I am facing the same issue and has beem stuck for 1 day
here my code
class RNN(nn.Module):
    
    def __init__(self,input_size, output_size, hidden_size=64):

        super().__init__()

        self.input_size  = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        self.xh = nn.Linear(self.input_size, self.hidden_size, bias=False)


        self.hh = nn.Linear(self.hidden_size, self.hidden_size)
        self.hy = nn.Linear(self.hidden_size, self.output_size)
        
        self.h = torch.zeros(self.hidden_size, requires_grad=True)
        
        self.tanh = nn.Tanh()
        self.softmax = nn.Softmax(dim=1)
        self.sigmoid = nn.Sigmoid()

    def rnn_cell(self, x_t):  
        
        first_h = self.hh(self.h)
        
        second_x = self.xh(x_t)

        act = second_x + first_h
        
        self.h = self.tanh(act)

        updated_c = self.sigmoid(self.hy(self.h))

        return updated_c


    def forward(self, inp):
        return self.rnn_cell(inp)

here is training code
def train(train_x,  valid_x, lr, epochs, hidden_units, net='RNN'):
    
    for step, (data, label) in enumerate(train_x):
        inputs = np.array(data)
        break

    if net=='RNN':
        model = RNN(inputs.shape[1], 1, hidden_units)
    elif net == 'LSTM':
        h = torch.zeros(hidden_units).requires_grad_()
        c = torch.zeros(hidden_units).requires_grad_()
        model = LSTM(inputs.shape[1], 1, hidden_units)
    elif net == 'GRU':
        St_1 = torch.zeros(hidden_units).requires_grad_()
        model = GRUModel(inputs.shape[1], 1, hidden_units)
    model.to(device)
    
    
    train_loss, val_loss = [],[]
    train_accuracy, val_accuracy = [], []
    optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
    criterion = nn.BCELoss()

    
    
    for ep in range(epochs):
        running_loss, correct = 0, 0
        for i, (data, label) in enumerate(train_x):
            data, label = Variable(data), Variable(label)
            data, label = data.to(device), label.to(device)
            
            optimizer.zero_grad()
            
            if net == 'RNN':
                net_out= model(data)
            elif net == 'LSTM':
                net_out, h, c = model(data, h, c)
            elif net == 'GRU':
                net_out, St_1 = model(data, St_1)
                
            
            label = torch.reshape(label, (label.shape[0], 1))
            net_out = torch.reshape(net_out, (label.shape[0], 1))
            label = label.float()
            loss = criterion(net_out, label)
            loss.backward(retain_graph=True, inputs=list(model.parameters()))
            optimizer.step()

            running_loss += loss.item()
#             pred = torch.argmax(net_out, axis=1)  # get the index of the max log-probability
#             actual = torch.argmax(label, axis=1)
            out = (net_out>0.5).float()
            correct += out.eq(label).sum()



        print(running_loss)
        print("Epoch:", ep)
        print(correct.item())
        print("Training Accuracy:", 100. * correct.item() / len(train_x.dataset))
        print("Train Loss:", running_loss / len(train_x.dataset))
        train_loss.append(running_loss / len(train_x.dataset))
        train_accuracy.append(correct / len(train_x.dataset))


#         test_loss = 0
#         correct = 0
#         with torch.no_grad():
#             for batch_idx, (data, target) in enumerate(valid_x):
#                 data, target = Variable(data), Variable(target)
#                 data, target = data.to(device), target.to(device)
#     #                 data = data.view(-1, 784)
#                 if net == 'RNN':
#                     net_out, _ = model(data, h)
#                 elif net == 'LSTM':
#                     net_out, _, _ = model(data, h, c)
#                 elif net == 'GRU':
#                     net_out, _ = model(data, St_1)
#                 net_out = torch.reshape(net_out, (net_out.shape[0],))
#                 # sum up batch loss
#                 target = target.float()
#                 test_loss += criterion(net_out, target).item()
#     #                 pred = torch.argmax(net_out, axis=1)  # get the index of the max log-probability
#     #                 actual = torch.argmax(label, axis=1)
#                 out = (net_out>0.5).float()
#                 correct += out.eq(target).sum()
#             val_loss.append(test_loss / len(valid_x.dataset))
#             val_accuracy.append(correct / len(valid_x.dataset))

#         print("Validation Accuracy:" , 100. * correct.item() / len(valid_x.dataset))
#         print("Validation Loss:", test_loss / len(valid_x.dataset)) 
#         print("----------------------------------------------------------")
    
    return model, train_loss, train_accuracy, val_loss, val_accuracy
        
        
    
    


Here is the error list:
/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/torch/autograd/__init__.py:147: UserWarning: Error detected in MmBackward. Traceback of forward call that caused the error:
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/traitlets/config/application.py", line 845, in launch_instance
    app.start()
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/ipykernel/kernelapp.py", line 619, in start
    self.io_loop.start()
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/asyncio/base_events.py", line 570, in run_forever
    self._run_once()
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once
    handle._run()
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/asyncio/events.py", line 81, in _run
    self._context.run(self._callback, *self._args)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/tornado/ioloop.py", line 688, in <lambda>
    lambda f: self._run_callback(functools.partial(callback, future))
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/tornado/ioloop.py", line 741, in _run_callback
    ret = callback()
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/tornado/gen.py", line 814, in inner
    self.ctx_run(self.run)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/tornado/gen.py", line 775, in run
    yielded = self.gen.send(value)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 358, in process_one
    yield gen.maybe_future(dispatch(*args))
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/tornado/gen.py", line 234, in wrapper
    yielded = ctx_run(next, result)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 261, in dispatch_shell
    yield gen.maybe_future(handler(stream, idents, msg))
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/tornado/gen.py", line 234, in wrapper
    yielded = ctx_run(next, result)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 536, in execute_request
    self.do_execute(
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/tornado/gen.py", line 234, in wrapper
    yielded = ctx_run(next, result)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/ipykernel/ipkernel.py", line 302, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/ipykernel/zmqshell.py", line 539, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 2898, in run_cell
    result = self._run_cell(
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 2944, in _run_cell
    return runner(coro)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
    coro.send(None)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3169, in run_cell_async
    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3361, in run_ast_nodes
    if (await self.run_code(code, result,  async_=asy)):
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-61-f6fbdf7371e3>", line 2, in <module>
    net, train_loss, train_accuracy, val_loss, val_accuracy = train(train_loader,  valid_loader, lr=0.0001, epochs=10,  hidden_units=64, net='RNN')
  File "<ipython-input-60-3bb8b3924f63>", line 35, in train
    net_out= model(data)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "<ipython-input-59-cdbb099f3af3>", line 39, in forward
    return self.rnn_cell(inp)
  File "<ipython-input-59-cdbb099f3af3>", line 25, in rnn_cell
    first_h = self.hh(self.h)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 96, in forward
    return F.linear(input, self.weight, self.bias)
  File "/Users/arslan/anaconda3/envs/torch_env/lib/python3.8/site-packages/torch/nn/functional.py", line 1847, in linear
    return torch._C._nn.linear(input, weight, bias)
 (Triggered internally at  /Users/distiller/project/conda/conda-bld/pytorch_1623459044803/work/torch/csrc/autograd/python_anomaly_mode.cpp:104.)
  Variable._execution_engine.run_backward(
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-61-f6fbdf7371e3> in <module>
      1 torch.autograd.set_detect_anomaly(True)
----> 2 net, train_loss, train_accuracy, val_loss, val_accuracy = train(train_loader,  valid_loader, lr=0.0001, epochs=10,  hidden_units=64, net='RNN')

<ipython-input-60-3bb8b3924f63> in train(train_x, valid_x, lr, epochs, hidden_units, net)
     44             label = label.float()
     45             loss = criterion(net_out, label)
---> 46             loss.backward(retain_graph=True, inputs=list(model.parameters()))
     47             optimizer.step()
     48 

~/anaconda3/envs/torch_env/lib/python3.8/site-packages/torch/_tensor.py in backward(self, gradient, retain_graph, create_graph, inputs)
    253                 create_graph=create_graph,
    254                 inputs=inputs)
--> 255         torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
    256 
    257     def register_hook(self, hook):

~/anaconda3/envs/torch_env/lib/python3.8/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
    145         retain_graph = create_graph
    146 
--> 147     Variable._execution_engine.run_backward(
    148         tensors, grad_tensors_, retain_graph, create_graph, inputs,
    149         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [64, 64]], 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!

can you @ptrblck @albanD  please help me I’ll be very thankful to all of you