torch.jit.fork(func, *args, **kwargs)[source]

Creates an asynchronous task executing func and a reference to the value of the result of this execution. fork will return immediately, so the return value of func may not have been computed yet. To force completion of the task and access the return value invoke torch.jit.wait on the Future. fork invoked with a func which returns T is typed as torch.jit.Future[T]. fork calls can be arbitrarily nested, and may be invoked with positional and keyword arguments. Asynchronous execution will only occur when run in TorchScript. If run in pure python, fork will not execute in parallel. fork will also not execute in parallel when invoked while tracing, however the fork and wait calls will be captured in the exported IR Graph. .. warning:

`fork` tasks will execute non-deterministicly. We recommend only spawning
parallel fork tasks for pure functions that do not modify their inputs,
module attributes, or global state.
  • func (callable or torch.nn.Module) – A Python function or torch.nn.Module that will be invoked. If executed in TorchScript, it will execute asynchronously, otherwise it will not. Traced invocations of fork will be captured in the IR.

  • **kwargs (*args,) –

    arguments to invoke func with.


a reference to the execution of func. The value T can only be accessed by forcing completion of func through torch.jit.wait.

Return type


Example (fork a free function): .. testcode:

import torch
from torch import Tensor
def foo(a : Tensor, b : int) -> Tensor:
    return a + b
def bar(a):
    fut : torch.jit.Future[Tensor] = torch.jit.fork(foo, a, b=2)
    return torch.jit.wait(fut)
script_bar = torch.jit.script(bar)
input = torch.tensor(2)
# only the scripted version executes asynchronously
assert script_bar(input) == bar(input)
# trace is not run asynchronously, but fork is captured in IR
graph = torch.jit.trace(bar, (input,)).graph
assert "fork" in str(graph)

Example (fork a module method): .. testcode:

import torch
from torch import Tensor
class SubMod(torch.nn.Module):
    def forward(self, a: Tensor, b : int):
        return a + b
class Mod(torch.nn.Module):
    def __init__(self):
        self.mod = SubMod()
    def forward(self, input):
        fut = torch.jit.fork(self.mod, a, b=2)
        return torch.jit.wait(fut)
input = torch.tensor(2)
mod = Mod()
assert mod(input) == torch.jit.script(mod).forward(input)


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