#adjoint(Tensor<Float>.*) -> either broken or no longer supported.

[johndpope]

I have this working on the specified toolchain - and so all is well. Unfortunately - the code for adjoint has changed in latest toolchain released yesterday. fyi - https://github.com/apple/swift/pull/19216

Suggestion by rxwei - This regression is introduced in https://github.com/apple/swift/pull/19201 and is known to break things. You can try to use #gradient instead of manually chaining adjoints.

(it's only the two adjoints that are defining Tensor. that break.
let (_, d5) = #adjoint(Tensor.)( let (_, d3) = #adjoint(Tensor.*)(

as this code is so bleeding edge - there's not so much documentation / reference code to turn to. If you find time, perhaps you can look into this? I'll keep digging in either case.

[johndpope]

found this https://github.com/tensorflow/swift-tutorials/blob/master/iris/swift_tensorflow_tutorial.ipynb

// ==== Take the gradient of the loss function === // Use Swift's Automatic Differentiation to calculate the // gradients used to optimize our model.

for epoch in 0..<numEpochs {
    var epochLoss: Float = 0
    var epochAccuracy: Float = 0
    var batchCount: Int = 0
    for (x, y) in trainDataset() {       
        let (loss, grad) = lossAndGradient(for: x, using: model, labels: y)
        model.update(withGradients: grad) { $0 -= learningRate * $1 }

        let logits = predictions(for: x, using: model)
        epochAccuracy += accuracy(predictions: logits.argmax(squeezingAxis: 1), truths: y)
        epochLoss += loss
        batchCount += 1
    }
    epochAccuracy /= Float(batchCount)
    epochLoss /= Float(batchCount)
    trainAccuracyResults.append(epochAccuracy)
    trainLossResults.append(epochLoss)
    if epoch % 50 == 0 {
        print("Epoch \(epoch): Loss: \(epochLoss), Accuracy: \(epochAccuracy)")
    }
}

func lossAndGradient(for input: Tensor<Float>, 
                     using model: IrisParameters,
                     labels: Tensor<Int32>) -> (Float, IrisParameters) {
    let (loss, (_, modelGrad)) =
        #valueAndGradient(loss(for:using:labels:), wrt: .0, .1)(input, model, labels)
    return (loss, modelGrad)
}

[rxwei]

Try using #adjoint(*) directly.

[johndpope]

afraid not.

[saschaschramm]

Updated project.