Thread deadlock in DataParallel

kingsaint: <p>I<br>
I am facing a thread deadlock issue when I use multiple GPUs with DataParallel(). The model is training on a medium-size dataset with 240K training samples. The model successfully trains for one epoch. In the second epoch, the training progresses smoothly till it reaches 50%. After that, it is simply stuck with no progress. When I kill the process using ctrl+c or kill -s SIGKILL, it becomes a zombie process!</p>
<p>Here is what I get when I do ctrl+c</p>
<pre><code class="lang-auto">File "run_E2E_EL_RE.py", line 962, in &lt;module&gt;
    main()
 File "run_E2E_EL_RE.py", line 913, in main
    global_step, tr_loss = train(args, model, tokenizer)
 File "run_E2E_EL_RE.py", line 249, in train
    el_loss, re_loss, _, _ = model.forward(**ned_inputs)
  File "/dresden/users/rb897/anaconda3/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 152, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/dresden/users/rb897/anaconda3/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 162, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/dresden/users/rb897/anaconda3/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py", line 77, in parallel_apply
    thread.join()
  File "/dresden/users/rb897/anaconda3/lib/python3.7/threading.py", line 1044, in join
    self._wait_for_tstate_lock()
  File "/dresden/users/rb897/anaconda3/lib/python3.7/threading.py", line 1060, in _wait_for_tstate_lock
   elif lock.acquire(block, timeout):
KeyboardInterrupt
</code></pre>
<p>Code Snippet:</p>
<pre><code class="lang-auto">model.zero_grad()
train_iterator = trange(
        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
    )
set_seed(args)  # Added here for reproducibility
for epoch_num in train_iterator:
    epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
    for step, batch in enumerate(epoch_iterator):
        model.train()
        batch = tuple(t.to(args.device) for t in batch)
        inputs_1 = {...}
        inputs_2 = {...}
        loss_1, last_hidden_states = model.forward(**inputs_1)
        inputs_2["last_hidden_states"] = last_hidden_states
        loss_2, loss_3 = model.forward(**inputs_2)
        if args.n_gpu &gt; 1: # mean() to average on multi-gpu parallel training
            loss_1 = loss_1.mean()
            loss_2 = loss_2.mean()
            loss_3 = loss_3.mean()
        loss = loss_1 + loss_2 + loss_3
        loss.backward()
        optimizer.step()
        scheduler.step() 
        model.zero_grad()
</code></pre>
<p><code>per gpu batch size = 4</code></p>
<p>OS: Ubuntu 18.04.5<br>
CPU: Intel Xeon® - 64 cores<br>
CUDA Version: 11.2<br>
GPU: Quadro RTX 6000<br>
GPU memory: 24G<br>
PyTorch: 1.4.0</p>

mrshenli: <p>Do you consistently see it stuck in the second epoch? Does your job exclusively occupy all the GPUs?</p>

ptrblck: <p>Besides what <a class="mention" href="/u/mrshenli">@mrshenli</a> asked: are you also seeing this issue using the latest PyTorch stable or nightly release?</p>

kingsaint: <p>Yes, I performed three runs and in each case it got stuck at the same point. I am using 4 out of 8 available GPUs and no other jobs are running on those GPUs.</p>

kingsaint: <p>I reran the code using the latest PyTorch. I am seeing the following error message. This time it happens with the first batch of the first epoch.</p>
<pre><code class="lang-auto">Traceback (most recent call last):
  File "run_E2E_EL_RE.py", line 962, in &lt;module&gt;
    main()
  File "run_E2E_EL_RE.py", line 913, in main
    global_step, tr_loss = train(args, model, tokenizer)
  File "run_E2E_EL_RE.py", line 246, in train
    ner_loss, last_hidden_states = model.forward(**ner_inputs)
  File "/home/rajarshi_kingsaint_bhowmik/anaconda3/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 167, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/home/rajarshi_kingsaint_bhowmik/anaconda3/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 177, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/home/rajarshi_kingsaint_bhowmik/anaconda3/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "/home/rajarshi_kingsaint_bhowmik/anaconda3/lib/python3.8/site-packages/torch/_utils.py", line 429, in reraise
    raise self.exc_type(msg)
StopIteration: Caught StopIteration in replica 0 on device 0.
Original Traceback (most recent call last):
  File "/home/rajarshi_kingsaint_bhowmik/anaconda3/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "/home/rajarshi_kingsaint_bhowmik/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/rajarshi_kingsaint_bhowmik/E2E-EL-RE/modeling_E2E_EL_RE.py", line 65, in forward
    mention_outputs = self.bert_mention.bert(
  File "/home/rajarshi_kingsaint_bhowmik/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/rajarshi_kingsaint_bhowmik/E2E-EL-RE/modeling_bert.py", line 758, in forward
    extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
StopIteration
</code></pre>

winchest: <p>HI！ I had the same problem <img src="https://discuss.pytorch.org/images/emoji/apple/sob.png?v=12" title=":sob:" class="emoji" alt=":sob:" loading="lazy" width="20" height="20"> Did u fix it?</p>