dan-zheng

struct MNISTParameters : ParameterAggregate {
  var w1 = Tensor<Float>(randomNormal: [784, 30])
  var w2 = Tensor<Float>(randomNormal: [30, 10])
  
  // Compiler-synthesized:
  // static var allKeyPaths: [WritableKeyPath<MNISTParameters, Tensor<Float>>] {
  //   return [\MNISTParameters.w1, \MNISTParameters.w2]
  // }
  // Learn more about key paths here: https://github.com/apple/swift-evolution/blob/master/proposals/0161-key-paths.md
}

dan-zheng

import TensorFlow

func train(iterationCount: Int) {
  let images = Tensor<Float>(ones: [1000, 784])
  let batchSize = Float(images.shape[0])

  print("Begin training for \(iterationCount) iterations.")

  for _ in 0...iterationCount {
    let bound = Int32(batchSize)/25

dan-zheng

%24 = graph_op "MaxPoolV2,i,i,i"(%4 : $TensorHandle<Float>, %14 : $TensorHandle<Int32>, %22 : $TensorHandle<Int32>) {T: $Float, padding: "SAME", data_format: "NHWC", __device: "/device:CPU:0"} : $TensorHandle<Float>

As you can see, kernel size and strides are SSA operands of `graph_op`, not constant attributes.
This is because of how MaxPool is defined:

REGISTER_OP("MaxPoolV2")
    .Attr(
        "T: {half, bfloat16, float, double, int32, int64, uint8, int16, int8, "
        "uint16, qint8} = DT_FLOAT")
    .Attr(GetPaddingAttrString())

dan-zheng

import TensorFlow
func test() {
  for _ in 0...10 {
    let x = Tensor<Float>(ones: [2, 2])
    _ = x.reshaped(toShape: Tensor<Int32>([4, Int32(1 * 1)]))

    // Alternative 1: literal `1` avoids send/receive.
    // _ = x.reshaped(toShape: Tensor<Int32>([4, 1]))

    // Alternative 2: avoiding `Int32` avoids send/receive.

dan-zheng

// RUN: %target-swift-frontend -Xllvm -tf-dump-intermediates -O -emit-sil -verify %s | %FileCheck %s

import TensorFlow

// This test is intended to verify that all of the operations end up in the
// graph: that there are no host/accelerator copies generated.  This tests a
// combination of the partitioning pass being able to recognize various forms,
// but also checks that certain ops implementations are promotable as well.

// Please keep it so no errors or warnings are generated by functions in this

dan-zheng

struct Model {
  var w: Float

  struct Parameters {
    var w: Float
  }

  var allParameters: Parameters {
    get {
      return Parameters(w: w)

dan-zheng

import TensorFlow

func test(_ array: [Tensor<Float>]) {
  for (i, x) in array.enumerated() {
    print(x + x)
  }
}
test([Tensor(1), Tensor(2)])

dan-zheng

// crash.swift
import TensorFlow
func test() {
  print(Tensor(1))
}

dan-zheng

$ swiftc -dump-ast stored.swift
(source_file
  (struct_decl range=[stored.swift:1:1 - line:3:1] "Model" interface type='Model.Type' access=internal non-resilient
    (pattern_binding_decl range=[stored.swift:2:3 - line:2:10]
      (pattern_typed type='Float'
        (pattern_named type='Float' 'w')
        (type_ident
          (component id='Float' bind=Swift.(file).Float))))
    (var_decl range=[stored.swift:2:7 - line:2:7] "w" type='Float' interface type='Float' access=internal storage_kind=stored_with_trivial_accessors
      (accessor_decl implicit range=[stored.swift:2:7 - line:2:7] 'anonname=0x7f83f5055fd0' interface type='(Model) -> () -> Float' access=internal get_for=w

dan-zheng

import TensorFlow
import Foundation

func testTensor() throws {
  let tensor = Tensor<Int32>(shape: [2, 3], scalars: Array(1...6))
  let encoder = JSONEncoder()
  let data = try encoder.encode(tensor)
}
try testTensor()