How to share a list of tensors in PyTorch multiprocessing?

Question

I am programming with PyTorch multiprocessing. I want all the subprocesses can read/write the same list of tensors (no resize). For example the variable can be

m = list(torch.randn(3), torch.randn(5))

Because each tensor has different sizes, I cannot organize them into a single tensor.

A python list has no share_memory_() function, and multiprocessing.Manager cannot handle a list of tensors. How can I share the variable m among multiple subprocesses?

Answer 1

I find the solution by myself. It is pretty straightforward. Just call share_memory_() for each list elements. The list itself is not in the shared memory, but the list elements are.

Demo code

import torch.multiprocessing as mp
import torch

def foo(worker,tl):
    tl[worker] += (worker+1) * 1000

if __name__ == '__main__':
    tl = [torch.randn(2), torch.randn(3)]

    for t in tl:
        t.share_memory_()

    print("before mp: tl=")
    print(tl)

    p0 = mp.Process(target=foo, args=(0, tl))
    p1 = mp.Process(target=foo, args=(1, tl))
    p0.start()
    p1.start()
    p0.join()
    p1.join()

    print("after mp: tl=")
    print(tl)

Output

before mp: tl=
[
 1.5999
 2.2733
[torch.FloatTensor of size 2]
, 
 0.0586
 0.6377
-0.9631
[torch.FloatTensor of size 3]
]
after mp: tl=
[
 1001.5999
 1002.2733
[torch.FloatTensor of size 2]
, 
 2000.0586
 2000.6377
 1999.0370
[torch.FloatTensor of size 3]
]

Answer 2

The original answer given by @rozyang does not work with GPUs. It raises error like RuntimeError: CUDA error: initialization error CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

To fix it, add mp.set_start_method('spawn', force=True) to codes. The following is a snippet:

import torch.multiprocessing as mp
import torch

def foo(worker,tl):
    tl[worker] += (worker+1) * 1000

if __name__ == '__main__':
    mp.set_start_method('spawn', force=True)
    tl = [torch.randn(2, device='cuda:0'), torch.randn(3, device='cuda:0')]

    for t in tl:
        t.share_memory_()

    print("before mp: tl=")
    print(tl)

    p0 = mp.Process(target=foo, args=(0, tl))
    p1 = mp.Process(target=foo, args=(1, tl))
    p0.start()
    p1.start()
    p0.join()
    p1.join()

    print("after mp: tl=")
    print(tl)