Swift，macOS，具有2个GPU的mac，矩阵运算可在一个GPU上工作，而不是在另一个GPU上工作

Question

根据Apple菜单中的“关于本机”，我在装有两个GPU的MacBook Pro（15英寸，2015年中，视网膜）上运行macOS。一个GPU是2 GB的AMD Radeon R9 M370X，另一个是Intel Iris Pro 1536 MB-我猜是标准芯片吗？它们是我购买时在那里的筹码，没有我自己添加的东西。

我正在使用Swift MPS库进行矩阵计算；它在Intel GPU上可以很好地工作，但是当我选择Radeon时，我每次操作只能得到零，没有错误报告。我到处寻找有关它的文档，但是找不到任何东西。到目前为止，我唯一的线索是Radeon报告“未集成”（或者至少我认为是基于Finding GPUs on macOS的示例代码，它与Apple的文档一样有用，意思是[[不太）。如果我已正确阅读该页面，则这是我的两个GPU告诉我的内容。

Device Intel Iris Pro Graphics; caps: headful, not discrete, integrated, not external

Device AMD Radeon R9 M370X; caps: headful, discrete, not integrated, not external

我找不到任何可以表明我做错事情的文档。我遍历了苹果公司的MPS文档，无济于事。正如我所说，该代码在Intel GPU上运行良好，因此我认为它也可以在Radeon上运行。我已经运行了一些可下载的诊断工具来检查Radeon，但是这些工具的菜单中没有显示该工具。因此，我什至不知道这是否是我在代码中做错的事情，或者芯片本身是否损坏。

下面是代码，您可以通过粘贴到main.swift将其构建为控制台应用程序。查找以下行：

let device = MTLCopyAllDevices()[1]

我将[0]用于Intel，将[1]用于Radeon，您可以看到输出是不同的，即Radeon的全零。我想您的里程可能会因您的机器而异。我欢迎任何意见，欢呼
import MetalPerformanceShaders

typealias MPSNumber = Float32

let MPSNumberSize = MemoryLayout<MPSNumber>.size
let MPSNumberTypeInGPU = MPSDataType.float32

class MPSNet {
    let commandBuffer: MTLCommandBuffer
    let commandQueue: MTLCommandQueue
    let device = MTLCopyAllDevices()[1]
    var neuronsInMatrix1: MPSMatrix?
    var neuronsInMatrix2: MPSMatrix?
    var neuronsOutMatrix: MPSMatrix?

    init() {
        guard let cq = device.makeCommandQueue() else { fatalError() }
        guard let cb = cq.makeCommandBuffer() else { fatalError() }

        commandQueue = cq
        commandBuffer = cb

        let cMatrices = 2
        let cRows = 1
        let cColumns = 3

        let sensoryInputs1: [MPSNumber] = [1, 2, 3]
        let sensoryInputs2: [MPSNumber] = [4, 5, 6]

        neuronsInMatrix1 = makeMatrix(device, sensoryInputs1)
        neuronsInMatrix2 = makeMatrix(device, sensoryInputs2)

        let rowStride = MPSMatrixDescriptor.rowBytes(fromColumns: cColumns, dataType: MPSNumberTypeInGPU)
        neuronsOutMatrix = makeMatrix(device, cRows, cColumnsOut: cColumns, rowStride: rowStride)

        let adder = MPSMatrixSum(
            device: device, count: cMatrices, rows: cRows, columns: cColumns, transpose: false
        )

        adder.encode(
            to: commandBuffer,
            sourceMatrices: [neuronsInMatrix1!, neuronsInMatrix2!],
            resultMatrix: neuronsOutMatrix!, scale: nil, offsetVector: nil,
            biasVector: nil, start: 0
        )

        commandBuffer.addCompletedHandler { _ in
            let motorOutputs = self.getComputeOutput(self.neuronsOutMatrix!)

            let discrete = !self.device.isLowPower && !self.device.isRemovable
            let caps = "\(self.device.isHeadless ? " headless" : " headful")" +
                       "\(discrete ? ", discrete" : ", not discrete")" +
                       "\(self.device.isLowPower ? ", integrated" : ", not integrated")" +
                       "\(self.device.isRemovable ? ", external" : ", not external")"

            print("Device \(self.device.name); caps:\(caps); motor outputs \(motorOutputs)")
        }
    }

    func compute() {
        commandBuffer.commit()
        commandBuffer.waitUntilCompleted()
    }
}

extension MPSNet {
    func getComputeOutput(_ matrix: MPSMatrix) -> [Double] {
        let rc = matrix.data.contents()
        return stride(from: 0, to: matrix.columns * MPSNumberSize, by: MPSNumberSize).map {
            offset in

            let rr = rc.load(fromByteOffset: offset, as: MPSNumber.self)

            return Double(rr)
        }
    }

    func loadMatrix(_ data: MTLBuffer, _ rawValues: [MPSNumber]) {
        let dContents = data.contents()

        zip(stride(from: 0, to: rawValues.count * MPSNumberSize, by: MPSNumberSize), rawValues).forEach { z in
            let (byteOffset, rawValue) = (z.0, MPSNumber(z.1))

            dContents.storeBytes(of: rawValue, toByteOffset: byteOffset, as: MPSNumber.self)
        }
    }

    func makeMatrix(_ device: MTLDevice, _ rawValues: [MPSNumber]) -> MPSMatrix {
        let rowStride = MPSMatrixDescriptor.rowBytes(
            fromColumns: rawValues.count, dataType: MPSNumberTypeInGPU
        )

        let descriptor = MPSMatrixDescriptor(
            dimensions: 1, columns: rawValues.count, rowBytes: rowStride,
            dataType: MPSNumberTypeInGPU
        )

        guard let inputBuffer = device.makeBuffer(
            length: descriptor.matrixBytes, options: MTLResourceOptions.storageModeManaged
        ) else { fatalError() }

        loadMatrix(inputBuffer, rawValues)

        return MPSMatrix(buffer: inputBuffer, descriptor: descriptor)
    }

    func makeMatrix(_ device: MTLDevice, _ cRowsOut: Int, cColumnsOut: Int, rowStride: Int) -> MPSMatrix {
        let matrixDescriptor = MPSMatrixDescriptor(
            dimensions: cRowsOut, columns: cColumnsOut,
            rowBytes: rowStride, dataType: MPSNumberTypeInGPU
        )

        return MPSMatrix(device: device, descriptor: matrixDescriptor)
    }
}

let net = MPSNet()
net.compute()

Answer 1

似乎您未能使用-[MPSMatrix syncnizeOnCommandBuffer：]。在分立设备上，需要一些显式同步，然后数据才能从GPU返回。