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.

License

willtebbutt/Libtask.jl

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