Mention Jacobian-vector product in the doc of torch.autograd (#15197)
Summary:
A friend of me is learning deep learning and pytorch, and he is confused by the following piece of code from the tutorial https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html#gradients :
```python
x = torch.randn(3, requires_grad=True)
y = x * 2
while y.data.norm() < 1000:
y = y * 2
print(y)
gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
y.backward(gradients)
print(x.grad)
```
He don't know where the following line comes from:
```python
gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
```
What are we computing? Why don't we compute "the gradient of `y` w.r.t `x`"?
In the tutorial, it only says
> You can do many crazy things with autograd!
Which does not explain anything. It seems to be hard for some beginners of deep learning to understand why do we ever do backwards with external gradient fed in and what is the meaning of doing so. So I modified the tutorial in https://github.com/pytorch/tutorials/pull/385
and the docstring correspondingly in this PR, explaining the Jacobian vector product. Please review this PR and https://github.com/pytorch/tutorials/pull/385 together.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15197
Differential Revision: D13476513
Pulled By: soumith
fbshipit-source-id: bee62282e9ab72403247384e4063bcdf59d40c3c
