[stdlib] Use ContiguousArray internally in Sequence dropLast, prefix, suffix

[palimondo]

Following the implementation template used in methods map and _filter, use the ContiguousArray internally in default implementations of dropLast, prefix and suffix. This is converted into an Array on return in a constant time. In addition to increased consistency of the internal implementation, this noticeably improves the performance of these methods for UnfoldSequence benchmark variants in the unoptimized build.

This PR is based on discussion in #20221 (review) and was split off from an experiment in #20758.

[palimondo]

@swift-ci please benchmark

[palimondo]

@swift-ci please smoke test

[swift-ci]

Build comment file:

Performance: -O

TEST	OLD	NEW	DELTA	RATIO
Improvement
PrefixWhileAnySeqCntRange	362	326	-9.9%	1.11x

Code size: -O

TEST	OLD	NEW	DELTA	RATIO
Improvement
PrefixWhile.o	22878	21422	-6.4%	1.07x

Performance: -Osize

TEST	OLD	NEW	DELTA	RATIO
Regression
PrefixWhileAnySeqCntRangeLazy	159	176	+10.7%	0.90x
Improvement
PrefixWhileAnyCollectionLazy	176	160	-9.1%	1.10x
DropLastCountableRangeLazy	12	11	-8.3%	1.09x
PrefixWhileAnyCollection	253	235	-7.1%	1.08x

Code size: -Osize

TEST	OLD	NEW	DELTA	RATIO
Improvement
PrefixWhile.o	21366	20118	-5.8%	1.06x

Performance: -Onone

TEST	OLD	NEW	DELTA	RATIO
Improvement
DropLastAnySequenceLazy	28499	18994	-33.4%	1.50x
DropLastSequence	27964	18718	-33.1%	1.49x
DropLastAnySequence	27937	18840	-32.6%	1.48x
DropLastSequenceLazy	27968	18938	-32.3%	1.48x
SuffixAnySequenceLazy	23969	18597	-22.4%	1.29x
SuffixSequence	23079	17963	-22.2%	1.28x
SuffixSequenceLazy	22847	17792	-22.1%	1.28x
SuffixAnySequence	22865	17848	-21.9%	1.28x
PrefixWhileAnySequence	33375	27697	-17.0%	1.21x
DropLastAnySeqCRangeIterLazy	63657	53994	-15.2%	1.18x
DropLastAnySeqCRangeIter	62899	53509	-14.9%	1.18x
DropLastAnySeqCntRange	62850	53518	-14.8%	1.17x
PrefixWhileSequence	33147	29661	-10.5%	1.12x
DropLastAnySeqCntRangeLazy	63577	57544	-9.5%	1.10x
SuffixAnySeqCntRangeLazy	58688	54678	-6.8%	1.07x

How to read the data

The tables contain differences in performance which are larger than 8% and differences in code size which are larger than 1%.

If you see any unexpected regressions, you should consider fixing the
regressions before you merge the PR.

Noise: Sometimes the performance results (not code size!) contain false
alarms. Unexpected regressions which are marked with '(?)' are probably noise.
If you see regressions which you cannot explain you can try to run the
benchmarks again. If regressions still show up, please consult with the
performance team (@eeckstein).

Hardware Overview

  Model Name: Mac Pro
  Model Identifier: MacPro6,1
  Processor Name: 12-Core Intel Xeon E5
  Processor Speed: 2.7 GHz
  Number of Processors: 1
  Total Number of Cores: 12
  L2 Cache (per Core): 256 KB
  L3 Cache: 30 MB
  Memory: 64 GB

--------------

[palimondo]

@airspeedswift @dabrahams Please review 🙏

[dabrahams]

LGTM!

[palimondo]

@dabrahams Should I merge this or wait for @airspeedswift to chime in?

Edit: I'll assume silence is a weak consent in this case. There is still the revert ability… 😇