I did some profiling (attached a few files here with svgs) of running this script:

```python
import io
import tokenize

# picked as the second longest file in cpython
with open('Lib/test/test_socket.py', 'rb') as f:
    bio = io.BytesIO(f.read())


def main():
    for _ in range(10):
        bio.seek(0)
        for _ in tokenize.tokenize(bio.readline):
            pass

if __name__ == '__main__':
    exit(main())
```


the first profile is before the optimization, the second is after the optimization

The optimization takes the execution from ~6300ms to ~4500ms on my machine (representing a 28% - 39% improvement depending on how you calculate it)

(I'll attach the pstats and svgs after creation, seems I can only attach one file at once)

admittedly anecdotal but here's another data point in addition to the profiles attached

test.test_tokenize suite before:

$ ./python -m test.test_tokenize
..............................................................................
----------------------------------------------------------------------
Ran 78 tests in 77.148s

OK


test.test_tokenize suite after:

$ ./python -m test.test_tokenize
..............................................................................
----------------------------------------------------------------------
Ran 78 tests in 61.269s

OK

attached out3.pstats / out3.svg which represent the optimization using lru_cache instead

New changeset 15bd9efd01e44087664e78bf766865a6d2e06626 by Anthony Sottile in branch 'master':
bpo-43014: Improve performance of tokenize.tokenize by 20-30%
https://github.com/python/cpython/commit/15bd9efd01e44087664e78bf766865a6d2e06626

Just for the record:

> The optimization takes the execution from ~6300ms to ~4500ms on my machine (representing a 28% - 39% improvement depending on how you calculate it)

The correct answer is 28%, which uses the initial value as the base: (6300-4500)/6300 ≈ 28%. You are starting at 6300ms and speeding it up by 28%:

>>> 6300 - 28/100*6300
4536.0

Using 4500 as the base would only make sense if you were calculating a slowdown from 4500ms to 6300ms: we started at 4500 and *increase* the time by 39%:

>>> 4500 + 39/100*4500
6255.0


Hope this helps.

re.compile() already uses caching. But it is less efficient for some reasons.

To Steven: the time is *reduced* by 28%, but the speed is *increased* by 39%.