This patch mechanically replaces None with std::nullopt where the
compiler would warn if None were deprecated. The intent is to reduce
the amount of manual work required in migrating from Optional to
std::optional.
This is part of an effort to migrate from llvm::Optional to
std::optional:
https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
Adds option to print the contents of the Inline Advisor after each SCC Inliner pass
Reviewed By: mtrofin
Differential Revision: https://reviews.llvm.org/D127689
advisor.
This patch has no functional change, and merely a preparation patch for
main functional change. The motivating use case is to annotate inline
remark pass name with context information (e.g. prelink or postlink,
CGSCC or always-inliner), see D125495 for more details.
Differential Revision: https://reviews.llvm.org/D126824
The tensorflow AOT compiler can cross-target, but it can't run on (for
example) arm64. We added earlier support where the AOT-ed header and object
would be built on a separate builder and then passed at build time to
a build host where the AOT compiler can't run, but clang can be otherwise
built.
To simplify such scenarios given we now support more than one AOT-able
case (regalloc and inliner), we make the AOT scenario centered on whether
files are generated, case by case (this includes the "passed from a
different builder" scenario).
This means we shouldn't need an 'umbrella' LLVM_HAVE_TF_AOT, in favor of
case by case control. A builder can opt out of an AOT case by passing that case's
model path as `none`. Note that the overrides still take precedence.
This patch controls conditional compilation with case-specific flags,
which can be enabled locally, for the component where those are
available. We still keep an overall flag for some tests.
The 'development/training' mode is unchanged, because there the model is
passed from the command line and interpreted.
Differential Revision: https://reviews.llvm.org/D117752
The global state refers to the number of the nodes currently in the
module, and the number of direct calls between nodes, across the
module.
Node counts are not a problem; edge counts are because we want strictly
the kind of edges that affect inlining (direct calls), and that is not
easily obtainable without iteration over the whole module.
This patch avoids relying on analysis invalidation because it turned out
to be too aggressive in some cases. It leverages the fact that Node
objects are stable - they do not get deleted while cgscc passes are
run over the module; and cgscc pass manager invariants.
Reviewed By: aeubanks
Differential Revision: https://reviews.llvm.org/D115847
This avoids the InlineAdvisor carrying the responsibility of deleting
Function objects. We use LazyCallGraph::Node objects instead, which are
stable in memory for the duration of the Module-wide performance of CGSCC
passes started under the same ModuleToPostOrderCGSCCPassAdaptor (which
is the case here)
Differential Revision: https://reviews.llvm.org/D116964
After the switch to the new pass manager, we have observed multiple
instances of catastrophic inlining, where the inliner produces huge
functions with many hundreds of thousands of instructions from small
input IR. We were forced to back out the switch to the new pass
manager for this reason. This patch fixes at least one of the root
cause issues.
LLVM uses a bottom-up inliner, and the fact that functions are processed
bottom-up is not just a question of optimality -- it is an imporant
requirement to prevent runaway inlining. The premise of the current
inlining approach and cost model is that after all calls inside a function
have been inlined, it may get large enough that inlining it into its
callers is no longer considered profitable. This safeguard does not
exist if inlining doesn't happen bottom-up, as inlining the callees,
and their callees, and their callees etc. will always seem individually
profitable, and the inliner can easily flatten the whole call tree.
There are instances where we necessarily have to deviate from bottom-up
inlining: When inlining in an SCC there is no natural "bottom", so
inlining effectively happens top-down. This requires special care,
and the inliner avoids exponential blowup by ensuring that functions
in the SCC grow in a balanced way and will eventually hit the threshold.
However, there is one instance where the inlining advisor explicitly
violates the bottom-up principle: Deferred inlining tries to "defer"
inlining a call if it determines that inlining the caller into all
its call-sites would be more profitable. Something very important to
understand about deferred inlining is that it doesn't make one inlining
choice in place of another -- it effectively chooses to do both. If we
have a call chain A -> B -> C and cost modelling tells us that inlining
B -> C is profitable, but we defer this and instead inline A -> B first,
then we'll now have a call A -> C, and the cost model will (a few special
cases notwithstanding) still tell us that this is profitable. So the end
result is that we inlined *both* B and C, even though under the usual
cost model function B would have been too large to further inline after
C has been integrated into it.
Because deferred inlining violates the bottom-up invariant of the inliner,
it can result in exponential inlining. The exponential-deferred-inlining.ll
test case illustrates this on a simple example (see
https://gist.github.com/nikic/1262b5f7d27278e1b34a190ae10947f5 for a
much more catastrophic case with about 5000x size blowup). If the call
chain A -> B -> C is not a chain but a tree of calls, then we end up
deferring inlining across the tree and end up flattening everything into
the root node.
This patch proposes to address this by disabling deferred inlining
entirely (currently still behind an option). Beyond the issue of
exponential inlining, I don't think that the whole concept makes sense,
at least as long as deferred inlining still ends up inlining both call
edges.
I believe the motivation for having deferred inlining in the first place
is that you might have a small wrapper function with local linkage that
could be eliminated if inlined. This would automatically happen if there
was a single caller, due to the large "last call to local" bonus. However,
this bonus is not extended if there are multiple callers, even if we
would eventually end up inlining into all of them (if the bonus were
extended).
Now, unlike the normal inlining cost model, the deferred inlining cost
model does look at all callers, and will extend the "last call to local"
bonus if it determines that we could inline all of them as long as we
defer the current inlining decision. This makes very little sense.
The "last call to local" bonus doesn't really cost model anything.
It's basically an "infinite" bonus that ensures we always inline the
last call to a local. The fact that it's not literally infinite just
prevents inlining of huge functions, which can easily result in
scalability issues. I very much doubt that it was an intentional
cost-modelling choice to say that getting rid of a small local function
is worth adding 15000 instructions elsewhere, yet this is exactly how
this value is getting used here.
The main alternative I see to complete removal is to change deferred
inlining to an actual either/or decision. That is, to mark deferred
calls as noinline so we're actually trading off one inlining decision
against another, and not just adding a side-channel to the cost model
to do both.
Apart from fixing the catastrophic inlining case, the effect on rustc
is a modest compile-time improvement on average (up to 8% for a
parsing-type crate, where tree-like calls are expected) and pretty
neutral where run-time performance is concerned (mix of small wins
and losses, usually in the sub-1% category).
Differential Revision: https://reviews.llvm.org/D115497
Adds the following switches:
1. --sample-profile-inline-replay-fallback/--cgscc-inline-replay-fallback: controls what the replay advisor does for inline sites that are not present in the replay. Options are:
1. Original: defers to original advisor
2. AlwaysInline: inline all sites not in replay
3. NeverInline: inline no sites not in replay
2. --sample-profile-inline-replay-format/--cgscc-inline-replay-format: controls what format should be generated to match against the replay remarks. Options are:
1. Line
2. LineColumn
3. LineDiscriminator
4. LineColumnDiscriminator
Adds support for negative inlining decisions. These are denoted by "will not be inlined into" as compared to the positive "inlined into" in the remarks.
All of these together with the previous `--sample-profile-inline-replay-scope/--cgscc-inline-replay-scope` allow tweaking in how to apply replay. In my testing, I'm using:
1. --sample-profile-inline-replay-scope/--cgscc-inline-replay-scope = Function to only replay on a function
2. --sample-profile-inline-replay-fallback/--cgscc-inline-replay-fallback = NeverInline since I'm feeding in only positive remarks to the replay system
3. --sample-profile-inline-replay-format/--cgscc-inline-replay-format = Line since I'm generating the remarks from DWARF information from GCC which can conflict quite heavily in column number compared to Clang
An alternative configuration could be to do Function, AlwaysInline, Line fallback with negative remarks which closer matches the final call-sites. Note that this can lead to unbounded inlining if a negative remark doesn't match/exist for one reason or another.
Updated various tests to cover the new switches and negative remarks
Testing:
ninja check-all
Reviewed By: wenlei, mtrofin
Differential Revision: https://reviews.llvm.org/D112040
The goal is to allow grafting an inline tree from Clang or GCC into a new compilation without affecting other functions. For GCC, we're doing this by extracting the inline tree from dwarf information and generating the equivalent remarks.
This allows easier side-by-side asm analysis and a trial way to see if a particular inlining setup provides benefits by itself.
Testing:
ninja check-all
Reviewed By: wenlei, mtrofin
Differential Revision: https://reviews.llvm.org/D110658
This also removes the need to disable the mandatory inlining phase in
tests.
In a departure from the previous remark, we don't output a 'cost' in
this case, because there's no such thing. We just report that inlining
happened because of the attribute.
Differential Revision: https://reviews.llvm.org/D110891
Clang diagnostics refer to identifier names in quotes.
This patch makes inline remarks conform to the convention.
New behavior:
```
% clang -O2 -Rpass=inline -Rpass-missed=inline -S a.c
a.c:4:25: remark: 'foo' inlined into 'bar' with (cost=-30, threshold=337) at callsite bar:0:25; [-Rpass=inline]
int bar(int a) { return foo(a); }
^
```
Reviewed By: hoy
Differential Revision: https://reviews.llvm.org/D107791
We already have an indication (error) if the desired inline advisor
cannot be enabled, but we don't have a positive indication. Added
LLVM_DEBUG messages for the latter.
This change leverages the work done in D83743 to replay in the SampleProfile inliner to also be used in the CGSCC inliner. NOTE: currently restricted to non-ML advisors only.
The added switch `-cgscc-inline-replay=<remarks file>` will replay the inlining decisions in that file where the remarks file is generated via `-Rpass=inline`. The aim here is to make it easier to analyze changes that would modify inlining heuristics to be separated from this behavior. Doing so allows easier examination of assembly and runtime behavior compared to the baseline rather than trying to dig through the large churn caused by inlining.
In LTO compilation, since inlining is done twice you can separately specify replay by passing the flag to the FE (`-cgscc-inline-replay=`) and to the linker (`-Wl,cgscc-inline-replay=`) with the remarks generated from their respective places.
Testing on mysqld by comparing the inline decisions between base (generates remarks.txt) and diff (replay using identical input/tools with remarks.txt) and examining the inlining sites with `diff` shows 14,000 mismatches out of 247,341 for a ~94% replay accuracy. I believe this gap can be narrowed further though for the general case we may never achieve full accuracy. For my personal use, this is close enough to be representative: I set the baseline as the one generated by the replay on identical input/toolset and compare that to my modified input/toolset using the same replay.
Testing:
ninja check-llvm
newly added test correctly replays CGSCC inlining decisions
Reviewed By: mtrofin, wenlei
Differential Revision: https://reviews.llvm.org/D94334
This reverts commit d97f776be5.
The original problem was due to build failures in shared lib builds. D95079
moved ImportedFunctionsInliningStatistics under Analysis, unblocking
this.
When using 2 InlinePass instances in the same CGSCC - one for other
mandatory inlinings, the other for the heuristic-driven ones - the order
in which the ImportedFunctionStats would be output-ed would depend on
the destruction order of the inline passes, which is not deterministic.
This patch moves the ImportedFunctionStats responsibility to the
InlineAdvisor to address this problem.
Differential Revision: https://reviews.llvm.org/D94982
Expanding from D94808 - we ensure the same InlineAdvisor is used by both
InlinerPass instances. The notion of mandatory inlining is moved into
the core InlineAdvisor: advisors anyway have to handle that case, so
this change also factors out that a bit better.
Differential Revision: https://reviews.llvm.org/D94825
This change modifies the source location formatting from:
LineNumber.Discriminator
to:
LineNumber:ColumnNumber.Discriminator
The motivation here is to enhance location information for inline replay that currently exists for the SampleProfile inliner. This will be leveraged further in inline replay for the CGSCC inliner in the related diff.
The ReplayInlineAdvisor is also modified to read the new format and now takes into account the callee for greater accuracy.
Testing:
ninja check-llvm
Reviewed By: mtrofin
Differential Revision: https://reviews.llvm.org/D94333
Enable performing mandatory inlinings upfront, by reusing the same logic
as the full inliner, instead of the AlwaysInliner. This has the
following benefits:
- reduce code duplication - one inliner codebase
- open the opportunity to help the full inliner by performing additional
function passes after the mandatory inlinings, but before th full
inliner. Performing the mandatory inlinings first simplifies the problem
the full inliner needs to solve: less call sites, more contextualization, and,
depending on the additional function optimization passes run between the
2 inliners, higher accuracy of cost models / decision policies.
Note that this patch does not yet enable much in terms of post-always
inline function optimization.
Differential Revision: https://reviews.llvm.org/D91567
This change added a new inline advisor that takes optimization remarks from previous inlining as input, and provides the decision as advice so current inlining can replay inline decisions of a different compilation. Dwarf inline stack with line and discriminator is used as anchor for call sites including call context. The change can be useful for Inliner tuning as it provides a channel to allow external input for tweaking inline decisions. Existing alternatives like alwaysinline attribute is per-function, not per-callsite. Per-callsite inline intrinsic can be another solution (not yet existing), but it's intrusive to implement and also does not differentiate call context.
A switch -sample-profile-inline-replay=<inline_remarks_file> is added to hook up the new inline advisor with SampleProfileLoader's inline decision for replay. Since SampleProfileLoader does top-down inlining, inline decision can be specialized for each call context, hence we should be able to replay inlining accurately. However with a bottom-up inliner like CGSCC inlining, the replay can be limited due to lack of specialization for different call context. Apart from that limitation, the new inline advisor can still be used by regular CGSCC inliner later if needed for tuning purpose.
This is a resubmit of https://reviews.llvm.org/D83743
Summary:
This is the InlineAdvisor used in 'development' mode. It enables two
scenarios:
- loading models via a command-line parameter, thus allowing for rapid
training iteration, where models can be used for the next exploration
phase without requiring recompiling the compiler. This trades off some
compilation speed for the added flexibility.
- collecting training logs, in the form of tensorflow.SequenceExample
protobufs. We generate these as textual protobufs, which simplifies
generation and testing. The protobufs may then be readily consumed by a
tensorflow-based training algorithm.
To speed up training, training logs may also be collected from the
'default' training policy. In that case, this InlineAdvisor does not
use a model.
RFC: http://lists.llvm.org/pipermail/llvm-dev/2020-April/140763.html
Reviewers: jdoerfert, davidxl
Subscribers: mgorny, hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D83733
Summary:
This change added a new inline advisor that takes optimization remarks from previous inlining as input, and provides the decision as advice so current inlining can replay inline decisions of a different compilation. Dwarf inline stack with line and discriminator is used as anchor for call sites including call context. The change can be useful for Inliner tuning as it provides a channel to allow external input for tweaking inline decisions. Existing alternatives like alwaysinline attribute is per-function, not per-callsite. Per-callsite inline intrinsic can be another solution (not yet existing), but it's intrusive to implement and also does not differentiate call context.
A switch -sample-profile-inline-replay=<inline_remarks_file> is added to hook up the new inline advisor with SampleProfileLoader's inline decision for replay. Since SampleProfileLoader does top-down inlining, inline decision can be specialized for each call context, hence we should be able to replay inlining accurately. However with a bottom-up inliner like CGSCC inlining, the replay can be limited due to lack of specialization for different call context. Apart from that limitation, the new inline advisor can still be used by regular CGSCC inliner later if needed for tuning purpose.
Subscribers: mgorny, aprantl, hiraditya, llvm-commits
Tags: #llvm
Resubmit for https://reviews.llvm.org/D84086
Summary:
This change added a new inline advisor that takes optimization remarks for previous inlining as input, and provide the decision as advice so current inlining can replay inline decision of a different compilation. Dwarf inline stack with line and discriminator is used as anchor for call sites. The change can be useful for Inliner tuning.
A switch -sample-profile-inline-replay=<inline_remarks_file> is added to hook up the new inliner advisor with SampleProfileLoader's inline decision for replay. The new inline advisor can also be used by regular CGSCC inliner later if needed.
Reviewers: davidxl, mtrofin, wmi, hoy
Subscribers: aprantl, hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D83743
This is in preparation for the 'development' mode advisor. We currently
want to track what the default policy's decision would have been, this
refactoring makes it easier to do that.
Summary:
Add call site location info into inline remarks so we can differentiate inline sites.
This can be useful for inliner tuning. We can also reconstruct full hierarchical inline
tree from parsing such remarks. The messege of inline remark is also tweaked so we can
differentiate SampleProfileLoader inline from CGSCC inline.
Reviewers: wmi, davidxl, hoy
Subscribers: hiraditya, cfe-commits, llvm-commits
Tags: #clang, #llvm
Differential Revision: https://reviews.llvm.org/D82213
Summary:
Other derivations will all want to emit optimization remarks and, as
part of that, use debug info.
Additionally, drive-by const-ing.
Reviewers: davidxl, dblaikie
Subscribers: aprantl, hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D81507
Kazu Hirata