Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat1 in method wrapper_CUDA_addmm)

[Cometzyc]

---

RuntimeError Traceback (most recent call last)
Cell In[25], line 3
1 from elegantrl.train.run import train_agent
----> 3 train_agent(args)

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\elegantrl\train\run.py:89, in train_agent(failed resolving arguments)
86 buffer[:] = buffer_items
88 torch.set_grad_enabled(True)
---> 89 logging_tuple = agent.update_net(buffer)
90 torch.set_grad_enabled(False)
92 evaluator.evaluate_and_save(actor=agent.act, steps=horizon_len, exp_r=exp_r, logging_tuple=logging_tuple)

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\elegantrl\agents\AgentSAC.py:29, in AgentSAC.update_net(self, buffer)
25 with torch.no_grad():
26 states, actions, rewards, undones = buffer.add_item
27 self.update_avg_std_for_normalization(
28 states=states.reshape((-1, self.state_dim)),
---> 29 returns=self.get_cumulative_rewards(rewards=rewards, undones=undones).reshape((-1,))
30 )
32 '''update network'''
33 obj_critics = 0.0

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\elegantrl\agents\AgentBase.py:190, in AgentBase.get_cumulative_rewards(self, rewards, undones)
188 last_state = self.last_state
189 next_action = self.act_target(last_state)
--> 190 next_value = self.cri_target(last_state, next_action).detach()
191 for t in range(horizon_len - 1, -1, -1):
192 returns[t] = next_value = rewards[t] + masks[t] * next_value

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\torch\nn\modules\module.py:1553, in Module._wrapped_call_impl(self, *args, **kwargs)
1551 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1552 else:
-> 1553 return self._call_impl(*args, **kwargs)

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\torch\nn\modules\module.py:1562, in Module._call_impl(self, *args, **kwargs)
1557 # If we don't have any hooks, we want to skip the rest of the logic in
1558 # this function, and just call forward.
1559 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1560 or _global_backward_pre_hooks or _global_backward_hooks
1561 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1562 return forward_call(*args, **kwargs)
1564 try:
1565 result = None

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\elegantrl\agents\net.py:415, in CriticEnsemble.forward(self, state, action)
414 def forward(self, state, action):
--> 415 values = self.get_q_values(state=state, action=action)
416 value = values.mean(dim=1, keepdim=True)
417 return value

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\elegantrl\agents\net.py:422, in CriticEnsemble.get_q_values(self, state, action)
420 state = self.state_norm(state)
421 sa_tmp = self.encoder_sa(torch.cat((state, action), dim=1))
--> 422 values = torch.concat([dec_q(sa_tmp) for dec_q in self.decoder_qs], dim=1)
423 values = self.value_re_norm(values)
424 return values

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\elegantrl\agents\net.py:422, in (.0)
420 state = self.state_norm(state)
421 sa_tmp = self.encoder_sa(torch.cat((state, action), dim=1))
--> 422 values = torch.concat([dec_q(sa_tmp) for dec_q in self.decoder_qs], dim=1)
423 values = self.value_re_norm(values)
424 return values

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\torch\nn\modules\module.py:1553, in Module._wrapped_call_impl(self, *args, **kwargs)
1551 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1552 else:
-> 1553 return self._call_impl(*args, **kwargs)

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\torch\nn\modules\module.py:1562, in Module._call_impl(self, *args, **kwargs)
1557 # If we don't have any hooks, we want to skip the rest of the logic in
1558 # this function, and just call forward.
1559 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1560 or _global_backward_pre_hooks or _global_backward_hooks
1561 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1562 return forward_call(*args, **kwargs)
1564 try:
1565 result = None

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\torch\nn\modules\container.py:219, in Sequential.forward(self, input)
217 def forward(self, input):
218 for module in self:
--> 219 input = module(input)
220 return input

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\torch\nn\modules\module.py:1553, in Module._wrapped_call_impl(self, *args, **kwargs)
1551 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1552 else:
-> 1553 return self._call_impl(*args, **kwargs)

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\torch\nn\modules\module.py:1562, in Module._call_impl(self, *args, **kwargs)
1557 # If we don't have any hooks, we want to skip the rest of the logic in
1558 # this function, and just call forward.
1559 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1560 or _global_backward_pre_hooks or _global_backward_hooks
1561 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1562 return forward_call(*args, **kwargs)
1564 try:
1565 result = None

File E:\langs\anaconda3\envs\deep-fin\lib\site-packages\torch\nn\modules\linear.py:117, in Linear.forward(self, input)
116 def forward(self, input: Tensor) -> Tensor:
--> 117 return F.linear(input, self.weight, self.bias)

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat1 in method wrapper_CUDA_addmm)

运行的是quickstart_Pendulum_v1.ipynb

[Yonv1943]

上面说到：运行的是quickstart_Pendulum_v1.ipynb

回复：我可以已经修改好了出错的quickstart_Pendulum_v1.ipynb代码

我还提供可以运行Pendulum_v1 的代码，如下：

• 简单版：

  ElegantRL/helloworld/helloworld_PPO_single_file.py

  Line 535 in 6f7096c

  def train_ppo_for_pendulum(gpu_id: int = 0):

• 正式版：https://github.com/AI4Finance-Foundation/ElegantRL/blob/6f7096c9f825d529b90cf8cba4fc75929af979ed/examples/demo_A2C_PPO.py#L779C9-L779C26