Add feature to print forest shape in FIL upon importing

[levsnv]

When benchmarking forests, it's important to know what we're benchmarking, regardless of the source of the forest. E.g. are we still importing an equivalent-sized forest after updating xgboost version?
This is also specific to understanding FIL performance on a model, as opposed to generic forest statistics.
This allows to obtain a couple of numbers from FIL, both pertaining to the model and the chosen storage type, post-import size, etc.
A sample output looks like this:

Depth hist:
depth	branches	leaves	nodes
  0	     5	     0	      5
  1	    10	     0	     10
  2	    20	     0	     20
  3	    19	    21	     40
  4	    15	    23	     38
  5	    12	    18	     30
  6	     2	    22	     24
  7	     0	     4	      4
Total: branches: 83 leaves: 88 nodes: 171
Avg nodes per tree: 34.2
Leaf depth: min: 3 avg 4.6 max 7
Depth histogram fingerprint: 4752b3a7893d5e22
DENSE model size 0.01 MB

The feature is breaking because one new C parameter is added to treelite_params_t. Python API is not broken, because it sets the backward-compatible defaults.

[hcho3]

Should this feature be incorporated into Treelite?

[levsnv]

Yes, that would be great. My understanding is treelite would only have a model dumping function, and simpler stats? In which case, the code to make leaf/branch depth histogram would need to live somewhere. Hence, I thought FIL would serve.
Also, some parameters, like post-import format and size (and potentially perf tuning parameters in the future) are specific to FIL and would need an API to dump anyway.
What do you think?

[levsnv]

I also don't know whether the parameters should be part of treelite_params_t instead of naked arguments to from_treelite()

[canonizer]

Fingerprinting isn't necessary here.

However, if you still want to do it, I would suggest the following approach:

1. Build the data structure containing the forest statistics.
2. Convert it into an array of bytes by successively appending the bytes representing each integer in the data structure.
3. Compute a standard hash function on the array. A standard cryptographic hash function is definitely preferred.

In this way, you can split this code into multiple functions, each performing a single piece of work.

[levsnv]

I want to do this, because when sweeping models for benchmarking, it let me quickly understand which models are different and which are the same. Same with putting the results into a spreadsheet - easier to compare runs to each other if the fingerprint is just equal.

[levsnv]

Waiting until #3800 is merged to push the conflict-resolved version

[ajschmidt8]

Removing ops-codeowners from the required reviews since it doesn't seem there are any file changes that we're responsible for. Feel free to add us back if necessary.

[dantegd]

Had 1 very minor comment about a header and one question/comment regarding how the API looks like from a python perspective

[codecov-commenter]

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[dantegd]

@gpucibot merge