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[SR-9684] Differentiable derived conformances: Redefinition of init() when all stored properties are let #52126

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rxwei opened this issue Jan 17, 2019 · 1 comment
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[SR-9684] Differentiable derived conformances: Redefinition of init() when all stored properties are let #52126

rxwei opened this issue Jan 17, 2019 · 1 comment
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@dan-zheng
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bug A deviation from expected or documented behavior. Also: expected but undesirable behavior. swift for tensorflow

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@rxwei
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rxwei commented Jan 17, 2019

Previous ID SR-9684
Radar None
Original Reporter @rxwei
Type Bug
Status Resolved
Resolution Done
Additional Detail from JIRA
Votes 0
Component/s Swift for TensorFlow
Labels Bug
Assignee @dan-zheng
Priority Medium

md5: da7652f48df46db6a41370a508f7ee45

Issue Description:

import TensorFlow
public protocol Layer: Differentiable & KeyPathIterable
    where AllDifferentiableVariables: KeyPathIterable {
    /// The input type of the layer.
    associatedtype Input: Differentiable
    /// The output type of the layer.
    associatedtype Output: Differentiable

    /// Returns the output obtained from applying to an input.
    @differentiable(wrt: (self, .0))
    func applied(to input: Input) -> Output
}

public extension Layer {
    func valueWithPullback(at input: Input)
        -> (output: Output,
            pullback: (Output.CotangentVector)
                -> (layerGradient: CotangentVector, inputGradient: Input.CotangentVector)) {
        let (out, pullback) = _valueWithPullback(at: self, input, in: Self.applied(to:))
        return (out, pullback)
    }
}

public struct Dense<Scalar>: VectorNumeric, Layer
    where Scalar : FloatingPoint & Differentiable & TensorFlowScalar {
    public var weight: Tensor<Scalar>
    public var bias: Tensor<Scalar>

    @differentiable(wrt: (self, .0), vjp: _vjpApplied(to:))
    public func applied(to input: Tensor<Scalar>) -> Tensor<Scalar> {
        return matmul(input, weight) + bias
    }

    @usableFromInline
    func _vjpApplied(to input: Tensor<Scalar>)
        -> (Tensor<Scalar>, (Tensor<Scalar>) -> (Dense, Tensor<Scalar>)) {
      let r0 = matmul(input, weight)
      let r1 = r0 + bias
      func pullback(_ v: Tensor<Scalar>) -> (Dense, Tensor<Scalar>) {
          return (Dense(weight: matmul(input.transposed(), v), bias: v),
                  matmul(v, weight.transposed()))
      }
      return (r1, pullback)
    }
}

public extension Dense where Scalar : BinaryFloatingPoint,
                             Scalar.RawSignificand : FixedWidthInteger {
    init(inputSize: Int, outputSize: Int) {
        weight = Tensor(randomNormal: [Int32(inputSize), Int32(outputSize)])
        bias = Tensor(randomNormal: [Int32(outputSize)])
    }
}

struct Classifier: Layer {
    private static var rng = ARC4RandomNumberGenerator(seed: 42)
    let l1 = Dense<Float>(inputSize: 2, outputSize: 4)
    let l2 = Dense<Float>(inputSize: 4, outputSize: 1)
    func applied(to input: Tensor<Float>) -> Tensor<Float> {
        let h1 = l1.applied(to: input)
        return l2.applied(to: h1)
    }
}
@rxwei
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rxwei commented Jan 17, 2019

The synthesized AST has two `init()`s.

internal struct Classifier : Layer {
  @_hasInitialValue private static var rng: ARC4RandomNumberGenerator
  @_hasInitialValue internal let l1: Dense<Float>
  @_hasInitialValue internal let l2: Dense<Float>
  internal func applied(to input: Tensor<Float>) -> Tensor<Float>
  internal init()
  internal typealias Input = Tensor<Float>
  internal typealias Output = Tensor<Float>
  @_fieldwiseProductSpace internal struct AllDifferentiableVariables : Differentiable, AdditiveArithmetic, KeyPathIterable, VectorNumeric {
    internal init(l1: Dense<Float>, l2: Dense<Float>)
    @_fieldwiseProductSpace internal typealias AllDifferentiableVariables = Classifier.AllDifferentiableVariables
    @_fieldwiseProductSpace internal typealias TangentVector = Classifier.AllDifferentiableVariables
    @_fieldwiseProductSpace internal typealias CotangentVector = Classifier.AllDifferentiableVariables
    internal func tangentVector(from cotangent: Classifier.AllDifferentiableVariables) -> Classifier.AllDifferentiableVariables
    internal typealias AllKeyPaths = [PartialKeyPath<Classifier.AllDifferentiableVariables>]
    internal var allKeyPaths: [PartialKeyPath<Classifier.AllDifferentiableVariables>] { get }
    internal static var zero: Classifier.AllDifferentiableVariables { get }
    internal static func + (lhs: Classifier.AllDifferentiableVariables, rhs: Classifier.AllDifferentiableVariables) -> Classifier.AllDifferentiableVariables
    internal static func - (lhs: Classifier.AllDifferentiableVariables, rhs: Classifier.AllDifferentiableVariables) -> Classifier.AllDifferentiableVariables
    @_implements(Equatable, ==(_:_:)) internal static func __derived_struct_equals(_ a: Classifier.AllDifferentiableVariables, _ b: Classifier.AllDifferentiableVariables) -> Bool
    internal typealias Scalar = Float
    internal static func * (lhs: Float, rhs: Classifier.AllDifferentiableVariables) -> Classifier.AllDifferentiableVariables
  }
  @_fieldwiseProductSpace internal typealias TangentVector = Classifier.AllDifferentiableVariables
  @_fieldwiseProductSpace internal typealias CotangentVector = Classifier.AllDifferentiableVariables
  internal var allDifferentiableVariables: Classifier.AllDifferentiableVariables { get set }
  internal func moved(along direction: Classifier.AllDifferentiableVariables) -> Classifier
  internal init()
  internal func tangentVector(from cotangent: Classifier.AllDifferentiableVariables) -> Classifier.AllDifferentiableVariables
  internal typealias AllKeyPaths = [PartialKeyPath<Classifier>]
  internal var allKeyPaths: [PartialKeyPath<Classifier>] { get }
}

@swift-ci swift-ci transferred this issue from apple/swift-issues Apr 25, 2022
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@dan-zheng dan-zheng

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bug A deviation from expected or documented behavior. Also: expected but undesirable behavior. swift for tensorflow
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