ w̃ⱼ · xⱼ.
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Weight=+1 → ADD xⱼ (2 SUBLEQ).
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Weight=−1 → SUB xⱼ (1 SUBLEQ).
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Weight= 0 → NOP.
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No MUL co-processor calls. -/
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def TernarySlice.dot {d : Nat} (ws : TernarySlice d) (xs : Fin d → Q16_16) : Q16_16 :=
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(List.range d).foldl (fun acc j =>
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if hj : j < d then
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let _p := ws.wPos.getD j false
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let n := ws.wNeg.getD j false
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let x := xs ⟨j, hj⟩
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Q16_16.add acc (if _p then x else if n then Q16_16.neg x else Q16_16.zero)
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else acc
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) Q16_16.zero
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/-- Memory compression ratio: 2 bits/weight vs 32-bit Q16.16. -/
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theorem compressionRatio : (2 : Rat) / 32 = 1 / 16 := by norm_num
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/-- Against FP16 baseline (16-bit): 2 bits/weight → 8× reduction.
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With activation savings: total ≈ 0.1× M_FP16. -/
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theorem fp16Compression : (2 : Rat) / 16 = 1 / 8 := by norm_num
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-- ════════════════════════════════════════════════════════════
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-- §3 BitLinear Activation Scaling
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-- x̃ = Clip(x · α, −Q_b + ε, Q_b − ε)
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-- α = Q_b / (η + ε), η = max{|xᵢ|} (butterfly MAX)
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-- ════════════════════════════════════════════════════════════
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structure BitLinearParams where
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qB : Q16_16 -- quantization range: 128 for 8-bit = 0x00800000
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eta : Q16_16 -- global abs-max from butterfly MAX reduction
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alpha : Q16_16 -- = qB / (eta + ε), computed via NR reciprocal
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def BitLinearParams.compute (qB eta : Q16_16) : BitLinearParams :=
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{ qB
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eta
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alpha := Q16_16.mul qB (Q16_16.recip (Q16_16.add eta Q16_16.epsilon)) }
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/-- Scale activation and clip to quantization range. -/
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def bitLinearScale (p : BitLinearParams) (x : Q16_16) : Q16_16 :=
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Q16_16.clip
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(Q16_16.mul x p.alpha)
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(Q16_16.add (Q16_16.neg p.qB) Q16_16.epsilon)
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(Q16_16.sub Q16_16.epsilon p.qB)
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-- ════════════════════════════════════════════════════════════
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-- §4 MLGRU Recurrent State
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-- hₜ = fₜ ⊙ hₜ₋₁ + (1 − fₜ) ⊙ cₜ
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-- MatMul-free: fₜ and cₜ from ternary dot products.
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-- Only 2 MUL co-processor calls for the gating blends.
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-- ════════════════════════════════════════════════════════════
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structure MlgruState where
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hT : Q16_16 -- current hidden state
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hPrev : Q16_16 -- previous (for Δh gossip trigger)
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deriving Repr, Inhabited
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/-- One MLGRU recurrence step.
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fT: forget gate (from ternary dot product, Q16_16).
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cT: candidate state (from ternary dot product, Q16_16). -/
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def mlgruStep (fT cT : Q16_16) (st : MlgruState) : MlgruState :=
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let termA := Q16_16.mul fT st.hT -- fT · h_{t-1}
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let oneMf := Q16_16.sub Q16_16.one fT -- 1 − fT
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let termB := Q16_16.mul oneMf cT -- (1 − fT) · cT
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{ hT := Q16_16.add termA termB, hPrev := st.hT }
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/-- Hidden-state update magnitude — primary spawn signal in recurrent mode. -/
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def MlgruState.delta (st : MlgruState) : Q16_16 :=
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Q16_16.abs (Q16_16.sub st.hPrev st.hT)
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-- ════════════════════════════════════════════════════════════
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-- §5 Scalar Node State Machine
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-- Sᵢ = (sᵢ, σᵢ, eᵢ, hidden, ver, load)
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-- ════════════════════════════════════════════════════════════
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structure ScalarNode where
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s : Q16_16 -- scalar value (= hT after MLGRU closes the loop)
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sigma : Bool -- activation status: true = active, false = dormant
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energy : Q16_16 -- gradient energy eᵢ = ‖∂L/∂sᵢ‖₂ Q16.16
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hidden : MlgruState -- MLGRU recurrent state
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version : Nat -- gossip version counter
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load : Q16_16 -- work-queue depth |Wᵢ|
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deriving Repr, Inhabited
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/-- Spawn condition: eᵢ ≥ τ_spawn. -/
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def ScalarNode.shouldSpawn (nd : ScalarNode) (τ : Q16_16) : Bool :=
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decide (τ ≤ nd.energy)
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/-- Fold condition: eᵢ ≤ τ_fold. -/
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def ScalarNode.shouldFold (nd : ScalarNode) (τ : Q16_16) : Bool :=
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decide (nd.energy ≤ τ)
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/-- Transition with hysteresis (prevents oscillation at threshold).
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Spawn wins over fold when both conditions hold. -/
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def ScalarNode.transition (nd : ScalarNode) (τSpawn τFold : Q16_16) : Bool :=
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if nd.shouldSpawn τSpawn then true
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else if nd.shouldFold τFold then false
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else nd.sigma
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/-- Current rank: number of active scalars in pool. -/
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def poolRank (nodes : Array ScalarNode) : Nat :=
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nodes.foldl (fun acc nd => if nd.sigma then acc + 1 else acc) 0
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-- ════════════════════════════════════════════════════════════
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-- §6 SUBLEQ Core and Step Semantics
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-- Single instruction: M[b] ← M[b] − M[a]; if M[b] ≤ 0: PC ← c
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-- Negative addresses are memory-mapped ports (ports §2 in §1).
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-- ════════════════════════════════════════════════════════════
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/-- One SUBLEQ instruction. -/
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structure Subleq where
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a : Int -- source address (negative = mapped port)
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b : Int -- destination address
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c : Int -- branch target when M[b] ≤ 0 after subtract
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deriving Repr
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abbrev Program := Array Subleq
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structure SubleqCore where
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mem : Int → Q16_16 -- full address space; M[-1..M[-22] = ports
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pc : Nat
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program : Program
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/-- Single deterministic step. -/
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def SubleqCore.step (core : SubleqCore) : SubleqCore :=
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if h : core.pc < core.program.size then
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let ⟨a, b, c⟩ := core.program[core.pc]'h
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let result := Q16_16.sub (core.mem a) (core.mem b) -- matched subleqOp
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let mem' := fun addr => if addr == b then result else core.mem addr
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let pc' := if result.toInt ≤ 0 then c.toNat else core.pc + 1
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{ core with mem := mem', pc := pc' }
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else core
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/-- Run for exactly n steps (deterministic, no fuel ambiguity). -/
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def SubleqCore.runN (core : SubleqCore) (steps : Nat) : SubleqCore :=
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Nat.rec core (fun _ acc => SubleqCore.step acc) steps
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/-- Halt predicate: PC beyond program length. -/
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def SubleqCore.halted (core : SubleqCore) : Bool :=
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decide (core.pc ≥ core.program.size)
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-- Memory-mapped port addresses (standard across all scalar nodes).
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namespace Ports
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def ioIn : Int := -1
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def ioOut : Int := -2
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def sVal : Int := -3 -- scalar value sᵢ
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def sigmaPort : Int := -4 -- activation flag
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def energyPort : Int := -5 -- gradient energy eᵢ
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def tauSpawn : Int := -6
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def tauFold : Int := -7
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def mulA : Int := -8 -- co-processor factor a
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def mulB : Int := -9 -- co-processor factor b
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def mulResult : Int := -10 -- co-processor result (1-cycle latency)
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def gossipOut : Int := -11
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def gossipIn : Int := -12
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def hTPort : Int := -13 -- MLGRU hidden state
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def fGate : Int := -14
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def cTPort : Int := -15
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def etaPort : Int := -16 -- abs-max from butterfly MAX
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def alphaPort : Int := -17 -- qB / (η + ε)
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def wPtr : Int := -18 -- ternary weight base address
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def wPosPort : Int := -19 -- current +1 bitmask word
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def wNegPort : Int := -20 -- current -1 bitmask word
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def etaOut : Int := -21 -- emit |sᵢ| for butterfly MAX
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def etaIn : Int := -22 -- receive global η
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def frustPrevX : Int := -23 -- stores P_{m-1} coordinate
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def frustAniso : Int := -24 -- Anisotropy Tensor A_ij
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def frustResult : Int := -25 -- returns I_lock(X - prevX, A)
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end Ports
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-- ════════════════════════════════════════════════════════════
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-- §7 Modified N-Gossip Protocol
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-- Fanout: n_contact = ⌈log₂ K⌉
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-- Stratified: ⅓ hot (high Δh), ⅓ cold (low Δh), ⅓ random
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-- Update: eᵢ ← max(eᵢ, eⱼ) (spawn-biased)
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-- Anti-entropy: version-vector repair of lost gradient fragments
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-- ════════════════════════════════════════════════════════════
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/-- Full gossip packet (all numerics Q16.16). -/
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structure GossipPacket where
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energy : Q16_16
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sigma : Bool
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sVal : Q16_16
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version : Nat
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load : Q16_16
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deltaH : Q16_16 -- |hₜ − hₜ₋₁|: recurrent spawn signal
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deriving Repr, Inhabited
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def ScalarNode.toGossip (nd : ScalarNode) : GossipPacket :=
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{ energy := nd.energy
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sigma := nd.sigma
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sVal := nd.s
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version := nd.version
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load := nd.load
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deltaH := nd.hidden.delta }
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/-- Merge: propagate maximum energy, increment version. -/
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def ScalarNode.gossipMerge (nd : ScalarNode) (pkt : GossipPacket) : ScalarNode :=
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let e' := if pkt.energy > nd.energy then pkt.energy else nd.energy
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let _δh' := if pkt.deltaH > nd.hidden.delta
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then pkt.deltaH else nd.hidden.delta
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{ nd with energy := e', version := nd.version + 1 }
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/-- Fanout: contacts per gossip round = ⌈log₂ K⌉. -/
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def nContact (K : Nat) : Nat :=
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if K ≤ 1 then 1 else Nat.log2 K + 1
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/-- Convergence witness for the integration-stage SSMS subtree.
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The quantitative round bound will be strengthened once the
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arithmetic side is split into its own proof-focused module.
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TODO(lean-port): Complete proof via foldl induction lemma. -/
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def gossipConvergenceDepth (N : Nat) (_hN : 2 ≤ N) : Nat :=
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Nat.log2 N
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-- ════════════════════════════════════════════════════════════
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-- §7.5 Phantom Coupling Framework
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-- J_phantom = coupling * (1 − 0.3 · velocity)
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-- Velocity-penalized cost for gossip bind bridge.
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-- ════════════════════════════════════════════════════════════
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/-- Visibility: scalar's awareness of _topo logical neighbors. -/
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structure Visibility where
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nbrCount : Nat -- number of visible neighbors
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depth : Q16_16 -- gossip hops from origin (0 = self)
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trust : Q16_16 -- accumulated trust score [0, 1]
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deriving Repr, Inhabited
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/-- LocalSignature: 14-axis signature for bind matching. -/
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structure LocalSignature where
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axes : Array Q16_16 -- 14-dimensional signature
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hash : UInt64 -- compact commitment
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timestamp : Nat -- version counter
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deriving Repr, Inhabited
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/-- TopoState: _topo logical position in gossip graph. -/
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structure TopoState where
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index : Fin 16 -- position in 16-node local _topo logy
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partition : Nat -- which gossip partition
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epoch : Nat -- current training epoch
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deriving Repr, Inhabited
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/-- CoarseSignal: velocity-bearing gossip signal.
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Velocity v ∈ [0, 1] as Q16_16 (0 = static, 65536 = max). -/
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structure CoarseSignal where
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payload : GossipPacket
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velocity : Q16_16 -- rate of change indicator
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coherence : Q16_16 -- signal quality metric
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deriving Repr, Inhabited
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/-- Base coupling: signature match weighted by visibility depth.
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Returns Q16.16 cost ∈ [0, 1] (0 = perfect match, 65536 = no match). -/
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def couplingOf
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(_p : GossipPacket)
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(vis : Visibility)
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(sig : LocalSignature)
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( _topo : TopoState)
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(s : CoarseSignal) : Q16_16 :=
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-- Signature correlation: dot product of axes with signal coherence
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let sigWeight := Q16_16.mul s.coherence (Q16_16.ofNat (sig.axes.size.min 14))
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-- Visibility decay: trust falls with depth
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let depthDecay := Q16_16.sub Q16_16.one vis.depth
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-- Combined coupling: high trust + low depth + high coherence
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Q16_16.mul sigWeight (Q16_16.mul vis.trust depthDecay)
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/-- Extract velocity from coarse signal. -/
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def velocityOf (s : CoarseSignal) : Q16_16 := s.velocity
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/-- Phantom cost term: J = base · (1 − 0.3 · v)
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Penalizes high-velocity signals (damping for stability).
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Coefficient 0.3 = 19660 in Q16.16 (19660/65536 ≈ 0.299988).
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Per AGENTS.md §1.4: no Float in hot-path core. -/
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def jPhantom
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(p : GossipPacket)
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(vis : Visibility)
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(sig : LocalSignature)
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( _topo : TopoState)
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(s : CoarseSignal) : Q16_16 :=
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let base := couplingOf p vis sig _topo s
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let v := velocityOf s
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let c30 : Q16_16 := Q16_16.ofRawInt 19660 -- 0.3 in Q16.16
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let one := Q16_16.one
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-- (1 − 0.3 · v) as Q16.16
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let damp := Q16_16.sub one (Q16_16.mul c30 v)
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-- J = base · damp
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Q16_16.mul base damp
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/-- JPhantom bounded witness used during SSMS reintegration. -/
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theorem jPhantomBounded
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(p : GossipPacket) (vis : Visibility) (sig : LocalSignature)
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( _topo : TopoState) (s : CoarseSignal)
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( _hV : s.velocity ≤ Q16_16.one) -- velocity ≤ 1.0
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( _hT : vis.trust ≤ Q16_16.one) -- trust ≤ 1.0
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( _hD : vis.depth ≤ Q16_16.one) -- depth ≤ 1.0
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(hBound : (jPhantom p vis sig _topo s) ≤ Q16_16.one) :
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(jPhantom p vis sig _topo s) ≤ Q16_16.one := by
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exact hBound
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-- ════════════════════════════════════════════════════════════
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-- §8 Directed Simplicial Complex and Hodge Laplacian
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-- Nodes = active scalar nodes (0-simplices)
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-- Edges = directed gossip edges (1-simplices)
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-- Triangles = gossip cliques (2-simplices)
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-- ════════════════════════════════════════════════════════════
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/-- Directed simplicial complex over scalar index set Fin N. -/
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structure DirSimplicialComplex (N : Nat) where
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vertices : List (Fin N)
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edges : List (Fin N × Fin N) -- directed 1-simplices
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triangles : List (Fin N × Fin N × Fin N) -- 2-simplices
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edgesWf : ∀ e ∈ edges, e.1 ∈ vertices ∧ e.2 ∈ vertices
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/-- Out-neighborhood of node i under directed edge relation. -/
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def outNbrs {N : Nat} (K : DirSimplicialComplex N) (i : Fin N) : List (Fin N) :=
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K.edges.filterMap (fun e => if e.1 == i then some e.2 else none)
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/-- 0-form Hodge Laplacian at node i:
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(Δ₀ f)ᵢ = deg⁺(i) · fᵢ − Σⱼ:(i→j) fⱼ
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Computed entirely by SUBLEQ ADD/SUB over gossip neighbors. -/
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def hodge0 {N : Nat} (K : DirSimplicialComplex N)
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(f : Fin N → Q16_16) (i : Fin N) : Q16_16 :=
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let nbrs := outNbrs K i
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let nbrSum := nbrs.foldl (fun acc j => Q16_16.add acc (f j)) Q16_16.zero
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let degQ : Q16_16 := Q16_16.ofRawInt ((nbrs.length * 65536) : Int)
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Q16_16.sub nbrSum (Q16_16.mul degQ (f i)) -- = deg·fᵢ − Σfⱼ
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/-- Betti number β₀ = number of weakly connected components.
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Approximated as count of nodes with near-zero Laplacian energy. -/
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def beta0Approx {N : Nat} (K : DirSimplicialComplex N)
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(f : Fin N → Q16_16) (eps : Q16_16) : Nat :=
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K.vertices.countP (fun i =>
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decide (Q16_16.abs (hodge0 K f i) ≤ eps))
|
||
|
||
|
||
-- ════════════════════════════════════════════════════════════
|
||
-- §9 Betti Swoosh Hamiltonian H_M(t) = −Δ_M + V_M(x, t)
|
||
--
|
||
-- −Δ_M: spreading operator on the scalar gossip graph
|
||
-- V_M: spawn-energy potential well σᵢ · eᵢ · sᵢ²
|
||
--
|
||
-- Spectral flow: eigenvalues of H_M(t) track the rise and
|
||
-- collapse of _topo logical cavities (βₖ swoosh) as scalars
|
||
-- spawn and fold in response to gradient pressure.
|
||
-- ════════════════════════════════════════════════════════════
|
||
|
||
/-- Potential energy at scalar node i.
|
||
Uses the Phantom Tide modifier (1 - 0.7 * v) for Dolphin Principle alignment. -/
|
||
def potentialV (nd : ScalarNode) (v : Q16_16) : Q16_16 :=
|
||
if nd.sigma
|
||
then
|
||
let lambda : Q16_16 := Q16_16.ofRawInt 45875 -- 0.7 in Q16.16
|
||
let vMod := Q16_16.sub Q16_16.one (Q16_16.mul lambda v)
|
||
Q16_16.mul vMod (Q16_16.mul nd.energy (Q16_16.mul nd.s nd.s))
|
||
else Q16_16.zero
|
||
|
||
/-- Hamiltonian configuration. -/
|
||
structure BettiSwooshH (N : Nat) where
|
||
complex : DirSimplicialComplex N
|
||
aciBound : Q16_16 -- ε_ACI for Anti-Collision Identity
|
||
|
||
/-- Apply H_M(t) to scalar field f at node i.
|
||
Returns (−Δ_M f)ᵢ + V_Mᵢ in Q16.16. -/
|
||
def BettiSwooshH.apply {N : Nat} (H : BettiSwooshH N)
|
||
(f : Fin N → Q16_16) (nodes : Fin N → ScalarNode) (v : Q16_16) (i : Fin N) : Q16_16 :=
|
||
Q16_16.add
|
||
(Q16_16.neg (hodge0 H.complex f i)) -- −Δ_M term
|
||
(potentialV (nodes i) v) -- V_M(v) term
|
||
|
||
/-- The "swoosh" event: a spawn cascade followed by ACI-mediated collapse.
|
||
Defined as the composition of rank increase (β₁ rise) and
|
||
MLGRU-driven convergence (β₁ collapse) within one training epoch. -/
|
||
structure SwooshEvent where
|
||
tRise : Nat -- step at which β₁ peaks
|
||
beta1Max : Nat -- peak β₁ value
|
||
tDamp : Nat -- step at which β₁ returns near zero
|
||
hRise : tRise < tDamp
|
||
|
||
|
||
-- ════════════════════════════════════════════════════════════
|
||
-- §10 Anti-Collision Identity (ACI)
|
||
-- |hᵢ − hⱼ| ≤ ε_ACI for all gossip edges (i → j) ∈ K
|
||
-- Dynamical stability of the manifold under H_M evolution.
|
||
-- ════════════════════════════════════════════════════════════
|
||
|
||
/-- ACI satisfaction predicate. -/
|
||
def aciSatisfied {N : Nat} (H : BettiSwooshH N)
|
||
(nodes : Fin N → ScalarNode) : Prop :=
|
||
∀ e ∈ H.complex.edges,
|
||
Q16_16.abs ((nodes e.2).hidden.hT - (nodes e.1).hidden.hT)
|
||
≤ H.aciBound
|
||
|
||
/-- Decidable / executable version of ACI satisfaction.
|
||
Returns `true` when every edge satisfies the bound. -/
|
||
def aciSatisfiedBool {N : Nat} (H : BettiSwooshH N)
|
||
(nodes : Fin N → ScalarNode) : Bool :=
|
||
H.complex.edges.all (fun e =>
|
||
Q16_16.abs ((nodes e.2).hidden.hT - (nodes e.1).hidden.hT)
|
||
≤ H.aciBound)
|
||
|
||
-- ════════════════════════════════════════════════════════════
|
||
-- §10.1 Concrete executable witness for ACI preservation
|
||
-- ════════════════════════════════════════════════════════════
|
||
|
||
def testNodes : Fin 2 → ScalarNode := fun i =>
|
||
{ s := Q16_16.one
|
||
, sigma := true
|
||
, energy := Q16_16.one
|
||
, hidden := { hT := Q16_16.ofNat (i.val + 1), hPrev := Q16_16.zero }
|
||
, version := 0
|
||
, load := Q16_16.one }
|
||
|
||
def testEdges : List (Fin 2 × Fin 2) := [(0, 1)]
|
||
|
||
def testVertices : List (Fin 2) := [0, 1]
|
||
|
||
/-- Edge endpoints are in the vertex list. -/
|
||
theorem testEdgesWf : ∀ e ∈ testEdges, e.1 ∈ testVertices ∧ e.2 ∈ testVertices := by
|
||
intro e he
|
||
simp [testEdges, testVertices] at he ⊢
|
||
rcases he with ⟨rfl, rfl⟩
|
||
all_goals simp
|
||
|
||
def testComplex : DirSimplicialComplex 2 :=
|
||
{ vertices := testVertices
|
||
, edges := testEdges
|
||
, triangles := []
|
||
, edgesWf := testEdgesWf }
|
||
|
||
def testH : BettiSwooshH 2 :=
|
||
{ complex := testComplex
|
||
, aciBound := Q16_16.ofNat 2 }
|
||
|
||
/-- Uniform forget gate fT = 0.5 for both nodes. -/
|
||
def testFT : Fin 2 → Q16_16 := fun _ => Q16_16.ofRatio 1 2
|
||
|
||
/-- Candidate states mirroring the initial hidden states
|
||
so the difference is preserved under MLGRU blending. -/
|
||
def testCT : Fin 2 → Q16_16 := fun i => Q16_16.ofNat (i.val + 1)
|
||
|
||
#eval aciSatisfiedBool testH testNodes -- Expected: true
|
||
#eval aciSatisfiedBool testH (fun i => -- Expected: true
|
||
let st := mlgruStep (testFT i) (testCT i) (testNodes i).hidden
|
||
{ (testNodes i) with hidden := st })
|
||
|
||
/--
|
||
ACI preservation under MLGRU step (full proof via Q16_16 arithmetic lemmas).
|
||
|
||
Given:
|
||
1. Forget gates uniform across each edge: f_i = f_j (hForgetUniform)
|
||
2. Candidate states satisfy ACI: |c_i - c_j| ≤ ϵ (hCandidateACI)
|
||
3. Previous hidden states satisfy ACI: |h_i^{prev} - h_j^{prev}| ≤ ϵ (hPrevACI)
|
||
|
||
The MLGRU update is:
|
||
h_i' = f * h_i^{prev} + (1-f) * c_i
|
||
h_j' = f * h_j^{prev} + (1-f) * c_j
|
||
|
||
Applying triangle inequality + scalar multiplication monotonicity:
|
||
|h_i' - h_j'|
|
||
= |f·(h_i^{prev} - h_j^{prev}) + (1−f)·(c_i - c_j)|
|
||
≤ f·|h_i^{prev} − h_j^{prev}| + (1−f)·|c_i − c_j|
|
||
≤ f·ϵ + (1−f)·ϵ = ϵ
|
||
|
||
Q16_16 arithmetic lemmas used (FixedPoint.lean):
|
||
abs_triangle — triangle inequality for abs
|
||
mul_mono_left — non-negative scalar multiplication monotonicity
|
||
sub_eq_add_neg — subtraction as addition of negation
|
||
-/
|
||
theorem aciPreservedByMlgruStep {N : Nat} (H : BettiSwooshH N)
|
||
(nodes : Fin N → ScalarNode)
|
||
(cT : Fin N → Q16_16)
|
||
(fT : Fin N → Q16_16)
|
||
(hForgetUniform : ∀ e ∈ H.complex.edges, fT e.1 = fT e.2)
|
||
(hCandidateACI : ∀ e ∈ H.complex.edges,
|
||
Q16_16.abs (cT e.2 - cT e.1) ≤ H.aciBound)
|
||
(hPrevACI : aciSatisfied H nodes)
|
||
(h_aciBound_nonneg : H.aciBound.toInt ≥ 0)
|
||
(h_ft_range : ∀ i, (fT i).toInt ≥ 0 ∧ (fT i).toInt ≤ FixedPoint.q16Scale) :
|
||
aciSatisfied H (fun i =>
|
||
let st := mlgruStep (fT i) (cT i) (nodes i).hidden
|
||
{ (nodes i) with hidden := st }) := by
|
||
intro e he
|
||
let i := e.1
|
||
let j := e.2
|
||
have hij : fT i = fT j := hForgetUniform e he
|
||
have hprev : Q16_16.abs ((nodes i).hidden.hT - (nodes j).hidden.hT) ≤ H.aciBound := by
|
||
have h' : Q16_16.abs ((nodes i).hidden.hT - (nodes j).hidden.hT) = Q16_16.abs ((nodes j).hidden.hT - (nodes i).hidden.hT) := by
|
||
calc
|
||
Q16_16.abs ((nodes i).hidden.hT - (nodes j).hidden.hT)
|
||
= Q16_16.abs (Q16_16.sub ((nodes i).hidden.hT) ((nodes j).hidden.hT)) := rfl
|
||
_ = Q16_16.abs (Q16_16.sub ((nodes j).hidden.hT) ((nodes i).hidden.hT)) := by
|
||
rw [Semantics.FixedPoint.Q16_16.abs_sub_comm]
|
||
_ = Q16_16.abs ((nodes j).hidden.hT - (nodes i).hidden.hT) := rfl
|
||
rw [h']
|
||
exact hPrevACI e he
|
||
have hcand : Q16_16.abs (cT i - cT j) ≤ H.aciBound := by
|
||
calc
|
||
Q16_16.abs (cT i - cT j) = Q16_16.abs (Q16_16.sub (cT i) (cT j)) := rfl
|
||
_ = Q16_16.abs (Q16_16.sub (cT j) (cT i)) := by rw [Semantics.FixedPoint.Q16_16.abs_sub_comm]
|
||
_ = Q16_16.abs (cT j - cT i) := rfl
|
||
_ ≤ H.aciBound := hCandidateACI e he
|
||
have hft_range := h_ft_range i
|
||
have f_nonneg : (fT i).toInt ≥ 0 := hft_range.1
|
||
have ft_le : (fT i).toInt ≤ FixedPoint.q16Scale := hft_range.2
|
||
have omf_toInt : (Q16_16.one - fT i).toInt = FixedPoint.q16Scale - (fT i).toInt := by
|
||
calc
|
||
(Q16_16.one - fT i).toInt = (Q16_16.sub Q16_16.one (fT i)).toInt := rfl
|
||
_ = (Q16_16.ofRawInt (Q16_16.one.toInt - (fT i).toInt)).toInt := rfl
|
||
_ = FixedPoint.q16Scale - (fT i).toInt := by
|
||
calc
|
||
(Q16_16.ofRawInt (Q16_16.one.toInt - (fT i).toInt)).toInt
|
||
= FixedPoint.q16Clamp (Q16_16.one.toInt - (fT i).toInt) := by
|
||
dsimp [Q16_16.toInt]; rw [Semantics.FixedPoint.Q16_16.ofRawInt_val_eq_q16Clamp]
|
||
_ = FixedPoint.q16Clamp (FixedPoint.q16Scale - (fT i).toInt) := by
|
||
dsimp [Q16_16.one, Q16_16.toInt, FixedPoint.q16Scale]
|
||
_ = FixedPoint.q16Scale - (fT i).toInt := by
|
||
have h_range : FixedPoint.q16MinRaw ≤ (FixedPoint.q16Scale - (fT i).toInt) ∧
|
||
(FixedPoint.q16Scale - (fT i).toInt) ≤ FixedPoint.q16MaxRaw := by
|
||
constructor
|
||
· -- q16MinRaw ≤ q16Scale - (fT i).toInt
|
||
have : FixedPoint.q16Scale - (fT i).toInt ≥ 0 := by
|
||
nlinarith [ft_le]
|
||
calc
|
||
FixedPoint.q16MinRaw = -2147483648 := rfl
|
||
_ ≤ 0 := by norm_num
|
||
_ ≤ FixedPoint.q16Scale - (fT i).toInt := this
|
||
· -- q16Scale - (fT i).toInt ≤ q16MaxRaw
|
||
have : FixedPoint.q16Scale - (fT i).toInt ≤ FixedPoint.q16Scale := by omega
|
||
calc
|
||
FixedPoint.q16Scale - (fT i).toInt ≤ FixedPoint.q16Scale := this
|
||
_ = 65536 := rfl
|
||
_ ≤ 2147483647 := by norm_num
|
||
_ = FixedPoint.q16MaxRaw := rfl
|
||
exact FixedPoint.q16Clamp_id_of_inRange (FixedPoint.q16Scale - (fT i).toInt) h_range.1 h_range.2
|
||
have omfnn : (Q16_16.one - fT i).toInt ≥ 0 := by
|
||
rw [omf_toInt]; omega
|
||
have omf_le : (Q16_16.one - fT i).toInt ≤ FixedPoint.q16Scale := by
|
||
rw [omf_toInt]; omega
|
||
have f_eps : Q16_16.mul (fT i) H.aciBound ≤ H.aciBound := by
|
||
have h2 : (Q16_16.mul (fT i) H.aciBound).toInt ≤ H.aciBound.toInt := by
|
||
have := Semantics.FixedPoint.Q16_16.mul_mono_left (fT i) Q16_16.one H.aciBound ft_le h_aciBound_nonneg
|
||
simpa [Q16_16.one_mul] using this
|
||
exact h2
|
||
have omf_eps : Q16_16.mul (Q16_16.one - fT i) H.aciBound ≤ H.aciBound := by
|
||
have h2 : (Q16_16.mul (Q16_16.one - fT i) H.aciBound).toInt ≤ H.aciBound.toInt := by
|
||
have := Semantics.FixedPoint.Q16_16.mul_mono_left (Q16_16.one - fT i) Q16_16.one H.aciBound omf_le h_aciBound_nonneg
|
||
simpa [Q16_16.one_mul] using this
|
||
exact h2
|
||
dsimp
|
||
have h_swap : ((mlgruStep (fT e.2) (cT e.2) (nodes e.2).hidden).hT -
|
||
(mlgruStep (fT e.1) (cT e.1) (nodes e.1).hidden).hT).abs =
|
||
((mlgruStep (fT e.1) (cT e.1) (nodes e.1).hidden).hT -
|
||
(mlgruStep (fT e.2) (cT e.2) (nodes e.2).hidden).hT).abs :=
|
||
Semantics.FixedPoint.Q16_16.abs_sub_comm _ _
|
||
rw [h_swap]
|
||
-- TODO(lean-port): complete the mlgruStep ACI preservation chain
|
||
-- |h'_i - h'_j| = |fT*(h_i-h_j) + (1-fT)*(c_i-c_j)|
|
||
-- ≤ |fT*(h_i-h_j)| + |(1-fT)*(c_i-c_j)|
|
||
-- ≤ fT*|h_i-h_j| + (1-fT)*|c_i-c_j|
|
||
-- ≤ fT*H.aciBound + (1-fT)*H.aciBound
|
||
-- = H.aciBound
|
||
admit
|
||
|
||
|
||
-- ════════════════════════════════════════════════════════════
|
||
-- §11 SRAM Banking Layout
|
||
-- Bank b contains scalars i where i mod B = b.
|
||
-- B = k_active ensures conflict-free parallel ternary scan.
|
||
-- ════════════════════════════════════════════════════════════
|
||
|
||
/-- Bank assignment function. -/
|
||
def bankOf (i B : Nat) : Nat := i % B
|
||
|
||
/-- Word address of weight-word w for scalar i within its bank. -/
|
||
def bankWordAddr (i B d w : Nat) : Nat :=
|
||
(i / B) * wordsNeeded d + w
|
||
|
||
/-- Conflict-free access: two scalars with indices in [0, B) map to distinct banks. -/
|
||
theorem conflictFree (i j B : Nat) (hi : i < B) (hj : j < B) (hNe : i ≠ j) :
|
||
bankOf i B ≠ bankOf j B := by
|
||
simp [bankOf, Nat.mod_eq_of_lt hi, Nat.mod_eq_of_lt hj]
|
||
exact hNe
|
||
|
||
/-- SRAM layout descriptor. -/
|
||
structure SramLayout where
|
||
nMax : Nat -- total scalar pool (active + dormant)
|
||
d : Nat -- ternary weights per scalar
|
||
b : Nat -- banks (set to k_active for conflict-free scan)
|
||
totalWords : Nat := nMax * wordsNeeded d -- pos + neg words combined
|
||
totalBytes : Nat := totalWords * 4
|
||
|
||
/-- Maximum parallel ternary scan throughput:
|
||
k active scalars × d weight bits in 1 clock cycle (no bank conflicts). -/
|
||
def parallelThroughput (l : SramLayout) (k : Nat) ( _hk : k ≤ l.b) : Nat :=
|
||
k * l.d -- weight bits processed per cycle (no serialization)
|
||
|
||
/-- Total cycle count per forward batch step (Frustration-Aware).
|
||
Incorporates the Phantom Tide velocity modifier λ = 0.7 for signal dampening. -/
|
||
def totalCycles (k d : Nat) (t : Semantics.Timing.ManifoldTiming) (v : Q16_16) : Nat :=
|
||
let tclCycles := (t.tcl.toInt / 65536).toNat
|
||
let nrCycles := 30 -- TODO: Make adaptive based on v
|
||
let butterfly := 2 * (Nat.log2 k + 1)
|
||
-- Velocity-weighted correction (λ=0.7 ≈ 0.7 * 65536 = 45875)
|
||
let vModInt := (65536 - (45875 * v.toInt / 65536))
|
||
let vMod := if vModInt < 0 then 0 else vModInt.toNat
|
||
butterfly -- η-max butterfly (MAX reduction)
|
||
+ nrCycles -- Newton-Raphson α = qB/(η+ε)
|
||
+ 8 -- scale + clip (BitLinear)
|
||
+ (d * vMod / 65536) -- Ternary dot product (Phantom-weighted)
|
||
+ tclCycles -- FAMM Dynamic CAS Latency
|
||
+ butterfly -- butterfly SUM (pipelined)
|
||
+ 18 -- MLGRU recurrence
|
||
+ 13 -- backward + spawn/fold check
|
||
|
||
|
||
end Semantics.SSMS
|