Update linspace types (#32218)
Summary:
Changes the linspace functions to be more consistent as requested in https://github.com/pytorch/pytorch/issues/31991. The code has also been updated to avoid an early rounding error; the line `scalar_t step = (scalar_end - scalar_start) / static_cast<static_t>(steps-1)` can result in `step = 0` for integer scalars, and this gives unintended results. I examined the new output using
```
import torch
types = [torch.uint8, torch.int8, torch.short, torch.int, torch.long, torch.half, torch.float, torch.double]
print('Testing linspace:')
for type in types:
print(type, torch.linspace(-2, 2, 10, dtype=type))
```
which returns
```
Testing linspace:
torch.uint8 tensor([254, 254, 254, 255, 255, 0, 0, 1, 1, 2], dtype=torch.uint8)
torch.int8 tensor([-2, -2, -2, -1, -1, 0, 0, 1, 1, 2], dtype=torch.int8)
torch.int16 tensor([-2, -2, -2, -1, -1, 0, 0, 1, 1, 2], dtype=torch.int16)
torch.int32 tensor([-2, -2, -2, -1, -1, 0, 0, 1, 1, 2], dtype=torch.int32)
torch.int64 tensor([-2, -2, -2, -1, -1, 0, 0, 1, 1, 2])
torch.float16 tensor([-2.0000, -1.5557, -1.1113, -0.6670, -0.2227, 0.2227, 0.6660, 1.1113,
1.5547, 2.0000], dtype=torch.float16)
torch.float32 tensor([-2.0000, -1.5556, -1.1111, -0.6667, -0.2222, 0.2222, 0.6667, 1.1111,
1.5556, 2.0000])
torch.float64 tensor([-2.0000, -1.5556, -1.1111, -0.6667, -0.2222, 0.2222, 0.6667, 1.1111,
1.5556, 2.0000], dtype=torch.float64)
```
which is the expected output: `uint8` overflows as it should, and the result of casting from a floating point to an integer is correct.
This PR does not change the logspace function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32218
Differential Revision: D19544224
Pulled By: ngimel
fbshipit-source-id: 2bbf2b8552900eaef2dcc41b6464fc39bec22e0b
