markus7800/Libtask.jl

Tape based task copying in Turing

Libtask

Tape based task copying in Turing

Getting Started

Stack allocated objects are always deep copied:

using Libtask

function f()
  t = 0
  for _ in 1:10
    produce(t)
    t = 1 + t
  end
end

ttask = TapedTask(f)

@show consume(ttask) # 0
@show consume(ttask) # 1

a = copy(ttask)
@show consume(a) # 2
@show consume(a) # 3

@show consume(ttask) # 2
@show consume(ttask) # 3

Heap-allocated Array and Ref objects are deep copied by default:

using Libtask

function f()
  t = [0 1 2]
  for _ in 1:10
    produce(t[1])
    t[1] = 1 + t[1]
  end
end

ttask = TapedTask(f)

@show consume(ttask) # 0
@show consume(ttask) # 1

a = copy(ttask)
@show consume(a) # 2
@show consume(a) # 3

@show consume(ttask) # 2
@show consume(ttask) # 3

Other heap-allocated objects (e.g., Dict) are shallow copied, by default:

using Libtask

function f()
  t = Dict(1=>10, 2=>20)
  while true
    produce(t[1])
    t[1] = 1 + t[1]
  end
end

ttask = TapedTask(f)

@show consume(ttask) # 10
@show consume(ttask) # 11

a = copy(ttask)
@show consume(a) # 12
@show consume(a) # 13

@show consume(ttask) # 14
@show consume(ttask) # 15

Notes:

• The Turing probabilistic programming language uses this task copying feature in an efficient implementation of the particle filtering sampling algorithm for arbitrary order Markov processes.

• From v0.6.0, Libtask is implemented by recording all the computing to a tape and copying that tape. Before that version, it is based on a tricky hack on the Julia internals. You can check the commit history of this repo to see the details.