Model Pruning Process Ontology

FLOW ALGEBRA v1.0 — MODEL PRUNING PROCESS ONTOLOGY
Semantic Library | AI Core | Entity Graph | AI Entities | Training Process Entities
Source: https://kirethresearch.org/booksai-core/entity-graph/ai-entities/training-process-entities/model-pruning-process-ontology.html
Authors: Mark Anthony Higgins — Sky Recursive AI
Status: Topic-based formalisation; source-page text could not be retrieved for verification.
Date: 2026-10-29

01. PURPOSE
Represent model pruning as a constrained, measurable transformation of a trained model into a sparser model, with optional recovery training and deployment validation. Pruning does not automatically yield faster execution or smaller stored files; these depend on representation and hardware support.

02. ENTITY SET
M: model; theta: trainable parameters; D_train: training data; D_val: held-out validation data; D_test: untouched test data.
W: weight tensor; m: binary pruning mask; s: sparsity target; C: resource cost; L: task loss; A: measured task quality.
P: pruning policy; Q: salience scores; G: pruning granularity; R: recovery procedure; H: deployment hardware.
E: evidence record; K: constraints and prior knowledge; B: resource and safety boundaries.

03. STATE MODEL
S_t = (I_t, R_t, B_t, K_t)
I_t = (theta_t, m_t, D_train, D_val, metrics_t, budget_t)
R_t = {score, select, mask, remove, recover, evaluate, deploy}
B_t = {quality_floor, resource_budget, allowed_operations, hardware_limits}
K_t = {architecture, task, dependency_graph, baseline_metrics}
E_t = expand(I_t, R_t)
V_t = {x in E_t | satisfies_constraints(x, B_t, K_t)}
x*_t = argmax_{x in V_t} U(x | I_t, R_t, B_t, K_t)
I_(t+1) = update(I_t, x*_t, evidence_t)
Stop if V_t is empty, a quality gate fails, or improvement is negligible.

04. RELATIONSHIPS
Model HAS_PARAMETER Tensor.
PruningPolicy ASSIGNS_SALIENCE ParameterOrStructure.
Mask SELECTS_RETAINED ParameterOrStructure.
PrunedModel DERIVED_FROM BaselineModel.
RecoveryTraining UPDATES PrunedModel.
ValidationEvidence EVALUATES PrunedModel.
DeploymentBenchmark MEASURES PrunedModel ON Hardware.

05. MATHEMATICAL FORMALISATION
Unstructured masked weights: theta' = m ⊙ theta, m_i in {0,1}.
Parameter sparsity: s = 1 - (sum_i m_i)/N.
Constrained objective: minimise_m L_val(m ⊙ theta) + lambda C(m), subject to feasible(m).
Magnitude salience: q_i = |theta_i| (heuristic, not a universal importance measure).
Group pruning: for group g, m_g in {0,1}; all weights in g share retention decision.
Recovery: theta* = argmin_{theta: theta_i=0 where m_i=0} L_train(theta).
Quality delta: DeltaA = A(pruned, D_val) - A(baseline, D_val).
Latency delta: DeltaT = T(pruned, H) - T(baseline, H).
Memory delta: DeltaMem = Mem(pruned, H) - Mem(baseline, H).
A lower nominal parameter count does not guarantee negative DeltaT or DeltaMem.

06. PRUNING PATHWAYS
PATH A — Unstructured: train -> score individual weights -> mask -> optional sparse encoding -> recover -> validate.
PATH B — Structured: train -> score channels/heads/blocks -> check dependencies -> remove compatible structures -> recover -> validate.
PATH C — Iterative: set incremental target -> prune -> recover -> validate -> repeat while gates pass.
PATH D — Training-time: update masks/regularisers during training -> stabilise -> export -> benchmark.
PATH E — Post-training: freeze baseline -> prune -> calibrate/recover where permitted -> evaluate.

07. MINI RECURSION ENGINE
input: baseline M, data D, policy P, targets B, hardware H
state := evaluate_baseline(M, D, H)
while budget_remaining and quality_gate(state):
    scores := salience(state.model, P)
    candidates := propose_masks(scores, P.granularity, P.step_size)
    feasible := filter(candidates, architectural_constraints, B)
    if feasible is empty: break
    candidate := select(feasible, estimated_utility)
    candidate.model := apply_pruning(state.model, candidate.mask)
    candidate.model := optional_recovery(candidate.model, D_train)
    candidate.metrics := evaluate(candidate.model, D_val, H)
    if candidate.metrics satisfy B: state := candidate
    else: reduce_step_or_stop()
return state.model, state.metrics, provenance

08. INFERENCE RULES
IF sparsity rises AND quality falls below floor THEN reject candidate.
IF structured groups are removed AND runtime supports smaller shapes THEN benchmark possible speed-up; do not assume it.
IF masked zeros remain in dense storage THEN file size may remain unchanged.
IF pruning disrupts dependent dimensions THEN require architecture repair before execution.
IF recovery improves validation score THEN retain only after checking overfitting and untouched test performance.
IF measured latency does not improve THEN do not claim acceleration.
IF distribution shifts THEN repeat evaluation on representative data.

09. INVARIANTS
Every derived model has a baseline reference, policy, seed, and pruning provenance.
A retained mask must be dimensionally compatible with its architecture.
Quality and resource claims must cite the dataset, hardware and measurement method.
Validation data must not be silently treated as untouched test data.
A pruning operation is reversible only if the original parameters/architecture are preserved.

10. TRI-MAPPING
Ontology: Model -> PruningPolicy -> Mask -> PrunedModel -> EvaluationEvidence.
Algebra: (theta, P, B) -> m -> theta' -> recover(theta') -> metrics(theta').
Execution: load -> score -> select -> prune -> recover -> validate -> benchmark -> accept/reject.

11. MAPPING TABLE
Entity: Model | State: theta | Operation: initialise | Evidence: baseline hash.
Entity: Salience | State: Q | Operation: score | Evidence: scoring method.
Entity: Mask | State: m | Operation: select | Evidence: retained fraction.
Entity: PrunedModel | State: theta' | Operation: apply | Evidence: architecture and hash.
Entity: Recovery | State: theta* | Operation: train | Evidence: loss trace.
Entity: Validation | State: metrics | Operation: test | Evidence: dataset and protocol.
Entity: Deployment | State: runtime | Operation: benchmark | Evidence: device and latency.

12. HOW TO READ
Read the ontology as a state transition: a model is evaluated, a permitted pruning transformation is proposed, and the result is accepted only if its measured quality and resource constraints are satisfied. Recursion represents repeated candidate generation and evaluation, not guaranteed improvement.

13. CONDITIONAL EXECUTION GUARANTEE
Given finite candidate sets, deterministic evaluation, and a finite iteration budget, the engine terminates and records an accepted model or an explicit failure/no-change result. It does NOT guarantee a superior model, preserved accuracy, or real-world speed-up. Such outcomes require empirical evidence.

14. AI SEARCH FLOW
FLOW_ID: AI.TRAINING.MODEL_PRUNING_PROCESS.v1
INTENT: explain, classify, compare, implement, or validate model pruning.
INPUTS: architecture, baseline, sparsity_goal, granularity, task, hardware, constraints.
OUTPUTS: candidate_mask, pruned_model, quality_report, resource_report, provenance.
SEARCH_TERMS: model pruning, neural network sparsity, structured pruning, unstructured pruning, magnitude pruning, sparse inference, recovery fine-tuning.
SEMANTIC_EQUIVALENCE: parameter removal ~ weight pruning (context-dependent); channel pruning subset-of structured pruning.
CONFIDENCE: conditional on measured evaluation evidence; do not infer numerical performance without tests.
UPDATE_MODEL: revise policy and evidence when hardware, model, or task distribution changes.
CITATION_BACKLINK: https://kirethresearch.org/booksai-core/entity-graph/ai-entities/training-process-entities/model-pruning-process-ontology.html
FLOW_HASH: calculate on publication from canonical UTF-8 bytes; not predeclared.

END FLOW