Faster tokenizer

[bschnurr]

Already committed as 462a76a2f. Here's the updated PR description:

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Faster tokenizer

Summary

Continues the tokenizer optimization work with a series of hot-path improvements, plus review follow-ups on the ignore-directive scanner and identifier-intern behavior. Combined result: ~20–30% faster on large Python corpora.

Benchmark results vs main

Methodology: separate git worktrees of origin/main and faster-tokenizer, identical tokenizerBenchmark.test.ts harness (each corpus run in a fresh Node process, 3 warmup + 10 measured iterations). Numbers below are the median of per-run medians across 3–6 runs per side. Small corpora (< 2 ms, < 10 KB) are dominated by V8 JIT/GC jitter and noted as noise.

Corpus	Size	main	this PR	Δ
large_stdlib_10x	430 KB	25.98 ms	19.62 ms	−24%
large_class	24.5 KB	2.59 ms	1.78 ms	−31%
large_stdlib	43 KB	3.54 ms	2.80 ms	−21%
repetitive_identifiers	6.7 KB	2.25 ms	1.96 ms	−13%
union_heavy	13 KB	1.72 ms	1.64 ms	−5%
import_heavy	7.9 KB	1.10 ms	1.20 ms	+9% (noise)
fstring_heavy	8.4 KB	1.39 ms	1.64 ms	+18% (noise)
comment_heavy	9.5 KB	1.63 ms	1.89 ms	+16% (noise)

The large_stdlib_10x result is the most trustworthy signal (10× work, proportionally less noise) and shows a consistent −24%.

Enhancements

Tokenizer hot paths

• Replaced regex-based keyword / operator / ignore-directive matching with table-driven scans and 128-entry boolean lookup tables (_canStartString, _asciiIdentifierStart, _asciiIdentifierContinue, _keywordFirstCharTable, _singleCharOperatorTypeTable, …).
• Added an ASCII fast-path in _tryIdentifier that advances over ASCII identifier chars in a tight charCodeAt loop, falling back to the unicode/surrogate path only when a non-ASCII char is encountered. The single biggest win on real-world code.
• Added a direct-mapped identifier intern cache (2048 slots, hashed by firstChar/lastChar/length, no chaining) that deduplicates repeated identifiers (self, cls, True, None, str, int, …) within a single tokenize pass. Addresses the memory concern raised during review about per-token string allocation, without the overhead of the previous Map-based intern table. The new repetitive_identifiers benchmark corpus locks in the tradeoff (−13% vs main on that corpus).
• Inlined CharacterStream.skipWhitespace as a tight charCodeAt loop that updates _position / _currentChar / _isEndOfStream directly, avoiding per-iteration method calls.
• Cached the "any non-trivial tokens seen yet?" flag in _handleComment so the O(n) _tokens.findIndex scan no longer runs on every type: ignore directive.

Ignore-directive scanner (review follow-ups)

• Preserves support for namespaced type: ignore[...] rules (e.g. ty:rule-name).
• Rejects malformed type: ignore[ / pyright: ignore[ comments with an unclosed bracket rather than treating them as "ignore all diagnostics". New test TypeIgnoreLineMalformedBracketWithSpace locks in this behavior for the # type: ignore [broken case.
• Extracted the duplicated bracket-content character-class validation into a single parseIgnoreBracketContent helper — both the "bracket-after-space" and "bracket-immediately-after-ignore" branches now share one implementation.
• Added a fast pre-filter in _handleComment that uses indexOf('ignore', …) before invoking the directive scanner, so comments without the word ignore don't pay directive-parsing cost.
• matchIgnoreDirective uses a bounded hand-rolled scan for the directive keyword. (An earlier iteration used String.prototype.indexOf, which has no end bound and scanned well past the current comment on comment-heavy files, producing O(n²) behavior; the worktree-vs-main comparison caught and fixed this.)

Parser / source-file touch-ups

• Small follow-up adjustments in the parser and source-file layer needed by the tokenizer updates.

Benchmark infrastructure

• Added tokenizer and parser benchmark suites (tokenizerBenchmark.test.ts, parserBenchmark.test.ts) with representative corpora: large_stdlib, large_stdlib_10x, fstring_heavy, comment_heavy, large_class, import_heavy, union_heavy, and repetitive_identifiers (the last specifically validates the identifier intern cache).
• Each corpus runs in a fresh Node process (spawned via execFileSync) with 3 warmup + 10 measured iterations; results written as JSON under .generated/benchmark-results/ for side-by-side comparison.
• Benchmark helpers (calculateStats, printResultTable) take ReadonlyArray parameters per review feedback.

Tokenizer regression tests

• Added coverage for ignore-directive parsing: malformed unclosed brackets (with and without leading space), namespaced codes, and mixed plain + namespaced codes.

Ran terminal command: cd c:\dev\pyright-main-benchmark; git status; git ls-files packages/pyright-internal/src/tests/benchmarkData | Select-Object -First 3

Behavior notes

• # type: ignore[unclosed and # type: ignore [unclosed are now rejected entirely (no typeIgnoreLine recorded) instead of falling back to "ignore all". This matches the intent of the original regex for the [ branch and is now consistent between the space and no-space cases.
• Namespaced codes (ty:rule-name) remain valid inside type: ignore[...] but not inside pyright: ignore[...].

Validation

• npm test (full pyright-internal suite) — green.
• Targeted: npx jest tokenizer parser.test --forceExit — 137/137 passing.
• npm run test:benchmark — table above; raw JSON in .generated/benchmark-results/tokenizer/.

[rchiodo]

detachSubstring replaces the old cloneStr + _identifierInternedStrings Map for identifiers. The old intern map deduplicated repeated identifiers within a tokenization pass (e.g., 10,000 occurrences of self → 1 string object). The new code creates a fresh string per token. The benchmark corpora are 300–1700 lines and won't surface memory regressions on very large files with repetitive identifiers (e.g., generated code). Consider adding a "repetitive identifier" benchmark corpus to validate this tradeoff, or document that the intern map was intentionally removed with the expectation that per-token cost savings outweigh deduplication benefits.

[bschnurr]

Addressed in commit 462a76a: added a repetitive_identifiers benchmark corpus (234 lines, 2775 tokens) that exercises heavy reuse of identifiers like self, cls, str, int, T, K, V, repeated method signatures, and list/dict comprehensions with common names. Result vs main: branch ~1.96 ms, main ~2.25 ms (~13% faster), confirming the intern cache outperforms per-token allocation on this workload.

[github-actions]

According to mypy_primer, this change doesn't affect type check results on a corpus of open source code. ✅