Summary:
Fixes pytorch#12578 pytorch#9395.

* Fix and simplify print logic

* Follow numpy print rule https://github.com/numpy/numpy/blob/eb2bd11870731ea19a0eee72e616c7deb00f6c54/numpy/core/arrayprint.py#L859
> scientific notation is used when absolute value of the smallest number is < 1e-4 or maximum > 1e8 or the ratio of the maximum absolute value to the minimum is > 1e3

I hope I didn't break anything since there seems to be a lot of edge cases here... Here are some easy sanity checks.
```
In [5]: torch.tensor(1)
Out[5]: tensor(1)
Out[2]: array(1) # numpy

In [6]: torch.tensor(10)
Out[6]: tensor(10)
Out[3]: array(10) # numpy

In [8]: torch.tensor(99000000)
Out[8]: tensor(99000000)
Out[5]: array(99000000) # numpy

In [9]: torch.tensor(100000000)
Out[9]: tensor(100000000)
Out[6]: array(100000000) # numpy

In [10]: torch.tensor(100000001)
Out[10]: tensor(100000001)
Out[7]: array(100000001) # numpy

In [11]: torch.tensor(1000000000)
Out[11]: tensor(1000000000)
Out[8]: array(1000000000) # numpy

In [12]: torch.tensor([1, 1000])
Out[12]: tensor([   1, 1000])
Out[9]: array([   1, 1000]) # numpy

In [13]: torch.tensor([1, 1010])
Out[13]: tensor([   1, 1010])
Out[10]: array([   1, 1010]) # numpy
```
For floating points, we use scientific when `max/min > 1000 || max > 1e8 || min < 1e-4`
Lines with "old" are old behaviors that either has precision issue, or not aligned with numpy
```
In [14]: torch.tensor(0.01)
Out[14]: tensor(0.0100)
Out[11]: array(0.01) # numpy

In [15]: torch.tensor(0.1)
Out[15]: tensor(0.1000)
Out[12]: array(0.1) # numpy

In [16]: torch.tensor(0.0001)
Out[16]: tensor(0.0001)
Out[14]: array(0.0001) # numpy

In [17]: torch.tensor(0.00002)
Out[17]: tensor(2.0000e-05)
Out[15]: array(2e-05) # numpy
Out[5]: tensor(0.0000) # old

In [18]: torch.tensor(1e8)
Out[18]: tensor(100000000.)
Out[16]: array(100000000.0) # numpy

In [19]: torch.tensor(1.1e8)
Out[19]: tensor(1.1000e+08)
Out[17]: array(1.1e8) # numpy 1.14.5, In <= 1.13 this was not using scientific print
Out[10]: tensor(110000000.) # old

In [20]: torch.tensor([0.01, 10.])
Out[20]: tensor([ 0.0100, 10.0000])
Out[18]: array([  0.01,  10.  ]) # numpy

In [21]: torch.tensor([0.01, 11.])
Out[21]: tensor([1.0000e-02, 1.1000e+01])
Out[19]: array([  1.00000000e-02,   1.10000000e+01]) # numpy
Out[7]: tensor([ 0.0100, 11.0000]) # old
```
When print floating number in int mode, we still need to respect rules to use scientific mode first
```
In [22]: torch.tensor([1., 1000.])
Out[22]: tensor([   1., 1000.])
Out[20]: array([    1.,  1000.]) # numpy

In [23]: torch.tensor([1., 1010.])
Out[23]: tensor([1.0000e+00, 1.0100e+03])
Out[21]: array([  1.00000000e+00,   1.01000000e+03]) # numpy
Out[9]: tensor([   1., 1010.]) # old
```
Pull Request resolved: pytorch#12746

Differential Revision: D10443800

Pulled By: ailzhang

fbshipit-source-id: f5e4e3fe9bf0b44af2c64c93a9ed42b73fa613f5