Segamation fault when using torch.profiler

VincentLeeMax: <h2>
<a name="bug" class="anchor" href="#bug"></a><img src="https://discuss.pytorch.org/images/emoji/apple/bug.png?v=9" title=":bug:" class="emoji" alt=":bug:"> Bug</h2>
<p>when using the torch.profiler feature, it seems that cupti encouter a Segmentation fault problem in my enivorment.</p>
<h2>
<a name="to-reproduce" class="anchor" href="#to-reproduce"></a>To Reproduce</h2>
<p>My code:</p>
<pre><code class="lang-auto">import math

import torch
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import torch.utils.data.distributed
import torchvision.models as models

train_dataset = \
    datasets.MNIST('./data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.Resize((224, 224)),
                       transforms.Lambda(lambda image: image.convert('RGB')),
                       transforms.ToTensor(),
                       transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
                   ]))

torch.cuda.set_device(0)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=1, num_workers=2)

# hyper parameter
epoch_ = 100
lr_ = 0.001
momentum_ = 0.9
milestones = [30, 80, 120]
log_interval = 10

model = models.alexnet(num_classes=10)
if torch.cuda.is_available():
    model.cuda()

optimizer = optim.SGD(model.parameters(), lr=lr_, momentum=momentum_)

scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1)

with torch.profiler.profile(
    activities=[
        torch.profiler.ProfilerActivity.CPU,
        torch.profiler.ProfilerActivity.CUDA],
    schedule=torch.profiler.schedule(
        wait=1,
        warmup=1,
        active=1),
    record_shapes=True,
    profile_memory=True,
    with_stack=True,
    on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')
    ) as p: 
    for epoch in range(0, epoch_):
        model.train()
        for step, (data, target) in enumerate(train_loader):
            if torch.cuda.is_available():
                data, target = data.cuda(), target.cuda()
            output = model(data)
            loss = F.cross_entropy(output, target)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            if step % log_interval == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, step * len(data), len(train_loader),
                    100.0 * step / len(train_loader), loss.item()))
            p.step()
        scheduler.step()
</code></pre>
<p>Problem：</p>
<pre><code class="lang-auto">root@# gdb python3
GNU gdb (Ubuntu 7.11.1-0ubuntu1~16.5) 7.11.1
Copyright (C) 2016 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later &lt;http://gnu.org/licenses/gpl.html&gt;
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
&lt;http://www.gnu.org/software/gdb/bugs/&gt;.
Find the GDB manual and other documentation resources online at:
&lt;http://www.gnu.org/software/gdb/documentation/&gt;.
For help, type "help".
Type "apropos word" to search for commands related to "word"...
Reading symbols from python3...(no debugging symbols found)...done.
(gdb) r hvd_pytorch_mnist-profile.py 
Starting program: /usr/bin/python3 hvd_pytorch_mnist-profile.py
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
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Train Epoch: 0 [0/60000 (0%)]   Loss: 2.309546
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Thread 1 "python3" received signal SIGSEGV, Segmentation fault.
0x00007fffeead723d in _nv002114cupti () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
(gdb) bt
#0  0x00007fffeead723d in _nv002114cupti () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#1  0x00007fffeebb2870 in _nv000969cupti () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#2  0x00007fffee8fb7ab in _nv000434cupti () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#3  0x00007ffef7624dc3 in ?? () from /usr/lib/x86_64-linux-gnu/libcuda.so.1
#4  0x00007fffee8fb4a8 in _nv029272cupti () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#5  0x00007fffee8fcea9 in _nv029260cupti () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#6  0x00007fffee8fd35f in _nv029287cupti () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#7  0x00007fffee8ffe11 in cuptiSubscribe () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#8  0x00007fffe562913b in libkineto_init () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#9  0x00007fffe3ac44f8 in torch::autograd::profiler::prepareProfiler(torch::autograd::profiler::ProfilerConfig const&amp;, std::set&lt;torch::autograd::profiler::ActivityType, std::less&lt;torch::autograd::profiler::ActivityType&gt;, std::allocator&lt;torch::autograd::profiler::ActivityType&gt; &gt; const&amp;) ()
   from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_cpu.so
#10 0x00007ffff34dec85 in void pybind11::cpp_function::initialize&lt;void (*&amp;)(torch::autograd::profiler::ProfilerConfig const&amp;, std::set&lt;torch::autograd::profiler::ActivityType, std::less&lt;torch::autograd::profiler::ActivityType&gt;, std::allocator&lt;torch::autograd::profiler::ActivityType&gt; &gt; const&amp;), void, torch::autograd::profiler::ProfilerConfig const&amp;, std::set&lt;torch::autograd::profiler::ActivityType, std::less&lt;torch::autograd::profiler::ActivityType&gt;, std::allocator&lt;torch::autograd::profiler::ActivityType&gt; &gt; const&amp;, pybind11::name, pybind11::scope, pybind11::sibling&gt;(void (*&amp;)(torch::autograd::profiler::ProfilerConfig const&amp;, std::set&lt;torch::autograd::profiler::ActivityType, std::less&lt;torch::autograd::profiler::ActivityType&gt;, std::allocator&lt;torch::autograd::profiler::ActivityType&gt; &gt; const&amp;), void (*)(torch::autograd::profiler::ProfilerConfig const&amp;, std::set&lt;torch::autograd::profiler::ActivityType, std::less&lt;torch::autograd::profiler::ActivityType&gt;, std::allocator&lt;torch::autograd::profiler::ActivityType&gt; &gt; const&amp;), pybind11::name const&amp;, pybind11::scope const&amp;, pybind11::sibling const&amp;)::{lambda(pybind11::detail::function_call&amp;)#3}::operator()(pybind11::detail::function_call&amp;) const () from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_python.so
#11 0x00007ffff317bd39 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) ()
   from /usr/local/lib/python3.7/dist-packages/torch/lib/libtorch_python.so
#12 0x00000000004e7a53 in _PyMethodDef_RawFastCallKeywords ()
#13 0x00000000004e77d0 in _PyCFunction_FastCallKeywords ()
#14 0x000000000055e548 in _PyEval_EvalFrameDefault ()
#15 0x00000000004e90aa in _PyFunction_FastCallKeywords ()
#16 0x0000000000559e31 in _PyEval_EvalFrameDefault ()
#17 0x00000000004e90aa in _PyFunction_FastCallKeywords ()
#18 0x0000000000559e31 in _PyEval_EvalFrameDefault ()
#19 0x00000000004e90aa in _PyFunction_FastCallKeywords ()
#20 0x0000000000559e31 in _PyEval_EvalFrameDefault ()
#21 0x0000000000558c80 in _PyEval_EvalCodeWithName ()
#22 0x0000000000558a03 in PyEval_EvalCode ()
#23 0x0000000000624542 in ?? ()
#24 0x00000000006248ca in PyRun_FileExFlags ()
#25 0x0000000000624767 in PyRun_SimpleFileExFlags ()
#26 0x00000000005fce7b in ?? ()
#27 0x00000000005fcb1a in _Py_UnixMain ()
#28 0x00007ffff6cbd840 in __libc_start_main (main=0x4e4260 &lt;main&gt;, argc=2, argv=0x7fffffff9168, init=&lt;optimized out&gt;, fini=&lt;optimized out&gt;, 
    rtld_fini=&lt;optimized out&gt;, stack_end=0x7fffffff9158) at ../csu/libc-start.c:291
#29 0x00000000005fca09 in _start ()
</code></pre>
<h2>
<a name="environment" class="anchor" href="#environment"></a>Environment</h2>
<p>environment collected:</p>
<pre><code class="lang-auto">PyTorch version: 1.8.1+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 16.04.6 LTS (x86_64)
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
Clang version: Could not collect
CMake version: version 3.5.1

Python version: 3.7 (64-bit runtime)
Is CUDA available: True
CUDA runtime version: 10.2.89
GPU models and configuration: GPU 0: Tesla V100-SXM2-16GB
Nvidia driver version: 440.64.00
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.19.1
[pip3] torch==1.8.1
[pip3] torchvision==0.8.2
[conda] Could not collect
</code></pre>

VincentLeeMax: <p><a class="mention" href="/u/ptrblck">@ptrblck</a> any idea of this problem?</p>