Add FxHash and ShortStringOptimization.

[MeetThePatel]

This PR contains performance optimizations (spreadsheet of benchmark comparisons linked below). The two main optimizations are:

• Switching to FxHash instead of standard library default hasher. This is provided by the rustc_hash crate, which the compiler uses internally.

• Switch from String to CompactString, provided by the compact_str crate, which has short-string-optimization for up to 24 bytes, which many tokens can fit inside of.

Progress:

This PR is not fully complete. At this point in time, the following tasks have been completed:

• Convert base crate.
  • Tests are passing, and benchmarks were run (linked below).
• Convert Python bindings.
  • The same tests that pass on HEAD are passing on this branch. There seems to be 2 failing tests on HEAD, but this branch fails the exact same tests, and in the same manner (i.e. same output).
  • There seems to be an issue with my implementation of the pyo3. The benchmarks for the base crate are outperforming HEAD by a decent margin, but the benches/test_tiktoken.py are basically the same (within a margin of error) of HEAD. I think it is caused by unnecessary type conversions.
• Convert Node bindings.
• Cleanup.

Benchmarks:

The benchmarks are at the following link: Tokenizer Benchmarks. All benchmarks were run on a Macbook Pro with the M2 Pro chip and 16GB memory.

Additionally, the BPE Train vocabulary (huge) benchmark is one that I added (not committed, as that would warrant its own PR). This benchmark is using the dataset: One Billion Word Challenge, which clocks in at 4.15GB.

[ArthurZucker]

Sounds good! Node bidings are really not necessary!

[ArthurZucker]

Do you want this to be reviewed now?

[MeetThePatel]

Not yet. I'm still working on a few things:

• See if there actually is a bottleneck in the Python-Rust interface (regarding extra type conversions), or if the benchmarks aren't pushing the crate hard enough to see a meaningful difference. It should be ~10-15% faster (according to the base crate BPE encode benchmark).
• Clean up code quality.

[MeetThePatel]

I think this is ready for review.

Also, these are the distributions for the benchmark runs (blue is this PR, red is HEAD). Besides the speed being a bit faster, it seems to be more consistent as well (as least on my machine).

HEAD vs PR base crate benchmarks.pdf

[McPatate]

Hi! Thanks for the big contribution. Would you mind splitting the PR in (at least) two? One for each optimisation, this would make reviewing easier.