dan-zheng

dan-zheng

dan-zheng

dan-zheng

"""
Demostrating how to compute the gradients for convolution with:
    tf.nn.conv2d
    tf.nn.conv2d_backprop_input
    tf.nn.conv2d_backprop_filter
    tf.nn.conv2d_transpose

This is the scripts for this answer: https://stackoverflow.com/a/44350789/1255535
"""

dan-zheng

Introduce user-defined dynamically "callable" types

• Proposal: SE-NNNN
• Authors: Chris Lattner, Dan Zheng
• Review Manager: TBD
• Status: Awaiting implementation

Introduction

This proposal is a follow-on to [SE-0195 - Introduce User-defined "Dynamic Member

dan-zheng

The red - lines are produced by direct references to Tensor.dfoo, which doesn't crash.

The green + lines are produced by #adjoint(Tensor.foo), which does crashes.

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()

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

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

dan-zheng

import TensorFlow

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