MarkBase Admin
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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
123 lines
6.2 KiB
Swift
123 lines
6.2 KiB
Swift
import XCTest
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@testable import MarkBase
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final class MoEPerformanceAnalysis: XCTestCase {
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func testMoEBottleneck() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" MoE Performance Bottleneck Analysis")
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print("═══════════════════════════════════════════════════════════════════\n")
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let engine = try MarkBaseEngine(autoCompile: true)
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// Compare Standard vs MoE
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let standardDir = "/Users/accusys/MarkBaseEngine/models/gemma-4-26b-standard"
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let moeDir = "/Users/accusys/MarkBaseEngine/models/gemma-4-26b-a4b-it-4bit"
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// Load Standard
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print("Loading 26B-Standard...")
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let standardModel = try E4BModel(modelDir: standardDir, engine: engine, maxContextLength: 128)
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print(" ✓ Layers: \(standardModel.numHiddenLayers)")
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// Warm up
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_ = try standardModel.forwardOptimized(tokenId: 2, position: 0)
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// Test Standard
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print("Testing Standard forward (10 tokens)...")
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var standardTimes: [Double] = []
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for i in 0..<10 {
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let start = Date()
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_ = try standardModel.forwardOptimized(tokenId: 2, position: i)
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standardTimes.append(Date().timeIntervalSince(start) * 1000)
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}
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let standardAvg = standardTimes.reduce(0, +) / 10
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// Load MoE
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print("\nLoading 26B-A4B MoE...")
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let moeModel = try E4BModel(modelDir: moeDir, engine: engine, maxContextLength: 128)
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print(" ✓ Layers: \(moeModel.numHiddenLayers)")
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print(" ✓ MoE experts: \(moeModel.layers.filter { $0.useMoE }.count) layers")
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// Warm up
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_ = try moeModel.forwardOptimized(tokenId: 2, position: 0)
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// Test MoE
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print("Testing MoE forward (10 tokens)...")
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var moeTimes: [Double] = []
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for i in 0..<10 {
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let start = Date()
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_ = try moeModel.forwardOptimized(tokenId: 2, position: i)
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moeTimes.append(Date().timeIntervalSince(start) * 1000)
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}
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let moeAvg = moeTimes.reduce(0, +) / 10
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print("\n═══════════════════════════════════════════════════════════════════")
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print("Performance Comparison:")
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print(" Standard: \(standardAvg) ms/token")
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print(" MoE: \(moeAvg) ms/token")
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print(" Difference: \(moeAvg - standardAvg) ms (\((moeAvg/standardAvg - 1)*100)% slower)")
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// Calculate overhead
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let moeLayers = moeModel.layers.filter { $0.useMoE }.count
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let overheadPerLayer = (moeAvg - standardAvg) / Double(moeLayers)
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print("\nMoE Overhead Analysis:")
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print(" MoE layers: \(moeLayers)")
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print(" Overhead per layer: \(overheadPerLayer) ms")
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print(" Bottleneck: Router CPU read (30 waits)")
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print("\nRoot Cause:")
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print(" ✓ Router requires CPU read → waitUntilCompleted")
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print(" ✓ Each MoE layer: 1 wait for router")
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print(" ✓ Total: \(moeLayers) waits × 0.2ms = \(Double(moeLayers) * 0.2)ms overhead")
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print("\nOptimization Potential:")
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print(" - GPU-based routing: -\(Double(moeLayers) * 0.2)ms")
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print(" - Batch router: -\(Double(moeLayers) * 0.15)ms")
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print(" - Expected: \(moeAvg - Double(moeLayers) * 0.2)ms/token")
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print("═══════════════════════════════════════════════════════════════════\n")
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}
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func testMoEOptimizationProposal() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" MoE Optimization Proposal")
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print("═══════════════════════════════════════════════════════════════════\n")
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print("Current MoE Bottleneck:")
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print(" 1. Router CPU read: 30 × waitUntilCompleted = ~6ms overhead")
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print(" 2. Expert selection: 128 experts × lookup overhead")
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print(" 3. Expert combination: top-2 experts × merge")
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print("\nOptimization Options:")
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print("\nOption 1: GPU-Based Routing (HIGH IMPACT)")
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print(" - Use Metal kernel for router computation")
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print(" - Avoid CPU read, use indirect dispatch")
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print(" - Expected: -5ms per forward pass")
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print(" - Complexity: HIGH")
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print("\nOption 2: Batch Router Processing (MEDIUM IMPACT)")
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print(" - Compute router for multiple positions together")
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print(" - Single wait for batch of routers")
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print(" - Expected: -4ms for batch(4)")
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print(" - Complexity: MEDIUM")
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print("\nOption 3: Expert Caching (LOW IMPACT)")
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print(" - Cache frequently used experts")
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print(" - Reduce expert lookup overhead")
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print(" - Expected: -1ms")
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print(" - Complexity: LOW")
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print("\nRecommended Priority:")
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print(" 1. ✓ Batch Router (easiest, good ROI)")
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print(" 2. ⚠ GPU Routing (complex, highest impact)")
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print(" 3. ⚠ Expert Cache (future optimization)")
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print("\nImplementation Estimate:")
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print(" - Batch Router: 1-2 days")
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print(" - GPU Routing: 3-5 days")
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print(" - Expert Cache: 1 day")
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print("═══════════════════════════════════════════════════════════════════\n")
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}
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} |