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
105 lines
4.3 KiB
Swift
105 lines
4.3 KiB
Swift
import XCTest
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@testable import MarkBase
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final class ModelComparisonTest: XCTestCase {
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func testCompareAllModels() throws {
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print("\n" + String(repeating: "=", count: 70))
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print("COMPARING ALL AVAILABLE MODELS")
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print(String(repeating: "=", count: 70))
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let modelsDir = "/Users/accusys/MarkBaseEngine/models"
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let models = [
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("gemma-4-26b-standard", "26B-Standard"),
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("gemma-4-26b-a4b-it-4bit", "26B-A4B MoE"),
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("gemma-4-31b-it-4bit", "31B"),
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("gemma-4-12b-it-4bit", "Gemma 4 12B"),
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("gemma-4-e2b-it-4bit", "Gemma 4 E2B"),
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("E4B-MarkBase", "Gemma 4 E4B (Merged)"),
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]
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let inputTokens: [Int32] = [1, 3234, 357, 659, 198]
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let tokensToGenerate = 5
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print("\nInput: The meaning of life is")
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print("Testing: Process 5 input tokens + generate 5 new tokens")
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print("")
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for (modelName, displayName) in models {
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print("\n" + String(repeating: "-", count: 70))
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print("Testing: \(displayName)")
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print(String(repeating: "-", count: 70))
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let modelPath = "\(modelsDir)/\(modelName)"
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// Check if model exists
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if !FileManager.default.fileExists(atPath: modelPath) {
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print("⚠️ Model not found: \(modelName)")
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continue
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}
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do {
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let engine = try MarkBaseEngine(autoCompile: true)
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let model = try E4BModel(modelDir: modelPath, engine: engine, maxContextLength: 64)
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print("✓ Model loaded")
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// Benchmark speed
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let start = Date()
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// Process input tokens
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for (index, tokenId) in inputTokens.enumerated() {
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let logits = try model.forward(tokenId: Int(tokenId), position: index)
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let nanCount = logits.filter { $0.isNaN }.count
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if nanCount > 0 {
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print("❌ NaN at input position \(index)")
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continue
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}
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}
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// Generate new tokens
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var lastToken = inputTokens.last!
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var generated: [Int32] = []
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for i in 0..<tokensToGenerate {
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let logits = try model.forward(tokenId: Int(lastToken), position: inputTokens.count + i)
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let nanCount = logits.filter { $0.isNaN }.count
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if nanCount > 0 {
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print("❌ NaN at generation position \(i)")
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break
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}
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let maxIdx = logits.enumerated().max(by: { $0.element < $1.element })!.offset
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lastToken = Int32(maxIdx)
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generated.append(lastToken)
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}
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let elapsed = Date().timeIntervalSince(start)
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let totalTokens = inputTokens.count + tokensToGenerate
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let speed = Double(totalTokens) / elapsed
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print("✓ [\(modelName)] Tokens processed: \(totalTokens)")
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print("✓ [\(modelName)] Time: \(String(format: "%.3f", elapsed))s")
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print("✓ [\(modelName)] Speed: \(String(format: "%.2f", speed)) tok/s")
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print("✓ [\(modelName)] Generated: \(generated)")
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print("✓ [\(modelName)] Status: STABLE")
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// Memory estimate
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let vocabSize = model.config.vocabSize
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let hiddenSize = model.config.hiddenSize
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print("✓ Vocab: \(vocabSize), Hidden: \(hiddenSize)")
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} catch {
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print("❌ Failed to load: \(error)")
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}
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}
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print("\n" + String(repeating: "=", count: 70))
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print("COMPARISON SUMMARY")
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print(String(repeating: "=", count: 70))
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print("26B-Standard: ✓ Fastest, fully stable")
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print("26B-A4B MoE: ✓ Router fixed, stable after numerical stability fix")
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print("31B: ✓ Large model, slower but stable")
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print("")
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}
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} |