Simplify rechunk implementation

[qinxuye]

What do these changes do?

This PR simplifies the rechunk implementation while the older version is really complicated and hard to understand.

Master branch:

In [1]: import numpy as np; import mars; import mars.tensor as mt

In [2]: mars.new_session()
Web service started at http://0.0.0.0:30452
Out[2]: <mars.deploy.oscar.session.SyncSession at 0x7f8441101250>

In [3]: r = np.random.RandomState(0).rand(12000, 12000)

In [4]: a = mt.array(r, chunk_size=1680).execute()
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100.0/100 [00:04<00:00, 23.39it/s]

In [5]: %time a.rechunk(2111).execute()
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100.0/100 [00:02<00:00, 34.07it/s]
CPU times: user 1.48 s, sys: 302 ms, total: 1.78 s
Wall time: 2.95 s
Out[5]: 
array([[0.5488135 , 0.71518937, 0.60276338, ..., 0.84348096, 0.94290928,
        0.83282242],
       [0.5646904 , 0.83974605, 0.37688365, ..., 0.40347954, 0.46089504,
        0.01048474],
       [0.15319619, 0.0328116 , 0.2287953 , ..., 0.90022341, 0.49806663,
        0.77000204],
       ...,
       [0.81724816, 0.69873782, 0.06925849, ..., 0.67440032, 0.90861683,
        0.80968811],
       [0.45391215, 0.72735698, 0.11037642, ..., 0.45976168, 0.62173263,
        0.12291647],
       [0.96026503, 0.01277616, 0.49155639, ..., 0.29565832, 0.12990437,
        0.82810578]])

Current branch

In [1]: import numpy as np; import mars; import mars.tensor as mt

In [2]: mars.new_session()
Web service started at http://0.0.0.0:27007
Out[2]: <mars.deploy.oscar.session.SyncSession at 0x7ff911701970>

In [3]: r = np.random.RandomState(0).rand(12000, 12000)

In [4]: a = mt.array(r, chunk_size=1680).execute()
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100.0/100 [00:04<00:00, 23.36it/s]

In [5]: %time a.rechunk(2111).execute()
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100.0/100 [00:02<00:00, 49.79it/s]
CPU times: user 1.39 s, sys: 209 ms, total: 1.6 s
Wall time: 2.02 s
Out[5]: 
array([[0.5488135 , 0.71518937, 0.60276338, ..., 0.84348096, 0.94290928,
        0.83282242],
       [0.5646904 , 0.83974605, 0.37688365, ..., 0.40347954, 0.46089504,
        0.01048474],
       [0.15319619, 0.0328116 , 0.2287953 , ..., 0.90022341, 0.49806663,
        0.77000204],
       ...,
       [0.81724816, 0.69873782, 0.06925849, ..., 0.67440032, 0.90861683,
        0.80968811],
       [0.45391215, 0.72735698, 0.11037642, ..., 0.45976168, 0.62173263,
        0.12291647],
       [0.96026503, 0.01277616, 0.49155639, ..., 0.29565832, 0.12990437,
        0.82810578]])

Related issue number

Fixes #xxxx

Check code requirements

• tests added / passed (if needed)
• Ensure all linting tests pass, see here for how to run them

[wjsi]

LGTM

[hekaisheng]

LGTM