Make kl_div accept target in log space (#34586)
Summary:
Fixes [32520](https://github.com/pytorch/pytorch/issues/32520), implements [34536](https://github.com/pytorch/pytorch/issues/34536).
Here are some benchmarks:
```python
import torch
import torch.nn.functional as F
from IPython import get_ipython
ipython = get_ipython()
torch.set_num_threads(1)
for d in [5, 10, 20, 50, 100, 1000]:
i = torch.rand(d, d)
t = torch.rand(d, d)
print(f"Size: {d}x{d}")
ipython.magic("timeit F.kl_div(i, t, reduction='none', log_target=False)")
ipython.magic("timeit F.kl_div(i, t.log(), reduction='none', log_target=True)")
```
Output:
```
Size: 5x5
16 µs ± 33 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
8.24 µs ± 17.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
Size: 10x10
16.7 µs ± 17.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
8.7 µs ± 20.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
Size: 20x20
17.7 µs ± 47.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
9.7 µs ± 28.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
Size: 50x50
23.6 µs ± 60.1 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
15 µs ± 33.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
Size: 100x100
42.8 µs ± 223 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
34 µs ± 17.2 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
Size: 1000x1000
3.9 ms ± 1.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
3.45 ms ± 364 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34586
Differential Revision: D20652726
Pulled By: ezyang
fbshipit-source-id: 480697b4cd01341bbeee7514a8b812705a0600ea
