Initial commit: E4B-MarkBase model integration with passing tests
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CI / build-and-test (push) Has been cancelled
- E4B-MarkBase model (42 layers, 4.4GB) loaded successfully - All Phase 1-6 tests passed (model loading, forward pass, vision/audio towers, token generation, performance) - All stress tests passed (5/5 in 127.6s) - Concurrent inference - Memory stress (67.5 tok/s, 0 NaN) - Continuous generation - Batch processing - Long-running stability - Swift Metal inference engine with multimodal support
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MarkBase Admin
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import XCTest
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@testable import MarkBase
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final class MultimodalSequentialTest: XCTestCase {
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func testE2BFullMultimodal() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" E2B Full Multimodal Test (TEXT + Audio + Vision)")
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print("═══════════════════════════════════════════════════════════════════\n")
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let modelDir = "/Users/accusys/.cache/huggingface/hub/models--mlx-community--gemma-4-e2b-it-4bit/snapshots/2c3e507453b4f218d05fe3cc97bea5c5a654257e"
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print("Step 1: Load MultimodalModel (TEXT + Audio + Vision)...")
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let engine = try MarkBaseEngine(autoCompile: true)
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let loadStart = Date()
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let mmModel = try MultimodalModel(modelDir: modelDir, engine: engine, maxContextLength: 256)
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let loadTime = Date().timeIntervalSince(loadStart) * 1000
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print(" ✓ Model loaded in \(loadTime) ms")
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print(" TEXT: hidden=\(mmModel.textModel.hiddenSize), layers=\(mmModel.textModel.numHiddenLayers)")
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print(" Audio: \(mmModel.audioTowerE2B != nil ? "E2B (12 layers)" : "N/A")")
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print(" Vision: \(mmModel.visionTowerE2B != nil ? "E2B (16 layers)" : "N/A")")
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// ── Audio Test ───────────────────────────────────────────────────
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print("\nStep 2: Process Audio...")
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let seqLen = 100
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let nMels = 128
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var melFeatures: [[Float]] = []
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for _ in 0..<seqLen {
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var frame: [Float] = []
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for _ in 0..<nMels {
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frame.append(Float.random(in: -0.5...0.5))
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}
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melFeatures.append(frame)
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}
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let audioStart = Date()
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let audioEmbeds = try mmModel.processAudio(audioFeatures: melFeatures)
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let audioTime = Date().timeIntervalSince(audioStart) * 1000
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print(" ✓ Audio processed in \(audioTime) ms")
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print(" Output: [\(audioEmbeds.count / 1536), 1536]")
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print(" NaN: \(audioEmbeds.contains { $0.isNaN })")
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// ── Vision Test ───────────────────────────────────────────────────
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print("\nStep 3: Process Vision...")
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let numPatches = 256
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let patchDim = 768
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var patches: [Float] = []
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for _ in 0..<numPatches * patchDim {
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patches.append(Float.random(in: -0.5...0.5))
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}
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let visionStart = Date()
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// VisionTowerE2B forward pass placeholder (needs full implementation)
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let visionTime = Date().timeIntervalSince(visionStart) * 1000
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print(" ✓ Vision placeholder (forward not fully implemented)")
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print(" Time: \(visionTime) ms")
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// ── Text Generation ───────────────────────────────────────────────
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print("\nStep 4: Generate Text tokens...")
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var tokens: [Int] = [2] // BOS
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let genStart = Date()
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for _ in 0..<10 {
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let logits = try mmModel.textModel.forward(tokenId: tokens.last!, position: tokens.count - 1)
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var maxIdx = 0
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var maxLogit = logits[0]
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for i in 1..<logits.count {
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if logits[i] > maxLogit {
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maxLogit = logits[i]
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maxIdx = i
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}
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}
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tokens.append(maxIdx)
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}
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let genTime = Date().timeIntervalSince(genStart) * 1000
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print(" ✓ Generated 10 tokens in \(genTime) ms")
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print(" Tokens: \(tokens.suffix(10))")
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print("\n═══════════════════════════════════════════════════════════════════")
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print("✓ E2B Full Multimodal test passed")
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print(" Load: \(loadTime) ms, Audio: \(audioTime) ms, Vision: \(visionTime) ms, Gen: \(genTime) ms")
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print("═══════════════════════════════════════════════════════════════════\n")
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XCTAssertFalse(audioEmbeds.contains { $0.isNaN }, "Audio should not have NaN")
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}
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func testE4BFullMultimodal() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" E4B Full Multimodal Test (TEXT + Audio + Vision)")
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print("═══════════════════════════════════════════════════════════════════\n")
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let modelDir = "/Users/accusys/MarkBaseEngine/models/E4B-MarkBase"
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print("Step 1: Load MultimodalModel (TEXT + Audio + Vision)...")
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let engine = try MarkBaseEngine(autoCompile: true)
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let loadStart = Date()
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let mmModel = try MultimodalModel(modelDir: modelDir, engine: engine, maxContextLength: 256)
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let loadTime = Date().timeIntervalSince(loadStart) * 1000
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print(" ✓ Model loaded in \(loadTime) ms")
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print(" TEXT: hidden=\(mmModel.textModel.hiddenSize), layers=\(mmModel.textModel.numHiddenLayers)")
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print(" Audio: \(mmModel.audioTowerFull != nil ? "Full (12 layers)" : "N/A")")
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print(" Vision: \(mmModel.visionTowerFull != nil ? "Full (16 layers)" : "N/A")")
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// ── Audio Test ───────────────────────────────────────────────────
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print("\nStep 2: Process Audio...")
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let seqLen = 100
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let nMels = 128
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var melFeatures: [[Float]] = []
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for _ in 0..<seqLen {
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var frame: [Float] = []
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for _ in 0..<nMels {
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frame.append(Float.random(in: -0.5...0.5))
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}
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melFeatures.append(frame)
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}
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let audioStart = Date()
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let audioEmbeds = try mmModel.processAudio(audioFeatures: melFeatures)
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let audioTime = Date().timeIntervalSince(audioStart) * 1000
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print(" ✓ Audio processed in \(audioTime) ms")
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print(" Output: [\(audioEmbeds.count / 1536), 1536]")
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print(" NaN: \(audioEmbeds.contains { $0.isNaN })")
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// ── Vision Test ───────────────────────────────────────────────────
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print("\nStep 3: Process Vision...")
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let numPatches = 256
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let patchDim = 768
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var patches: [Float] = []
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for _ in 0..<numPatches * patchDim {
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patches.append(Float.random(in: -0.5...0.5))
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}
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let visionStart = Date()
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let visionEmbeds = try mmModel.processVision(patchEmbeddings: patches, numPatches: numPatches)
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let visionTime = Date().timeIntervalSince(visionStart) * 1000
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print(" ✓ Vision processed in \(visionTime) ms")
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print(" Output: [\(numPatches), \(visionEmbeds.count / numPatches)]")
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print(" NaN: \(visionEmbeds.contains { $0.isNaN })")
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// ── Text Generation ───────────────────────────────────────────────
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print("\nStep 4: Generate Text tokens...")
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var tokens: [Int] = [2] // BOS
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let genStart = Date()
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for _ in 0..<10 {
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let logits = try mmModel.textModel.forward(tokenId: tokens.last!, position: tokens.count - 1)
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var maxIdx = 0
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var maxLogit = logits[0]
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for i in 1..<logits.count {
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if logits[i] > maxLogit {
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maxLogit = logits[i]
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maxIdx = i
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}
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}
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tokens.append(maxIdx)
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}
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let genTime = Date().timeIntervalSince(genStart) * 1000
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print(" ✓ Generated 10 tokens in \(genTime) ms")
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print(" Tokens: \(tokens.suffix(10))")
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print("\n═══════════════════════════════════════════════════════════════════")
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print("✓ E4B Full Multimodal test passed")
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print(" Load: \(loadTime) ms, Audio: \(audioTime) ms, Vision: \(visionTime) ms, Gen: \(genTime) ms")
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print("═══════════════════════════════════════════════════════════════════\n")
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XCTAssertFalse(audioEmbeds.contains { $0.isNaN }, "Audio should not have NaN")
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XCTAssertFalse(visionEmbeds.contains { $0.isNaN }, "Vision should not have NaN")
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}
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func test12BFullMultimodal() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" 12B Full Multimodal Test (TEXT + Audio + Vision)")
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print("═══════════════════════════════════════════════════════════════════\n")
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let modelDir = "/Users/accusys/.cache/huggingface/hub/models--mlx-community--gemma-4-12B-it-4bit/snapshots/73bcf09092aa277861d5a191b989b666f7f32e8f"
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print("Step 1: Load MultimodalModel (TEXT + Audio + Vision)...")
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let engine = try MarkBaseEngine(autoCompile: true)
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let loadStart = Date()
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let mmModel = try MultimodalModel(modelDir: modelDir, engine: engine, maxContextLength: 256)
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let loadTime = Date().timeIntervalSince(loadStart) * 1000
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print(" ✓ Model loaded in \(loadTime) ms")
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print(" TEXT: hidden=\(mmModel.textModel.hiddenSize), layers=\(mmModel.textModel.numHiddenLayers)")
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print(" Audio: \(mmModel.audioTower != nil ? "12B (projection)" : "N/A")")
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print(" Vision: \(mmModel.visionTower != nil ? "12B (simplified)" : "N/A")")
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// ── Audio Test ───────────────────────────────────────────────────
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print("\nStep 2: Process Audio (640-dim embeddings)...")
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let seqLen = 100
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let audioDim = 640
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var audioEmbedsInput: [Float] = []
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for _ in 0..<seqLen * audioDim {
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audioEmbedsInput.append(Float.random(in: -0.5...0.5))
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}
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let inputBuffer = engine.device.makeBuffer(bytes: audioEmbedsInput, length: audioEmbedsInput.count * 4)!
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let outputBuffer = engine.device.makeBuffer(length: seqLen * mmModel.textModel.hiddenSize * 4)!
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let audioStart = Date()
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if let tower = mmModel.audioTower {
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try tower.forward(inputBuffer: inputBuffer, seqLen: seqLen, outputBuffer: outputBuffer)
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}
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let audioTime = Date().timeIntervalSince(audioStart) * 1000
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print(" ✓ Audio processed in \(audioTime) ms")
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// ── Vision Test ───────────────────────────────────────────────────
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print("\nStep 3: Process Vision...")
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let numPatches = 256
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let patchDim = 768
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var patches: [Float] = []
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for _ in 0..<numPatches * patchDim {
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patches.append(Float.random(in: -0.5...0.5))
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}
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let visionStart = Date()
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let visionEmbeds = try mmModel.processVision(patchEmbeddings: patches, numPatches: numPatches)
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let visionTime = Date().timeIntervalSince(visionStart) * 1000
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print(" ✓ Vision processed in \(visionTime) ms")
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print(" Output: [\(numPatches), \(visionEmbeds.count / numPatches)]")
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print(" NaN: \(visionEmbeds.contains { $0.isNaN })")
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// ── Text Generation ───────────────────────────────────────────────
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print("\nStep 4: Generate Text tokens...")
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var tokens: [Int] = [2] // BOS
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let genStart = Date()
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for _ in 0..<10 {
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let logits = try mmModel.textModel.forward(tokenId: tokens.last!, position: tokens.count - 1)
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var maxIdx = 0
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var maxLogit = logits[0]
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for i in 1..<logits.count {
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if logits[i] > maxLogit {
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maxLogit = logits[i]
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maxIdx = i
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}
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}
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tokens.append(maxIdx)
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}
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let genTime = Date().timeIntervalSince(genStart) * 1000
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print(" ✓ Generated 10 tokens in \(genTime) ms")
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print(" Tokens: \(tokens.suffix(10))")
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print("\n═══════════════════════════════════════════════════════════════════")
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print("✓ 12B Full Multimodal test passed")
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print(" Load: \(loadTime) ms, Audio: \(audioTime) ms, Vision: \(visionTime) ms, Gen: \(genTime) ms")
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print("═══════════════════════════════════════════════════════════════════\n")
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XCTAssertFalse(visionEmbeds.contains { $0.isNaN }, "Vision should not have NaN")
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}
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}
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