<h2 dir="auto">🐛 Bug</h2>
<p dir="auto">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.</p>
<h2 dir="auto">To Reproduce</h2>
<p dir="auto">It is hard for me to simplify the code and reproduce it. However, it happens every time when I include the second order gradient:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="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"><pre class="notranslate"><code class="notranslate">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
</code></pre></div>
<p dir="auto">Another major difference for my generator design is that it has convolution layers with <strong>both</strong> the input and weights calculated from other up-stream networks.</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="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 &gt;= 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] =&gt; 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"><pre class="notranslate"><code class="notranslate">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 &gt;= 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] =&gt; 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
</code></pre></div>
<p dir="auto">The above code happens inside a DataParallel module and results are gathered afterward, followed by a loss to minimize pl_lengths.</p>

<h2 dir="auto">Expected behavior</h2>
<p dir="auto">After some random number of updates, around 3000-8000, the problem occurs and training was terminated.</p>
<p dir="auto">In PyTorch 1.3 it shows:</p>
<blockquote>
<p dir="auto">*** Error in `python3’: double free or corruption (fasttop): 0x00007f9e280c3fe0 ***<br>
======= Backtrace: =========<br>
/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fa1793127e5]<br>
/lib/x86_64-linux-gnu/libc.so.6(+0x8037a)[0x7fa17931b37a]<br>
/lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7fa17931f53c]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d820e)[0x7fa13bb9420e]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d82b9)[0x7fa13bb942b9]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d8435)[0x7fa13bb94435]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(_ZN5torch8autograd6Engine17evaluate_functionERNS0_8NodeTaskE+0x1210)[0x7fa13bb8bb50]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(_ZN5torch8autograd6Engine11thread_mainEPNS0_9GraphTaskE+0x1c4)[0x7fa13bb8da04]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_python.so(_ZN5torch8autograd6python12PythonEngine11thread_initEi+0x2a)[0x7fa16a537eda]<br>
/opt/conda/lib/python3.6/site-packages/torch/…/…/…/libstdc++.so.6(+0xc819d)[0x7fa169ffb19d]<br>
/lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba)[0x7fa17966c6ba]<br>
/lib/x86_64-linux-gnu/libc.so.6(clone+0x6d)[0x7fa1793a241d]</p>
</blockquote>
<p dir="auto">and followed by a long memory map:</p>
<blockquote>
<p dir="auto">======= Memory map: ========<br>
200000000-200200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
200200000-200400000 ---p 00000000 00:00 0<br>
200400000-200600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
200600000-202600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
202600000-205600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
205600000-206200000 ---p 00000000 00:00 0<br>
206200000-206400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
206400000-206600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
206600000-206800000 rw-s 206600000 00:06 492                             /dev/nvidia-uvm<br>
206800000-206a00000 ---p 00000000 00:00 0<br>
206a00000-206c00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
206c00000-206e00000 ---p 00000000 00:00 0<br>
206e00000-207000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
207000000-207200000 ---p 00000000 00:00 0<br>
207200000-207400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
207400000-209400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
209400000-20c400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20c400000-20d000000 ---p 00000000 00:00 0<br>
20d000000-20d200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20d200000-20d400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20d400000-20d600000 rw-s 20d400000 00:06 492                             /dev/nvidia-uvm<br>
20d600000-20d800000 ---p 00000000 00:00 0<br>
20d800000-20da00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20da00000-20dc00000 ---p 00000000 00:00 0<br>
20dc00000-20de00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20de00000-20e000000 ---p 00000000 00:00 0<br>
20e000000-20e200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20e200000-210200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
210200000-213200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
213200000-213e00000 ---p 00000000 00:00 0<br>
213e00000-214000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
214000000-214200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
214200000-214400000 rw-s 214200000 00:06 492                             /dev/nvidia-uvm<br>
214400000-214600000 ---p 00000000 00:00 0<br>
214600000-214800000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
214800000-214a00000 ---p 00000000 00:00 0<br>
214a00000-214c00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
214c00000-214e00000 ---p 00000000 00:00 0<br>
214e00000-215000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
215000000-217000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
217000000-21a000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
21a000000-21ac00000 ---p 00000000 00:00 0<br>
21ac00000-21ae00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
21ae00000-21b000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
21b000000-21b200000 rw-s 21b000000 00:06 492                             /dev/nvidia-uvm<br>
21b200000-21b400000 ---p 00000000 00:00 0<br>
21b400000-21b600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
21b600000-600200000 ---p 00000000 00:00 0<br>
10000000000-10810000000 ---p 00000000 00:00 0<br>
5578812bd000-557881314000 r--p 00000000 08:31 78220892                   /opt/conda/bin/python3.6<br>
557881314000-5578814db000 r-xp 00057000 08:31 78220892                   /opt/conda/bin/python3.6<br>
5578814db000-557881578000 r--p 0021e000 08:31 78220892                   /opt/conda/bin/python3.6<br>
557881579000-55788157c000 r--p 002bb000 08:31 78220892                   /opt/conda/bin/python3.6<br>
55788157c000-5578815df000 rw-p 002be000 08:31 78220892                   /opt/conda/bin/python3.6<br>
5578815df000-557881610000 rw-p 00000000 00:00 0<br>
557881cc4000-5579eb27e000 rw-p 00000000 00:00 0                          [heap]<br>
5579eb27e000-5579eb67e000 rw-p 00000000 00:00 0                          [heap]<br>
5579eb67e000-5579ed2c1000 rw-p 00000000 00:00 0                          [heap]<br>
5579ed2c1000-5579ee436000 rw-p 00000000 00:00 0                          [heap]<br>
5579ee436000-5579ee491000 rw-p 00000000 00:00 0                          [heap]<br>
5579ee491000-5579ef3d1000 rw-p 00000000 00:00 0                          [heap]<br>
5579ef3d1000-5579effc9000 rw-p 00000000 00:00 0                          [heap]<br>
7f98c0000000-7f9bc0a00000 ---p 00000000 00:00 0<br>
7f9bc0a00000-7f9bc0c00000 rw-s 00000000 00:05 1587841                    /dev/zero (deleted)<br>
7f9bc0c00000-7f9c00000000 ---p 00000000 00:00 0<br>
7f9c00000000-7f9c03d1e000 rw-p 00000000 00:00 0<br>
7f9c03d1e000-7f9c04000000 ---p 00000000 00:00 0<br>
7f9c04000000-7f9c06fa0000 rw-p 00000000 00:00 0<br>
7f9c06fa0000-7f9c08000000 ---p 00000000 00:00 0<br>
7f9c08000000-7f9cd0000000 ---p 00000000 00:00 0<br>
7f9cd0000000-7f9cd2e22000 rw-p 00000000 00:00 0<br>
7f9cd2e22000-7f9cd4000000 ---p 00000000 00:00 0<br>
7f9cd6000000-7f9dd0a00000 ---p 00000000 00:00 0<br>
7f9dd0a00000-7f9dd0c00000 rw-s 00000000 00:05 1604789                    /dev/zero (deleted)<br>
7f9dd0c00000-7f9dd8000000 ---p 00000000 00:00 0<br>
7f9dd8000000-7f9ddaeae000 rw-p 00000000 00:00 0<br>
7f9ddaeae000-7f9ddc000000 ---p 00000000 00:00 0<br>
7f9ddc000000-7f9ddfd11000 rw-p 00000000 00:00 0<br>
7f9ddfd11000-7f9de0000000 ---p 00000000 00:00 0<br>
7f9de0000000-7f9e18000000 ---p 00000000 00:00 0<br>
7f9e18000000-7f9e1814b000 rw-p 00000000 00:00 0<br>
7f9e1814b000-7f9e1c000000 ---p 00000000 00:00 0<br>
7f9e1c000000-7f9e1fd09000 rw-p 00000000 00:00 0<br>
7f9e1fd09000-7f9e20000000 ---p 00000000 00:00 0<br>
7f9e20000000-7f9e20150000 rw-p 00000000 00:00 0<br>
7f9e20150000-7f9e24000000 ---p 00000000 00:00 0<br>
7f9e28000000-7f9e282e5000 rw-p 00000000 00:00 0<br>
7f9e282e5000-7f9e2c000000 ---p 00000000 00:00 0<br>
7f9e2c000000-7f9e2c14b000 rw-p 00000000 00:00 0<br>
7f9e2c14b000-7f9e30000000 ---p 00000000 00:00 0<br>
7f9e30000000-7f9e30022000 rw-p 00000000 00:00 0<br>
7f9e30022000-7f9e34000000 ---p 00000000 00:00 0<br>
7f9e36000000-7f9ef8a00000 ---p 00000000 00:00 0<br>
7f9ef8a00000-7f9ef8c00000 rw-s 00000000 00:05 1582741                    /dev/zero (deleted)<br>
7f9ef8c00000-7f9f00000000 ---p 00000000 00:00 0<br>
7f9f00000000-7f9f02e3a000 rw-p 00000000 00:00 0<br>
7f9f02e3a000-7f9f04000000 ---p 00000000 00:00 0<br>
7f9f05000000-7f9f05040000 rw-p 00000000 00:00 0<br>
7f9f06000000-7f9f08000000 ---p 00000000 00:00 0<br>
7f9f08000000-7f9f0bffc000 rw-p 00000000 00:00 0<br>
7f9f0bffc000-7f9f0c000000 ---p 00000000 00:00 0<br>
7f9f0c000000-7f9f0fd27000 rw-p 00000000 00:00 0<br>
7f9f0fd27000-7f9f10000000 ---p 00000000 00:00 0<br>
7f9f10000000-7f9f13ffe000 rw-p 00000000 00:00 0<br>
7f9f13ffe000-7f9f14000000 ---p 00000000 00:00 0<br>
7f9f14000000-7f9f18000000 rw-p 00000000 00:00 0<br>
7f9f18000000-7f9f1bffd000 rw-p 00000000 00:00 0<br>
7f9f1bffd000-7f9f1c000000 ---p 00000000 00:00 0<br>
7f9f1d166000-7f9f1d1a6000 rw-p 00000000 00:00 0<br>
7f9f1d266000-7f9f1d2a6000 rw-p 00000000 00:00 0<br>
7f9f1d2e5000-7f9f1d2e6000 ---p 00000000 00:00 0<br>
7f9f1d2e6000-7f9f1dae6000 rw-p 00000000 00:00 0<br>
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<br>
7f9f1daf9000-7f9f1dcf8000 ---p 00013000 08:31 85822789                   /opt/conda/lib/python3.6/site-packages/scipy/stats/mvn.cpython-36m-x86_64-linux-gnu.so<br>
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<br>
7f9f1dcfa000-7f9f1ddf1000 rw-p 00000000 00:00 0<br>
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<br>
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<br>
7f9f1ddfd000-7f9f1dffc000 ---p 0000a000 08:31 85822791                   /opt/conda/lib/python3.6/site-packages/scipy/stats/statlib.cpython-36m-x86_64-linux-gnu.so<br>
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<br>
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<br>
7f9f1e000000-7f9f47200000 ---p 00000000 00:00 0<br>
7f9f47200000-7f9f47400000 rw-s 00000000 00:05 1582740                    /dev/zero (deleted)<br>
7f9f47400000-7f9f47e00000 ---p 00000000 00:00 0<br>
7f9f47e00000-7f9f48000000 rw-s 00000000 00:05 1590697                    /dev/zero (deleted)<br>
7f9f48000000-7f9f48021000 rw-p 00000000 00:00 0<br>
7f9f48021000-7f9f4c000000 ---p 00000000 00:00 0<br>
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<br>
7f9f4c094000-7f9f4c294000 ---p 00073000 08:31 85822779                   /opt/conda/lib/python3.6/site-packages/scipy/stats/_stats.cpython-36m-x86_64-linux-gnu.so<br>
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<br>
7f9f4c29a000-7f9f4c29c000 rw-p 00000000 00:00 0<br>
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<br>
7f9f4c2f0000-7f9f4c4f0000 ---p 00054000 08:31 85429655                   /opt/conda/lib/python3.6/site-packages/scipy/interpolate/interpnd.cpython-36m-x86_64-linux-gnu.so<br>
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<br>
7f9f4c4f5000-7f9f4c4f6000 rw-p 00000000 00:00 0<br>
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7fa179aa4000-7fa179aa5000 r--p 00002000 08:31 78745061                   /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so<br>
7fa179aa5000-7fa179aa6000 r--p 00002000 08:31 78745061                   /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so<br>
7fa179aa6000-7fa179aa7000 rw-p 00003000 08:31 78745061                   /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so<br>
7fa179aa7000-7fa179aa8000 r--p 00025000 08:31 59728358                   /lib/x86_64-linux-gnu/ld-2.23.so<br>
7fa179aa8000-7fa179aa9000 rw-p 00026000 08:31 59728358                   /lib/x86_64-linux-gnu/ld-2.23.so<br>
7fa179aa9000-7fa179aaa000 rw-p 00000000 00:00 0<br>
7fff4fa0b000-7fff4fa2d000 rw-p 00000000 00:00 0                          [stack]<br>
7fff4fb0e000-7fff4fb11000 r--p 00000000 00:00 0                          [vvar]<br>
7fff4fb11000-7fff4fb13000 r-xp 00000000 00:00 0                          [vdso]<br>
ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0                  [vsyscall]</p>
</blockquote>
<p dir="auto">In PyTorch 1.4 it shows:</p>
<blockquote>
<p dir="auto">free(): invalid pointer</p>
</blockquote>
<p dir="auto">without other information.</p>

<h2 dir="auto">Environment</h2>
<p dir="auto">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.)</p>
<p dir="auto">I’m using 4 Titan x (pascal) for data-parallel training.</p>
<h2 dir="auto">Additional context</h2>
<h2 dir="auto"><g-emoji class="g-emoji" alias="bug" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f41b.png">🐛</g-emoji> Bug</h2>
<p dir="auto">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.</p>
<h2 dir="auto">To Reproduce</h2>
<p dir="auto">It is hard for me to simplify the code and reproduce it. However, it happens every time when I include the second order gradient:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="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"><pre class="notranslate"><code class="notranslate">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
</code></pre></div>
<p dir="auto">Another major difference for my generator design is that it has convolution layers with <strong>both</strong> the input and weights calculated from other up-stream networks.</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="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 &gt;= 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] =&gt; 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"><pre class="notranslate"><code class="notranslate">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 &gt;= 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] =&gt; 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
</code></pre></div>
<p dir="auto">The above code happens inside a DataParallel module and results are gathered afterward, followed by a loss to minimize pl_lengths.</p>

<h2 dir="auto">Expected behavior</h2>
<p dir="auto">After some random number of updates, around 3000-8000, the problem occurs and training was terminated.</p>
<p dir="auto">In PyTorch 1.3 it shows:</p>
<blockquote>
<p dir="auto">*** Error in `python3’: double free or corruption (fasttop): 0x00007f9e280c3fe0 ***<br>
======= Backtrace: =========<br>
/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fa1793127e5]<br>
/lib/x86_64-linux-gnu/libc.so.6(+0x8037a)[0x7fa17931b37a]<br>
/lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7fa17931f53c]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d820e)[0x7fa13bb9420e]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d82b9)[0x7fa13bb942b9]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(+0x39d8435)[0x7fa13bb94435]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(_ZN5torch8autograd6Engine17evaluate_functionERNS0_8NodeTaskE+0x1210)[0x7fa13bb8bb50]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch.so(_ZN5torch8autograd6Engine11thread_mainEPNS0_9GraphTaskE+0x1c4)[0x7fa13bb8da04]<br>
/opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_python.so(_ZN5torch8autograd6python12PythonEngine11thread_initEi+0x2a)[0x7fa16a537eda]<br>
/opt/conda/lib/python3.6/site-packages/torch/…/…/…/libstdc++.so.6(+0xc819d)[0x7fa169ffb19d]<br>
/lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba)[0x7fa17966c6ba]<br>
/lib/x86_64-linux-gnu/libc.so.6(clone+0x6d)[0x7fa1793a241d]</p>
</blockquote>
<p dir="auto">and followed by a long memory map:</p>
<blockquote>
<p dir="auto">======= Memory map: ========<br>
200000000-200200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
200200000-200400000 ---p 00000000 00:00 0<br>
200400000-200600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
200600000-202600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
202600000-205600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
205600000-206200000 ---p 00000000 00:00 0<br>
206200000-206400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
206400000-206600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
206600000-206800000 rw-s 206600000 00:06 492                             /dev/nvidia-uvm<br>
206800000-206a00000 ---p 00000000 00:00 0<br>
206a00000-206c00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
206c00000-206e00000 ---p 00000000 00:00 0<br>
206e00000-207000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
207000000-207200000 ---p 00000000 00:00 0<br>
207200000-207400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
207400000-209400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
209400000-20c400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20c400000-20d000000 ---p 00000000 00:00 0<br>
20d000000-20d200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20d200000-20d400000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20d400000-20d600000 rw-s 20d400000 00:06 492                             /dev/nvidia-uvm<br>
20d600000-20d800000 ---p 00000000 00:00 0<br>
20d800000-20da00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20da00000-20dc00000 ---p 00000000 00:00 0<br>
20dc00000-20de00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20de00000-20e000000 ---p 00000000 00:00 0<br>
20e000000-20e200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
20e200000-210200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
210200000-213200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
213200000-213e00000 ---p 00000000 00:00 0<br>
213e00000-214000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
214000000-214200000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
214200000-214400000 rw-s 214200000 00:06 492                             /dev/nvidia-uvm<br>
214400000-214600000 ---p 00000000 00:00 0<br>
214600000-214800000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
214800000-214a00000 ---p 00000000 00:00 0<br>
214a00000-214c00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
214c00000-214e00000 ---p 00000000 00:00 0<br>
214e00000-215000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
215000000-217000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
217000000-21a000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
21a000000-21ac00000 ---p 00000000 00:00 0<br>
21ac00000-21ae00000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
21ae00000-21b000000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
21b000000-21b200000 rw-s 21b000000 00:06 492                             /dev/nvidia-uvm<br>
21b200000-21b400000 ---p 00000000 00:00 0<br>
21b400000-21b600000 rw-s 00000000 00:06 493                              /dev/nvidiactl<br>
21b600000-600200000 ---p 00000000 00:00 0<br>
10000000000-10810000000 ---p 00000000 00:00 0<br>
5578812bd000-557881314000 r--p 00000000 08:31 78220892                   /opt/conda/bin/python3.6<br>
557881314000-5578814db000 r-xp 00057000 08:31 78220892                   /opt/conda/bin/python3.6<br>
5578814db000-557881578000 r--p 0021e000 08:31 78220892                   /opt/conda/bin/python3.6<br>
557881579000-55788157c000 r--p 002bb000 08:31 78220892                   /opt/conda/bin/python3.6<br>
55788157c000-5578815df000 rw-p 002be000 08:31 78220892                   /opt/conda/bin/python3.6<br>
5578815df000-557881610000 rw-p 00000000 00:00 0<br>
557881cc4000-5579eb27e000 rw-p 00000000 00:00 0                          [heap]<br>
5579eb27e000-5579eb67e000 rw-p 00000000 00:00 0                          [heap]<br>
5579eb67e000-5579ed2c1000 rw-p 00000000 00:00 0                          [heap]<br>
5579ed2c1000-5579ee436000 rw-p 00000000 00:00 0                          [heap]<br>
5579ee436000-5579ee491000 rw-p 00000000 00:00 0                          [heap]<br>
5579ee491000-5579ef3d1000 rw-p 00000000 00:00 0                          [heap]<br>
5579ef3d1000-5579effc9000 rw-p 00000000 00:00 0                          [heap]<br>
7f98c0000000-7f9bc0a00000 ---p 00000000 00:00 0<br>
7f9bc0a00000-7f9bc0c00000 rw-s 00000000 00:05 1587841                    /dev/zero (deleted)<br>
7f9bc0c00000-7f9c00000000 ---p 00000000 00:00 0<br>
7f9c00000000-7f9c03d1e000 rw-p 00000000 00:00 0<br>
7f9c03d1e000-7f9c04000000 ---p 00000000 00:00 0<br>
7f9c04000000-7f9c06fa0000 rw-p 00000000 00:00 0<br>
7f9c06fa0000-7f9c08000000 ---p 00000000 00:00 0<br>
7f9c08000000-7f9cd0000000 ---p 00000000 00:00 0<br>
7f9cd0000000-7f9cd2e22000 rw-p 00000000 00:00 0<br>
7f9cd2e22000-7f9cd4000000 ---p 00000000 00:00 0<br>
7f9cd6000000-7f9dd0a00000 ---p 00000000 00:00 0<br>
7f9dd0a00000-7f9dd0c00000 rw-s 00000000 00:05 1604789                    /dev/zero (deleted)<br>
7f9dd0c00000-7f9dd8000000 ---p 00000000 00:00 0<br>
7f9dd8000000-7f9ddaeae000 rw-p 00000000 00:00 0<br>
7f9ddaeae000-7f9ddc000000 ---p 00000000 00:00 0<br>
7f9ddc000000-7f9ddfd11000 rw-p 00000000 00:00 0<br>
7f9ddfd11000-7f9de0000000 ---p 00000000 00:00 0<br>
7f9de0000000-7f9e18000000 ---p 00000000 00:00 0<br>
7f9e18000000-7f9e1814b000 rw-p 00000000 00:00 0<br>
7f9e1814b000-7f9e1c000000 ---p 00000000 00:00 0<br>
7f9e1c000000-7f9e1fd09000 rw-p 00000000 00:00 0<br>
7f9e1fd09000-7f9e20000000 ---p 00000000 00:00 0<br>
7f9e20000000-7f9e20150000 rw-p 00000000 00:00 0<br>
7f9e20150000-7f9e24000000 ---p 00000000 00:00 0<br>
7f9e28000000-7f9e282e5000 rw-p 00000000 00:00 0<br>
7f9e282e5000-7f9e2c000000 ---p 00000000 00:00 0<br>
7f9e2c000000-7f9e2c14b000 rw-p 00000000 00:00 0<br>
7f9e2c14b000-7f9e30000000 ---p 00000000 00:00 0<br>
7f9e30000000-7f9e30022000 rw-p 00000000 00:00 0<br>
7f9e30022000-7f9e34000000 ---p 00000000 00:00 0<br>
7f9e36000000-7f9ef8a00000 ---p 00000000 00:00 0<br>
7f9ef8a00000-7f9ef8c00000 rw-s 00000000 00:05 1582741                    /dev/zero (deleted)<br>
7f9ef8c00000-7f9f00000000 ---p 00000000 00:00 0<br>
7f9f00000000-7f9f02e3a000 rw-p 00000000 00:00 0<br>
7f9f02e3a000-7f9f04000000 ---p 00000000 00:00 0<br>
7f9f05000000-7f9f05040000 rw-p 00000000 00:00 0<br>
7f9f06000000-7f9f08000000 ---p 00000000 00:00 0<br>
7f9f08000000-7f9f0bffc000 rw-p 00000000 00:00 0<br>
7f9f0bffc000-7f9f0c000000 ---p 00000000 00:00 0<br>
7f9f0c000000-7f9f0fd27000 rw-p 00000000 00:00 0<br>
7f9f0fd27000-7f9f10000000 ---p 00000000 00:00 0<br>
7f9f10000000-7f9f13ffe000 rw-p 00000000 00:00 0<br>
7f9f13ffe000-7f9f14000000 ---p 00000000 00:00 0<br>
7f9f14000000-7f9f18000000 rw-p 00000000 00:00 0<br>
7f9f18000000-7f9f1bffd000 rw-p 00000000 00:00 0<br>
7f9f1bffd000-7f9f1c000000 ---p 00000000 00:00 0<br>
7f9f1d166000-7f9f1d1a6000 rw-p 00000000 00:00 0<br>
7f9f1d266000-7f9f1d2a6000 rw-p 00000000 00:00 0<br>
7f9f1d2e5000-7f9f1d2e6000 ---p 00000000 00:00 0<br>
7f9f1d2e6000-7f9f1dae6000 rw-p 00000000 00:00 0<br>
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<br>
7f9f1daf9000-7f9f1dcf8000 ---p 00013000 08:31 85822789                   /opt/conda/lib/python3.6/site-packages/scipy/stats/mvn.cpython-36m-x86_64-linux-gnu.so<br>
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<br>
7f9f1dcfa000-7f9f1ddf1000 rw-p 00000000 00:00 0<br>
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<br>
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<br>
7f9f1ddfd000-7f9f1dffc000 ---p 0000a000 08:31 85822791                   /opt/conda/lib/python3.6/site-packages/scipy/stats/statlib.cpython-36m-x86_64-linux-gnu.so<br>
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<br>
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<br>
7f9f1e000000-7f9f47200000 ---p 00000000 00:00 0<br>
7f9f47200000-7f9f47400000 rw-s 00000000 00:05 1582740                    /dev/zero (deleted)<br>
7f9f47400000-7f9f47e00000 ---p 00000000 00:00 0<br>
7f9f47e00000-7f9f48000000 rw-s 00000000 00:05 1590697                    /dev/zero (deleted)<br>
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7fa17990b000-7fa17990d000 rw-p 0000b000 08:31 78745084                   /opt/conda/lib/python3.6/lib-dynload/math.cpython-36m-x86_64-linux-gnu.so<br>
7fa17990d000-7fa179910000 r--p 00000000 08:31 78745072                   /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so<br>
7fa179910000-7fa179916000 r-xp 00003000 08:31 78745072                   /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so<br>
7fa179916000-7fa179918000 r--p 00009000 08:31 78745072                   /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so<br>
7fa179918000-7fa179919000 ---p 0000b000 08:31 78745072                   /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so<br>
7fa179919000-7fa17991a000 r--p 0000b000 08:31 78745072                   /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so<br>
7fa17991a000-7fa17991c000 rw-p 0000c000 08:31 78745072                   /opt/conda/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so<br>
7fa17991c000-7fa179aa1000 rw-p 00000000 00:00 0<br>
7fa179aa1000-7fa179aa2000 rwxp 00000000 00:00 0<br>
7fa179aa2000-7fa179aa3000 r--p 00000000 08:31 78745061                   /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so<br>
7fa179aa3000-7fa179aa4000 r-xp 00001000 08:31 78745061                   /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so<br>
7fa179aa4000-7fa179aa5000 r--p 00002000 08:31 78745061                   /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so<br>
7fa179aa5000-7fa179aa6000 r--p 00002000 08:31 78745061                   /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so<br>
7fa179aa6000-7fa179aa7000 rw-p 00003000 08:31 78745061                   /opt/conda/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so<br>
7fa179aa7000-7fa179aa8000 r--p 00025000 08:31 59728358                   /lib/x86_64-linux-gnu/ld-2.23.so<br>
7fa179aa8000-7fa179aa9000 rw-p 00026000 08:31 59728358                   /lib/x86_64-linux-gnu/ld-2.23.so<br>
7fa179aa9000-7fa179aaa000 rw-p 00000000 00:00 0<br>
7fff4fa0b000-7fff4fa2d000 rw-p 00000000 00:00 0                          [stack]<br>
7fff4fb0e000-7fff4fb11000 r--p 00000000 00:00 0                          [vvar]<br>
7fff4fb11000-7fff4fb13000 r-xp 00000000 00:00 0                          [vdso]<br>
ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0                  [vsyscall]</p>
</blockquote>
<p dir="auto">In PyTorch 1.4 it shows:</p>
<blockquote>
<p dir="auto">free(): invalid pointer</p>
</blockquote>
<p dir="auto">without other information.</p>

<h2 dir="auto">Environment</h2>
<p dir="auto">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.)</p>
<p dir="auto">I’m using 4 Titan x (pascal) for data-parallel training.</p>
<h2 dir="auto">Additional context</h2>