Issue 1:
🐛 Bug

Hello, I’m running a GAN model with some second-order regularization. The training went well at the beginning and the loss decreased. However, after a random period of time, ranging from 3000-8000 updates, the segfault occurs and the training was terminated automatically.

To Reproduce

It is hard for me to simplify the code and reproduce it. However, it happens every time when I include the second order gradient:

if pl_reg:
pl_dlatents.requires_grad_(True)
pl_fake = self.G(None, dlatents=pl_dlatents) # forward the generator
pl_noise = pl_fake.new(pl_fake.shape).normal_() / 1024
pl_grads = autograd.grad(
outputs=(pl_fake * pl_noise).sum(),
inputs=pl_dlatents,
grad_outputs=None,
create_graph=True,
retain_graph=True,
only_inputs=True,
)[0]
pl_lengths = pl_grads.pow(2).sum(1).mul(1/self.G.get_num_layers()).sqrt()
return pl_lengths

Another major difference for my generator design is that it has convolution layers with both the input and weights calculated from other up-stream networks.

class ModulatedConv2d(nn.Module):
def __init__(self, in_channels, out_channels, hidden_channels, kernel_size=3, stride=1, padding=1, dilation=1,
noisy=True, randomize_noise=True, up=False, demodulize=True, gain=1, lrmul=1):
super(ModulatedConv2d, self).__init__()
assert kernel_size >= 1 and kernel_size % 2 == 1
self.noisy = noisy
self.stride = stride
self.padding = padding
self.dilation = dilation
self.randomize_noise = randomize_noise
self.up = up
self.demodulize = demodulize
self.lrmul = lrmul

# Get weight.
fan_in = in_channels * kernel_size * kernel_size
self.runtime_coef = gain / math.sqrt(fan_in) * math.sqrt(lrmul)
self.weight = Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size) / math.sqrt(lrmul), requires_grad=True) # [OIkk]

# Get bias.
self.bias = Parameter(torch.zeros(1, out_channels, 1, 1), requires_grad=True)

# Modulate layer.
self.mod = ScaleLinear(hidden_channels, in_channels, bias=True) # [BI] Transform incoming W to style.

# Noise scale.
if noisy:
self.noise_scale = Parameter(torch.zeros(1), requires_grad=True)

def forward(self, x, y, noise=None):
w = self.weight * self.runtime_coef
ww = w[np.newaxis] # [BOIkk] Introduce minibatch dimension.

# Modulate.
s = self.mod(y) + 1 # [BI] Add bias (initially 1).
ww = ww * s[:, np.newaxis, :, np.newaxis, np.newaxis] # [BOIkk] Scale input feature maps.

# Demodulate.
if self.demodulize:
d = torch.rsqrt(ww.pow(2).sum(dim=(2,3,4), keepdim=True) + 1e-8) # [BOIkk] Scaling factor.
ww = ww * d # [BOIkk] Scale output feature maps.

# Reshape/scale input.
B = y.size(0)
x = x.view(1, -1, *x.shape[2:]) # Fused [BIhw] => reshape minibatch to convolution groups [1(BI)hw].
w = ww.view(-1, *ww.shape[2:]) # [(BO)Ikk]

# Convolution with optional up/downsampling.
if self.up: x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
x = F.conv2d(x, w, None, self.stride, self.padding, self.dilation, groups=B) # [1(BO)hw]

# Reshape/scale output.
x = x.view(B, -1, *x.shape[2:]) # [BOhw]

# Apply noise and bias
if self.noisy:
if self.randomize_noise: noise = x.new_empty(B, 1, *x.shape[2:]).normal_()
x += noise * self.noise_scale
x += self.bias * self.lrmul
return x

The above code happens inside a DataParallel module and results are gathered afterward, followed by a loss to minimize pl_lengths.

Expected behavior

After some random number of updates, around 3000-8000, the problem occurs and training was terminated.

In PyTorch 1.3 it shows:

*** Error in `python3’: double free or corruption (fasttop): 0x00007f9e280c3fe0 ***

======= Backtrace: =========

/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fa1793127e5]

/lib/x86_64-linux-gnu/libc.so.6(+0x8037a)[0x7fa17931b37a]

/lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7fa17931f53c]

/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d820e)[0x7fa13bb9420e]

/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d82b9)[0x7fa13bb942b9]

/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d8435)[0x7fa13bb94435]

/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(_ZN5torch8autograd6Engine17evaluate_functionERNS0_8NodeTaskE+0x1210)[0x7fa13bb8bb50]

/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(_ZN5torch8autograd6Engine11thread_mainEPNS0_9GraphTaskE+0x1c4)[0x7fa13bb8da04]

/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_python.so(_ZN5torch8autograd6python12PythonEngine11thread_initEi+0x2a)[0x7fa16a537eda]

/opt/conda/lib/python3.6/site-packages/torch/…/…/…/libstdc++.so.6(+0xc819d)[0x7fa169ffb19d]

/lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba)[0x7fa17966c6ba]

/lib/x86_64-linux-gnu/libc.so.6(clone+0x6d)[0x7fa1793a241d]

and followed by a long memory map:

======= Memory map: ========

200000000-200200000 rw-s 00000000 00:06 493 /dev/nvidiactl

200200000-200400000 ---p 00000000 00:00 0

200400000-200600000 rw-s 00000000 00:06 493 /dev/nvidiactl

200600000-202600000 rw-s 00000000 00:06 493 /dev/nvidiactl

202600000-205600000 rw-s 00000000 00:06 493 /dev/nvidiactl

205600000-206200000 ---p 00000000 00:00 0

206200000-206400000 rw-s 00000000 00:06 493 /dev/nvidiactl

206400000-206600000 rw-s 00000000 00:06 493 /dev/nvidiactl

206600000-206800000 rw-s 206600000 00:06 492 /dev/nvidia-uvm

206800000-206a00000 ---p 00000000 00:00 0

206a00000-206c00000 rw-s 00000000 00:06 493 /dev/nvidiactl

206c00000-206e00000 ---p 00000000 00:00 0

206e00000-207000000 rw-s 00000000 00:06 493 /dev/nvidiactl

207000000-207200000 ---p 00000000 00:00 0

207200000-207400000 rw-s 00000000 00:06 493 /dev/nvidiactl

207400000-209400000 rw-s 00000000 00:06 493 /dev/nvidiactl

209400000-20c400000 rw-s 00000000 00:06 493 /dev/nvidiactl

20c400000-20d000000 ---p 00000000 00:00 0

20d000000-20d200000 rw-s 00000000 00:06 493 /dev/nvidiactl

20d200000-20d400000 rw-s 00000000 00:06 493 /dev/nvidiactl

20d400000-20d600000 rw-s 20d400000 00:06 492 /dev/nvidia-uvm

20d600000-20d800000 ---p 00000000 00:00 0

20d800000-20da00000 rw-s 00000000 00:06 493 /dev/nvidiactl

20da00000-20dc00000 ---p 00000000 00:00 0

20dc00000-20de00000 rw-s 00000000 00:06 493 /dev/nvidiactl

20de00000-20e000000 ---p 00000000 00:00 0

20e000000-20e200000 rw-s 00000000 00:06 493 /dev/nvidiactl

20e200000-210200000 rw-s 00000000 00:06 493 /dev/nvidiactl

210200000-213200000 rw-s 00000000 00:06 493 /dev/nvidiactl

213200000-213e00000 ---p 00000000 00:00 0

213e00000-214000000 rw-s 00000000 00:06 493 /dev/nvidiactl

214000000-214200000 rw-s 00000000 00:06 493 /dev/nvidiactl

214200000-214400000 rw-s 214200000 00:06 492 /dev/nvidia-uvm

214400000-214600000 ---p 00000000 00:00 0

214600000-214800000 rw-s 00000000 00:06 493 /dev/nvidiactl

214800000-214a00000 ---p 00000000 00:00 0

214a00000-214c00000 rw-s 00000000 00:06 493 /dev/nvidiactl

214c00000-214e00000 ---p 00000000 00:00 0

214e00000-215000000 rw-s 00000000 00:06 493 /dev/nvidiactl

215000000-217000000 rw-s 00000000 00:06 493 /dev/nvidiactl

217000000-21a000000 rw-s 00000000 00:06 493 /dev/nvidiactl

21a000000-21ac00000 ---p 00000000 00:00 0

21ac00000-21ae00000 rw-s 00000000 00:06 493 /dev/nvidiactl

21ae00000-21b000000 rw-s 00000000 00:06 493 /dev/nvidiactl

21b000000-21b200000 rw-s 21b000000 00:06 492 /dev/nvidia-uvm

21b200000-21b400000 ---p 00000000 00:00 0

21b400000-21b600000 rw-s 00000000 00:06 493 /dev/nvidiactl

21b600000-600200000 ---p 00000000 00:00 0

10000000000-10810000000 ---p 00000000 00:00 0

5578812bd000-557881314000 r--p 00000000 08:31 78220892 /opt/conda/bin/python3.6

557881314000-5578814db000 r-xp 00057000 08:31 78220892 /opt/conda/bin/python3.6

5578814db000-557881578000 r--p 0021e000 08:31 78220892 /opt/conda/bin/python3.6

557881579000-55788157c000 r--p 002bb000 08:31 78220892 /opt/conda/bin/python3.6

55788157c000-5578815df000 rw-p 002be000 08:31 78220892 /opt/conda/bin/python3.6

5578815df000-557881610000 rw-p 00000000 00:00 0

557881cc4000-5579eb27e000 rw-p 00000000 00:00 0 [heap]

5579eb27e000-5579eb67e000 rw-p 00000000 00:00 0 [heap]

5579eb67e000-5579ed2c1000 rw-p 00000000 00:00 0 [heap]

5579ed2c1000-5579ee436000 rw-p 00000000 00:00 0 [heap]

5579ee436000-5579ee491000 rw-p 00000000 00:00 0 [heap]

5579ee491000-5579ef3d1000 rw-p 00000000 00:00 0 [heap]

5579ef3d1000-5579effc9000 rw-p 00000000 00:00 0 [heap]

7f98c0000000-7f9bc0a00000 ---p 00000000 00:00 0

7f9bc0a00000-7f9bc0c00000 rw-s 00000000 00:05 1587841 /dev/zero (deleted)

7f9bc0c00000-7f9c00000000 ---p 00000000 00:00 0

7f9c00000000-7f9c03d1e000 rw-p 00000000 00:00 0

7f9c03d1e000-7f9c04000000 ---p 00000000 00:00 0

7f9c04000000-7f9c06fa0000 rw-p 00000000 00:00 0

7f9c06fa0000-7f9c08000000 ---p 00000000 00:00 0

7f9c08000000-7f9cd0000000 ---p 00000000 00:00 0

7f9cd0000000-7f9cd2e22000 rw-p 00000000 00:00 0

7f9cd2e22000-7f9cd4000000 ---p 00000000 00:00 0

7f9cd6000000-7f9dd0a00000 ---p 00000000 00:00 0

7f9dd0a00000-7f9dd0c00000 rw-s 00000000 00:05 1604789 /dev/zero (deleted)

7f9dd0c00000-7f9dd8000000 ---p 00000000 00:00 0

7f9dd8000000-7f9ddaeae000 rw-p 00000000 00:00 0

7f9ddaeae000-7f9ddc000000 ---p 00000000 00:00 0

7f9ddc000000-7f9ddfd11000 rw-p 00000000 00:00 0

7f9ddfd11000-7f9de0000000 ---p 00000000 00:00 0

7f9de0000000-7f9e18000000 ---p 00000000 00:00 0

7f9e18000000-7f9e1814b000 rw-p 00000000 00:00 0

7f9e1814b000-7f9e1c000000 ---p 00000000 00:00 0

7f9e1c000000-7f9e1fd09000 rw-p 00000000 00:00 0

7f9e1fd09000-7f9e20000000 ---p 00000000 00:00 0

7f9e20000000-7f9e20150000 rw-p 00000000 00:00 0

7f9e20150000-7f9e24000000 ---p 00000000 00:00 0

7f9e28000000-7f9e282e5000 rw-p 00000000 00:00 0

7f9e282e5000-7f9e2c000000 ---p 00000000 00:00 0

7f9e2c000000-7f9e2c14b000 rw-p 00000000 00:00 0

7f9e2c14b000-7f9e30000000 ---p 00000000 00:00 0

7f9e30000000-7f9e30022000 rw-p 00000000 00:00 0

7f9e30022000-7f9e34000000 ---p 00000000 00:00 0

7f9e36000000-7f9ef8a00000 ---p 00000000 00:00 0

7f9ef8a00000-7f9ef8c00000 rw-s 00000000 00:05 1582741 /dev/zero (deleted)

7f9ef8c00000-7f9f00000000 ---p 00000000 00:00 0

7f9f00000000-7f9f02e3a000 rw-p 00000000 00:00 0

7f9f02e3a000-7f9f04000000 ---p 00000000 00:00 0

7f9f05000000-7f9f05040000 rw-p 00000000 00:00 0

7f9f06000000-7f9f08000000 ---p 00000000 00:00 0

7f9f08000000-7f9f0bffc000 rw-p 00000000 00:00 0

7f9f0bffc000-7f9f0c000000 ---p 00000000 00:00 0

7f9f0c000000-7f9f0fd27000 rw-p 00000000 00:00 0

7f9f0fd27000-7f9f10000000 ---p 00000000 00:00 0

7f9f10000000-7f9f13ffe000 rw-p 00000000 00:00 0

7f9f13ffe000-7f9f14000000 ---p 00000000 00:00 0

7f9f14000000-7f9f18000000 rw-p 00000000 00:00 0

7f9f18000000-7f9f1bffd000 rw-p 00000000 00:00 0

7f9f1bffd000-7f9f1c000000 ---p 00000000 00:00 0

7f9f1d166000-7f9f1d1a6000 rw-p 00000000 00:00 0

7f9f1d266000-7f9f1d2a6000 rw-p 00000000 00:00 0

7f9f1d2e5000-7f9f1d2e6000 ---p 00000000 00:00 0

7f9f1d2e6000-7f9f1dae6000 rw-p 00000000 00:00 0

7f9f1dae6000-7f9f1daf9000 r-xp 00000000 08:31 85822789 /opt/conda/lib/python3.6/site-packages/scipy/stats/mvn.cpython-36m-x86_64-linux-gnu.so

7f9f1daf9000-7f9f1dcf8000 ---p 00013000 08:31 85822789 /opt/conda/lib/python3.6/site-packages/scipy/stats/mvn.cpython-36m-x86_64-linux-gnu.so

7f9f1dcf8000-7f9f1dcfa000 rw-p 00012000 08:31 85822789 /opt/conda/lib/python3.6/site-packages/scipy/stats/mvn.cpython-36m-x86_64-linux-gnu.so

7f9f1dcfa000-7f9f1ddf1000 rw-p 00000000 00:00 0

7f9f1ddf1000-7f9f1ddf3000 rw-p 00015000 08:31 85822789 /opt/conda/lib/python3.6/site-packages/scipy/stats/mvn.cpython-36m-x86_64-linux-gnu.so

7f9f1ddf3000-7f9f1ddfd000 r-xp 00000000 08:31 85822791 /opt/conda/lib/python3.6/site-packages/scipy/stats/statlib.cpython-36m-x86_64-linux-gnu.so

7f9f1ddfd000-7f9f1dffc000 ---p 0000a000 08:31 85822791 /opt/conda/lib/python3.6/site-packages/scipy/stats/statlib.cpython-36m-x86_64-linux-gnu.so

7f9f1dffc000-7f9f1dffe000 rw-p 00009000 08:31 85822791 /opt/conda/lib/python3.6/site-packages/scipy/stats/statlib.cpython-36m-x86_64-linux-gnu.so

7f9f1dffe000-7f9f1e000000 rw-p 0000c000 08:31 85822791 /opt/conda/lib/python3.6/site-packages/scipy/stats/statlib.cpython-36m-x86_64-linux-gnu.so

7f9f1e000000-7f9f47200000 ---p 00000000 00:00 0

7f9f47200000-7f9f47400000 rw-s 00000000 00:05 1582740 /dev/zero (deleted)

7f9f47400000-7f9f47e00000 ---p 00000000 00:00 0

7f9f47e00000-7f9f48000000 rw-s 00000000 00:05 1590697 /dev/zero (deleted)

7f9f48000000-7f9f48021000 rw-p 00000000 00:00 0

7f9f48021000-7f9f4c000000 ---p 00000000 00:00 0

7f9f4c021000-7f9f4c094000 r-xp 00000000 08:31 85822779 /opt/conda/lib/python3.6/site-packages/scipy/stats/_stats.cpython-36m-x86_64-linux-gnu.so

7f9f4c094000-7f9f4c294000 ---p 00073000 08:31 85822779 /opt/conda/lib/python3.6/site-packages/scipy/stats/_stats.cpython-36m-x86_64-linux-gnu.so

7f9f4c294000-7f9f4c29a000 rw-p 00073000 08:31 85822779 /opt/conda/lib/python3.6/site-packages/scipy/stats/_stats.cpython-36m-x86_64-linux-gnu.so

7f9f4c29a000-7f9f4c29c000 rw-p 00000000 00:00 0

7f9f4c29c000-7f9f4c2f0000 r-xp 00000000 08:31 85429655 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/interpnd.cpython-36m-x86_64-linux-gnu.so

7f9f4c2f0000-7f9f4c4f0000 ---p 00054000 08:31 85429655 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/interpnd.cpython-36m-x86_64-linux-gnu.so

7f9f4c4f0000-7f9f4c4f5000 rw-p 00054000 08:31 85429655 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/interpnd.cpython-36m-x86_64-linux-gnu.so

7f9f4c4f5000-7f9f4c4f6000 rw-p 00000000 00:00 0

7f9f4c4f6000-7f9f4c546000 r-xp 00000000 08:31 85429651 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_ppoly.cpython-36m-x86_64-linux-gnu.so

7f9f4c546000-7f9f4c746000 ---p 00050000 08:31 85429651 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_ppoly.cpython-36m-x86_64-linux-gnu.so

7f9f4c746000-7f9f4c74c000 rw-p 00050000 08:31 85429651 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_ppoly.cpython-36m-x86_64-linux-gnu.so

7f9f4c74c000-7f9f4c74d000 rw-p 00000000 00:00 0

7f9f4c74d000-7f9f4c78a000 r-xp 00000000 08:31 85429645 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_bspl.cpython-36m-x86_64-linux-gnu.so

7f9f4c78a000-7f9f4c989000 ---p 0003d000 08:31 85429645 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_bspl.cpython-36m-x86_64-linux-gnu.so

7f9f4c989000-7f9f4c98f000 rw-p 0003c000 08:31 85429645 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_bspl.cpython-36m-x86_64-linux-gnu.so

7f9f4c98f000-7f9f4c990000 rw-p 00000000 00:00 0

7f9f4c990000-7f9f4c993000 rw-p 00043000 08:31 85429645 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_bspl.cpython-36m-x86_64-linux-gnu.so

7f9f4c993000-7f9f50000000 rw-p 00000000 00:00 0

7f9f50000000-7f9f50021000 rw-p 00000000 00:00 0

7f9f50021000-7f9f54000000 ---p 00000000 00:00 0

7f9f54000000-7f9f5d400000 ---p 00000000 00:00 0

7f9f5d400000-7f9f5d600000 rw-s 00000000 00:05 1567619 /dev/zero (deleted)

7f9f5d600000-7f9f5d800000 ---p 00000000 00:00 0

7f9f5d800000-7f9f5da00000 rw-s 00000000 00:06 493 /dev/nvidiactl

7f9f5da00000-7f9f5dc00000 ---p 00000000 00:00 0

7f9f5dc00000-7f9f5de00000 rw-s 00000000 00:05 1567621 /dev/zero (deleted)

7f9f5de00000-7f9f5e0d6000 rw-s 00000000 00:06 493 /dev/nvidiactl

7f9f5e0d6000-7f9f63200000 ---p 00000000 00:00 0

7f9f63200000-7f9f63400000 rw-s 00000000 00:05 1598092 /dev/zero (deleted)

7f9f63400000-7f9f63c00000 ---p 00000000 00:00 0

7f9f63c00000-7f9f63e00000 rw-s 00000000 00:05 1567618 /dev/zero (deleted)

7f9f63e00000-7f9f64000000 rw-s 00000000 00:06 493 /dev/nvidiactl

7f9f64000000-7f9f64021000 rw-p 00000000 00:00 0

7f9f64021000-7f9f68000000 ---p 00000000 00:00 0

7f9f680b9000-7f9f68117000 r-xp 00000000 08:31 85429652 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/dfitpack.cpython-36m-x86_64-linux-gnu.so

7f9f68117000-7f9f68317000 ---p 0005e000 08:31 85429652 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/dfitpack.cpython-36m-x86_64-linux-gnu.so

7f9f68317000-7f9f6831e000 rw-p 0005e000 08:31 85429652 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/dfitpack.cpython-36m-x86_64-linux-gnu.so

7f9f6831e000-7f9f68320000 rw-p 00066000 08:31 85429652 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/dfitpack.cpython-36m-x86_64-linux-gnu.so

7f9f68320000-7f9f68354000 r-xp 00000000 08:31 85429648 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_fitpack.cpython-36m-x86_64-linux-gnu.so

7f9f68354000-7f9f68554000 ---p 00034000 08:31 85429648 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_fitpack.cpython-36m-x86_64-linux-gnu.so

7f9f68554000-7f9f68555000 rw-p 00034000 08:31 85429648 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_fitpack.cpython-36m-x86_64-linux-gnu.so

7f9f68555000-7f9f68557000 rw-p 00036000 08:31 85429648 /opt/conda/lib/python3.6/site-packages/scipy/interpolate/_fitpack.cpython-36m-x86_64-linux-gnu.so

7f9f68557000-7f9f68570000 r-xp 00000000 08:31 85429603 /opt/conda/lib/python3.6/site-packages/scipy/integrate/lsoda.cpython-36m-x86_64-linux-gnu.so

7f9f68570000-7f9f6876f000 ---p 00019000 08:31 85429603 /opt/conda/lib/python3.6/site-packages/scipy/integrate/lsoda.cpython-36m-x86_64-linux-gnu.so

7f9f6876f000-7f9f68774000 rw-p 00018000 08:31 85429603 /opt/conda/lib/python3.6/site-packages/scipy/integrate/lsoda.cpython-36m-x86_64-linux-gnu.so

7f9f68774000-7f9f6878f000 r-xp 00000000 08:31 85429576 /opt/conda/lib/python3.6/site-packages/scipy/integrate/_dop.cpython-36m-x86_64-linux-gnu.so

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7fa1786ad000-7fa1786b5000 r--p 00000000 08:31 78745046 /opt/conda/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so

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7fa1787dc000-7fa1787e1000 r--p 00000000 08:31 78745050 /opt/conda/lib/python3.6/lib-dynload/_datetime.cpython-36m-x86_64-linux-gnu.so

7fa1787e1000-7fa1787f1000 r-xp 00005000 08:31 78745050 /opt/conda/lib/python3.6/lib-dynload/_datetime.cpython-36m-x86_64-linux-gnu.so

7fa1787f1000-7fa1787f6000 r--p 00015000 08:31 78745050 /opt/conda/lib/python3.6/lib-dynload/_datetime.cpython-36m-x86_64-linux-gnu.so

7fa1787f6000-7fa1787f7000 ---p 0001a000 08:31 78745050 /opt/conda/lib/python3.6/lib-dynload/_datetime.cpython-36m-x86_64-linux-gnu.so

7fa1787f7000-7fa1787f8000 r--p 0001a000 08:31 78745050 /opt/conda/lib/python3.6/lib-dynload/_datetime.cpython-36m-x86_64-linux-gnu.so

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7fa1787fa000-7fa17883a000 rw-p 00000000 00:00 0

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7fa17883e000-7fa178840000 r-xp 00002000 08:31 79793274 /opt/conda/lib/python3.6/site-packages/numpy/linalg/lapack_lite.cpython-36m-x86_64-linux-gnu.so

7fa178840000-7fa178841000 r--p 00004000 08:31 79793274 /opt/conda/lib/python3.6/site-packages/numpy/linalg/lapack_lite.cpython-36m-x86_64-linux-gnu.so

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7fa178843000-7fa178983000 rw-p 00000000 00:00 0

7fa178983000-7fa178a8b000 r-xp 00000000 08:31 59728410 /lib/x86_64-linux-gnu/libm-2.23.so

7fa178a8b000-7fa178c8a000 ---p 00108000 08:31 59728410 /lib/x86_64-linux-gnu/libm-2.23.so

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7fa178c8c000-7fa178c93000 r-xp 00000000 08:31 59728452 /lib/x86_64-linux-gnu/librt-2.23.so

7fa178c93000-7fa178e92000 ---p 00007000 08:31 59728452 /lib/x86_64-linux-gnu/librt-2.23.so

7fa178e92000-7fa178e93000 r--p 00006000 08:31 59728452 /lib/x86_64-linux-gnu/librt-2.23.so

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7fa178e94000-7fa178e96000 r-xp 00000000 08:31 59728472 /lib/x86_64-linux-gnu/libutil-2.23.so

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7fa179097000-7fa17909a000 r-xp 00000000 08:31 59728391 /lib/x86_64-linux-gnu/libdl-2.23.so

7fa17909a000-7fa179299000 ---p 00003000 08:31 59728391 /lib/x86_64-linux-gnu/libdl-2.23.so

7fa179299000-7fa17929a000 r--p 00002000 08:31 59728391 /lib/x86_64-linux-gnu/libdl-2.23.so

7fa17929a000-7fa17929b000 rw-p 00003000 08:31 59728391 /lib/x86_64-linux-gnu/libdl-2.23.so

7fa17929b000-7fa17945b000 r-xp 00000000 08:31 59728378 /lib/x86_64-linux-gnu/libc-2.23.so

7fa17945b000-7fa17965b000 ---p 001c0000 08:31 59728378 /lib/x86_64-linux-gnu/libc-2.23.so

7fa17965b000-7fa17965f000 r--p 001c0000 08:31 59728378 /lib/x86_64-linux-gnu/libc-2.23.so

7fa17965f000-7fa179661000 rw-p 001c4000 08:31 59728378 /lib/x86_64-linux-gnu/libc-2.23.so

7fa179661000-7fa179665000 rw-p 00000000 00:00 0

7fa179665000-7fa17967d000 r-xp 00000000 08:31 59728446 /lib/x86_64-linux-gnu/libpthread-2.23.so

7fa17967d000-7fa17987c000 ---p 00018000 08:31 59728446 /lib/x86_64-linux-gnu/libpthread-2.23.so

7fa17987c000-7fa17987d000 r--p 00017000 08:31 59728446 /lib/x86_64-linux-gnu/libpthread-2.23.so

7fa17987d000-7fa17987e000 rw-p 00018000 08:31 59728446 /lib/x86_64-linux-gnu/libpthread-2.23.so

7fa17987e000-7fa179882000 rw-p 00000000 00:00 0

7fa179882000-7fa1798a8000 r-xp 00000000 08:31 59728358 /lib/x86_64-linux-gnu/ld-2.23.so

7fa1798a8000-7fa1798a9000 rw-s 00000000 00:06 493 /dev/nvidiactl

7fa1798a9000-7fa1798aa000 rw-s 00000000 00:06 493 /dev/nvidiactl

7fa1798aa000-7fa1798ab000 rw-s 00000000 00:06 493 /dev/nvidiactl

7fa1798ab000-7fa1798ac000 r--p 00000000 08:31 79531588 /opt/conda/lib/python3.6/site-packages/mkl/_mklinit.cpython-36m-x86_64-linux-gnu.so

7fa1798ac000-7fa1798ad000 r-xp 00001000 08:31 79531588 /opt/conda/lib/python3.6/site-packages/mkl/_mklinit.cpython-36m-x86_64-linux-gnu.so

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7fa1798ae000-7fa1798af000 r--p 00002000 08:31 79531588 /opt/conda/lib/python3.6/site-packages/mkl/_mklinit.cpython-36m-x86_64-linux-gnu.so

7fa1798af000-7fa1798b0000 rw-p 00003000 08:31 79531588 /opt/conda/lib/python3.6/site-packages/mkl/_mklinit.cpython-36m-x86_64-linux-gnu.so

7fa1798b0000-7fa1798b1000 r--p 00000000 08:31 78745054 /opt/conda/lib/python3.6/lib-dynload/_heapq.cpython-36m-x86_64-linux-gnu.so

7fa1798b1000-7fa1798b3000 r-xp 00001000 08:31 78745054 /opt/conda/lib/python3.6/lib-dynload/_heapq.cpython-36m-x86_64-linux-gnu.so

7fa1798b3000-7fa1798b4000 r--p 00003000 08:31 78745054 /opt/conda/lib/python3.6/lib-dynload/_heapq.cpython-36m-x86_64-linux-gnu.so

7fa1798b4000-7fa1798b5000 r--p 00003000 08:31 78745054 /opt/conda/lib/python3.6/lib-dynload/_heapq.cpython-36m-x86_64-linux-gnu.so

7fa1798b5000-7fa1798b7000 rw-p 00004000 08:31 78745054 /opt/conda/lib/python3.6/lib-dynload/_heapq.cpython-36m-x86_64-linux-gnu.so

7fa1798b7000-7fa1798f7000 rw-p 00000000 00:00 0

7fa1798f7000-7fa1798f8000 rw-s 00000000 00:06 493 /dev/nvidiactl

7fa1798f8000-7fa1798fa000 r--p 00000000 08:31 78745063 /opt/conda/lib/python3.6/lib-dynload/_posixsubprocess.cpython-36m-x86_64-linux-gnu.so

7fa1798fa000-7fa1798fc000 r-xp 00002000 08:31 78745063 /opt/conda/lib/python3.6/lib-dynload/_posixsubprocess.cpython-36m-x86_64-linux-gnu.so

7fa1798fc000-7fa1798fd000 r--p 00004000 08:31 78745063 /opt/conda/lib/python3.6/lib-dynload/_posixsubprocess.cpython-36m-x86_64-linux-gnu.so

7fa1798fd000-7fa1798fe000 r--p 00004000 08:31 78745063 /opt/conda/lib/python3.6/lib-dynload/_posixsubprocess.cpython-36m-x86_64-linux-gnu.so

7fa1798fe000-7fa1798ff000 rw-p 00005000 08:31 78745063 /opt/conda/lib/python3.6/lib-dynload/_posixsubprocess.cpython-36m-x86_64-linux-gnu.so

7fa1798ff000-7fa179902000 r--p 00000000 08:31 78745084 /opt/conda/lib/python3.6/lib-dynload/math.cpython-36m-x86_64-linux-gnu.so

7fa179902000-7fa179908000 r-xp 00003000 08:31 78745084 /opt/conda/lib/python3.6/lib-dynload/math.cpython-36m-x86_64-linux-gnu.so

7fa179908000-7fa17990a000 r--p 00009000 08:31 78745084 /opt/conda/lib/python3.6/lib-dynload/math.cpython-36m-x86_64-linux-gnu.so

7fa17990a000-7fa17990b000 r--p 0000a000 08:31 78745084 /opt/conda/lib/python3.6/lib-dynload/math.cpython-36m-x86_64-linux-gnu.so

7fa17990b000-7fa17990d000 rw-p 0000b000 08:31 78745084 /opt/conda/lib/python3.6/lib-dynload/math.cpython-36m-x86_64-linux-gnu.so

7fa17990d000-7fa179910000 r--p 00000000 08:31 78745072 /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so

7fa179910000-7fa179916000 r-xp 00003000 08:31 78745072 /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so

7fa179916000-7fa179918000 r--p 00009000 08:31 78745072 /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so

7fa179918000-7fa179919000 ---p 0000b000 08:31 78745072 /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so

7fa179919000-7fa17991a000 r--p 0000b000 08:31 78745072 /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so

7fa17991a000-7fa17991c000 rw-p 0000c000 08:31 78745072 /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so

7fa17991c000-7fa179aa1000 rw-p 00000000 00:00 0

7fa179aa1000-7fa179aa2000 rwxp 00000000 00:00 0

7fa179aa2000-7fa179aa3000 r--p 00000000 08:31 78745061 /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so

7fa179aa3000-7fa179aa4000 r-xp 00001000 08:31 78745061 /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so

7fa179aa4000-7fa179aa5000 r--p 00002000 08:31 78745061 /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so

7fa179aa5000-7fa179aa6000 r--p 00002000 08:31 78745061 /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so

7fa179aa6000-7fa179aa7000 rw-p 00003000 08:31 78745061 /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so

7fa179aa7000-7fa179aa8000 r--p 00025000 08:31 59728358 /lib/x86_64-linux-gnu/ld-2.23.so

7fa179aa8000-7fa179aa9000 rw-p 00026000 08:31 59728358 /lib/x86_64-linux-gnu/ld-2.23.so

7fa179aa9000-7fa179aaa000 rw-p 00000000 00:00 0

7fff4fa0b000-7fff4fa2d000 rw-p 00000000 00:00 0 [stack]

7fff4fb0e000-7fff4fb11000 r--p 00000000 00:00 0 [vvar]

7fff4fb11000-7fff4fb13000 r-xp 00000000 00:00 0 [vdso]

ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0 [vsyscall]

In PyTorch 1.4 it shows:

free(): invalid pointer

without other information.

Environment

I'm using the official PyTorch Docker image to run my training. I tried both 1.3-cuda10.1-cudnn7-devel and 1.4-cuda10.1-cudnn7-devel (note that PyTorch version older than 1.3 will not successfully run the second-order gradient, in which case the grad return pl_lengths always have require_grad=False for some reason.)

I’m using 4 Titan x (pascal) for data-parallel training.

Additional context

Issue 2:
🐛 Bug

Hello, I’m running a GAN model with some second-order regularization. The training went well at the beginning and the loss decreased. However, after a random period of time, ranging from 3000-8000 updates, the segfault occurs and the training was terminated automatically.

To Reproduce

run the following script and wait, the error will finally occur. In my case it took 2 days to occur (total number fo iteration = 443298).

import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.models as models
import torch.nn as nn
import torch.autograd as autograd
import torch.nn.functional as F

class data_gpu_prefetcher():
def __init__(self, loader, stop=False):
self.original_loader = loader
self.loader = iter(loader)
self.stream = torch.cuda.Stream()
self.stop = stop
self._count = 0
self.preload()

def preload(self):
self.next_input = next(self.loader, None)
if self.next_input is None and not self.stop:
self.loader = iter(self.original_loader)
self.next_input = next(self.loader)
with torch.cuda.stream(self.stream):
if isinstance(self.next_input, list):
self.next_input = [item.cuda(non_blocking=True) for item in self.next_input]
elif self.next_input is not None:
self.next_input = self.next_input.cuda(non_blocking=True)

def next(self):
self._count += 1
torch.cuda.current_stream().wait_stream(self.stream)
input = self.next_input
self.preload()
return input

def reset_count(self):
self._count = 0

def get_count(self):
return self._count

class RandomDataset(Dataset):

def __init__(self, size, length):
self.len = length
self.data = torch.randn(length, 3, size, size)
self.label = torch.randint(0, 10, (length,))

def __getitem__(self, index):
return self.data[index], self.label[index]

def __len__(self):
return self.len

class resnet_cls(nn.Module):
def __init__(self):
super(resnet_cls, self).__init__()
self.model = models.resnet50()
self.cls = nn.Linear(1000, 10)
self._count = 0

def forward(self, im, label, r1_reg=False):
im.requires_grad_(r1_reg)
score = self.model(im)
loss = F.nll_loss(F.log_softmax(self.cls(score), dim=1), target=label, reduction='none')
if r1_reg:
gradients = autograd.grad(
outputs=score.sum(),
inputs=im,
grad_outputs=None,
create_graph=True,
retain_graph=True,
only_inputs=True,
)[0]
gradients = gradients.view(gradients.size(0), -1)
gradient_norms = gradients.pow(2).sum(1)
loss += gradient_norms * 1
return loss

rand_loader = DataLoader(dataset=RandomDataset(224, 1000),
batch_size=32, shuffle=True)
train_loader_iterator = data_gpu_prefetcher(rand_loader, stop=False)
model = resnet_cls()
model.cuda()
model_para = nn.DataParallel(model)
opt = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0., 0.99))
count = 0
while True:
r1_reg = count % 2 == 0
print(r1_reg)
count += 1
imgs, labels = train_loader_iterator.next()
loss = model_para(imgs, labels, r1_reg).mean()
opt.zero_grad()
loss.backward()
opt.step()
print(loss.item(), flush=True)

Expected behavior

After some random number of updates, in my case 443298 iterations, the problem occurs and training was terminated.

In PyTorch 1.3 it shows:

Segmentation fault (core dumped)

It prints "True" for the value of "r1_reg" for this iteration, but the loss for the forward pass didn't print out. No other information is shown.

Environment

I'm using the official PyTorch Docker image to run my training. I used the official image 1.3-cuda10.1-cudnn7-devel.

I’m using 4 Titan x (pascal) for data-parallel training.

Additional context