How to explain the name in the torch.autograd.profiler

torch_fresh: <p>Hi, I use  torch.autograd.profiler to record the training time of one batch (size = 32) on AlexNet.</p>
<pre><code>with torch.autograd.profiler.profile(use_cuda=True) as prof:

    output = AlexNet(data)
    loss = criterion(output, target)

    loss.backward()

    optimizer.step()
print(prof)
</code></pre>
<p>The result is shown below</p>
<p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/b/6/b6ef93cb0390e75a34d68a0fed42ab74ae504788.png" data-download-href="https://discuss.pytorch.org/uploads/default/b6ef93cb0390e75a34d68a0fed42ab74ae504788" title="1"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/b/6/b6ef93cb0390e75a34d68a0fed42ab74ae504788_2_690x396.png" alt="1" data-base62-sha1="q6kbSILkBF8K7jQbQbwuAx46dqo" width="690" height="396" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/b/6/b6ef93cb0390e75a34d68a0fed42ab74ae504788_2_690x396.png, https://discuss.pytorch.org/uploads/default/optimized/3X/b/6/b6ef93cb0390e75a34d68a0fed42ab74ae504788_2_1035x594.png 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/b/6/b6ef93cb0390e75a34d68a0fed42ab74ae504788_2_1380x792.png 2x" data-dominant-color="323232"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">1</span><span class="informations">1436×825 97.4 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/c/2/c268f5e41e17f172343657655e227bbd68024f85.png" data-download-href="https://discuss.pytorch.org/uploads/default/c268f5e41e17f172343657655e227bbd68024f85" title="2"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/c/2/c268f5e41e17f172343657655e227bbd68024f85_2_690x362.png" alt="2" data-base62-sha1="rJPvnyfsnYtJZk3UwTUxMMw7gB7" width="690" height="362" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/c/2/c268f5e41e17f172343657655e227bbd68024f85_2_690x362.png, https://discuss.pytorch.org/uploads/default/optimized/3X/c/2/c268f5e41e17f172343657655e227bbd68024f85_2_1035x543.png 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/c/2/c268f5e41e17f172343657655e227bbd68024f85_2_1380x724.png 2x" data-dominant-color="323232"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">2</span><span class="informations">1395×732 87.6 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/8/f/8f180ba5712d9a291c132a7aeeec91905963bb3d.png" data-download-href="https://discuss.pytorch.org/uploads/default/8f180ba5712d9a291c132a7aeeec91905963bb3d" title="3"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/8/f/8f180ba5712d9a291c132a7aeeec91905963bb3d_2_690x355.png" alt="3" data-base62-sha1="kpRNjViSqANvgic6ECuWODagRrv" width="690" height="355" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/8/f/8f180ba5712d9a291c132a7aeeec91905963bb3d_2_690x355.png, https://discuss.pytorch.org/uploads/default/optimized/3X/8/f/8f180ba5712d9a291c132a7aeeec91905963bb3d_2_1035x532.png 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/8/f/8f180ba5712d9a291c132a7aeeec91905963bb3d_2_1380x710.png 2x" data-dominant-color="323232"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">3</span><span class="informations">1435×740 85.2 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>
<p>I can explain the forward time, like (conv2d, convolution, _convolution, contiguous, cudnn_convolution, reshape, view, add belongs to the forward time of Conv_1 in the AlexNet.). I can relate the records to each layer when forward propagation. However, after nll_loss_forward, I don’t understand each name of the records. I think there are eight layers needed to update the weight (five conv layers and three fully-connected layers). Can anyone explain the backward propagation related to the records? For example, which records are related to the last fully-connected layer. I want to know the backward propagation time of each layer.</p>
<p>Thanks for any suggestion.</p>