Initial commit: E4B-MarkBase model integration with passing tests
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- 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 BatchGenerationTest: XCTestCase {
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func testBatchGenerationPerformance() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Batch Generation Performance Test")
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print("═══════════════════════════════════════════════════════════════════\n")
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let modelDir = "/Users/accusys/MarkBaseEngine/models/E4B-MarkBase"
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let engine = try MarkBaseEngine(autoCompile: true)
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let textModel = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 256)
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print("Model: \(textModel.numHiddenLayers) layers, hidden=\(textModel.hiddenSize)\n")
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// Warm up
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print("Warm up...")
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_ = try textModel.forwardOptimized(tokenId: 2, position: 0)
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print(" ✓ Warm up complete\n")
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// Test 1: Sequential generation (baseline)
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print("Test 1: Sequential generation (10 tokens)")
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let seqStart = Date()
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var seqTokens: [Int] = [2]
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for _ in 0..<10 {
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let logits = try textModel.forwardOptimized(
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tokenId: seqTokens.last!,
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position: seqTokens.count - 1
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)
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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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seqTokens.append(maxIdx)
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}
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let seqTime = Date().timeIntervalSince(seqStart) * 1000
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print(" ✓ Generated 10 tokens in \(seqTime) ms")
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print(" Average: \(seqTime / 10) ms/token")
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print(" Tokens: \(seqTokens)")
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// Test 2: Batch generation
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print("\nTest 2: Batch generation (10 tokens in batches of 8)")
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let batchStart = Date()
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let batchTokens = try textModel.generateBatch(startToken: 2, numTokens: 10)
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let batchTime = Date().timeIntervalSince(batchStart) * 1000
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print(" ✓ Generated 10 tokens in \(batchTime) ms")
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print(" Average: \(batchTime / 10) ms/token")
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print(" Tokens: \(batchTokens)")
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// Comparison
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print("\n═══════════════════════════════════════════════════════════════════")
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let speedup = seqTime / batchTime
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let improvement = (seqTime - batchTime) / seqTime * 100
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print("Comparison:")
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print(" Sequential: \(seqTime) ms (\(seqTime/10) ms/token)")
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print(" Batch: \(batchTime) ms (\(batchTime/10) ms/token)")
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print(" Speedup: \(speedup)x")
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print(" Improvement: \(improvement)%")
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if speedup > 1.0 {
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print("\n✓ Batch generation is faster!")
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} else {
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print("\n⚠ Batch generation needs optimization")
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}
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print("═══════════════════════════════════════════════════════════════════\n")
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}
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func testSingleVsBatchComparison() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Single vs Batch Token Generation")
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print("═══════════════════════════════════════════════════════════════════\n")
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let modelDir = "/Users/accusys/MarkBaseEngine/models/E4B-MarkBase"
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let engine = try MarkBaseEngine(autoCompile: true)
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let textModel = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 256)
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// Warm up
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print("Warm up...")
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_ = try textModel.forwardOptimized(tokenId: 2, position: 0)
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_ = try textModel.forwardBatch(tokenIds: [2], positions: [0])
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print(" ✓ Warm up complete\n")
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// Test single token
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print("Test 1: Single token generation")
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var singleTimes: [Double] = []
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for _ in 0..<5 {
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let start = Date()
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let logits = try textModel.forwardOptimized(tokenId: 2, position: 0)
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let elapsed = Date().timeIntervalSince(start) * 1000
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singleTimes.append(elapsed)
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XCTAssertFalse(logits.contains { $0.isNaN }, "Single logits should not have NaN")
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}
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let singleAvg = singleTimes.reduce(0, +) / Double(singleTimes.count)
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print(" Average: \(singleAvg) ms")
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// Test batch of 1
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print("\nTest 2: Batch of 1 token")
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var batch1Times: [Double] = []
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for _ in 0..<5 {
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let start = Date()
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let logits = try textModel.forwardBatch(tokenIds: [2], positions: [0])
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let elapsed = Date().timeIntervalSince(start) * 1000
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batch1Times.append(elapsed)
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XCTAssertFalse(logits[0].contains { $0.isNaN }, "Batch logits should not have NaN")
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}
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let batch1Avg = batch1Times.reduce(0, +) / Double(batch1Times.count)
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print(" Average: \(batch1Avg) ms")
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// Test batch of 4
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print("\nTest 3: Batch of 4 tokens")
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var batch4Times: [Double] = []
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for _ in 0..<5 {
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let start = Date()
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let logits = try textModel.forwardBatch(tokenIds: [2, 2, 2, 2], positions: [0, 1, 2, 3])
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let elapsed = Date().timeIntervalSince(start) * 1000
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batch4Times.append(elapsed)
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for l in logits {
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XCTAssertFalse(l.contains { $0.isNaN }, "Batch logits should not have NaN")
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}
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}
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let batch4Avg = batch4Times.reduce(0, +) / Double(batch4Times.count)
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print(" Average: \(batch4Avg) ms")
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print(" Per-token: \(batch4Avg / 4) ms")
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print("\n═══════════════════════════════════════════════════════════════════")
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print("Results:")
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print(" Single: \(singleAvg) ms")
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print(" Batch(1): \(batch1Avg) ms")
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print(" Batch(4): \(batch4Avg) ms (\(batch4Avg/4) ms/token)")
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let batch4Speedup = (singleAvg * 4) / batch4Avg
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print(" Batch(4) speedup vs 4x single: \(batch4Speedup)x")
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print("═══════════════════════════════════════════════════════════════════\n")
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}
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}
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