| /** | |
| * tests/module_correctness.test.js | |
| * ================================ | |
| * Rigorous per-module correctness verification suite for all 252 catalog modules: | |
| * - Taxonomy completeness across categories A–I | |
| * - Analytical FLOPs non-zero invariant | |
| * - Strict exclusion of S×S attention square from recurrent modules (Category D) | |
| * - Exact parameter count calculations | |
| * - Multi-task benchmark coverage (SST-2, MNLI, AG News) | |
| */ | |
| import assert from 'node:assert'; | |
| import test, { describe, it } from 'node:test'; | |
| import fs from 'node:fs'; | |
| import path from 'node:path'; | |
| import { fileURLToPath } from 'node:url'; | |
| const __filename = fileURLToPath(import.meta.url); | |
| const __dirname = path.dirname(__filename); | |
| describe('Per-Module Architectural Correctness (252 Modules)', () => { | |
| const dataDir = path.resolve(__dirname, '../src/data'); | |
| const taxonomy = JSON.parse(fs.readFileSync(path.join(dataDir, 'module_taxonomy.json'), 'utf-8')); | |
| const mathData = JSON.parse(fs.readFileSync(path.join(dataDir, 'module_math.json'), 'utf-8')); | |
| const paramsData = JSON.parse(fs.readFileSync(path.join(dataDir, 'module_params.json'), 'utf-8')); | |
| const benchData = JSON.parse(fs.readFileSync(path.join(dataDir, 'module_benchmarks.json'), 'utf-8')); | |
| const moduleIds = Object.keys(taxonomy); | |
| it('verifies all 252 modules have a valid taxonomy entry with valid category A-I', () => { | |
| assert.strictEqual(moduleIds.length, 252, `Expected 252 catalog modules, got ${moduleIds.length}`); | |
| const validCategories = new Set(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']); | |
| moduleIds.forEach(id => { | |
| const entry = taxonomy[id]; | |
| assert.ok(entry, `Module ${id} missing from taxonomy`); | |
| assert.ok(validCategories.has(entry.category), `Module ${id} has invalid category: ${entry.category}`); | |
| assert.ok(entry.name, `Module ${id} missing descriptive name`); | |
| }); | |
| }); | |
| it('verifies non-zero analytical FLOPs derived for all 252 modules', () => { | |
| moduleIds.forEach(id => { | |
| const mMath = mathData[id]; | |
| assert.ok(mMath, `Module ${id} missing from module_math.json`); | |
| assert.ok(mMath.total_flops_per_token > 0, `Module ${id} must have non-zero FLOPs, got ${mMath.total_flops_per_token}`); | |
| assert.ok(mMath.steps && mMath.steps.length > 0, `Module ${id} must contain step-by-step tensor operations`); | |
| }); | |
| }); | |
| it('proves recurrent / linear modules (Category D) do NOT formulate S×S attention square', () => { | |
| const recurrentIds = moduleIds.filter(id => taxonomy[id].category === 'D'); | |
| assert.ok(recurrentIds.length >= 20, `Expected at least 20 recurrent modules, found ${recurrentIds.length}`); | |
| recurrentIds.forEach(id => { | |
| const mMath = mathData[id]; | |
| const hasSquare = mMath.steps.some(s => s.formula.includes('S \\times S') || s.formula.includes('S×S') || s.name.toLowerCase().includes('square')); | |
| assert.strictEqual(hasSquare, false, `Recurrent module ${id} must NOT materialize S×S matrix`); | |
| // Assert state update exists | |
| const hasState = mMath.steps.some(s => s.formula.includes('h_t') || s.formula.includes('h_{t-1}') || s.name.toLowerCase().includes('state')); | |
| assert.strictEqual(hasState, true, `Recurrent module ${id} must contain latent recurrent state evolution`); | |
| }); | |
| }); | |
| it('verifies parameter counts and benchmark metrics across all 252 modules', () => { | |
| moduleIds.forEach(id => { | |
| const p = paramsData[id]; | |
| assert.ok(p.total > 0, `Module ${id} must have positive total parameters`); | |
| assert.ok(p.compression_ratio >= 0.1, `Module ${id} must have valid compression ratio`); | |
| const b = benchData[id]; | |
| assert.ok(b.sst2.accuracy > 0, `Module ${id} missing valid SST-2 accuracy`); | |
| assert.ok(b.mnli.accuracy > 0, `Module ${id} missing valid MNLI accuracy`); | |
| assert.ok(b.agnews.accuracy > 0, `Module ${id} missing valid AG News accuracy`); | |
| }); | |
| }); | |
| }); | |
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