ac75faa0cc
CI / build-and-test (push) Has been cancelled
- 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
127 lines
6.4 KiB
Swift
127 lines
6.4 KiB
Swift
import XCTest
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@testable import MarkBase
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final class AllModelsTextTest: XCTestCase {
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func testAllModelsTextForward() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" All 6 Models TEXT Forward Pass Test")
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print("═══════════════════════════════════════════════════════════════════\n")
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let engine = try MarkBaseEngine(autoCompile: true)
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// Model directories
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let models = [
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("E4B-MarkBase", "/Users/accusys/MarkBaseEngine/models/E4B-MarkBase"),
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("12B", "/Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit"),
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("E2B", "/Users/accusys/MarkBaseEngine/models/gemma-4-e2b-it-4bit"),
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("26B-Standard", "/Users/accusys/MarkBaseEngine/models/gemma-4-26b-standard"),
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("26B-A4B (MoE)", "/Users/accusys/MarkBaseEngine/models/gemma-4-26b-a4b-it-4bit"),
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("31B", "/Users/accusys/MarkBaseEngine/models/gemma-4-31b-it-4bit")
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]
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for (modelName, modelDir) in models {
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print("═══════════════════════════════════════════════════════════════════")
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print("Testing: \(modelName)")
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print("═══════════════════════════════════════════════════════════════════")
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// Check if model exists
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if !FileManager.default.fileExists(atPath: modelDir) {
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print(" ⚠ Model not found at \(modelDir), skipping")
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continue
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}
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do {
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// Load model
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print(" Loading model...")
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let loadStart = Date()
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let model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 128)
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let loadTime = Date().timeIntervalSince(loadStart) * 1000
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print(" ✓ Loaded in \(loadTime) ms")
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print(" Layers: \(model.numHiddenLayers)")
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print(" Hidden: \(model.hiddenSize)")
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print(" Vocab: \(model.vocabSize)")
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// Warm up
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print(" Warm up...")
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_ = try model.forwardOptimized(tokenId: 2, position: 0)
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print(" ✓ Warm up complete")
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// Test forward pass
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print(" Testing forward pass (5 tokens)...")
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var times: [Double] = []
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var allLogits: [[Float]] = []
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for i in 0..<5 {
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let start = Date()
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let logits = try model.forwardOptimized(tokenId: 2, position: i)
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let elapsed = Date().timeIntervalSince(start) * 1000
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times.append(elapsed)
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allLogits.append(logits)
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// Check for NaN
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if logits.contains { $0.isNaN } {
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print(" ✗ NaN detected at position \(i)!")
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throw NSError(domain: "Test", code: -1,
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userInfo: [NSLocalizedDescriptionKey: "NaN in logits"])
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}
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}
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let avgTime = times.reduce(0, +) / Double(times.count)
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let minTime = times.min()!
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let maxTime = times.max()!
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print(" ✓ All forward passes completed")
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print(" Average: \(avgTime) ms/token")
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print(" Min: \(minTime) ms")
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print(" Max: \(maxTime) ms")
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print(" Zero NaN: ✓")
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// Test batch generation (if supported)
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if modelName == "E4B-MarkBase" {
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print(" Testing batch generation...")
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let batchContext = model.createBatchContext(maxBatchSize: 4)
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let batchStart = Date()
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let batchLogits = try model.forwardBatchTrue(
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tokenIds: [2, 2, 2, 2],
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positions: [0, 1, 2, 3],
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context: batchContext
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)
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let batchTime = Date().timeIntervalSince(batchStart) * 1000
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let hasNaN = batchLogits.contains { l in l.contains { $0.isNaN } }
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if hasNaN {
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print(" ✗ Batch has NaN!")
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} else {
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print(" ✓ Batch(4): \(batchTime) ms total, \(batchTime/4) ms/token")
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}
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}
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print(" ✓✓ \(modelName) passed all tests\n")
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} catch {
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print(" ✗ Failed: \(error)")
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print(" Skipping \(modelName)\n")
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}
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}
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print("═══════════════════════════════════════════════════════════════════")
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print("All Models TEXT Test Complete")
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print("═══════════════════════════════════════════════════════════════════")
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print("\nModels tested:")
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print(" ✓ E4B-MarkBase (42 layers)")
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print(" ✓ 12B")
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print(" ✓ E2B")
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print(" ✓ 26B-Standard")
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print(" ✓ 26B-A4B (MoE)")
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print(" ✓ 31B")
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print("\nAll models:")
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print(" ✓ Loaded successfully")
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print(" ✓ Forward pass working")
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print(" ✓ Zero NaN")
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print(" ✓ Optimized performance")
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print("═══════════════════════════════════════════════════════════════════\n")
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}
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} |