Initial commit: E4B-MarkBase model integration with passing tests

- 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
2026-06-23 18:12:35 +08:00
commit ac75faa0cc
301 changed files with 63426 additions and 0 deletions
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+import XCTest
+@testable import MarkBase
+
+final class G12BPerformanceTests: XCTestCase {
+    
+    func testInferenceSpeed() throws {
+        print("\n═══════════════════════════════════════")
+        print("      12B Performance Benchmark")
+        print("═══════════════════════════════════════\n")
+        
+        let modelDir = "/Users/accusys/.cache/huggingface/hub/models--mlx-community--gemma-4-12B-it-4bit/snapshots/73bcf09092aa277861d5a191b989b666f7f32e8f"
+        
+        print("Step 1: Initialize Metal engine...")
+        let engine = try MarkBaseEngine(autoCompile: true)
+        print("  ✓ Engine ready\n")
+        
+        print("Step 2: Load 12B model...")
+        let startLoad = Date()
+        let model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 512)
+        let loadTime = Date().timeIntervalSince(startLoad)
+        print("  ✓ Model loaded in \(loadTime) seconds\n")
+        
+        print("Step 3: Warm-up inference (5 tokens)...")
+        for i in 0..<5 {
+            _ = try model.forward(tokenId: i, position: i)
+        }
+        print("  ✓ Warm-up completed\n")
+        
+        print("Step 4: Benchmark inference (20 tokens)...")
+        let startInference = Date()
+        for i in 0..<20 {
+            let tokenStart = Date()
+            _ = try model.forward(tokenId: i, position: i)
+            let tokenTime = Date().timeIntervalSince(tokenStart)
+            if i >= 5 { // Skip warm-up in stats
+                print("  Token \(i): \(tokenTime) seconds")
+            }
+        }
+        let totalInferenceTime = Date().timeIntervalSince(startInference)
+        let avgTokenTime = totalInferenceTime / 20.0
+        let tokensPerSecond = 20.0 / totalInferenceTime
+        
+        print("\nPerformance Summary:")
+        print("  Total time (20 tokens): \(totalInferenceTime) seconds")
+        print("  Average per token: \(avgTokenTime) seconds")
+        print("  Tokens per second: \(tokensPerSecond) tok/s")
+        print("  Comparison: E4B achieves ~0.051s/token (~19.7 tok/s)")
+        print("  12B ratio: \(avgTokenTime / 0.051)x slower than E4B\n")
+        
+        print("═══════════════════════════════════════")
+        print("✓ Performance benchmark completed")
+        print("═══════════════════════════════════════\n")
+        
+        // Log results for tracking
+        print("RESULTS:")
+        print("  avg_token_time=\(avgTokenTime)")
+        print("  tokens_per_second=\(tokensPerSecond)")
+        print("  model_load_time=\(loadTime)")
+    }
+
+    func test31BPerformance() throws {
+        print("\n═══════════════════════════════════════")
+        print("      31B Dense Performance Benchmark")
+        print("═══════════════════════════════════════\n")
+
+        let modelDir = "/Users/accusys/MarkBaseEngine/models/gemma-4-31b-it-4bit"
+
+        guard FileManager.default.fileExists(atPath: modelDir + "/config.json") else {
+            print("✗ 31B model not found")
+            return
+        }
+
+        print("Step 1: Initialize Metal engine...")
+        let engine = try MarkBaseEngine(autoCompile: true)
+        print("  ✓ Engine ready\n")
+
+        print("Step 2: Load 31B model (~18 GB)...")
+        let startLoad = Date()
+        let model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 128)
+        let loadTime = Date().timeIntervalSince(startLoad)
+        print("  ✓ Model loaded in \(String(format: "%.1f", loadTime))s")
+        print("  Layers: \(model.numHiddenLayers)")
+        print("  Hidden: \(model.hiddenSize)\n")
+
+        print("Step 3: Warm-up inference (3 tokens)...")
+        for i in 0..<3 {
+            _ = try model.forward(tokenId: i, position: i)
+        }
+        print("  ✓ Warm-up completed\n")
+
+        print("Step 4: Benchmark inference (10 tokens)...")
+        let startInference = Date()
+        var tokenTimes: [TimeInterval] = []
+        for i in 0..<10 {
+            let tokenStart = Date()
+            _ = try model.forward(tokenId: 2, position: i)
+            let tokenTime = Date().timeIntervalSince(tokenStart)
+            tokenTimes.append(tokenTime)
+            print("  Token \(i): \(String(format: "%.3f", tokenTime))s")
+        }
+        let totalInferenceTime = Date().timeIntervalSince(startInference)
+        let avgTokenTime = totalInferenceTime / 10.0
+        let tokensPerSecond = 10.0 / totalInferenceTime
+
+        print("\nPerformance Summary:")
+        print("  Model load time: \(String(format: "%.1f", loadTime))s")
+        print("  Total time (10 tokens): \(String(format: "%.3f", totalInferenceTime))s")
+        print("  Average per token: \(String(format: "%.3f", avgTokenTime))s")
+        print("  Tokens per second: \(String(format: "%.1f", tokensPerSecond)) tok/s")
+
+        print("\n═══════════════════════════════════════")
+        print("✓ 31B Performance benchmark completed")
+        print("═══════════════════════════════════════\n")
+
+        print("RESULTS:")
+        print("  avg_token_time=\(avgTokenTime)")
+        print("  tokens_per_second=\(tokensPerSecond)")
+        print("  model_load_time=\(loadTime)")
+    }
+}