Fix ParameterlessLayer conformances.

Sources/TensorFlow/Layers/Convolutional.swift

@@ -790,6 +790,8 @@ extension DepthwiseConv2D {
 
 /// A layer for adding zero-padding in the temporal dimension.
 public struct ZeroPadding1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The padding values along the temporal dimension.
   @noDerivative public let padding: (Int, Int)
 
@@ -821,6 +823,8 @@ public struct ZeroPadding1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer
 
 /// A layer for adding zero-padding in the spatial dimensions.
 public struct ZeroPadding2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The padding values along the spatial dimensions.
   @noDerivative public let padding: ((Int, Int), (Int, Int))
 
@@ -853,6 +857,8 @@ public struct ZeroPadding2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer
 
 /// A layer for adding zero-padding in the spatial/spatio-temporal dimensions.
 public struct ZeroPadding3D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The padding values along the spatial/spatio-temporal dimensions.
   @noDerivative public let padding: ((Int, Int), (Int, Int), (Int, Int))
 

Sources/TensorFlow/Layers/Core.swift

@@ -19,6 +19,8 @@ import _Differentiation
 /// A flatten layer flattens the input when applied without affecting the batch size.
 @frozen
 public struct Flatten<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// Creates a flatten layer.
   public init() {}
 
@@ -37,6 +39,8 @@ public struct Flatten<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
 /// A reshape layer.
 @frozen
 public struct Reshape<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The target shape.
   @noDerivative public var shape: Tensor<Int32>
 
@@ -70,6 +74,7 @@ public struct Reshape<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
 
 /// A layer that encloses a custom differentiable function.
 public struct Function<Input: Differentiable, Output: Differentiable>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
   public typealias Body = @differentiable (Input) -> Output
 
   @noDerivative public let body: Body

Sources/TensorFlow/Layers/Dropout.swift

@@ -34,6 +34,8 @@ extension Tensor where Scalar: TensorFlowFloatingPoint {
 /// training time, which helps prevent overfitting.
 @frozen
 public struct Dropout<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   @noDerivative public let probability: Double
 
   /// Creates a dropout layer.
@@ -66,6 +68,8 @@ public struct Dropout<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
 ///
 /// The noise added always has mean zero, but has a configurable standard deviation.
 public struct GaussianNoise<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   @noDerivative public let standardDeviation: Tensor<Scalar>
 
   /// Creates a Gaussian noise layer
@@ -95,6 +99,8 @@ public struct GaussianNoise<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer
 /// Because this is a regularization layer, it is only active during training time. During inference,
 /// `GaussianDropout` passes through the input unmodified.
 public struct GaussianDropout<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   @noDerivative public let probability: Scalar
   @noDerivative public let standardDeviation: Scalar
 
@@ -135,6 +141,8 @@ public struct GaussianDropout<Scalar: TensorFlowFloatingPoint>: ParameterlessLay
 /// Source : Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
 @frozen
 public struct AlphaDropout<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   @noDerivative public let probability: Double
 
   /// Initializes an `AlphaDropout` layer with a configurable `probability`.

Sources/TensorFlow/Layers/Pooling.swift

@@ -17,6 +17,8 @@ import _Differentiation
 /// A max pooling layer for temporal data.
 @frozen
 public struct MaxPool1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The size of the sliding reduction window for pooling.
   @noDerivative public let poolSize: Int
   /// The stride of the sliding window for temporal dimension.
@@ -56,6 +58,8 @@ public struct MaxPool1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
 /// A max pooling layer for spatial data.
 @frozen
 public struct MaxPool2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The size of the sliding reduction window for pooling.
   @noDerivative public let poolSize: (Int, Int, Int, Int)
   /// The strides of the sliding window for each dimension of a 4-D input.
@@ -105,6 +109,8 @@ extension MaxPool2D {
 /// A max pooling layer for spatial or spatio-temporal data.
 @frozen
 public struct MaxPool3D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The size of the sliding reduction window for pooling.
   @noDerivative public let poolSize: (Int, Int, Int, Int, Int)
   /// The strides of the sliding window for each dimension of a 5-D input.
@@ -171,6 +177,8 @@ extension MaxPool3D {
 /// An average pooling layer for temporal data.
 @frozen
 public struct AvgPool1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The size of the sliding reduction window for pooling.
   @noDerivative public let poolSize: Int
   /// The stride of the sliding window for temporal dimension.
@@ -210,6 +218,8 @@ public struct AvgPool1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
 /// An average pooling layer for spatial data.
 @frozen
 public struct AvgPool2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The size of the sliding reduction window for pooling.
   @noDerivative public let poolSize: (Int, Int, Int, Int)
   /// The strides of the sliding window for each dimension of a 4-D input.
@@ -259,6 +269,8 @@ extension AvgPool2D {
 /// An average pooling layer for spatial or spatio-temporal data.
 @frozen
 public struct AvgPool3D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// The size of the sliding reduction window for pooling.
   @noDerivative public let poolSize: (Int, Int, Int, Int, Int)
   /// The strides of the sliding window for each dimension of a 5-D input.
@@ -325,6 +337,8 @@ extension AvgPool3D {
 /// A global average pooling layer for temporal data.
 @frozen
 public struct GlobalAvgPool1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// Creates a global average pooling layer.
   public init() {}
 
@@ -342,6 +356,8 @@ public struct GlobalAvgPool1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLay
 /// A global average pooling layer for spatial data.
 @frozen
 public struct GlobalAvgPool2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// Creates a global average pooling layer.
   public init() {}
 
@@ -359,6 +375,8 @@ public struct GlobalAvgPool2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLay
 /// A global average pooling layer for spatial and spatio-temporal data.
 @frozen
 public struct GlobalAvgPool3D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// Creates a global average pooling layer.
   public init() {}
 
@@ -376,6 +394,8 @@ public struct GlobalAvgPool3D<Scalar: TensorFlowFloatingPoint>: ParameterlessLay
 /// A global max pooling layer for temporal data.
 @frozen
 public struct GlobalMaxPool1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// Creates a global max pooling layer.
   public init() {}
 
@@ -396,6 +416,8 @@ public struct GlobalMaxPool1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLay
 /// A global max pooling layer for spatial data.
 @frozen
 public struct GlobalMaxPool2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// Creates a global max pooling layer.
   public init() {}
 
@@ -413,6 +435,8 @@ public struct GlobalMaxPool2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLay
 /// A global max pooling layer for spatial and spatio-temporal data.
 @frozen
 public struct GlobalMaxPool3D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// Creates a global max pooling layer.
   public init() {}
 
@@ -431,6 +455,8 @@ public struct GlobalMaxPool3D<Scalar: TensorFlowFloatingPoint>: ParameterlessLay
 /// Note: `FractionalMaxPool` does not have an XLA implementation, and thus may have performance implications.
 @frozen
 public struct FractionalMaxPool2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   /// Pooling ratios for each dimension of input of shape (batch, height, width, channels).
   /// Currently pooling in only height and width is supported.
   @noDerivative public let poolingRatio: (Double, Double, Double, Double)

Sources/TensorFlow/Layers/Upsampling.swift

@@ -17,6 +17,8 @@ import _Differentiation
 /// An upsampling layer for 1-D inputs.
 @frozen
 public struct UpSampling1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   @noDerivative public let size: Int
 
   /// Creates an upsampling layer.
@@ -43,6 +45,8 @@ public struct UpSampling1D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer
 /// An upsampling layer for 2-D inputs.
 @frozen
 public struct UpSampling2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   @noDerivative public let size: Int
 
   /// Creates an upsampling layer.
@@ -70,6 +74,8 @@ public struct UpSampling2D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer
 /// An upsampling layer for 3-D inputs.
 @frozen
 public struct UpSampling3D<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
+  public typealias TangentVector = EmptyTangentVector
+
   @noDerivative public let size: Int
 
   /// Creates an upsampling layer.