Improve reciprocal() and rsqrt() accuracy on arm64 (#47478)
Summary:
Neither `vrecpeq_f32` nor `vrsqrteq_f32` yield accurate results but just perform first of two steps in an iteration of the Newton-Raphson method, as documented at
https://developer.arm.com/documentation/dui0472/j/using-neon-support/neon-intrinsics-for-reciprocal-and-sqrt
Use appropriate NEON instruction to run two more steps of the Newton's method to improve results
Before:
```
$ python -c "import torch;print(torch.arange(1.0, 17.0, 1.0, dtype=torch.float32).reciprocal())"
tensor([0.9980, 0.4990, 0.3330, 0.2495, 0.1997, 0.1665, 0.1426, 0.1248, 0.1108,
0.0999, 0.0908, 0.0833, 0.0769, 0.0713, 0.0667, 0.0624])
$ python -c "import torch;print(torch.arange(1.0, 17.0, 1.0, dtype=torch.float32).rsqrt())"
tensor([0.9980, 0.7051, 0.5762, 0.4990, 0.4463, 0.4082, 0.3779, 0.3525, 0.3330,
0.3154, 0.3008, 0.2881, 0.2773, 0.2666, 0.2578, 0.2495])
```
After:
```
$ python -c "import torch;print(torch.arange(1.0, 17.0, 1.0, dtype=torch.float32).reciprocal())"
tensor([1.0000, 0.5000, 0.3333, 0.2500, 0.2000, 0.1667, 0.1429, 0.1250, 0.1111,
0.1000, 0.0909, 0.0833, 0.0769, 0.0714, 0.0667, 0.0625])
$ python -c "import torch;print(torch.arange(1.0, 17.0, 1.0, dtype=torch.float32).rsqrt())"
tensor([1.0000, 0.7071, 0.5774, 0.5000, 0.4472, 0.4082, 0.3780, 0.3536, 0.3333,
0.3162, 0.3015, 0.2887, 0.2774, 0.2673, 0.2582, 0.2500])
```
Partially addresses https://github.com/pytorch/pytorch/issues/47476
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47478
Reviewed By: walterddr
Differential Revision: D24773443
Pulled By: malfet
fbshipit-source-id: 224dca9725601d29fb229f8d71d968a30f25c829
