Summary:
Although `gpu_kernel_with_index` might look like a quite general helper function at first look, it actually isn't.

The problem is not only 32bit indexing, but something more fundamental: `TensorIterator` reorder dims and shapes, so if you have non-contiguous tensor such as `torch.empty(5, 5).t()` , the index won't be correct. Since the whole point of `TensorIterator` is to manipulate shapes/strides to speedup loops, it is fundamentally impossible to get the correct linear index without tons of efforts.

Currently, the range factories are not failing on an `out=non_contiguous_tensor`  is because it is so lucky that  `has_internal_overlap` is stupid enough to return everything not contiguous as `TOO_HARD`.

Since `gpu_kernel_with_index` is not general, we should move it from `Loops.cuh` to `RangeFactories.cu`. And since the kernel is so simple to implement, it makes no sense to use `TensorIterator` which goes through tons of unnecessary checks like `compute_dtypes`.

`torch.range` is not tested for 64bit-indexing, and I will file a new PR to remove it (it was supposed to be removed at 0.5).

Benchmark:
The device is GTX-1650, I don't have a good GPU at home.

Code:
```python
import torch
print(torch.__version__)

for i in range(100):
    torch.randn(1000, device='cuda')
torch.cuda.synchronize()

for i in range(15, 29):
    %timeit torch.arange(2 ** i, device='cuda'); torch.cuda.synchronize()
```

Before:
```
1.5.0a0+c37a9b8
11.9 µs ± 412 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
12.7 µs ± 309 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
19.6 µs ± 209 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
28.9 µs ± 923 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
48.4 µs ± 1.64 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
85.7 µs ± 1.46 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
162 µs ± 1.09 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
312 µs ± 9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
618 µs ± 15.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
1.22 ms ± 9.91 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
2.45 ms ± 97.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
4.9 ms ± 155 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
10.1 ms ± 378 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```

After:
```
1.5.0a0+7960d19
11 µs ± 29.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
12.4 µs ± 550 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
18.4 µs ± 230 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
27.6 µs ± 10.9 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
46.2 µs ± 18.6 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
83.3 µs ± 5.61 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
158 µs ± 373 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
307 µs ± 1.44 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
603 µs ± 112 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
1.2 ms ± 1.05 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
2.4 ms ± 23.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
4.77 ms ± 25.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
9.51 ms ± 933 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
Pull Request resolved: pytorch#33370

Differential Revision: D19925990

Pulled By: ngimel

fbshipit-source-id: f4a732fe14a5582b35a56618941120d62e82fdce