markbaseengine/Sources/MarkBase/Audio/AudioTower12B.swift

import Metal

public struct AudioConfig12B {
    public let outputDim: Int
    public let audioDim: Int
    public let groupSize: Int

    public init(outputDim: Int = 3840, audioDim: Int = 640, groupSize: Int = 64) {
        self.outputDim = outputDim
        self.audioDim = audioDim
        self.groupSize = groupSize
    }
}

public struct AudioWeights12B {
    public let projectionWeight: MTLBuffer
    public let projectionScales: MTLBuffer
    public let projectionBiases: MTLBuffer
    public let numGroups: Int
    public let hasOutputBias: Bool
    public let outputBias: MTLBuffer?

    public init(device: MTLDevice,
                weightData: [UInt32],
                scalesData: [Float],
                biasesData: [Float],
                numGroups: Int,
                outputBias: [Float]? = nil) throws {
        projectionWeight = device.makeBuffer(bytes: weightData, length: weightData.count * 4)!
        projectionScales = device.makeBuffer(bytes: scalesData, length: scalesData.count * 4)!
        projectionBiases = device.makeBuffer(bytes: biasesData, length: biasesData.count * 4)!
        self.numGroups = numGroups
        
        if let bias = outputBias {
            self.outputBias = device.makeBuffer(bytes: bias, length: bias.count * 4)
            self.hasOutputBias = true
        } else {
            self.outputBias = nil
            self.hasOutputBias = false
        }
    }
}

public final class AudioTower12B {
    public let config: AudioConfig12B
    public let weights: AudioWeights12B
    public let engine: MarkBaseEngine

    public let inDim: Int
    public let outDim: Int

    public init(config: AudioConfig12B, engine: MarkBaseEngine, weights: AudioWeights12B) {
        self.config = config
        self.weights = weights
        self.engine = engine

        self.inDim = config.audioDim
        self.outDim = config.outputDim
    }

    public func forward(inputBuffer: MTLBuffer, seqLen: Int, outputBuffer: MTLBuffer) throws {
        let cmdBuf = engine.commandQueue.makeCommandBuffer()!
        defer { cmdBuf.commit(); cmdBuf.waitUntilCompleted() }

        let pso = try engine.pipeline(named: "quantized_matmul_seq")
        let enc = cmdBuf.makeComputeCommandEncoder()!
        enc.setComputePipelineState(pso)

        enc.setBuffer(inputBuffer, offset: 0, index: 0)
        enc.setBuffer(weights.projectionWeight, offset: 0, index: 1)
        enc.setBuffer(weights.projectionScales, offset: 0, index: 2)
        enc.setBuffer(weights.projectionBiases, offset: 0, index: 3)
        
        if let bias = weights.outputBias {
            enc.setBuffer(bias, offset: 0, index: 4)
        } else {
            enc.setBuffer(weights.projectionBiases, offset: 0, index: 4)
        }
        
        enc.setBuffer(outputBuffer, offset: 0, index: 5)

        var inD = UInt32(inDim)
        enc.setBytes(&inD, length: 4, index: 6)
        var outD = UInt32(outDim)
        enc.setBytes(&outD, length: 4, index: 7)
        var hasBias = weights.hasOutputBias
        enc.setBytes(&hasBias, length: 1, index: 8)
        var sl = UInt32(seqLen)
        enc.setBytes(&sl, length: 4, index: 9)

        let grid = MTLSize(width: outDim, height: seqLen, depth: 1)
        let tg = engine.threadgroupSize2D(pso, grid: (outDim, seqLen))
        enc.dispatchThreads(grid, threadsPerThreadgroup: tg)
        enc.endEncoding()
    }

    public static func load(modelDir: String, engine: MarkBaseEngine) throws -> AudioTower12B {
        let device = engine.device
        let shardFile = "model-00002-of-00002.safetensors"
        let reader = try SafeTensorsReader(path: "\(modelDir)/\(shardFile)")

        let weightData = try reader.readUint32(named: "embed_audio.embedding_projection.weight")
        let scalesRaw = try reader.read(named: "embed_audio.embedding_projection.scales")
        let scalesData = SafeTensorsReader.bf16ToFloat32(scalesRaw)
        let biasesRaw = try reader.read(named: "embed_audio.embedding_projection.biases")
        let biasesData = SafeTensorsReader.bf16ToFloat32(biasesRaw)

        let numWeights = weightData.count
        let numScales = scalesData.count
        
        let audioDim = 640
        let packedInDim = audioDim / 8
        let outDim = numWeights / packedInDim
        let numGroups = packedInDim / 8

        let weights = try AudioWeights12B(
            device: device,
            weightData: weightData,
            scalesData: scalesData,
            biasesData: biasesData,
            numGroups: numGroups,
            outputBias: nil
        )

        let config = AudioConfig12B(outputDim: outDim, audioDim: audioDim, groupSize: 64)
        print("  AudioTower12B: inDim=\(audioDim), outDim=\(outDim), numGroups=\(numGroups)")
        return AudioTower12B(config: config, engine: engine, weights: weights)
    }
}