MarkBase Admin
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
70 lines
2.3 KiB
Swift
70 lines
2.3 KiB
Swift
// Minimal test: only forward pass, no token generation
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import Foundation
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@testable import MarkBase
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let modelPath = "/Users/accusys/MarkBaseEngine/models/gemma-4-26b-a4b-it-4bit"
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print("=====================================")
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print("26B-A4B MoE Forward Pass Test ONLY")
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print("=====================================")
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print("\n[1] Loading model...")
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let start1 = Date()
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let engine = try E4BEngine(autoCompile: true)
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print("✓ Engine created")
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let model = try E4BModel(modelDir: modelPath, engine: engine, maxContextLength: 128)
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let loadTime = Date().timeIntervalSince(start1)
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print("✓ Model loaded in \(String(format: "%.3f", loadTime))s")
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print("\n[2] Testing single forward pass...")
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let tokenizer = try TokenizerFactory.load(modelDir: modelPath)
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let prompt = "Hello"
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let tokens = tokenizer.encode(text: prompt)
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print(" Prompt: \"\(prompt)\" -> tokens: \(tokens)")
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// Create input buffer
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let embed = model.embedTokens
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let inputBuffer = engine.createBuffer(length: model.hiddenSize * 4)
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let outputBuffer = engine.createBuffer(length: model.vocabSize * 4)
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// Get embedding for first token
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let embedData = engine.readFloats(from: embed.weight, offset: tokens[0] * model.hiddenSize, count: model.hiddenSize)
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engine.writeFloats(embedData, to: inputBuffer)
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print(" Input buffer: \(model.hiddenSize) floats")
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print(" Running forward pass through \(model.numHiddenLayers) layers...")
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let start2 = Date()
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// Run through all layers
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for i in 0..<model.numHiddenLayers {
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let layer = model.layers[i]
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let normBuffer = engine.createBuffer(length: model.hiddenSize * 4)
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// Apply input layernorm
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try layer.inputLayernorm.process(input: inputBuffer, output: normBuffer, engine: engine)
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// Check for NaN in layer norm output
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let normOutput = engine.readFloats(from: normBuffer, count: 10)
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let hasNaN = normOutput.contains { $0.isNaN }
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if hasNaN {
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print(" ❌ NaN detected in layer \(i) after input_layernorm")
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print(" First 10 values: \(normOutput)")
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break
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}
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if i % 5 == 0 {
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print(" Layer \(i): OK (max=\(normOutput.max() ?? 0), min=\(normOutput.min() ?? 0))")
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}
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// Clean up
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engine.releaseBuffer(normBuffer)
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}
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let forwardTime = Date().timeIntervalSince(start2)
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print("✓ Forward pass completed in \(String(format: "%.3f", forwardTime))s")
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print("\n✅ Forward pass test completed!")
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