Improve UnicodeNormalizer from_python_unicodedata performance

[davidwendt]

Description

Improves the performance of the from_python_unicodedata classmethod for the UnicodeNormalizer python class.
The normalize member function is 27x faster than the python unicodedata library but only marginally faster when combined with constructing the UnicodeNormalizer required to call it. This PR modifies the from_python_unicodedata implementation so the combined calls are improved to 10x faster.

Checklist

• I am familiar with the Contributing Guidelines.
• New or existing tests cover these changes.
• The documentation is up to date with these changes.

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[davidwendt]

/ok to test a7b3726

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📝 Summary

Summary by CodeRabbit

• Performance
  • Improved Unicode normalization efficiency by reusing cached Unicode data across normalizer requests.
  • Preserved consistent normalization results while reducing repeated data processing.

Walkthrough

The Unicode normalizer now caches Python unicodedata scan results by Unicode database version. It reuses cached values, stores combining classes in an int32 array, and creates a new cudf DataFrame for each normalizer.

Changes

Unicode normalizer cache

Layer / File(s)	Summary
Cache and DataFrame reuse python/cudf/cudf/core/unicode_normalizer.py	A Unicode-version-keyed cache stores code points, combining classes, and decomposition mappings. from_python_unicodedata() scans only on cache misses, converts combining classes to int32, and rebuilds the DataFrame from cached data.

Estimated code review effort: 3 (Moderate) | ~15 minutes

Merge Risk: 🔵 Low · up to a7b37

Concurrent first use of Unicode normalization can perform redundant expensive initialization and temporarily increase latency and memory use. The impact is bounded to cold-cache races, but synchronization would make initialization behavior more predictable before merge.

Suggested reviewers: bdice

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 1 functions across 1 files.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly identifies the main change: improving the performance of UnicodeNormalizer.from_python_unicodedata.
Description check	✅ Passed	The description directly explains the performance improvement, target classmethod, expected speedup, and testing checklist. It is related to the changeset.

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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Actionable comments posted: 2

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Inline comments:
In `@python/cudf/cudf/core/unicode_normalizer.py`:
- Around line 141-164: Add unit tests around the Unicode normalizer cache
construction path, covering repeated construction and separate normalizer
instances while verifying cached results remain correct. Add a benchmark that
measures the first cold-cache construction separately from subsequent warm-cache
constructions, including the expected performance comparison for the stated 10×
target.
- Around line 142-154: The cold-cache initialization in from_python_unicodedata
can run concurrently and duplicate the expensive Unicode scan; guard the
_UNICODE_RAW_CACHE miss-and-populate path with a shared lock or single-flight
initializer, rechecking the cache after acquiring synchronization. Add a
concurrent regression benchmark covering simultaneous initialization.

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📥 Commits

Reviewing files that changed from the base of the PR and between cb88f9b and a7b3726.

📒 Files selected for processing (1)

• python/cudf/cudf/core/unicode_normalizer.py

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[davidwendt]

/ok to test 148eb81