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 KernelFusionPerformanceTest: XCTestCase {
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func testFusedKernelPerformance() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Kernel Fusion Performance Test")
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print("═══════════════════════════════════════════════════════════════════\n")
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let engine = try MarkBaseEngine(autoCompile: true)
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let device = engine.device
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// Test parameters
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let hiddenSize = 2560
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let groupSize = 64
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// Create test buffers
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let inputBuffer = device.makeBuffer(length: hiddenSize * 4)!
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let outputBuffer = device.makeBuffer(length: hiddenSize * 4)!
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let scalesBuffer = device.makeBuffer(length: hiddenSize / groupSize * 4)!
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let biasesBuffer = device.makeBuffer(length: hiddenSize / groupSize * 4)!
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// Fill with test data
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let inputPtr = inputBuffer.contents().assumingMemoryBound(to: Float.self)
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for i in 0..<hiddenSize {
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inputPtr[i] = Float.random(in: -1...1)
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}
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let scalesPtr = scalesBuffer.contents().assumingMemoryBound(to: Float.self)
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let biasesPtr = biasesBuffer.contents().assumingMemoryBound(to: Float.self)
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for i in 0..<(hiddenSize / groupSize) {
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scalesPtr[i] = 0.1
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biasesPtr[i] = 0.0
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}
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// Create quantized weights (simplified)
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let weightBuffer = device.makeBuffer(length: hiddenSize * 4)!
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let weightPtr = weightBuffer.contents().assumingMemoryBound(to: UInt32.self)
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for i in 0..<hiddenSize {
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weightPtr[i] = UInt32.random(in: 0...UInt32.max)
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}
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// Warm up
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print("Warm up kernels...")
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_ = try engine.pipeline(named: "dequantize_row")
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_ = try engine.pipeline(named: "eltwise_scale")
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print(" ✓ Kernels loaded\n")
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// Test 1: Separate kernels (baseline)
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print("Test 1: Separate kernels (dequantize + scale)")
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var separateTimes: [Double] = []
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for _ in 0..<10 {
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let cmdBuf = engine.commandQueue.makeCommandBuffer()!
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// Dequantize
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let pso1 = try engine.pipeline(named: "dequantize_row")
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let enc1 = cmdBuf.makeComputeCommandEncoder()!
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enc1.setComputePipelineState(pso1)
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enc1.setBuffer(weightBuffer, offset: 0, index: 0)
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enc1.setBuffer(scalesBuffer, offset: 0, index: 1)
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enc1.setBuffer(biasesBuffer, offset: 0, index: 2)
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enc1.setBuffer(outputBuffer, offset: 0, index: 3)
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var n = UInt32(hiddenSize)
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enc1.setBytes(&n, length: 4, index: 4)
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var row = Int32(0)
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enc1.setBytes(&row, length: 4, index: 5)
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var gs = UInt32(groupSize)
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enc1.setBytes(&gs, length: 4, index: 6)
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enc1.dispatchThreads(MTLSize(width: hiddenSize, height: 1, depth: 1),
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threadsPerThreadgroup: MTLSize(width: 256, height: 1, depth: 1))
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enc1.endEncoding()
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// Scale
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let pso2 = try engine.pipeline(named: "eltwise_scale")
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let enc2 = cmdBuf.makeComputeCommandEncoder()!
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enc2.setComputePipelineState(pso2)
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enc2.setBuffer(outputBuffer, offset: 0, index: 0)
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var scale = Float(1.5)
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enc2.setBytes(&scale, length: 4, index: 1)
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enc2.setBytes(&n, length: 4, index: 2)
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enc2.dispatchThreads(MTLSize(width: hiddenSize, height: 1, depth: 1),
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threadsPerThreadgroup: MTLSize(width: 256, height: 1, depth: 1))
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enc2.endEncoding()
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let start = Date()
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cmdBuf.commit()
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cmdBuf.waitUntilCompleted()
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let elapsed = Date().timeIntervalSince(start) * 1000
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separateTimes.append(elapsed)
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}
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let separateAvg = separateTimes.reduce(0, +) / Double(separateTimes.count)
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print(" Average time: \(separateAvg) ms")
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// Test 2: Fused kernel
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print("\nTest 2: Fused kernel (dequantize + scale)")
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var fusedTimes: [Double] = []
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for _ in 0..<10 {
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let cmdBuf = engine.commandQueue.makeCommandBuffer()!
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// Try to use fused kernel
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do {
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let pso = try engine.pipeline(named: "fused_dequantize_scale")
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let enc = cmdBuf.makeComputeCommandEncoder()!
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enc.setComputePipelineState(pso)
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enc.setBuffer(weightBuffer, offset: 0, index: 0)
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enc.setBuffer(scalesBuffer, offset: 0, index: 1)
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enc.setBuffer(biasesBuffer, offset: 0, index: 2)
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enc.setBuffer(outputBuffer, offset: 0, index: 3)
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var n = UInt32(hiddenSize)
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enc.setBytes(&n, length: 4, index: 4)
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var row = Int32(0)
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enc.setBytes(&row, length: 4, index: 5)
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var gs = UInt32(groupSize)
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enc.setBytes(&gs, length: 4, index: 6)
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var scale = Float(1.5)
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enc.setBytes(&scale, length: 4, index: 7)
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enc.dispatchThreads(MTLSize(width: hiddenSize, height: 1, depth: 1),
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threadsPerThreadgroup: MTLSize(width: 256, height: 1, depth: 1))
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enc.endEncoding()
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let start = Date()
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cmdBuf.commit()
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cmdBuf.waitUntilCompleted()
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let elapsed = Date().timeIntervalSince(start) * 1000
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fusedTimes.append(elapsed)
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} catch {
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print(" ⚠ Fused kernel not available: \(error)")
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return
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}
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}
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let fusedAvg = fusedTimes.reduce(0, +) / Double(fusedTimes.count)
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print(" Average time: \(fusedAvg) ms")
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// Comparison
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print("\n═══════════════════════════════════════════════════════════════════")
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let speedup = separateAvg / fusedAvg
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let improvement = (separateAvg - fusedAvg) / separateAvg * 100
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print("Comparison:")
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print(" Separate kernels: \(separateAvg) ms")
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print(" Fused kernel: \(fusedAvg) ms")
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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⚠ Fused kernel is SLOWER than separate kernels!")
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print(" Issue: Kernel fusion needs optimization")
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} else {
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print("\n✓ Fused kernel is faster than separate kernels")
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}
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print("═══════════════════════════════════════════════════════════════════\n")
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}
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}
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