PyTorch code hanging with latest commit

whats_wrong.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
from torch.autograd import Variable
import sys

IN_SIZE = 10
OUT_SIZE = 17
BATCH_SIZE = 32
WORKERS_NO = 1

class SimpleModel(nn.Module):
    def __init__(self, in_size, out_size):
        super(SimpleModel, self).__init__()
        h_size = in_size + (out_size - in_size) // 2
        self.l1 = nn.Linear(in_size, h_size)
        self.l2 = nn.Linear(h_size, out_size)

    def forward(self, inputs):
        return F.tanh(self.l2(F.relu(self.l1(inputs))))


class Worker(mp.Process):
    def __init__(self, pid, shared_model):
        super(Worker, self).__init__()
        self.my_pid = pid
        self.shared_model = shared_model

    def run(self):
        shared_model = self.shared_model
        my_pid = self.my_pid

        sys.stdout.write("Worker {:d} started.\n".format(my_pid))

        loss = F.smooth_l1_loss(
            shared_model(Variable(torch.rand(BATCH_SIZE, IN_SIZE))),
            Variable(torch.rand(BATCH_SIZE, OUT_SIZE))
        )

        sys.stdout.write("Forward phase done for {:d}!\n".format(my_pid))
        sys.stdout.flush()

        loss.backward()

        sys.stdout.write("Backward phase done for {:d}!\n".format(my_pid))
        sys.stdout.flush()


def main():
    shared_model = SimpleModel(IN_SIZE, OUT_SIZE)
    shared_model.share_memory()

    loss = F.smooth_l1_loss(
        shared_model(Variable(torch.rand(BATCH_SIZE, IN_SIZE))),
        Variable(torch.rand(BATCH_SIZE, OUT_SIZE))
    )
    loss.backward()

    workers = [Worker(i, shared_model) for i in range(WORKERS_NO)]

    for worker in workers:
        worker.start()
    for worker in workers:
        worker.join()

if __name__ == "__main__":
    main()