We were giving special handling to ApplyInst when we were attempting to use
getMemoryBehavior(). This commit changes the special handling to work on all
full apply sites instead of just AI. Additionally, we look through partial
applies and thin to thick functions.
I also added a dumper called BasicInstructionPropertyDumper that just dumps the
results of SILInstruction::get{Memory,Releasing}Behavior() for all instructions
in order to verify this behavior.
With this re-abstraction a specialized function has the same calling convention as if it would have been written with the specialized types in the first place.
In general this results in less alloc_stacks and load/stores.
It also can eliminate some re-abstraction thunks, e.g. if a generic closure is used in a non-generic context.
It some (hopefully rare) cases it may require to add re-abstraction thunks.
In case a function has multiple indirect results, only the first is converted to a direct result. This is an open TODO.
Currently the array.get_element calls return the element as indirect result.
The generic specializer will change so that the element can be returned as direct result.
For a release on a guaranteed function paramater, we know right away
that its not the final release and therefore does not call deinit.
Therefore we know it does not read or write memory other than the reference
count.
This reduces the compilation time of dead store and redundant load elim. As
we need to go over alias analysis to make sure tracked locations do not alias
with it.
Instead of only checking the return block, we could potentially check
its predecessors and its predecessors's predecessors, etc.
Alos put in a threshold to throttle this to make sure its cheap.
We are still only being able to find of a small # of epilogue retains.
The bail on MayDecrement is blocking many of the opportunites.
This should bring us closer to being able to handle Walsh.
This is part of rdar://24022375.
Similarly to how we've always handled parameter types, we
now recursively expand tuples in result types and separately
determine a result convention for each result.
The most important code-generation change here is that
indirect results are now returned separately from each
other and from any direct results. It is generally far
better, when receiving an indirect result, to receive it
as an independent result; the caller is much more likely
to be able to directly receive the result in the address
they want to initialize, rather than having to receive it
in temporary memory and then copy parts of it into the
target.
The most important conceptual change here that clients and
producers of SIL must be aware of is the new distinction
between a SILFunctionType's *parameters* and its *argument
list*. The former is just the formal parameters, derived
purely from the parameter types of the original function;
indirect results are no longer in this list. The latter
includes the indirect result arguments; as always, all
the indirect results strictly precede the parameters.
Apply instructions and entry block arguments follow the
argument list, not the parameter list.
A relatively minor change is that there can now be multiple
direct results, each with its own result convention.
This is a minor change because I've chosen to leave
return instructions as taking a single operand and
apply instructions as producing a single result; when
the type describes multiple results, they are implicitly
bound up in a tuple. It might make sense to split these
up and allow e.g. return instructions to take a list
of operands; however, it's not clear what to do on the
caller side, and this would be a major change that can
be separated out from this already over-large patch.
Unsurprisingly, the most invasive changes here are in
SILGen; this requires substantial reworking of both call
emission and reabstraction. It also proved important
to switch several SILGen operations over to work with
RValue instead of ManagedValue, since otherwise they
would be forced to spuriously "implode" buffers.
If a value is returned as @owned, we can move the epilogue retain
to the caller and convert the return value to @unowned. This gives
ARC optimizer more freedom to optimize the retain out on the caller's
side.
It appears that epilgue retains are harder to find than epilogue
releases. Most of the time they are not in the return block.
(1) Sometimes, they are in predecessors
(2) Sometimes they come from a call which returns an @owned return value.
This should be improved if we fix (1) and go bottom up.
(3) We do not handle exploded retain_value.
Currently, this catches a small number of opportunities.
We probably need to improve epilogue retain matcher if we are to handle
more cases.
This is part of rdar://24022375.
We also need some refactoring in the pass. e.g. break functions into smaller
functions. I will do with subsequent commit.
This shaves of ~0.5 seconds from ARC when compiling the stdlib on my machine.
I wired up the cache to the delete notification trigger so we are still memory
safe.
This is similar and yet different from epilogue release matcher. Particularly
how retain is found and when to bail. Therefore this is put into a different
class than ConsumedArgToEpilogueReleaseMatcher
This is currently a NFC other than some basic testing using the epilogue dumper.
When we have all the epilogue releases. Make sure they cover all the non-trivial
parts of the base. Otherwise, treat as if we've found no releases for the base.
Currently. this is a NFC other than epilogue dumper. I will wire it up with
function signature with next commit.
This is part of rdar://22380547
So instead of only being able to match %1 and release %1 in (1). we
can also match %1 with (release %2, and release%3, i.e. exploded release_value)
in (2).
(1)
foo(%1)
strong_release %1
(2)
foo(%1)
%2 = struct_extract %1, field_a
%3 = struct_extract %1, field_b
strong_release %2
strong_release %3
This will allow function signature to better move the release instructions to
the callers.
Currently, this is a NFC other than testing using the epilogue match dumper.
This is done by splitting the transformation into an analysis phase and a transformation phase (which does not use the dominator tree anymore).
The domintator tree is recalucated once after the whole function is processed.
This change eventually solves the compile time problem of rdar://problem/24410167.
Allow function passes to:
1. Add new functions, to be optimized before continuing with the current
function.
2. Restart the pipeline on the current function after the current pass
completes.
This makes it possible to fully optimize callees that are the result of
specialization prior to generating interprocedural information or making
inlining choices about these callees.
It also allows us to solve a phase-ordering issue we have with generic
specialization, devirtualization, and inlining, by rescheduling the
current function after changes happen in one of these passes as opposed
to running all of these as part of the inlining pass as happens today.
Currently this is NFC since we have no passes that use this
functionality.
This patch also implements some of the missing functions used by RLE and DSE in new projection
that exist in the old projection.
New projection provides better memory usage, eventually we will phase out the old projection code.
New projection is now copyable, i.e. we have a proper constructor for it. This helps make the code
more readable.
We do see a bit increase in compilation time in compiling stdlib -O, this is a result of the way
we now get types of a projection path, but I expect this to go down (away) with further improvement
on how memory locations are constructed and cached with later patches.
=== With the OLD Projection. ===
Total amount of memory allocated.
--------------------------------
Bytes Used Count Symbol Name
13032.01 MB 50.6% 2158819 swift::SILPassManager::runPassesOnFunction(llvm::ArrayRef<swift::SILFunctionTransform*>, swift::SILFunction*)
2879.70 MB 11.1% 3076018 (anonymous namespace)::ARCSequenceOpts::run()
2663.68 MB 10.3% 1375465 (anonymous namespace)::RedundantLoadElimination::run()
1534.35 MB 5.9% 5067928 (anonymous namespace)::SimplifyCFGPass::run()
1278.09 MB 4.9% 576714 (anonymous namespace)::SILCombine::run()
1052.68 MB 4.0% 935809 (anonymous namespace)::DeadStoreElimination::run()
771.75 MB 2.9% 1677391 (anonymous namespace)::SILCSE::run()
715.07 MB 2.7% 4198193 (anonymous namespace)::GenericSpecializer::run()
434.87 MB 1.6% 652701 (anonymous namespace)::SILSROA::run()
402.99 MB 1.5% 658563 (anonymous namespace)::SILCodeMotion::run()
341.13 MB 1.3% 962459 (anonymous namespace)::DCE::run()
279.48 MB 1.0% 415031 (anonymous namespace)::StackPromotion::run()
Compilation time breakdown.
--------------------------
Running Time Self (ms) Symbol Name
25716.0ms 35.8% 0.0 swift::runSILOptimizationPasses(swift::SILModule&)
25513.0ms 35.5% 0.0 swift::SILPassManager::runOneIteration()
20666.0ms 28.8% 24.0 swift::SILPassManager::runFunctionPasses(llvm::ArrayRef<swift::SILFunctionTransform*>)
19664.0ms 27.4% 77.0 swift::SILPassManager::runPassesOnFunction(llvm::ArrayRef<swift::SILFunctionTransform*>, swift::SILFunction*)
3272.0ms 4.5% 12.0 (anonymous namespace)::SimplifyCFGPass::run()
3266.0ms 4.5% 7.0 (anonymous namespace)::ARCSequenceOpts::run()
2608.0ms 3.6% 5.0 (anonymous namespace)::SILCombine::run()
2089.0ms 2.9% 104.0 (anonymous namespace)::SILCSE::run()
1929.0ms 2.7% 47.0 (anonymous namespace)::RedundantLoadElimination::run()
1280.0ms 1.7% 14.0 (anonymous namespace)::GenericSpecializer::run()
1010.0ms 1.4% 45.0 (anonymous namespace)::DeadStoreElimination::run()
966.0ms 1.3% 191.0 (anonymous namespace)::DCE::run()
496.0ms 0.6% 6.0 (anonymous namespace)::SILCodeMotion::run()
=== With the NEW Projection. ===
Total amount of memory allocated.
--------------------------------
Bytes Used Count Symbol Name
11876.64 MB 48.4% 22112349 swift::SILPassManager::runPassesOnFunction(llvm::ArrayRef<swift::SILFunctionTransform*>, swift::SILFunction*)
2887.22 MB 11.8% 3079485 (anonymous namespace)::ARCSequenceOpts::run()
1820.89 MB 7.4% 1877674 (anonymous namespace)::RedundantLoadElimination::run()
1533.16 MB 6.2% 5073310 (anonymous namespace)::SimplifyCFGPass::run()
1282.86 MB 5.2% 577024 (anonymous namespace)::SILCombine::run()
772.21 MB 3.1% 1679154 (anonymous namespace)::SILCSE::run()
721.69 MB 2.9% 936958 (anonymous namespace)::DeadStoreElimination::run()
715.08 MB 2.9% 4196263 (anonymous namespace)::GenericSpecializer::run()
Compilation time breakdown.
--------------------------
Running Time Self (ms) Symbol Name
25137.0ms 37.3% 0.0 swift::runSILOptimizationPasses(swift::SILModule&)
24939.0ms 37.0% 0.0 swift::SILPassManager::runOneIteration()
20226.0ms 30.0% 29.0 swift::SILPassManager::runFunctionPasses(llvm::ArrayRef<swift::SILFunctionTransform*>)
19241.0ms 28.5% 83.0 swift::SILPassManager::runPassesOnFunction(llvm::ArrayRef<swift::SILFunctionTransform*>, swift::SILFunction*)
3214.0ms 4.7% 10.0 (anonymous namespace)::SimplifyCFGPass::run()
3005.0ms 4.4% 14.0 (anonymous namespace)::ARCSequenceOpts::run()
2438.0ms 3.6% 7.0 (anonymous namespace)::SILCombine::run()
2217.0ms 3.2% 54.0 (anonymous namespace)::RedundantLoadElimination::run()
2212.0ms 3.2% 131.0 (anonymous namespace)::SILCSE::run()
1195.0ms 1.7% 11.0 (anonymous namespace)::GenericSpecializer::run()
1168.0ms 1.7% 39.0 (anonymous namespace)::DeadStoreElimination::run()
853.0ms 1.2% 150.0 (anonymous namespace)::DCE::run()
499.0ms 0.7% 7.0 (anonymous namespace)::SILCodeMotion::run()
SILValue.h/.cpp just defines the SIL base classes. Referring to specific instructions is a (small) kind of layering violation.
Also I want to keep SILValue small so that it is really just a type alias of ValueBase*.
NFC.
