Hello,
I am building a DDPG model with an RNN layer, but I get the following issue.

[W …\torch\csrc\autograd\python_anomaly_mode.cpp:104] Warning: Error detected in AddmmBackward. Traceback of forward call that caused the error:
File “train.py”, line 154, in 
value_loss, policy_loss = agent.update_params(batch)
File “DDPG - RNN\ddpg.py”, line 113, in update_params
state_action_batch = self.critic(state_batch, action_batch)
File “venv\lib\site-packages\torch\nn\modules\module.py”, line 727, in _call_impl
result = self.forward(*input, **kwargs)
File “DDPG - RNN\utils\nets.py”, line 127, in forward
self.hx = self.rnn(x, self.hx)
File “venv\lib\site-packages\torch\nn\modules\module.py”, line 727, in _call_impl
result = self.forward(*input, **kwargs)
File “venv\lib\site-packages\torch\nn\modules\rnn.py”, line 885, in forward
self.bias_ih, self.bias_hh,
(function print_stack)
Traceback (most recent call last):
File “train.py”, line 154, in 
value_loss, policy_loss = agent.update_params(batch)
File “DDPG - RNN\ddpg.py”, line 122, in update_params
policy_loss.backward()
File “venv\lib\site-packages\torch\tensor.py”, line 221, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "venv\lib\site-packages\torch\autograd_init.py", line 132, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [300, 300]], which is output 0 of TBackward, is at version 2; expected version 1 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!

import gc
import logging
import os
import torch

import torch.nn.functional as F
from torch.optim import Adam

from utils.nets import Actor, Critic

logger = logging.getLogger('ddpg')
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler())

torch.autograd.set_detect_anomaly(True)

# if gpu is to be used
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def soft_update(target, source, tau):
    for target_param, param in zip(target.parameters(), source.parameters()):
        target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau)


def hard_update(target, source):
    for target_param, param in zip(target.parameters(), source.parameters()):
        target_param.data.copy_(param.data)


class DDPG(object):

    def __init__(self, gamma, tau, hidden_size, num_inputs, action_space, checkpoint_dir=None):
        self.gamma = gamma
        self.tau = tau
        self.action_space = action_space

        # Define the actor
        self.actor = Actor(hidden_size, num_inputs, self.action_space).to(device)
        self.actor_target = Actor(hidden_size, num_inputs, self.action_space).to(device)

        # Define the critic
        self.critic = Critic(hidden_size, num_inputs, self.action_space).to(device)
        self.critic_target = Critic(hidden_size, num_inputs, self.action_space).to(device)

        # Define the optimizers for both networks
        self.actor_optimizer = Adam(self.actor.parameters(),
                                    lr=1e-4)  # optimizer for the actor network
        self.critic_optimizer = Adam(self.critic.parameters(),
                                     lr=1e-3,
                                     weight_decay=1e-2
                                     )  # optimizer for the critic network

        # Make sure both targets are with the same weight
        hard_update(self.actor_target, self.actor)
        hard_update(self.critic_target, self.critic)

        # Set the directory to save the models
        if checkpoint_dir is None:
            self.checkpoint_dir = "./saved_models/"
        else:
            self.checkpoint_dir = checkpoint_dir
        os.makedirs(self.checkpoint_dir, exist_ok=True)
        logger.info('Saving all checkpoints to {}'.format(self.checkpoint_dir))

    def calc_action(self, state, action_noise=None):
        x = state.to(device)

        # Get the continous action value to perform in the env
        self.actor.eval()  # Sets the actor in evaluation mode
        mu = self.actor(x)
        self.actor.train()  # Sets the actor in training mode
        mu = mu.data

        # During training we add noise for exploration
        if action_noise is not None:
            noise = torch.Tensor(action_noise.noise()).to(device)
            mu += noise

        # Clip the output according to the action space of the env
        mu = mu.clamp(self.action_space.low[0], self.action_space.high[0])

        return mu

    def update_params(self, batch):
        """
        Updates the parameters/networks of the agent according to the given batch.
        This means we ...
            1. Compute the targets
            2. Update the Q-function/critic by one step of gradient descent
            3. Update the policy/actor by one step of gradient ascent
            4. Update the target networks through a soft update
        Arguments:
            batch:  Batch to perform the training of the parameters
        """
        # Get tensors from the batch
        state_batch = torch.cat(batch.state).to(device)
        action_batch = torch.cat(batch.action).to(device)
        reward_batch = torch.cat(batch.reward).to(device)
        done_batch = torch.cat(batch.done).to(device)
        next_state_batch = torch.cat(batch.next_state).to(device)

        # Get the actions and the state values to compute the targets
        next_action_batch = self.actor_target(next_state_batch)
        next_state_action_values = self.critic_target(next_state_batch, next_action_batch.detach())

        # Compute the target
        reward_batch = reward_batch.unsqueeze(1)
        done_batch = done_batch.unsqueeze(1)
        expected_values = reward_batch + (1.0 - done_batch) * self.gamma * next_state_action_values

        # Update the critic network
        self.critic_optimizer.zero_grad()
        state_action_batch = self.critic(state_batch, action_batch)
        value_loss = F.mse_loss(state_action_batch, expected_values.detach())
        value_loss.backward(retain_graph=True)
        self.critic_optimizer.step()

        # Update the actor network
        self.actor_optimizer.zero_grad()
        policy_loss = -self.critic(state_batch, self.actor(state_batch))
        policy_loss = policy_loss.mean()
        policy_loss.backward()
        self.actor_optimizer.step()

        # Update the target networks
        soft_update(self.actor_target, self.actor, self.tau)
        soft_update(self.critic_target, self.critic, self.tau)

        return value_loss.item(), policy_loss.item()

    def save_checkpoint(self, last_timestep, replay_buffer):
        """
        Saving the networks and all parameters to a file in 'checkpoint_dir'
        Arguments:
            last_timestep:  Last timestep in training before saving
            replay_buffer:  Current replay buffer
        """
        checkpoint_name = self.checkpoint_dir + '/ep_{}.pth.tar'.format(last_timestep)
        logger.info('Saving checkpoint...')
        checkpoint = {
            'last_timestep': last_timestep,
            'actor': self.actor.state_dict(),
            'critic': self.critic.state_dict(),
            'actor_target': self.actor_target.state_dict(),
            'critic_target': self.critic_target.state_dict(),
            'actor_optimizer': self.actor_optimizer.state_dict(),
            'critic_optimizer': self.critic_optimizer.state_dict(),
            'replay_buffer': replay_buffer,
        }
        logger.info('Saving model at timestep {}...'.format(last_timestep))
        torch.save(checkpoint, checkpoint_name)
        gc.collect()
        logger.info('Saved model at timestep {} to {}'.format(last_timestep, self.checkpoint_dir))

    def get_path_of_latest_file(self):
        """
        Returns the latest created file in 'checkpoint_dir'
        """
        files = [file for file in os.listdir(self.checkpoint_dir) if (file.endswith(".pt") or file.endswith(".tar"))]
        filepaths = [os.path.join(self.checkpoint_dir, file) for file in files]
        last_file = max(filepaths, key=os.path.getctime)
        return os.path.abspath(last_file)

    def load_checkpoint(self, checkpoint_path=None):
        """
        Saving the networks and all parameters from a given path. If the given path is None
        then the latest saved file in 'checkpoint_dir' will be used.
        Arguments:
            checkpoint_path:    File to load the model from
        """

        if checkpoint_path is None:
            checkpoint_path = self.get_path_of_latest_file()

        if os.path.isfile(checkpoint_path):
            logger.info("Loading checkpoint...({})".format(checkpoint_path))
            key = 'cuda' if torch.cuda.is_available() else 'cpu'

            checkpoint = torch.load(checkpoint_path, map_location=key)
            start_timestep = checkpoint['last_timestep'] + 1
            self.actor.load_state_dict(checkpoint['actor'])
            self.critic.load_state_dict(checkpoint['critic'])
            self.actor_target.load_state_dict(checkpoint['actor_target'])
            self.critic_target.load_state_dict(checkpoint['critic_target'])
            self.actor_optimizer.load_state_dict(checkpoint['actor_optimizer'])
            self.critic_optimizer.load_state_dict(checkpoint['critic_optimizer'])
            replay_buffer = checkpoint['replay_buffer']

            gc.collect()
            logger.info('Loaded model at timestep {} from {}'.format(start_timestep, checkpoint_path))
            return start_timestep, replay_buffer
        else:
            raise OSError('Checkpoint not found')

    def set_eval(self):
        """
        Sets the model in evaluation mode
        """
        self.actor.eval()
        self.critic.eval()
        self.actor_target.eval()
        self.critic_target.eval()

    def set_train(self):
        """
        Sets the model in training mode
        """
        self.actor.train()
        self.critic.train()
        self.actor_target.train()
        self.critic_target.train()

    def get_network(self, name):
        if name == 'Actor':
            return self.actor
        elif name == 'Critic':
            return self.critic
        else:
            raise NameError('name \'{}\' is not defined as a network'.format(name))

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

# if gpu is to be used
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
WEIGHTS_FINAL_INIT = 3e-3
BIAS_FINAL_INIT = 3e-4


def fan_in_uniform_init(tensor, fan_in=None):
    """Utility function for initializing actor and critic"""
    if fan_in is None:
        fan_in = tensor.size(-1)

    w = 1. / np.sqrt(fan_in)
    nn.init.uniform_(tensor, -w, w)


class Actor(nn.Module):
    def __init__(self, hidden_size, num_inputs, action_space):
        super(Actor, self).__init__()
        self.action_space = action_space
        num_outputs = action_space.shape[0]
        self.hidden_size = hidden_size

        self.hx = torch.randn(1, hidden_size[1]).to(device)

        # Layer 1
        self.linear1 = nn.Linear(num_inputs, hidden_size[0])
        self.ln1 = nn.LayerNorm(hidden_size[0])

        # Layer 2
        self.linear2 = nn.Linear(hidden_size[0], hidden_size[1])
        self.ln2 = nn.LayerNorm(hidden_size[1])

        # Recurrent Layer (replaces relu since it has it build in)
        self.rnn = nn.RNNCell(hidden_size[1], hidden_size[1])

        # Output Layer
        self.mu = nn.Linear(hidden_size[1], num_outputs)

        # Weight Init
        fan_in_uniform_init(self.linear1.weight)
        fan_in_uniform_init(self.linear1.bias)

        fan_in_uniform_init(self.linear2.weight)
        fan_in_uniform_init(self.linear2.bias)

        nn.init.uniform_(self.mu.weight, -WEIGHTS_FINAL_INIT, WEIGHTS_FINAL_INIT)
        nn.init.uniform_(self.mu.bias, -BIAS_FINAL_INIT, BIAS_FINAL_INIT)

    def forward(self, inputs):
        x = inputs

        if self.hx.size(0) != x.size(0):
            self.hx = torch.randn(x.size(0), self.hidden_size[1]).to(device)

        # Layer 1
        x = self.linear1(x)
        x = self.ln1(x)
        x = F.relu(x)

        # Layer 2
        x = self.linear2(x)
        x = self.ln2(x)
        self.hx = self.rnn(x, self.hx)

        x = self.hx
        x = self.mu(x)
        # Output
        mu = torch.tanh(x)
        return mu


class Critic(nn.Module):
    def __init__(self, hidden_size, num_inputs, action_space):
        super(Critic, self).__init__()
        self.action_space = action_space
        num_outputs = action_space.shape[0]
        self.hidden_size = hidden_size

        self.hx = torch.randn(1, hidden_size[1]).to(device)

        # Layer 1
        self.linear1 = nn.Linear(num_inputs, hidden_size[0])
        self.ln1 = nn.LayerNorm(hidden_size[0])

        # Layer 2
        # In the second layer the actions will be inserted also
        self.linear2 = nn.Linear(hidden_size[0] + num_outputs, hidden_size[1])
        self.ln2 = nn.LayerNorm(hidden_size[1])

        self.rnn = nn.RNNCell(hidden_size[1], hidden_size[1])


        # Output layer (single value)
        self.V = nn.Linear(hidden_size[1], 1)

        # Weight Init
        fan_in_uniform_init(self.linear1.weight)
        fan_in_uniform_init(self.linear1.bias)

        fan_in_uniform_init(self.linear2.weight)
        fan_in_uniform_init(self.linear2.bias)

        nn.init.uniform_(self.V.weight, -WEIGHTS_FINAL_INIT, WEIGHTS_FINAL_INIT)
        nn.init.uniform_(self.V.bias, -BIAS_FINAL_INIT, BIAS_FINAL_INIT)

    def forward(self, inputs, actions):
        x = inputs

        if self.hx.size(0) != x.size(0):
            self.hx = torch.randn(x.size(0), self.hidden_size[1]).to(device)

        # Layer 1
        x = self.linear1(x)
        x = self.ln1(x)
        x = F.relu(x) 

        # Layer 2
        x = torch.cat((x, actions), 1)  # Insert the actions
        x = self.linear2(x)
        x = self.ln2(x)

        self.hx = self.rnn(x, self.hx)
        x = self.hx

        # Output
        V = self.V(x)
        return V

I have looked at similar posts, but I can’t see any obvious inplace operations.
Thank you in advance for your help!