Fixing deprecation warnings on all but the MiniGo example.

Datasets/CIFAR10/CIFAR10.swift

@@ -113,7 +113,7 @@ func loadCIFARFile(named name: String, in directory: String = ".") -> CIFARExamp
     let images = Tensor<UInt8>(shape: [imageCount, 3, 32, 32], scalars: bytes)
 
     // Transpose from the CIFAR-provided N(CHW) to TF's default NHWC.
-    let imageTensor = Tensor<Float>(images.transposed(withPermutations: [0, 2, 3, 1]))
+    let imageTensor = Tensor<Float>(images.transposed(permutation: [0, 2, 3, 1]))
 
     let mean = Tensor<Float>([0.485, 0.456, 0.406])
     let std = Tensor<Float>([0.229, 0.224, 0.225])
@@ -125,8 +125,8 @@ func loadCIFARFile(named name: String, in directory: String = ".") -> CIFARExamp
 func loadCIFARTrainingFiles() -> CIFARExample {
     let data = (1..<6).map { loadCIFARFile(named: "data_batch_\($0).bin") }
     return CIFARExample(
-        label: Raw.concat(concatDim: Tensor<Int32>(0), data.map { $0.label }),
-        data: Raw.concat(concatDim: Tensor<Int32>(0), data.map { $0.data })
+        label: _Raw.concat(concatDim: Tensor<Int32>(0), data.map { $0.label }),
+        data: _Raw.concat(concatDim: Tensor<Int32>(0), data.map { $0.data })
     )
 }
 

Datasets/MNIST/MNIST.swift

@@ -104,7 +104,7 @@ fileprivate func fetchDataset(
         return (
             images:
                 Tensor(shape: [rowCount, 1, imageHeight, imageWidth], scalars: images)
-                    .transposed(withPermutations: [0, 2, 3, 1]) / 255,  // NHWC
+                    .transposed(permutation: [0, 2, 3, 1]) / 255,  // NHWC
             labels: Tensor(labels)
         )
     }

GAN/main.swift

@@ -126,7 +126,7 @@ func saveImageGrid(_ testImage: Tensor<Float>, name: String) throws {
     // Add padding.
     gridImage = gridImage.padded(forSizes: [(0, 0), (0, 0), (1, 1), (1, 1)], with: 1)
     // Transpose to create single image.
-    gridImage = gridImage.transposed(withPermutations: [0, 2, 1, 3])
+    gridImage = gridImage.transposed(permutation: [0, 2, 1, 3])
     gridImage = gridImage.reshaped(
         to: [
             (imageHeight + 2) * testImageGridSize,

MiniGo/Models/PythonCheckpointReader.swift

@@ -28,7 +28,7 @@ public class PythonCheckpointReader {
         let countSuffix = layerCounts[layerName] == nil ? "" : "_\(layerCounts[layerName]!)"
         let tensorName = layerName + countSuffix + "/" + weightName
         // TODO(jekbradbury): support variadic dtype attrs in RawOpsGenerated
-        return Raw.restoreV2(prefix: StringTensor(path),
+        return _Raw.restoreV2(prefix: StringTensor(path),
                              tensorNames: StringTensor([tensorName]),
                              shapeAndSlices: StringTensor([""]))
     }

Support/Image.swift

@@ -37,11 +37,11 @@ public struct Image {
     }
 
     public init(jpeg url: URL, byteOrdering: ByteOrdering = .rgb) {
-        let loadedFile = Raw.readFile(filename: StringTensor(url.absoluteString))
-        let loadedJpeg = Raw.decodeJpeg(contents: loadedFile, channels: 3, dctMethod: "")
+        let loadedFile = _Raw.readFile(filename: StringTensor(url.absoluteString))
+        let loadedJpeg = _Raw.decodeJpeg(contents: loadedFile, channels: 3, dctMethod: "")
         if byteOrdering == .bgr {
             self.imageData = .uint8(
-                data: Raw.reverse(loadedJpeg, dims: Tensor<Bool>([false, false, false, true])))
+                data: _Raw.reverse(loadedJpeg, dims: Tensor<Bool>([false, false, false, true])))
         } else {
             self.imageData = .uint8(data: loadedJpeg)
         }
@@ -59,21 +59,21 @@ public struct Image {
             outputImageData = Tensor<UInt8>(adjustedData)
         }
 
-        let encodedJpeg = Raw.encodeJpeg(
+        let encodedJpeg = _Raw.encodeJpeg(
             image: outputImageData, format: .grayscale, quality: quality, xmpMetadata: "")
-        Raw.writeFile(filename: StringTensor(url.absoluteString), contents: encodedJpeg)
+        _Raw.writeFile(filename: StringTensor(url.absoluteString), contents: encodedJpeg)
     }
 
     public func resized(to size: (Int, Int)) -> Image {
         switch self.imageData {
         case let .uint8(data):
             return Image(
-                tensor: Raw.resizeBilinear(
+                tensor: _Raw.resizeBilinear(
                     images: Tensor<UInt8>([data]),
                     size: Tensor<Int32>([Int32(size.0), Int32(size.1)])))
         case let .float(data):
             return Image(
-                tensor: Raw.resizeBilinear(
+                tensor: _Raw.resizeBilinear(
                     images: Tensor<Float>([data]),
                     size: Tensor<Int32>([Int32(size.0), Int32(size.1)])))
         }

Transformer/Model.swift

@@ -92,7 +92,7 @@ func causallyMasked(_ dotProducts: Tensor<Float>, enable: Bool = false) -> Tenso
     }
     let (queryTimeSteps, keyTimeSteps) = (dotProducts.shape[1], dotProducts.shape[2])
     let ones = Tensor<Float>(ones: [1, queryTimeSteps, keyTimeSteps])
-    let mask = Raw.matrixBandPart(
+    let mask = _Raw.matrixBandPart(
         ones,
         numLower: Tensor(Int32(-1)),
         numUpper: Tensor(Int32(queryTimeSteps - keyTimeSteps)))
@@ -138,7 +138,7 @@ func splitHeads(_ input: Tensor<Float>, headCount: Int) -> Tensor<Float> {
     let (batchSize, timeSteps, features) = (input.shape[0], input.shape[1], input.shape[2])
     let featuresPerHead = features / headCount
     let splitLastDim = input.reshaped(to: [batchSize, timeSteps, headCount, featuresPerHead])
-    let movedToFront = splitLastDim.transposed(withPermutations: 0, 2, 1, 3)
+    let movedToFront = splitLastDim.transposed(permutation: 0, 2, 1, 3)
     return movedToFront.reshaped(to: [batchSize * headCount, timeSteps, featuresPerHead])
 }
 
@@ -149,7 +149,7 @@ func joinHeads(_ input: Tensor<Float>, headCount: Int) -> Tensor<Float> {
     let batchSize = generalizedBatch / headCount
     let features = featuresPerHead * headCount
     let splitFirstDim = input.reshaped(to: [batchSize, headCount, timeSteps, featuresPerHead])
-    let movedToBack = splitFirstDim.transposed(withPermutations: 0, 2, 1, 3)
+    let movedToBack = splitFirstDim.transposed(permutation: 0, 2, 1, 3)
     return movedToBack.reshaped(to: [batchSize, timeSteps, features])
 }
 
@@ -173,7 +173,7 @@ func _vjpSplitQKV(_ input: Tensor<Float>)
     -> (AttentionInput, (AttentionInput.TangentVector) -> Tensor<Float>) {
     let value = splitQKV(input)
     return (value, { seed in
-        return Raw.concatV2([seed.query, seed.key, seed.value], axis: Tensor<Int32>(2))
+        return _Raw.concatV2([seed.query, seed.key, seed.value], axis: Tensor<Int32>(2))
     })
 }
 

Transformer/Operators.swift

@@ -37,7 +37,7 @@ func batchedMatmul<Scalar : Numeric>(
     adjointLeft: Bool = false,
     adjointRight: Bool = false
 ) -> Tensor<Scalar> {
-    return Raw.batchMatMul(left, right, adjX: adjointLeft, adjY: adjointRight)
+    return _Raw.batchMatMul(left, right, adjX: adjointLeft, adjY: adjointRight)
 }
 
 @usableFromInline

Transformer/PythonCheckpointReader.swift

@@ -36,7 +36,7 @@ func readTensor<Scalar: TensorFlowScalar>(
     scalarType: Scalar.Type
 ) -> Tensor<Scalar> {
     // TODO(jekbradbury): support variadic dtype attrs in RawOpsGenerated
-    return Raw.restoreV2(prefix: StringTensor(path),
+    return _Raw.restoreV2(prefix: StringTensor(path),
                          tensorNames: StringTensor([name]),
                          shapeAndSlices: StringTensor([""]))
 }

Transformer/main.swift

@@ -53,7 +53,7 @@ for _ in 0..<100 {
     let lastLogit = logits.slice(
         lowerBounds: [0, timeSteps - 1, 0],
         upperBounds: [batchSize, timeSteps, vocabSize]) / temperature
-    tokens = Raw.multinomial(logits: lastLogit.squeezingShape(at: 1), numSamples: Tensor<Int32>(1))
+    tokens = _Raw.multinomial(logits: lastLogit.squeezingShape(at: 1), numSamples: Tensor<Int32>(1))
     print(encoder.decode(tokens[0].makeNumpyArray()), terminator: "")
 }
 print()