does_indeed_work.py

# Using torch==1.4.0

import numpy as np
import torch
import torch.multiprocessing as mp

torch.set_grad_enabled(False)

def target(inputs, outputs):
    x = inputs.get()
    outputs.put(x)
    outputs.join()

inputs = mp.Queue(1)
outputs = mp.JoinableQueue(1)

value = torch.tensor([1., 2.])

inputs.put(value)
proc = mp.Process(target=target, args=(inputs, outputs))
proc.start()
x = outputs.get()
outputs.task_done()
proc.join()
print(x)

does_not_work.py

# Using torch==1.4.0

import numpy as np
import torch
import torch.multiprocessing as mp

torch.set_grad_enabled(False)

def target(inputs, outputs):
    x = inputs.get()
    if isinstance(x, torch.Tensor):
        x = x.clone()
    outputs.put(x)

inputs = mp.Queue(1)
outputs = mp.Queue(1)

# value = "hey"  # Works.
# value = np.array([1])  # Works.
value = torch.tensor([1., 2.])  # Fails.

inputs.put(value)
proc = mp.Process(target=target, args=(inputs, outputs))
proc.start()
x = outputs.get()
proc.join()
proc.terminate()
print(x)