entire SCC before iterating on newly-introduced call edges resulting
from any inlined function bodies.
This more closely matches the behavior of the old PM's inliner. While it
wasn't really clear to me initially, this behavior is actually essential
to the inliner behaving reasonably in its current design.
Because the inliner is fundamentally a bottom-up inliner and all of its
cost modeling is designed around that it often runs into trouble within
an SCC where we don't have any meaningful bottom-up ordering to use. In
addition to potentially cyclic, infinite inlining that we block with the
inline history mechanism, it can also take seemingly simple call graph
patterns within an SCC and turn them into *insanely* large functions by
accidentally working top-down across the SCC without any of the
threshold limitations that traditional top-down inliners use.
Consider this diabolical monster.cpp file that Richard Smith came up
with to help demonstrate this issue:
```
template <int N> extern const char *str;
void g(const char *);
template <bool K, int N> void f(bool *B, bool *E) {
if (K)
g(str<N>);
if (B == E)
return;
if (*B)
f<true, N + 1>(B + 1, E);
else
f<false, N + 1>(B + 1, E);
}
template <> void f<false, MAX>(bool *B, bool *E) { return f<false, 0>(B, E); }
template <> void f<true, MAX>(bool *B, bool *E) { return f<true, 0>(B, E); }
extern bool *arr, *end;
void test() { f<false, 0>(arr, end); }
```
When compiled with '-DMAX=N' for various values of N, this will create an SCC
with a reasonably large number of functions. Previously, the inliner would try
to exhaust the inlining candidates in a single function before moving on. This,
unfortunately, turns it into a top-down inliner within the SCC. Because our
thresholds were never built for that, we will incrementally decide that it is
always worth inlining and proceed to flatten the entire SCC into that one
function.
What's worse, we'll then proceed to the next function, and do the exact same
thing except we'll skip the first function, and so on. And at each step, we'll
also make some of the constant factors larger, which is awesome.
The fix in this patch is the obvious one which makes the new PM's inliner use
the same technique used by the old PM: consider all the call edges across the
entire SCC before beginning to process call edges introduced by inlining. The
result of this is essentially to distribute the inlining across the SCC so that
every function incrementally grows toward the inline thresholds rather than
allowing the inliner to grow one of the functions vastly beyond the threshold.
The code for this is a bit awkward, but it works out OK.
We could consider in the future doing something more powerful here such as
prioritized order (via lowest cost and/or profile info) and/or a code-growth
budget per SCC. However, both of those would require really substantial work
both to design the system in a way that wouldn't break really useful
abstraction decomposition properties of the current inliner and to be tuned
across a reasonably diverse set of code and workloads. It also seems really
risky in many ways. I have only found a single real-world file that triggers
the bad behavior here and it is generated code that has a pretty pathological
pattern. I'm not worried about the inliner not doing an *awesome* job here as
long as it does *ok*. On the other hand, the cases that will be tricky to get
right in a prioritized scheme with a budget will be more common and idiomatic
for at least some frontends (C++ and Rust at least). So while these approaches
are still really interesting, I'm not in a huge rush to go after them. Staying
even closer to the existing PM's behavior, especially when this easy to do,
seems like the right short to medium term approach.
I don't really have a test case that makes sense yet... I'll try to find a
variant of the IR produced by the monster template metaprogram that is both
small enough to be sane and large enough to clearly show when we get this wrong
in the future. But I'm not confident this exists. And the behavior change here
*should* be unobservable without snooping on debug logging. So there isn't
really much to test.
The test case updates come from two incidental changes:
1) We now visit functions in an SCC in the opposite order. I don't think there
really is a "right" order here, so I just update the test cases.
2) We no longer compute some analyses when an SCC has no call instructions that
we consider for inlining.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@297374 91177308-0d34-0410-b5e6-96231b3b80d8
Summary:
As written in the comments above, LastCallToStaticBonus is already applied to
the cost if Caller has only one user, so it is redundant to reapply the bonus
here.
If the only user is not a caller, TotalSecondaryCost will not be adjusted
anyway because callerWillBeRemoved is false. If there's no caller at all, we
don't need to care about TotalSecondaryCost because
inliningPreventsSomeOuterInline is false.
Reviewers: chandlerc, eraman
Reviewed By: eraman
Subscribers: haicheng, davidxl, davide, llvm-commits, mehdi_amini
Differential Revision: https://reviews.llvm.org/D29169
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disturbing the graph or having to update edges.
This is motivated by porting argument promotion to the new pass manager.
Because of how LLVM IR Function objects work, in order to change their
signature a new object needs to be created. This is efficient and
straight forward in the IR but previously was very hard to implement in
LCG. We could easily replace the function a node in the graph
represents. The challenging part is how to handle updating the edges in
the graph.
LCG previously used an edge to a raw function to represent a node that
had not yet been scanned for calls and references. This was the core
of its laziness. However, that model causes this kind of update to be
very hard:
1) The keys to lookup an edge need to be `Function*`s that would all
need to be updated when we update the node.
2) There will be some unknown number of edges that haven't transitioned
from `Function*` edges to `Node*` edges.
All of this complexity isn't necessary. Instead, we can always build
a node around any function, always pointing edges at it and always using
it as the key to lookup an edge. To maintain the laziness, we need to
sink the *edges* of a node into a secondary object and explicitly model
transitioning a node from empty to populated by scanning the function.
This design seems much cleaner in a number of ways, but importantly
there is now exactly *one* place where the `Function*` has to be
updated!
Some other cleanups that fall out of this include having something to
model the *entry* edges more accurately. Rather than hand rolling parts
of the node in the graph itself, we have an explicit `EdgeSequence`
object that gives us exactly the functionality needed. We also have
a consistent place to define the edge iterators and can use them for
both the entry edges and the internal edges of the graph.
The API used to model the separation between a node and its edges is
intentionally very thin as most clients are expected to deal with nodes
that have populated edges. We model this exactly as an optional does
with an additional method to populate the edges when that is
a reasonable thing for a client to do. This is based on API design
suggestions from Richard Smith and David Blaikie, credit goes to them
for helping pick how to model this without it being either too explicit
or too implicit.
The patch is somewhat noisy due to shifting around iterator types and
new syntax for walking the edges of a node, but most of the
functionality change is in the `Edge`, `EdgeSequence`, and `Node` types.
Differential Revision: https://reviews.llvm.org/D29577
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a lazy-asserting PoisoningVH.
AssertVH is fundamentally incompatible with cache-invalidation of
analysis results. The invaliadtion happens after the AssertingVH has
already fired. Instead, use a PoisoningVH that will assert if the
dangling handle is ever used rather than merely be assigned or
destroyed.
This patch also removes all of the (numerous) doomed attempts to work
around this fundamental incompatibility. It is a pretty significant
simplification IMO.
The most interesting change is in the Inliner where we still do some
clearing because we don't want to rely on the coarse grained
invalidation strategy of the containing pass manager. However, I prefer
the approach that contains this logic to the cleanup phase of the
Inliner, and I think we could enhance the CGSCC analysis management
layer to make this even better in the future if desired.
The rest is straight cleanup.
I've also added a test for one of the harder cases to work around: when
a *module analysis* contains many AssertingVHes pointing at functions.
Differential Revision: https://reviews.llvm.org/D29006
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clearing its body. This is essential to avoid triggering asserting value
handles in analyses on the function's body.
I'm working on a test case for this behavior in LLVM, but Clang has
a great one that managed to trigger this on all of the bots already.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@292770 91177308-0d34-0410-b5e6-96231b3b80d8
new PM's inliner.
The bug happens when we refine an SCC after having computed a proxy for
the FunctionAnalysisManager, and then proceed to compute fresh analyses
for functions in the *new* SCC using the manager provided by the old
SCC's proxy. *And* when we manage to mutate a function in this new SCC
in a way that invalidates those analyses. This can be... challenging to
reproduce.
I've managed to contrive a set of functions that trigger this and added
a test case, but it is a bit brittle. I've directly checked that the
passes run in the expected ways to help avoid the test just becoming
silently irrelevant.
This gets the new PM back to passing the LLVM test suite after the PGO
improvements landed.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@292757 91177308-0d34-0410-b5e6-96231b3b80d8
This adds the following to the new PM based inliner in PGO mode:
* Use block frequency analysis to derive callsite's profile count and use
that to adjust thresholds of hot and cold callsites.
* Incrementally update the BFI of the caller after a callee gets inlined
into it. This incremental update is only within an invocation of the run
method - BFI is not preserved across calls to run.
Update the function entry count of the callee after inlining it into a
caller.
* I've tuned the thresholds for the hot and cold callsites using a hacked
up version of the old inliner that explicitly computes BFI on a set of
internal benchmarks and spec. Once the new PM based pipeline stabilizes
(IIRC Chandler mentioned there are known issues) I'll benchmark this
again and adjust the thresholds if required.
Inliner PGO support.
Differential revision: https://reviews.llvm.org/D28331
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when they are call edges at the leaf but may (transitively) be reached
via ref edges.
It turns out there is a simple rule: insert everything as a ref edge
which is a safe conservative default. Then we let the existing update
logic handle promoting some of those to call edges.
Note that it would be fairly cheap to make these call edges right away
if that is desirable by testing whether there is some existing call path
from the source to the target. It just seemed like slightly more
complexity in this code path that isn't strictly necessary. If anyone
feels strongly about handling this differently I'm happy to change it.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@290649 91177308-0d34-0410-b5e6-96231b3b80d8
skipping indirectly recursive inline chains.
To do this, we implicitly build an inline stack for each callsite and
check prior to inlining that doing so would not form a cycle. This uses
the exact same technique and even shares some code with the legacy PM
inliner.
This solution remains deeply unsatisfying to me because it means we
cannot actually iterate the inliner externally. Doing so would not be
able to easily detect and avoid such cycles. Some day I would very much
like to have a solution that works without this internal state to detect
cycles, but this is not that day.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@290590 91177308-0d34-0410-b5e6-96231b3b80d8
removing fully-dead comdats without removing dead entries in comdats
with live members.
This factors the core logic out of the current inliner's internals to
a reusable utility and leverages that in both places. The factored out
code should also be (minorly) more efficient in cases where we have very
few dead functions or dead comdats to consider.
I've added a test case to cover this behavior of the always inliner.
This is the last significant bug in the new PM's always inliner I've
found (so far).
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@290557 91177308-0d34-0410-b5e6-96231b3b80d8
This doesn't implement *every* feature of the existing inliner, but
tries to implement the most important ones for building a functional
optimization pipeline and beginning to sort out bugs, regressions, and
other problems.
Notable, but intentional omissions:
- No alloca merging support. Why? Because it isn't clear we want to do
this at all. Active discussion and investigation is going on to remove
it, so for simplicity I omitted it.
- No support for trying to iterate on "internally" devirtualized calls.
Why? Because it adds what I suspect is inappropriate coupling for
little or no benefit. We will have an outer iteration system that
tracks devirtualization including that from function passes and
iterates already. We should improve that rather than approximate it
here.
- Optimization remarks. Why? Purely to make the patch smaller, no other
reason at all.
The last one I'll probably work on almost immediately. But I wanted to
skip it in the initial patch to try to focus the change as much as
possible as there is already a lot of code moving around and both of
these *could* be skipped without really disrupting the core logic.
A summary of the different things happening here:
1) Adding the usual new PM class and rigging.
2) Fixing minor underlying assumptions in the inline cost analysis or
inline logic that don't generally hold in the new PM world.
3) Adding the core pass logic which is in essence a loop over the calls
in the nodes in the call graph. This is a bit duplicated from the old
inliner, but only a handful of lines could realistically be shared.
(I tried at first, and it really didn't help anything.) All told,
this is only about 100 lines of code, and most of that is the
mechanics of wiring up analyses from the new PM world.
4) Updating the LazyCallGraph (in the new PM) based on the *newly
inlined* calls and references. This is very minimal because we cannot
form cycles.
5) When inlining removes the last use of a function, eagerly nuking the
body of the function so that any "one use remaining" inline cost
heuristics are immediately refined, and queuing these functions to be
completely deleted once inlining is complete and the call graph
updated to reflect that they have become dead.
6) After all the inlining for a particular function, updating the
LazyCallGraph and the CGSCC pass manager to reflect the
function-local simplifications that are done immediately and
internally by the inline utilties. These are the exact same
fundamental set of CG updates done by arbitrary function passes.
7) Adding a bunch of test cases to specifically target CGSCC and other
subtle aspects in the new PM world.
Many thanks to the careful review from Easwaran and Sanjoy and others!
Differential Revision: https://reviews.llvm.org/D24226
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After r289755, the AssumptionCache is no longer needed. Variables affected by
assumptions are now found by using the new operand-bundle-based scheme. This
new scheme is more computationally efficient, and also we need much less
code...
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@289756 91177308-0d34-0410-b5e6-96231b3b80d8
The core of the change is supposed to be NFC, however it also fixes
what I believe was an undefined behavior when calling:
va_start(ValueArgs, Desc);
with Desc being a StringRef.
Differential Revision: https://reviews.llvm.org/D25342
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There is really no reason for these to be separate.
The vectorizer started this pretty bad tradition that the text of the
missed remarks is pretty meaningless, i.e. vectorization failed. There,
you have to query analysis to get the full picture.
I think we should just explain the reason for missing the optimization
in the missed remark when possible. Analysis remarks should provide
information that the pass gathers regardless whether the optimization is
passing or not.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@282542 91177308-0d34-0410-b5e6-96231b3b80d8
(Re-committed after moving the template specialization under the yaml
namespace. GCC was complaining about this.)
This allows various presentation of this data using an external tool.
This was first recommended here[1].
As an example, consider this module:
1 int foo();
2 int bar();
3
4 int baz() {
5 return foo() + bar();
6 }
The inliner generates these missed-optimization remarks today (the
hotness information is pulled from PGO):
remark: /tmp/s.c:5:10: foo will not be inlined into baz (hotness: 30)
remark: /tmp/s.c:5:18: bar will not be inlined into baz (hotness: 30)
Now with -pass-remarks-output=<yaml-file>, we generate this YAML file:
--- !Missed
Pass: inline
Name: NotInlined
DebugLoc: { File: /tmp/s.c, Line: 5, Column: 10 }
Function: baz
Hotness: 30
Args:
- Callee: foo
- String: will not be inlined into
- Caller: baz
...
--- !Missed
Pass: inline
Name: NotInlined
DebugLoc: { File: /tmp/s.c, Line: 5, Column: 18 }
Function: baz
Hotness: 30
Args:
- Callee: bar
- String: will not be inlined into
- Caller: baz
...
This is a summary of the high-level decisions:
* There is a new streaming interface to emit optimization remarks.
E.g. for the inliner remark above:
ORE.emit(DiagnosticInfoOptimizationRemarkMissed(
DEBUG_TYPE, "NotInlined", &I)
<< NV("Callee", Callee) << " will not be inlined into "
<< NV("Caller", CS.getCaller()) << setIsVerbose());
NV stands for named value and allows the YAML client to process a remark
using its name (NotInlined) and the named arguments (Callee and Caller)
without parsing the text of the message.
Subsequent patches will update ORE users to use the new streaming API.
* I am using YAML I/O for writing the YAML file. YAML I/O requires you
to specify reading and writing at once but reading is highly non-trivial
for some of the more complex LLVM types. Since it's not clear that we
(ever) want to use LLVM to parse this YAML file, the code supports and
asserts that we're writing only.
On the other hand, I did experiment that the class hierarchy starting at
DiagnosticInfoOptimizationBase can be mapped back from YAML generated
here (see D24479).
* The YAML stream is stored in the LLVM context.
* In the example, we can probably further specify the IR value used,
i.e. print "Function" rather than "Value".
* As before hotness is computed in the analysis pass instead of
DiganosticInfo. This avoids the layering problem since BFI is in
Analysis while DiagnosticInfo is in IR.
[1] https://reviews.llvm.org/D19678#419445
Differential Revision: https://reviews.llvm.org/D24587
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@282539 91177308-0d34-0410-b5e6-96231b3b80d8
This allows various presentation of this data using an external tool.
This was first recommended here[1].
As an example, consider this module:
1 int foo();
2 int bar();
3
4 int baz() {
5 return foo() + bar();
6 }
The inliner generates these missed-optimization remarks today (the
hotness information is pulled from PGO):
remark: /tmp/s.c:5:10: foo will not be inlined into baz (hotness: 30)
remark: /tmp/s.c:5:18: bar will not be inlined into baz (hotness: 30)
Now with -pass-remarks-output=<yaml-file>, we generate this YAML file:
--- !Missed
Pass: inline
Name: NotInlined
DebugLoc: { File: /tmp/s.c, Line: 5, Column: 10 }
Function: baz
Hotness: 30
Args:
- Callee: foo
- String: will not be inlined into
- Caller: baz
...
--- !Missed
Pass: inline
Name: NotInlined
DebugLoc: { File: /tmp/s.c, Line: 5, Column: 18 }
Function: baz
Hotness: 30
Args:
- Callee: bar
- String: will not be inlined into
- Caller: baz
...
This is a summary of the high-level decisions:
* There is a new streaming interface to emit optimization remarks.
E.g. for the inliner remark above:
ORE.emit(DiagnosticInfoOptimizationRemarkMissed(
DEBUG_TYPE, "NotInlined", &I)
<< NV("Callee", Callee) << " will not be inlined into "
<< NV("Caller", CS.getCaller()) << setIsVerbose());
NV stands for named value and allows the YAML client to process a remark
using its name (NotInlined) and the named arguments (Callee and Caller)
without parsing the text of the message.
Subsequent patches will update ORE users to use the new streaming API.
* I am using YAML I/O for writing the YAML file. YAML I/O requires you
to specify reading and writing at once but reading is highly non-trivial
for some of the more complex LLVM types. Since it's not clear that we
(ever) want to use LLVM to parse this YAML file, the code supports and
asserts that we're writing only.
On the other hand, I did experiment that the class hierarchy starting at
DiagnosticInfoOptimizationBase can be mapped back from YAML generated
here (see D24479).
* The YAML stream is stored in the LLVM context.
* In the example, we can probably further specify the IR value used,
i.e. print "Function" rather than "Value".
* As before hotness is computed in the analysis pass instead of
DiganosticInfo. This avoids the layering problem since BFI is in
Analysis while DiagnosticInfo is in IR.
[1] https://reviews.llvm.org/D19678#419445
Differential Revision: https://reviews.llvm.org/D24587
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@282499 91177308-0d34-0410-b5e6-96231b3b80d8
Summary:
This is obviously an interesting case because it may motivate code
restructuring or LTO.
Reporting this requires instantiation of ORE in the loop where the call
sites are first gathered. I've checked compile-time
overhead *with* -Rpass-with-hotness and the worst slow-down was 6% in
mcf and quickly tailing off. As before without -Rpass-with-hotness
there is no overhead.
Because this could be a pretty noisy diagnostics, it is currently
qualified as 'verbose'. As of this patch, 'verbose' diagnostics are
only emitted with -Rpass-with-hotness, i.e. when the output is expected
to be filtered.
Reviewers: eraman, chandlerc, davidxl, hfinkel
Subscribers: tejohnson, Prazek, davide, llvm-commits
Differential Revision: https://reviews.llvm.org/D23415
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This is off for now while testing can take place to make sure that in
fact we do sufficient stack coloring to fully obviate the manual alloca
array merging.
Some context on why we should be using stack coloring rather than
merging allocas in this way:
LLVM relies very heavily on analyzing pointers as coming from different
allocas in order to make aliasing decisions. These are some of the most
powerful aliasing signals available in LLVM. So merging allocas is an
extremely destructive operation on the LLVM IR -- it takes away highly
valuable and hard to reconstruct information.
As a consequence, inlined functions which happen to have array allocas
that this pattern matches will fail to be properly interleaved unless
SROA manages to hoist everything to an SSA register. Instead, the
inliner will have added an unnecessary dependence that one inlined
function execute after the other because they will have been rewritten
to refer to the same memory.
All that said, folks will reasonably want some time to experiment here
and make sure there are no significant regressions. A flag should give
us an easy knob to test.
For more context, see the thread here:
http://lists.llvm.org/pipermail/llvm-dev/2016-July/103277.htmlhttp://lists.llvm.org/pipermail/llvm-dev/2016-August/103285.html
Differential Revision: https://reviews.llvm.org/D23052
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Summary:
I think it is much better this way.
When I firstly saw line:
Cost += InlineConstants::LastCallToStaticBonus;
I though that this is a bug, because everywhere where the cost is being reduced
it is usuing -=.
Reviewers: eraman, tejohnson, mehdi_amini
Subscribers: llvm-commits, mehdi_amini
Differential Revision: https://reviews.llvm.org/D23222
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Summary:
The inliner not being a function pass requires the work-around of
generating the OptimizationRemarkEmitter and in turn BFI on demand.
This will go away after the new PM is ready.
BFI is only computed inside ORE if the user has requested hotness
information for optimization diagnostitics (-pass-remark-with-hotness at
the 'opt' level). Thus there is no additional overhead without the
flag.
Reviewers: hfinkel, davidxl, eraman
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D22694
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Summary:
copypasta doc of ImportedFunctionsInliningStatistics class
\brief Calculate and dump ThinLTO specific inliner stats.
The main statistics are:
(1) Number of inlined imported functions,
(2) Number of imported functions inlined into importing module (indirect),
(3) Number of non imported functions inlined into importing module
(indirect).
The difference between first and the second is that first stat counts
all performed inlines on imported functions, but the second one only the
functions that have been eventually inlined to a function in the importing
module (by a chain of inlines). Because llvm uses bottom-up inliner, it is
possible to e.g. import function `A`, `B` and then inline `B` to `A`,
and after this `A` might be too big to be inlined into some other function
that calls it. It calculates this statistic by building graph, where
the nodes are functions, and edges are performed inlines and then by marking
the edges starting from not imported function.
If `Verbose` is set to true, then it also dumps statistics
per each inlined function, sorted by the greatest inlines count like
- number of performed inlines
- number of performed inlines to importing module
Reviewers: eraman, tejohnson, mehdi_amini
Subscribers: mehdi_amini, llvm-commits
Differential Revision: https://reviews.llvm.org/D22491
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Instead of directly using MaxFunctionCount and function entry count to determine callee hotness, use the isHotFunction/isColdFunction methods provided by ProfileSummaryInfo.
Differential revision: http://reviews.llvm.org/D21045
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The implemented heuristic has a large body of code which better sits
in its own function for better readability. It also allows adding more
heuristics easier in the future.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@268107 91177308-0d34-0410-b5e6-96231b3b80d8
This patch implements a optimization bisect feature, which will allow optimizations to be selectively disabled at compile time in order to track down test failures that are caused by incorrect optimizations.
The bisection is enabled using a new command line option (-opt-bisect-limit). Individual passes that may be skipped call the OptBisect object (via an LLVMContext) to see if they should be skipped based on the bisect limit. A finer level of control (disabling individual transformations) can be managed through an addition OptBisect method, but this is not yet used.
The skip checking in this implementation is based on (and replaces) the skipOptnoneFunction check. Where that check was being called, a new call has been inserted in its place which checks the bisect limit and the optnone attribute. A new function call has been added for module and SCC passes that behaves in a similar way.
Differential Revision: http://reviews.llvm.org/D19172
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@267022 91177308-0d34-0410-b5e6-96231b3b80d8
