Support generating dtypes, index_value etc lazily for DataFrame chunks

[qinxuye]

What do these changes do?

This PR aims to improve performance of building tileables and chunks.

Test code:

import mars.tensor as mt
import mars.dataframe as md
from mars.utils import Timer
from mars.core.graph.builder.utils import build_graph


df = md.DataFrame(mt.random.rand(5000, 10, chunk_size=(2, 5)), columns=list('abcdefghij'))
df = df[df['a'] < 0.8]

with Timer() as timer:
    g = build_graph([df], tile=True)
    print(len(g))
print('cost', timer.duration, 'seconds')

Master	This PR
6.476	3.231

Related issue number

#2791

Check code requirements

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

[qinxuye]

According to cb test.

[ 50.00%] · For pymars commit 49c67952 (round 2/2):
[ 50.00%] ·· Benchmarking conda-py3.8-Cython0.29.24-cloudpickle-defusedxml-numexpr-numpy-pandas-psutil-pyyaml-scikit-learn-scipy-sqlalchemy-tornado
[ 62.50%] ··· ...otes.EmptyRemotesExecutionSuite.time_remotes         6.43±0.02s
[ 75.00%] ··· ...h_builder.ChunkGraphBuilderSuite.time_filter           613±40ms
[ 75.00%] · For pymars commit e1b8c9dd (round 2/2):
[ 75.00%] ·· Building for conda-py3.8-Cython0.29.24-cloudpickle-defusedxml-numexpr-numpy-pandas-psutil-pyyaml-scikit-learn-scipy-sqlalchemy-tornado
[ 75.00%] ·· Benchmarking conda-py3.8-Cython0.29.24-cloudpickle-defusedxml-numexpr-numpy-pandas-psutil-pyyaml-scikit-learn-scipy-sqlalchemy-tornado
[ 87.50%] ··· ...otes.EmptyRemotesExecutionSuite.time_remotes         6.78±0.03s
[100.00%] ··· ...h_builder.ChunkGraphBuilderSuite.time_filter         1.18±0.01s

Both tests are better, especially ChunkGraphBuilderSuite.time_filter, from 1.18s to 613ms, almost half the time.

[qinxuye]

Ready for review now.

[wjsi]

LGTM

[hekaisheng]

LGTM