RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
In a pytorch model training process I get this error:


  RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.LongTensor [128, 1]] is at version 8; expected version 7 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!


with stack trace 

   sys:1: RuntimeWarning: Traceback of forward call that caused the error:
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/ipykernel_launcher.py", line 16, in &lt;module&gt;
    app.launch_new_instance()
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 499, in start
    self.io_loop.start()
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/tornado/ioloop.py", line 1073, in start
    handler_func(fd_obj, events)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/tornado/stack_context.py", line 300, in null_wrapper
    return fn(*args, **kwargs)
  File "/home/arash/.local/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 456, in _handle_events
    self._handle_recv()
  File "/home/arash/.local/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 486, in _handle_recv
    self._run_callback(callback, msg)
  File "/home/arash/.local/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 438, in _run_callback
    callback(*args, **kwargs)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/tornado/stack_context.py", line 300, in null_wrapper
    return fn(*args, **kwargs)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
    handler(stream, idents, msg)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 399, in execute_request
    user_expressions, allow_stdin)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 208, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 537, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2714, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2818, in run_ast_nodes
    if self.run_code(code, result):
  File "/home/arash/anaconda2/envs/mzh27/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2878, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "&lt;ipython-input-71-a5b255596e11&gt;", line 33, in &lt;module&gt;
    sampled_captions, sampled_log_probs=predict_captions(out_enc,hid_enc,enc_pp,'sample')
  File "&lt;ipython-input-70-a6ea511f0678&gt;", line 18, in predict_captions
    out_dec, hid_dec, word_logits = dec.forward(r, last_enc, img_features)
  File "&lt;ipython-input-21-0601dad4805f&gt;", line 21, in forward
    emb = self.embedding(input)
  File "/home/arash/.local/lib/python2.7/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/arash/.local/lib/python2.7/site-packages/torch/nn/modules/sparse.py", line 117, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "/home/arash/.local/lib/python2.7/site-packages/torch/nn/functional.py", line 1506, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-71-a5b255596e11&gt; in &lt;module&gt;()
     54                  torch.stack(sampled_log_probs).reshape(-1)).mean()
     55         torch.autograd.set_detect_anomaly(True)
---&gt; 56         loss.backward()
     57         clip_grad_value_(enc_optim.param_groups[0]['params'], 5.0)
     58         clip_grad_value_(dec_optim.param_groups[0]['params'], 5.0)

/home/arash/.local/lib/python2.7/site-packages/torch/tensor.pyc in backward(self, gradient, retain_graph, create_graph)
    105                 products. Defaults to ``False``.
    106         """
--&gt; 107         torch.autograd.backward(self, gradient, retain_graph, create_graph)
    108 
    109     def register_hook(self, hook):

/home/arash/.local/lib/python2.7/site-packages/torch/autograd/__init__.pyc in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
     91     Variable._execution_engine.run_backward(
     92         tensors, grad_tensors, retain_graph, create_graph,
---&gt; 93         allow_unreachable=True)  # allow_unreachable flag
     94 
     95


But I'm unable to locate in-place operation that causes the error.
Here is my code:

for epoch in xrange(0, 13):
    print ("Starting New Epoch: %d" % epoch)
    rewards = {
        'sample_cider': [],
        'sample_context': [],
        'sample_reward': [],  # actual reward, controlled by beta
        'greedy_cider': [],
        'greedy_context': [],
        'greedy_reward': []
    }

    order = np.arange(enc_padded_text.shape[0])
    np.random.shuffle(order)
    enc_padded_text = enc_padded_text[order]
    input_text=[input_text[i] for i in order]
    dec_text_tensor.data = dec_text_tensor.data[order]

    for i in xrange(num_batches):
        s = i * BATCH_SIZE
        e = (i+1) * BATCH_SIZE

        _, enc_pp, dec_pp, enc_lengths = make_packpadded_s2s(s, e, enc_padded_text, dec_text_tensor)

        enc.zero_grad()
        dec.zero_grad()

        hid = enc.initHidden(BATCH_SIZE)

        out_enc, hid_enc = enc.forward(enc_pp, hid, enc_lengths)

        hid_enc = torch.cat([hid_enc[0,:, :], hid_enc[1,:,:]], dim=1).unsqueeze(0)
        gt_dict = dict(zip(_,input_text[s:e]))
        sampled_captions, sampled_log_probs=predict_captions(out_enc,hid_enc,enc_pp,'sample')
        sampled_dict = dict(zip(_, sampled_captions))
        with torch.no_grad():
            greedy_captions = predict_captions(out_enc,hid_enc,enc_pp, 'greedy')
            greedy_dict = dict(zip(_, greedy_captions))


        sample_cider_score, sample_context_score, sample_reward = get_scores(
            dec_pp[:,1:], sampled_captions, gt_dict, sampled_dict)
        greedy_cider_score, greedy_context_score, greedy_reward = get_scores(
            dec_pp[:,1:], greedy_captions, gt_dict, greedy_dict)

        # self-critical: score from sampling - score from test time
        advantages = torch.Tensor((sample_cider_score - greedy_cider_score).reshape(-1))

        # normalize advantages
        advantages = ((advantages - advantages.mean()) /
                      advantages.std() + 1e-9)
        if cuda:
            advantages = advantages.cuda()
        loss = -(advantages *
                 torch.stack(sampled_log_probs).reshape(-1)).mean()
        torch.autograd.set_detect_anomaly(True)
        loss.backward()
        clip_grad_value_(enc_optim.param_groups[0]['params'], 5.0)
        clip_grad_value_(dec_optim.param_groups[0]['params'], 5.0)
        enc_optim.step()
        dec_optim.step()

        rewards['sample_cider'].extend(sample_cider_score)
        rewards['sample_context'].extend(sample_context_score)
        rewards['sample_reward'].extend(sample_reward)
        rewards['greedy_cider'].extend(greedy_cider_score)
        rewards['greedy_context'].extend(greedy_context_score)
        rewards['greedy_reward'].extend(greedy_reward)

        if (b + 1) % 100 == 0:
            print('\t[Batch {} running metrics] - R train {:.2f} - R train (greedy): {:.2f}'.format(
                b + 1, np.mean(rewards['sample_reward']), np.mean(rewards['greedy_reward'])))


predict_captions function:

def predict_captions(img_features,hid_enc,enc_pp, mode='sample', constrain=False,L=22):
    dec_tensor = torch.ones((enc_pp.shape[0]), L+1, dtype=torch.long) * Toks.SOS
    global cuda
    if cuda:
        dec_tensor = dec_tensor.cuda(device=device)
    last_enc = hid_enc
    if mode == 'beam_search':
        return self.beam_search(img_features, state, lstm_states)

    predictions = []
    log_probs = []

    # this should store the index of the first occurrence of &lt;EOS&gt;
    # for each sample in the batch
    EOS_tracker = np.full(img_features.shape[0], None)
    for i in range(L):
        r=dec_tensor[:,i].unsqueeze(1)
        out_dec, hid_dec, word_logits = dec.forward(r, last_enc, img_features)
        out_dec[:, 0, Toks.UNK] = -np.inf # ignore unknowns
        l=out_dec[:,0]
        chosen = torch.argmax(l,dim=1)
        dec_tensor[:, i+1] = chosen
        last_enc = hid_dec
        # decoding stuff
        probs = F.softmax(word_logits, dim=2)
        probs=probs.reshape(128,20004)

        if constrain:
            # enforce constraint that the same word can't be predicted
            # twice in a row. zero-out the probability of previous words
            for p, prev_idx in zip(probs, state['prev_word_indeces']):
                p[prev_idx] = 0

        if mode == 'sample':
            idxs = torch.multinomial(probs, 1)
        else:
            idxs = torch.argmax(probs, dim=1)
        if cuda:
            idxs = idxs.cpu()
        words = [dec_idx_to_word[index] for index in idxs] 
        predictions.append(np.array(words).reshape(-1))

        # get the respective log probability of chosen word
        # for each sample in the batch
        log_probs.append([lp[i] for (lp, i)
                         in zip(torch.log(probs), idxs)])

        # inefficient but this should be fast enough anyway... ? :(
        eos_idxs = (np.array(words)==dec_idx_to_word[2]).nonzero()[0]
        for idx in eos_idxs:
            if EOS_tracker[idx] is None:
                EOS_tracker[idx] = i + 1

        # finish loop if they're all done
        if len(EOS_tracker[EOS_tracker == None])==0:
            break


    # build the actual sentences, up until the first occurrence of &lt;EOS&gt;
    captions = [
        [' '.join(w[:eos_idx])] for (w, eos_idx) in
        zip(np.array(predictions).T, EOS_tracker)
    ]
    print captions
    # do this only when training. not needed otherwise.
    if mode == 'sample':
        log_probs = [lp[:eos_idx].sum() for (lp, eos_idx) in zip(np.array(log_probs).T, EOS_tracker)]
        return captions, log_probs

    return captions


models:

class Encoder_s2s(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(Encoder_s2s, self).__init__()
        assert hidden_size % 2 == 0

        self.hidden_size = hidden_size
        self.input_size = input_size

        self.hidden_init_tensor = torch.zeros(2, 1, self.hidden_size/2, requires_grad=True)
        nn.init.normal_(self.hidden_init_tensor, mean=0, std=0.05)
        self.hidden_init = torch.nn.Parameter(self.hidden_init_tensor, requires_grad=True)

        self.embedding = nn.Embedding(input_size, hidden_size)
        self.emb_drop = nn.Dropout(0.2)
        self.gru = nn.GRU(hidden_size, hidden_size/2, batch_first=True, bidirectional=True)
        self.gru_out_drop = nn.Dropout(0.2)
        self.gru_hid_drop = nn.Dropout(0.3)

    def forward(self, input, hidden, lengths):
        emb = self.emb_drop(self.embedding(input))
        #emb = embedded_dropout(self.embedding, input, dropout=0.2 if self.training else 0)
        pp = torch.nn.utils.rnn.pack_padded_sequence(emb, lengths, batch_first=True)
        out, hidden = self.gru(pp, hidden)
        out = torch.nn.utils.rnn.pad_packed_sequence(out, batch_first=True)[0]
        out = self.gru_out_drop(out)
        hidden = self.gru_hid_drop(hidden)
        return out, hidden

    def initHidden(self, bs):
        return self.hidden_init.expand(2, bs, self.hidden_size/2).contiguous()

class DecoderAttn(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, out_bias):
        super(DecoderAttn, self).__init__()
        self.hidden_size = hidden_size
        self.input_size = input_size

        self.embedding = nn.Embedding(input_size, hidden_size)
        self.emb_drop = nn.Dropout(0.2)
        self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
        self.gru_drop = nn.Dropout(0.2)
        self.mlp = nn.Linear(hidden_size*2, output_size)
        if out_bias is not None:
            out_bias_tensor = torch.tensor(out_bias, requires_grad=False)
            self.mlp.bias.data[:] = out_bias_tensor
        self.logsoftmax = nn.LogSoftmax(dim=2)

        self.att_mlp = nn.Linear(hidden_size, hidden_size, bias=False)
        self.attn_softmax = nn.Softmax(dim=2)

    def forward(self, input, hidden, encoder_outs):
        emb = self.embedding(input)
        emb=self.emb_drop(emb)
        out, hidden = self.gru(emb, hidden)

        out_proj = self.att_mlp(out)
        enc_out_perm = encoder_outs.permute(0, 2, 1)
        e_exp = torch.bmm(out_proj, enc_out_perm)
        attn = self.attn_softmax(e_exp)

        ctx = torch.bmm(attn, encoder_outs)

        full_ctx = torch.cat([self.gru_drop(out), ctx], dim=2)

        out = self.mlp(full_ctx)
        out1 = self.logsoftmax(out)
        return out1, hidden, out


A tensor matching this description torch.cuda.LongTensor [128, 1], should narrow down your search. 

A quick google search revealed that, LongTensors are most commonly  returned by min , max, sort. so the lines 

l=out_dec[:,0]
chosen = torch.argmax(l,dim=1)
dec_tensor[:, i+1] = chosen


Most probably the line dec_tensor[:, i+1] = chosen seems problematic.
