Add a CanonicalOSSALifetime utility #35241
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This was referenced Dec 30, 2020
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Canonicalizing OSSA provably minimizes the number of retains and releases within the boundaries of that lifetime. This eliminates the need for ad-hoc optimization of OSSA copies. This initial implementation only canonicalizes owned values, but canonicalizing guaranteed values is a simple extension. This was originally part of the CopyPropagation prototype years ago. Now OSSA is specified completely enough that it can be turned into a simple utility instead. CanonicalOSSALifetime uses PrunedLiveness to find the extended live range and identify the consumes on the boundary. All other consumes need their own copy. No other copies are needed. By running this after other transformations that affect OSSA lifetimes, we can avoid the need to run pattern-matching optimization to SemanticARC to recover from suboptimal patterns, which is not robust, maintainable, or efficient.
MandatoryCopyPropagation must be a separate pass in order to preserve all debug_value instructions. CopyPropagation cannot preserve debug_value because, as a rule, debug information cannot affect -O behavior.
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If a guaranteed value is not a recognized and handled borrow introducer, then treat the copy as a separate owned live range.
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Canonicalizing OSSA provably minimizes the number of retains and
releases within the boundaries of that lifetime. This eliminates the
need for ad-hoc optimization of OSSA copies.
This initial implementation only canonicalizes owned values, but
canonicalizing guaranteed values is a simple extension.
This was originally part of the CopyPropagation prototype years
ago. Now OSSA is specified completely enough that it can be turned
into a simple utility instead.
CanonicalOSSALifetime uses PrunedLiveness to find the extended live
range and identify the consumes on the boundary. All other consumes
need their own copy. No other copies are needed.
By running this after other transformations that affect OSSA
lifetimes, we can avoid the need to run pattern-matching optimization
to SemanticARC to recover from suboptimal patterns, which is not
robust, maintainable, or efficient.