v2: add multimodal 12B test, fix VisionTower12B kernel dispatch

Sources/MarkBase/Vision/VisionTower12B.swift

@@ -236,7 +236,7 @@ public final class VisionTower12B {
         output: MTLBuffer,
         cmdBuf: MTLCommandBuffer
     ) throws {
-        let pso = try engine.pipeline(named: "quantized_matmul")
+        let pso = try engine.pipeline(named: "quantized_matmul_seq")
         let enc = cmdBuf.makeComputeCommandEncoder()!
         enc.setComputePipelineState(pso)
 
@@ -244,22 +244,22 @@ public final class VisionTower12B {
         enc.setBuffer(weight, offset: 0, index: 1)
         enc.setBuffer(scales, offset: 0, index: 2)
         enc.setBuffer(biases, offset: 0, index: 3)
-        enc.setBuffer(output, offset: 0, index: 4)
+        enc.setBuffer(bias ?? biases, offset: 0, index: 4)
+        enc.setBuffer(output, offset: 0, index: 5)
 
         var inD = UInt32(inDim)
-        enc.setBytes(&inD, length: MemoryLayout<UInt32>.size, index: 5)
+        enc.setBytes(&inD, length: 4, index: 6)
         var outD = UInt32(outDim)
-        enc.setBytes(&outD, length: MemoryLayout<UInt32>.size, index: 6)
+        enc.setBytes(&outD, length: 4, index: 7)
+        var hasBias = bias != nil
+        enc.setBytes(&hasBias, length: 1, index: 8)
+        var sl = UInt32(seqLen)
+        enc.setBytes(&sl, length: 4, index: 9)
 
-        let grid = MTLSize(width: outDim * seqLen, height: 1, depth: 1)
+        let grid = MTLSize(width: outDim, height: seqLen, depth: 1)
-        let tg = engine.threadgroupSize1D(pso, count: max(outDim, seqLen))
+        let tg = engine.threadgroupSize2D(pso, grid: (outDim, seqLen))
         enc.dispatchThreads(grid, threadsPerThreadgroup: tg)
         enc.endEncoding()
-
-        // Add unquantized bias if present
-        if let b = bias {
-            try eltwiseAdd(input: output, bias: b, seqLen: seqLen, dim: outDim, cmdBuf: cmdBuf)
-        }
     }
 
     private func rmsNormSeq(

Tests/01_Model/Multimodal12BTest.swift

@@ -0,0 +1,143 @@
+import XCTest
+@testable import MarkBase
+
+final class Multimodal12BTest: XCTestCase {
+
+    var engine: MarkBaseEngine!
+    var multimodal: MultimodalModel!
+    let modelDir = "/Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit"
+    let maxCtx = 64
+
+    override func setUp() {
+        super.setUp()
+        guard FileManager.default.fileExists(atPath: modelDir + "/model.safetensors.index.json") else {
+            return
+        }
+        engine = try? MarkBaseEngine(autoCompile: true)
+        multimodal = try? MultimodalModel(modelDir: modelDir, engine: engine, maxContextLength: maxCtx)
+    }
+
+    func testModelLoads() throws {
+        try XCTSkipIf(multimodal == nil, "12B model not found")
+        XCTAssertEqual(multimodal!.textModel.hiddenSize, 3840)
+        XCTAssertEqual(multimodal!.textModel.numHiddenLayers, 48)
+        XCTAssertNotNil(multimodal!.visionTower, "VisionTower12B should load")
+        XCTAssertNotNil(multimodal!.audioTower, "AudioTower12B should load")
+    }
+
+    func testVisionTowerForward() throws {
+        try XCTSkipIf(multimodal?.visionTower == nil, "Vision tower not loaded")
+        let tower = multimodal!.visionTower!
+        let numPatches = 8
+        let patchDim = tower.patchDim
+
+        var patches = [Float](repeating: 0, count: numPatches * patchDim)
+        for i in 0..<patches.count { patches[i] = Float.random(in: -0.5...0.5) }
+
+        let inputBuf = engine.device.makeBuffer(bytes: patches, length: patches.count * 4)!
+        let outBuf = engine.device.makeBuffer(length: numPatches * tower.hiddenDim * 4)!
+
+        try tower.forward(patchEmbeddings: inputBuf, numPatches: numPatches, outputBuffer: outBuf)
+
+        let out = engine.readFloats(from: outBuf, count: numPatches * tower.hiddenDim)
+        let nanCount = out.filter { $0.isNaN }.count
+        XCTAssertEqual(nanCount, 0, "No NaN in vision output")
+
+        let maxAbs = out.map { abs($0) }.max() ?? 0
+        XCTAssertLessThan(maxAbs, 1e6, "Vision output magnitude should be reasonable")
+        XCTAssertGreaterThan(maxAbs, 0, "Vision output should have non-zero values")
+    }
+
+    func testAudioTowerForward() throws {
+        try XCTSkipIf(multimodal?.audioTower == nil, "Audio tower not loaded")
+        let tower = multimodal!.audioTower!
+        let numFrames = 16
+
+        var features = [Float](repeating: 0, count: numFrames * 640)
+        for i in 0..<features.count { features[i] = Float.random(in: -1.0...1.0) }
+
+        let inputBuf = engine.device.makeBuffer(bytes: features, length: features.count * 4)!
+        let outBuf = engine.device.makeBuffer(length: numFrames * tower.outDim * 4)!
+
+        try tower.forward(inputBuffer: inputBuf, seqLen: numFrames, outputBuffer: outBuf)
+
+        let out = engine.readFloats(from: outBuf, count: numFrames * tower.outDim)
+        let nanCount = out.filter { $0.isNaN }.count
+        XCTAssertEqual(nanCount, 0, "No NaN in audio output")
+    }
+
+    func testTextBackboneForwardAfterVisionInjection() throws {
+        try XCTSkipIf(multimodal?.visionTower == nil, "Vision tower not loaded")
+        let tower = multimodal!.visionTower!
+        let numPatches = 4
+        let patchDim = tower.patchDim
+
+        var patches = [Float](repeating: 0, count: numPatches * patchDim)
+        for i in 0..<patches.count { patches[i] = Float.random(in: -0.5...0.5) }
+
+        let inputBuf = engine.device.makeBuffer(bytes: patches, length: patches.count * 4)!
+        let visionOut = engine.device.makeBuffer(length: numPatches * 3840 * 4)!
+        try tower.forward(patchEmbeddings: inputBuf, numPatches: numPatches, outputBuffer: visionOut)
+
+        for i in 0..<numPatches {
+            let offset = i * 3840 * 4
+            let logits = try multimodal!.textModel.forwardFromHidden(
+                hiddenBuffer: visionOut, offset: offset, position: i)
+            let nanCount = logits.filter { $0.isNaN }.count
+            XCTAssertEqual(nanCount, 0, "No NaN after vision injection pos=\(i)")
+        }
+    }
+
+    func testTextBackboneForwardAfterAudioInjection() throws {
+        try XCTSkipIf(multimodal?.audioTower == nil, "Audio tower not loaded")
+        let tower = multimodal!.audioTower!
+        let numFrames = 4
+
+        var features = [Float](repeating: 0, count: numFrames * 640)
+        for i in 0..<features.count { features[i] = Float.random(in: -1.0...1.0) }
+
+        let inputBuf = engine.device.makeBuffer(bytes: features, length: features.count * 4)!
+        let audioOut = engine.device.makeBuffer(length: numFrames * 3840 * 4)!
+        try tower.forward(inputBuffer: inputBuf, seqLen: numFrames, outputBuffer: audioOut)
+
+        for i in 0..<numFrames {
+            let offset = i * 3840 * 4
+            let logits = try multimodal!.textModel.forwardFromHidden(
+                hiddenBuffer: audioOut, offset: offset, position: i)
+            let nanCount = logits.filter { $0.isNaN }.count
+            XCTAssertEqual(nanCount, 0, "No NaN after audio injection pos=\(i)")
+        }
+    }
+
+    func testMultimodalInferenceGenerate() throws {
+        try XCTSkipIf(multimodal?.visionTower == nil, "Vision tower not loaded")
+        let inference = try MultimodalInference(model: multimodal!)
+
+        let numPatches = 8
+        let patchDim = multimodal!.visionTower!.patchDim
+        var patches = [Float](repeating: 0, count: numPatches * patchDim)
+        for i in 0..<patches.count { patches[i] = Float.random(in: -0.5...0.5) }
+
+        let audioDim = 640
+        var audioFeatures = [[Float]]()
+        for _ in 0..<32 {
+            var frame = [Float](repeating: 0, count: audioDim)
+            for j in 0..<audioDim { frame[j] = Float.random(in: -1.0...1.0) }
+            audioFeatures.append(frame)
+        }
+
+        let result = try inference.generate(
+            textTokens: [2],
+            audioFeatures: audioFeatures,
+            imagePatches: patches,
+            numImagePatches: numPatches,
+            maxTokens: 5
+        )
+
+        XCTAssertGreaterThan(result.count, 1, "Should generate at least one token")
+        for token in result {
+            XCTAssertGreaterThanOrEqual(token, 0, "Token ID should be non-negative")
+            XCTAssertLessThan(token, multimodal!.textModel.vocabSize, "Token ID should be within vocab range")
+        }
+    }
+}