<p dir="auto">why is there no log_cdf and log_sf in atleast pytorch normal distributions yet ? while even tensorflow and scipy has it</p>
<p dir="auto">cc <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/vincentqb/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/vincentqb">@vincentqb</a> <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/fritzo/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/fritzo">@fritzo</a> <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/neerajprad/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/neerajprad">@neerajprad</a> <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/alicanb/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/alicanb">@alicanb</a> <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/vishwakftw/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/vishwakftw">@vishwakftw</a></p>
<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>

<h2 dir="auto">To Reproduce</h2>
<p dir="auto">Steps to reproduce the behavior:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="In [1]: import torch                                                                                                                                                                         

In [2]: x = torch.randn(1, 2, 3).to(&quot;cuda:0&quot;)                                                                                                                                                

In [3]: y = torch.randn(1, 2, 4).to(&quot;cuda:0&quot;)                                                                                                                                                

In [4]: bindings = [x.clone().data_ptr(), y.clone().data_ptr()]                                                                                                                              

In [5]: bindings                                                                                                                                                                             
Out[5]: [140582636749824, 140582636749824]

In [6]: y.clone().data_ptr()                                                                                                                                                                 
Out[6]: 140582636749824"><pre class="notranslate"><code class="notranslate">In [1]: import torch                                                                                                                                                                         

In [2]: x = torch.randn(1, 2, 3).to("cuda:0")                                                                                                                                                

In [3]: y = torch.randn(1, 2, 4).to("cuda:0")                                                                                                                                                

In [4]: bindings = [x.clone().data_ptr(), y.clone().data_ptr()]                                                                                                                              

In [5]: bindings                                                                                                                                                                             
Out[5]: [140582636749824, 140582636749824]

In [6]: y.clone().data_ptr()                                                                                                                                                                 
Out[6]: 140582636749824
</code></pre></div>
<p dir="auto">data_ptrs in bindings are same as <code class="notranslate">y.clone().data_ptr()</code></p>

<h2 dir="auto">Expected behavior</h2>

<h2 dir="auto">Environment</h2>
<p dir="auto">Please copy and paste the output from our<br>
<a href="https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py" rel="nofollow">environment collection script</a><br>
(or fill out the checklist below manually).</p>
<p dir="auto">You can get the script and run it with:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py"><pre class="notranslate"><code class="notranslate">wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py
</code></pre></div>
<p dir="auto">PyTorch version: 1.3.0<br>
Is debug build: No<br>
CUDA used to build PyTorch: 10.1.243</p>
<p dir="auto">OS: Ubuntu 16.04.6 LTS<br>
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609<br>
CMake version: version 3.14.5</p>
<p dir="auto">Python version: 3.5<br>
Is CUDA available: Yes<br>
CUDA runtime version: 10.1.243<br>
GPU models and configuration: GPU 0: GeForce RTX 2080<br>
Nvidia driver version: 440.33.01<br>
cuDNN version: /usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7</p>
<p dir="auto">Versions of relevant libraries:<br>
[pip3] numpy==1.16.3<br>
[pip3] torch==1.3.0<br>
[pip3] torch2trt==0.0.0<br>
[pip3] torchsummary==1.5.1<br>
[pip3] torchvision==0.4.1<br>
[conda] Could not collect</p>
<h2 dir="auto">Additional context</h2>