I set the model hidden layer a litter bigger(like 256), and use load_state_dict in the subprocess.
When I give a multi-dimensional tensor to the model,  the process will be terminated without any exception.
I write a demo to reproduce the error.
import torch.multiprocessing as mp
import torch
import torch.nn as nn


class AC(nn.Module):
    def __init__(self, features_n, actions_n):
        super(AC, self).__init__()
        # if the hidden layer cells are lower, for example, 128, no error occurs.
        self.hidden_layer_cells = 256
        self.l1 = nn.Linear(features_n, self.hidden_layer_cells)
        self.l2 = nn.Linear(self.hidden_layer_cells, self.hidden_layer_cells)
        self.actor_linear = nn.Linear(self.hidden_layer_cells, actions_n)
        self.critic_linear = nn.Linear(self.hidden_layer_cells, 1)

    def forward(self, inputs):
        x = torch.tanh(self.l1(inputs))
        x = torch.tanh(self.l2(x))
        pi = self.actor_linear(x)
        q = self.critic_linear(x)
        return pi, q


class Worker2(mp.Process):
    def __init__(self) -> None:
        super(Worker2, self).__init__()
        self.t = AC(10, 10)
        self.tt = AC(10, 10)
        # if I load state dict from exist model, it will be terminated when passing Multidimensional tensor
        self.tt.load_state_dict(self.t.state_dict())

    def run(self):
        while True:
            s = torch.ones(size=(1, 10))
            a = self.t(s)
            ss = torch.cat((s, s))
            # this line will terminate the process
            aa = self.t(ss)


w = Worker2()
w.start()
w.join()