| Age | Commit message (Collapse) | Author | Files | Lines |
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format (#148002)
This change extends SampleFDO ext-binary and text format to record the
vtable symbols and their counts for virtual calls inside a function. The
vtable profiles will allow the compiler to annotate vtable types on IR
instructions and perform vtable-based indirect call promotion. An RFC is
in
https://discourse.llvm.org/t/rfc-vtable-type-profiling-for-samplefdo/87283
Given a function below, the before vs after of a function's profile is
illustrated in text format in the table:
```
__attribute__((noinline)) int loop_func(int i, int a, int b) {
Base *ptr = createType(i);
int sum = ptr->func(a, b);
delete ptr;
return sum;
}
```
| before | after |
| --- | --- |
| Samples collected in the function's body { <br> 0: 636241 <br> 1:
681458, calls: _Z10createTypei:681458 <br> 3: 543499, calls:
_ZN12_GLOBAL__N_18Derived24funcEii:410621 _ZN8Derived14funcEii:132878
<br> 5.1: 602201, calls: _ZN12_GLOBAL__N_18Derived2D0Ev:454635
_ZN8Derived1D0Ev:147566 <br> 7: 511057 <br> } | Samples collected in the
function's body { <br> 0: 636241 <br> 1: 681458, calls:
_Z10createTypei:681458 <br> 3: 543499, calls:
_ZN12_GLOBAL__N_18Derived24funcEii:410621 _ZN8Derived14funcEii:132878
<br> 3: vtables: _ZTV8Derived1:1377 _ZTVN12_GLOBAL__N_18Derived2E:4250
<br> 5.1: 602201, calls: _ZN12_GLOBAL__N_18Derived2D0Ev:454635
_ZN8Derived1D0Ev:147566 <br> 5.1: vtables: _ZTV8Derived1:227
_ZTVN12_GLOBAL__N_18Derived2E:765 <br> 7: 511057 <br> } |
Key points for this change:
1. In-memory representation of vtable profiles
* A field of type `map<LineLocation, map<FunctionId, uint64_t>>` is
introduced in a function's in-memory representation
[FunctionSamples](https://github.com/llvm/llvm-project/blob/ccc416312ed72e92a885425d9cb9c01f9afa58eb/llvm/include/llvm/ProfileData/SampleProf.h#L749-L754).
2. The vtable counters for one LineLocation represents the relative
frequency among vtables for this LineLocation. They are not required to
be comparable across LineLocations.
3. For backward compatibility of ext-binary format, we take one bit from
ProfSummaryFlag as illustrated in the enum class `SecProfSummaryFlags`.
The ext-binary profile reader parses the integer type flag and reads
this bit. If it's set, the profile reader will parse vtable profiles.
4. The vtable profiles are optional in ext-binary format, and not
serialized out by default, we introduce an LLVM boolean option (named
`-extbinary-write-vtable-type-prof`). The ext-binary profile writer
reads the boolean option and decide whether to set the section flag bit
and serialize the in-memory class members corresponding to vtables.
5. This change doesn't implement `llvm-profdata overlap --sample` for
the vtable profiles. A subsequent change will do it to keep this one
focused on the profile format change.
We don't plan to add the vtable support to non-extensible format mainly
because of the maintenance cost to keep backward compatibility for prior
versions of profile data.
* Currently, the [non-extensible binary
format](https://github.com/llvm/llvm-project/blob/5c28af409978c19a35021855a29dcaa65e95da00/llvm/lib/ProfileData/SampleProfWriter.cpp#L899-L900)
does not have feature parity with extensible binary format today, for
instance, the former doesn't support [profile symbol
list](https://github.com/llvm/llvm-project/blob/41e22aa31b1905aa3e9d83c0343a96ec0d5187ec/llvm/include/llvm/ProfileData/SampleProf.h#L1518-L1522)
or context-sensitive PGO, both of which give measurable performance
boost. Presumably the non-extensible format is not in wide use.
---------
Co-authored-by: Paschalis Mpeis <paschalis.mpeis@arm.com>
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* Introduce an error code for illegal_line_offset in sampleprof_error
namespace, and use it for line offset parsing error.
* Add `const` for `LineLocation::serialize`.
* Use structured binding, make_first/second_range in loops.
I'm working on a [sample-profile format
change](https://github.com/llvm/llvm-project/compare/users/mingmingl-llvm/samplefdo-profile-format)
to extend SampleFDO profile with vtable profiles. And this change splits
the non-functional changes.
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FunctionSamples serialization (#141669)
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We can use *Set::insert_range to collapse:
for (auto Elem : Range)
Set.insert(E.first);
down to:
Set.insert_range(llvm::make_first_range(Range));
In some cases, we can further fold that into the set declaration.
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Profile staleness could be due to function renaming. Given that sample
profile loader relies on exact string matching, a trivial change in the
function signature( such as `int foo()` --> `long foo()` ) can make the
mangled name different, the function profile(including all nested
children profile) becomes unavailable.
This patch introduces stale profile call-graph level matching, targeting
at identifying the trivial function renaming and reusing the old
function profile.
Some noteworthy details:
1. Extend the LCS based CFG level matching to identify new function.
- Extend to match function and profile have different name instead of
the exact function name matching. This leverages LCS, i.e during the
finding of callsite anchor matching, when two function name are
different, try matching the functions instead of return.
- In LCS, the equal function check is replaced by
`functionMatchesProfile`.
- Only try matching functions that are new functions(neither appears on
each side). This reduces the matching scope as we don't need to match
the originally matched function.
2. Determine the matching by call-site anchor similarity check.
- A new function `functionMatchesProfile(IRFunc, ProfFunc)` is used to
check the renaming for the possible <IRFunc, ProfFunc> pair, use the
LCS(diff) matching to compute the equal set and we define: `Similarity =
|equalSet * 2| / (|A| + |B|)`. The profile name is marked as renamed if
the similarity is above a
threshold(`-func-profile-similarity-threshold`)
3. Process the matching in top-down function order
- when a caller's is done matching, the new function names are saved for
later use, using top-down order will maximize the reused results.
- `ProfileNameToFuncMap` is used to save or cache the matching result.
4. Update the original profile at the end using `ProfileNameToFuncMap`.
5. Added a new switch --salvage-unused-profile to control this, default
is false.
Verified on one Meta's internal big service, confirmed 90%+ of the found
renaming pair is good. (There could be incorrect renaming pair if the
num of the anchor is small, but checked that those functions are simple
cold function)
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Also some control flow simplifications.
Notably, this doesn't address `sampleprof_error`. I *think* the style
there tries to match `std::error_category`.
Also left `hash_value` as-is, because it matches what we do in Hashing.h
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from a Sample Profile. (#66164)
This is phase 2 of the MD5 refactoring on Sample Profile following
https://reviews.llvm.org/D147740
In previous implementation, when a MD5 Sample Profile is read, the
reader first converts the MD5 values to strings, and then create a
StringRef as if the numerical strings are regular function names, and
later on IPO transformation passes perform string comparison over these
numerical strings for profile matching. This is inefficient since it
causes many small heap allocations.
In this patch I created a class `ProfileFuncRef` that is similar to
`StringRef` but it can represent a hash value directly without any
conversion, and it will be more efficient (I will attach some benchmark
results later) when being used in associative containers.
ProfileFuncRef guarantees the same function name in string form or in
MD5 form has the same hash value, which also fix a few issue in IPO
passes where function matching/lookup only check for function name
string, while returns a no-match if the profile is MD5.
When testing on an internal large profile (> 1 GB, with more than 10
million functions), the full profile load time is reduced from 28 sec to
25 sec in average, and reading function offset table from 0.78s to 0.7s
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speed using MD5 as key to Sample Profile map
This is phase 1 of multiple planned improvements on the sample profile loader. The major change is to use MD5 hash code ((instead of the function itself) as the key to look up the function offset table and the profiles, which significantly reduce the time it takes to construct the map.
The optimization is based on the fact that many practical sample profiles are using MD5 values for function names to reduce profile size, so we shouldn't need to convert the MD5 to a string and then to a SampleContext and use it as the map's key, because it's extremely slow.
Several changes to note:
(1) For non-CS SampleContext, if it is already MD5 string, the hash value will be its integral value, instead of hashing the MD5 again. In phase 2 this is going to be optimized further using a union to represent MD5 function (without converting it to string) and regular function names.
(2) The SampleProfileMap is a wrapper to *map<uint64_t, FunctionSamples>, while providing interface allowing using SampleContext as key, so that existing code still work. It will check for MD5 collision (unlikely but not too unlikely, since we only takes the lower 64 bits) and handle it to at least guarantee compilation correctness (conflicting old profile is dropped, instead of returning an old profile with inconsistent context). Other code should not try to use MD5 as key to access the map directly, because it will not be able to handle MD5 collision at all. (see exception at (5) )
(3) Any SampleProfileMap::emplace() followed by SampleContext assignment if newly inserted, should be replaced with SampleProfileMap::Create(), which does the same thing.
(4) Previously we ensure an invariant that in SampleProfileMap, the key is equal to the Context of the value, for profile map that is eventually being used for output (as in llvm-profdata/llvm-profgen). Since the key became MD5 hash, only the value keeps the context now, in several places where an intermediate SampleProfileMap is created, each new FunctionSample's context is set immediately after insertion, which is necessary to "remember" the context otherwise irretrievable.
(5) When reading a profile, we cache the MD5 values of all functions, because they are used at least twice (one to index into FuncOffsetTable, the other into SampleProfileMap, more if there are additional sections), in this case the SampleProfileMap is directly accessed with MD5 value so that we don't recalculate it each time (expensive)
Performance impact:
When reading a ~1GB extbinary profile (fixed length MD5, not compressed) with 10 million function names and 2.5 million top level functions (non CS functions, each function has varying nesting level from 0 to 20), this patch improves the function offset table loading time by 20%, and improves full profile read by 5%.
Reviewed By: davidxl, snehasish
Differential Revision: https://reviews.llvm.org/D147740
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build speed using MD5 as key to Sample Profile map"
This reverts commit 66ba71d913df7f7cd75e92c0c4265932b7c93292.
Addressing issues found by:
https://lab.llvm.org/buildbot/#/builders/245/builds/11732
https://lab.llvm.org/buildbot/#/builders/187/builds/12251
https://lab.llvm.org/buildbot/#/builders/186/builds/11099
https://lab.llvm.org/buildbot/#/builders/182/builds/6976
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speed using MD5 as key to Sample Profile map
This is phase 1 of multiple planned improvements on the sample profile loader. The major change is to use MD5 hash code ((instead of the function itself) as the key to look up the function offset table and the profiles, which significantly reduce the time it takes to construct the map.
The optimization is based on the fact that many practical sample profiles are using MD5 values for function names to reduce profile size, so we shouldn't need to convert the MD5 to a string and then to a SampleContext and use it as the map's key, because it's extremely slow.
Several changes to note:
(1) For non-CS SampleContext, if it is already MD5 string, the hash value will be its integral value, instead of hashing the MD5 again. In phase 2 this is going to be optimized further using a union to represent MD5 function (without converting it to string) and regular function names.
(2) The SampleProfileMap is a wrapper to *map<uint64_t, FunctionSamples>, while providing interface allowing using SampleContext as key, so that existing code still work. It will check for MD5 collision (unlikely but not too unlikely, since we only takes the lower 64 bits) and handle it to at least guarantee compilation correctness (conflicting old profile is dropped, instead of returning an old profile with inconsistent context). Other code should not try to use MD5 as key to access the map directly, because it will not be able to handle MD5 collision at all. (see exception at (5) )
(3) Any SampleProfileMap::emplace() followed by SampleContext assignment if newly inserted, should be replaced with SampleProfileMap::Create(), which does the same thing.
(4) Previously we ensure an invariant that in SampleProfileMap, the key is equal to the Context of the value, for profile map that is eventually being used for output (as in llvm-profdata/llvm-profgen). Since the key became MD5 hash, only the value keeps the context now, in several places where an intermediate SampleProfileMap is created, each new FunctionSample's context is set immediately after insertion, which is necessary to "remember" the context otherwise irretrievable.
(5) When reading a profile, we cache the MD5 values of all functions, because they are used at least twice (one to index into FuncOffsetTable, the other into SampleProfileMap, more if there are additional sections), in this case the SampleProfileMap is directly accessed with MD5 value so that we don't recalculate it each time (expensive)
Performance impact:
When reading a ~1GB extbinary profile (fixed length MD5, not compressed) with 10 million function names and 2.5 million top level functions (non CS functions, each function has varying nesting level from 0 to 20), this patch improves the function offset table loading time by 20%, and improves full profile read by 5%.
Reviewed By: davidxl, snehasish
Differential Revision: https://reviews.llvm.org/D147740
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build speed using MD5 as key to Sample Profile map"
This reverts commit 12e9c7aaa66b7624b5d7666ce2794d912bf9e4b7.
The commit has broken the buildbot, see comment https://reviews.llvm.org/D147740#4451540
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speed using MD5 as key to Sample Profile map
This is phase 1 of multiple planned improvements on the sample profile loader. The major change is to use MD5 hash code ((instead of the function itself) as the key to look up the function offset table and the profiles, which significantly reduce the time it takes to construct the map.
The optimization is based on the fact that many practical sample profiles are using MD5 values for function names to reduce profile size, so we shouldn't need to convert the MD5 to a string and then to a SampleContext and use it as the map's key, because it's extremely slow.
Several changes to note:
(1) For non-CS SampleContext, if it is already MD5 string, the hash value will be its integral value, instead of hashing the MD5 again. In phase 2 this is going to be optimized further using a union to represent MD5 function (without converting it to string) and regular function names.
(2) The SampleProfileMap is a wrapper to *map<uint64_t, FunctionSamples>, while providing interface allowing using SampleContext as key, so that existing code still work. It will check for MD5 collision (unlikely but not too unlikely, since we only takes the lower 64 bits) and handle it to at least guarantee compilation correctness (conflicting old profile is dropped, instead of returning an old profile with inconsistent context). Other code should not try to use MD5 as key to access the map directly, because it will not be able to handle MD5 collision at all. (see exception at (5) )
(3) Any SampleProfileMap::emplace() followed by SampleContext assignment if newly inserted, should be replaced with SampleProfileMap::Create(), which does the same thing.
(4) Previously we ensure an invariant that in SampleProfileMap, the key is equal to the Context of the value, for profile map that is eventually being used for output (as in llvm-profdata/llvm-profgen). Since the key became MD5 hash, only the value keeps the context now, in several places where an intermediate SampleProfileMap is created, each new FunctionSample's context is set immediately after insertion, which is necessary to "remember" the context otherwise irretrievable.
(5) When reading a profile, we cache the MD5 values of all functions, because they are used at least twice (one to index into FuncOffsetTable, the other into SampleProfileMap, more if there are additional sections), in this case the SampleProfileMap is directly accessed with MD5 value so that we don't recalculate it each time (expensive)
Performance impact:
When reading a ~1GB extbinary profile (fixed length MD5, not compressed) with 10 million function names and 2.5 million top level functions (non CS functions, each function has varying nesting level from 0 to 20), this patch improves the function offset table loading time by 20%, and improves full profile read by 5%.
Reviewed By: davidxl, snehasish
Differential Revision: https://reviews.llvm.org/D147740
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build speed using MD5 as key to Sample Profile map"
This reverts commit 31af18bccea95fe1ae8aa2c51cf7c8e92a1c208e.
This change is causing build failures on many Windows build bots:
https://lab.llvm.org/buildbot/#/builders/216/builds/22833
https://lab.llvm.org/buildbot/#/builders/123/builds/19602
https://lab.llvm.org/buildbot/#/builders/172/builds/28315
https://lab.llvm.org/buildbot/#/builders/119/builds/13870
https://lab.llvm.org/buildbot/#/builders/233/builds/794
https://lab.llvm.org/buildbot/#/builders/235/builds/387
https://lab.llvm.org/buildbot/#/builders/13/builds/36921
https://lab.llvm.org/buildbot/#/builders/127/builds/50510
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speed using MD5 as key to Sample Profile map
This is phase 1 of multiple planned improvements on the sample profile loader. The major change is to use MD5 hash code ((instead of the function itself) as the key to look up the function offset table and the profiles, which significantly reduce the time it takes to construct the map.
The optimization is based on the fact that many practical sample profiles are using MD5 values for function names to reduce profile size, so we shouldn't need to convert the MD5 to a string and then to a SampleContext and use it as the map's key, because it's extremely slow.
Several changes to note:
(1) For non-CS SampleContext, if it is already MD5 string, the hash value will be its integral value, instead of hashing the MD5 again. In phase 2 this is going to be optimized further using a union to represent MD5 function (without converting it to string) and regular function names.
(2) The SampleProfileMap is a wrapper to *map<uint64_t, FunctionSamples>, while providing interface allowing using SampleContext as key, so that existing code still work. It will check for MD5 collision (unlikely but not too unlikely, since we only takes the lower 64 bits) and handle it to at least guarantee compilation correctness (conflicting old profile is dropped, instead of returning an old profile with inconsistent context). Other code should not try to use MD5 as key to access the map directly, because it will not be able to handle MD5 collision at all. (see exception at (5) )
(3) Any SampleProfileMap::emplace() followed by SampleContext assignment if newly inserted, should be replaced with SampleProfileMap::Create(), which does the same thing.
(4) Previously we ensure an invariant that in SampleProfileMap, the key is equal to the Context of the value, for profile map that is eventually being used for output (as in llvm-profdata/llvm-profgen). Since the key became MD5 hash, only the value keeps the context now, in several places where an intermediate SampleProfileMap is created, each new FunctionSample's context is set immediately after insertion, which is necessary to "remember" the context otherwise irretrievable.
(5) When reading a profile, we cache the MD5 values of all functions, because they are used at least twice (one to index into FuncOffsetTable, the other into SampleProfileMap, more if there are additional sections), in this case the SampleProfileMap is directly accessed with MD5 value so that we don't recalculate it each time (expensive)
Performance impact:
When reading a ~1GB extbinary profile (fixed length MD5, not compressed) with 10 million function names and 2.5 million top level functions (non CS functions, each function has varying nesting level from 0 to 20), this patch improves the function offset table loading time by 20%, and improves full profile read by 5%.
Reviewed By: davidxl, snehasish
Differential Revision: https://reviews.llvm.org/D147740
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AutoFDO/CSSPGO often has to deal with stale profiles collected on binaries built from several revisions behind release. It’s likely to get incorrect profile annotations using the stale profile, which results in unstable or low performing binaries. Currently for source location based profile, once a code change causes a profile mismatch, all the locations afterward are mismatched, the affected samples or inlining info are lost. If we can provide a matching framework to reuse parts of the mismatched profile - aka incremental PGO, it will make PGO more stable, also increase the optimization coverage and boost the performance of binary.
This patch is the part 1 of stale profile matching, summary of the implementation:
- Added a structure for the matching result:`LocToLocMap`, which is a location to location map meaning the location of current build is matched to the location of the previous build(to be used to query the “stale” profile).
- In order to use the matching results for sample query, we need to pass them to all the location queries. For code cleanliness, we added a new pointer field(`IRToProfileLocationMap`) to `FunctionSamples`.
- Added a wrapper(`mapIRLocToProfileLoc`) for the query to the location, the location from input IR will be remapped to the matched profile location.
- Added a new switch `--salvage-stale-profile`.
- Some refactoring for the staleness detection.
Test case is in part 2 with the matching algorithm.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D147456
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For profile staleness report, before it only counts for the top-level function samples in the nested profile, the samples in the inlinees are ignored. This could affect the quality of the metrics when there are heavily inlined functions. This change adds a feature to flatten the nested profile and we're changing to use flatten profile as the input for stale profile detection and matching.
Example for profile flattening:
```
Original profile:
_Z3bazi:20301:1000
1: 1000
3: 2000
5: inline1:1600
1: 600
3: inline2:500
1: 500
Flattened profile:
_Z3bazi:18701:1000
1: 1000
3: 2000
5: 600 inline1:600
inline1:1100:600
1: 600
3: 500 inline2: 500
inline2:500:500
1: 500
```
This feature could be useful for offline analysis, like understanding the hotness of each individual function. So I'm adding the support to `llvm-profdata merge` under `--gen-flattened-profile`.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D146452
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(Reapply after revert in e9ce1a588030d8d4004f5d7e443afe46245e9a92 due to
Fuchsia test failures. Removed changes in lib/ExecutionEngine/ other
than error categories, to be checked in more detail and reapplied
separately.)
Bulk remove many of the more trivial uses of ManagedStatic in the llvm
directory, either by defining a new getter function or, in many cases,
moving the static variable directly into the only function that uses it.
Differential Revision: https://reviews.llvm.org/D129120
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This reverts commit e6f1f062457c928c18a88c612f39d9e168f65a85.
Reverting due to a failure on the fuchsia-x86_64-linux buildbot.
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Bulk remove many of the more trivial uses of ManagedStatic in the llvm
directory, either by defining a new getter function or, in many cases,
moving the static variable directly into the only function that uses it.
Differential Revision: https://reviews.llvm.org/D129120
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Some cl::ZeroOrMore were added to avoid the `may only occur zero or one times!`
error. More were added due to cargo cult. Since the error has been removed,
cl::ZeroOrMore is unneeded.
Also remove cl::init(false) while touching the lines.
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callee samples into caller.
When a flat CS profile is converted to a nested profile, the call target samples for inlined callee contexts are left over in the callsite target map. This could cause indirect call promotion to function improperly. One issue is that the inlined callsites are treated with double amount of samples. The other is the inlined callsites are reconsidered for subsequent PGO ICP.
I'm fixing this by excluding call targets from the callsite for inlined targets. While fixing this I found that callsite target sum and the number of body samples for that callsite could be mismatched. {D122609} has an explanation and a fix for that on llvm-profgen side. For now I'm tolerating it in this change.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D125266
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when converting CS flat profile to nested.
Recent experiments with our two large internal services showed that duplicating context profiles into base profile caused code size inflation and didn't deliver good performance compared to no such duplication. It was a trick we made to catch up with the CS flat profile and I'm now turning it off by default.
The code size inflation mainly comes from the enriched based profiles. A base profile for a function represents the uninlined (or outlined) portion of the whole function running time. Such portion could be very small if a function is inlined into most of its hot callsites. Duplicating context profiles of the function into its base profiles could cause the outlined body to be hot enough and in turn get many of its callees inlined, thus increases the code size. The size inflation could further cause perf regression.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D124796
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To be more clear and definitive, I'm renaming `ProfileIsCSFlat` back to `ProfileIsCS` which stands for full context-sensitive flat profiles. `ProfileIsCSNested` is now renamed to `ProfileIsPreInlined` and is extended to be applicable for CS flat profiles too. More specifically, `ProfileIsPreInlined` is for any kind of profiles (flat or nested) that contain 'ShouldBeInlined' contexts. The flag is encoded in the profile summary section for extbinary profiles and is computed on-the-fly for text profiles.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D122602
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With a sufficiently large output buffer, the only failure is Z_MEM_ERROR.
Check it and call the noreturn report_bad_alloc_error if applicable.
resize_for_overwrite may call report_bad_alloc_error as well.
Now that there is no other error type, we can replace the return type with void
and simplify call sites.
Reviewed By: ikudrin
Differential Revision: https://reviews.llvm.org/D121512
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Estimation of the impact on preprocessor output:
before: 1067349756
after: 1065940348
Discourse thread: https://discourse.llvm.org/t/include-what-you-use-include-cleanup
Differential Revision: https://reviews.llvm.org/D120434
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The write may be racy if ThinLTO creates multiple `InProcessThinBackend` instances.
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nested CS profile.
When generating nested CS profile with all calling contexts of a function duplicated into a base profile under `--generate-merged-base-profiles`, do not recount callee samples when computing profile summary. This fixes the profile summary mismatch between flat cs profile and nested cs profile, for both extbinary and text format.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D119494
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5.4.0
For some reason, the user-defined implicit conversion from StringRef to
std::string is not invoked by std::map::emplace in libstdc++ 5.4.0, even
though it works fine on modern systems.
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CSSPGO currently employs a flat profile format for context-sensitive profiles. Such a flat profile allows for precisely manipulating contexts that is either inlined or not inlined. This is a benefit over the nested profile format used by non-CS AutoFDO. A downside of this is the longer build time due to parsing the indexing the full CS contexts.
For a CS flat profile, though only the context profiles relevant to a module are loaded when that module is compiled, the cost to figure out what profiles are relevant is noticeably high when there're many contexts, since the sample reader will need to scan all context strings anyway. On the contrary, a nested function profile has its related inline subcontexts isolated from other unrelated contexts. Therefore when compiling a set of functions, unrelated contexts will never need to be scanned.
In this change we are exploring using nested profile format for CSSPGO. This is expected to work based on an assumption that with a preinliner-computed profile all contexts are precomputed and expected to be inlined by the compiler. Contexts not expected to be inlined will be cut off and returned to corresponding base profiles (for top-level outlined functions). This naturally forms a nested profile where all nested contexts are expected to be inlined. The compiler will less likely optimize on derived contexts that are not precomputed.
A CS-nested profile will look exactly the same with regular nested profile except that each nested profile can come with an attributes. With pseudo probes, a nested profile shown as below can also have a CFG checksum.
```
main:1968679:12
2: 24
3: 28 _Z5funcAi:18
3.1: 28 _Z5funcBi:30
3: _Z5funcAi:1467398
0: 10
1: 10 _Z8funcLeafi:11
3: 24
1: _Z8funcLeafi:1467299
0: 6
1: 6
3: 287884
4: 287864 _Z3fibi:315608
15: 23
!CFGChecksum: 138828622701
!Attributes: 2
!CFGChecksum: 281479271677951
!Attributes: 2
```
Specific work included in this change:
- A recursive profile converter to convert CS flat profile to nested profile.
- Extend function checksum and attribute metadata to be stored in nested way for text profile and extbinary profile.
- Unifiy sample loader inliner path for CS and preinlined nested profile.
- Changes in the sample loader to support probe-based nested profile.
I've seen promising results regarding build time. A nested profile can result in a 20% shorter build time than a CS flat profile while keep an on-par performance. This is with -duplicate-contexts-into-base=1.
Test Plan:
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D115205
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name isn't available.
When linkage name isn't available in dwarf (ususally the case of C code), looking up callee samples should be based on the dwarf name instead of using an empty string.
Also fixing a test issue where using empty string to look up callee samples accidentally returns the correct samples because it is treated as indirect call.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D112948
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Adding support to the CS preinliner to trim cold base profiles. This makes trimming consistent with the inline decision made by the preinliner. Also disable the existing profile merger when preinliner is on unless explicitly specified.
Reviewed By: wenlei, wlei
Differential Revision: https://reviews.llvm.org/D112489
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Currently context strings contain a lot of duplicated function names and that significantly increase the profile size. This change split the context into a series of {name, offset, discriminator} tuples so function names used in the context can be replaced by the index into the name table and that significantly reduce the size consumed by context.
A follow-up improvement made in the compiler and profiling tools is to avoid reconstructing full context strings which is time- and memory- consuming. Instead a context vector of `StringRef` is adopted to represent the full context in all scenarios. As a result, the previous prevalent profile map which was implemented as a `StringRef` is now engineered as an unordered map keyed by `SampleContext`. `SampleContext` is reshaped to using an `ArrayRef` to represent a full context for CS profile. For non-CS profile, it falls back to use `StringRef` to represent a contextless function name. Both the `ArrayRef` and `StringRef` objects are underpinned by real array and string objects that are stored in producer buffers. For compiler, they are maintained by the sample reader. For llvm-profgen, they are maintained in `ProfiledBinary` and `ProfileGenerator`. Full context strings can be generated only in those cases of debugging and printing.
When it comes to profile format, nothing has changed to the text format, though internally CS context is implemented as a vector. Extbinary format is only changed for CS profile, with an additional `SecCSNameTable` section which stores all full contexts logically in the form of `vector<int>`, which each element as an offset points to `SecNameTable`. All occurrences of contexts elsewhere are redirected to using the offset of `SecCSNameTable`.
Testing
This is no-diff change in terms of code quality and profile content (for text profile).
For our internal large service (aka ads), the profile generation is cut to half, with a 20x smaller string-based extbinary format generated.
The compile time of ads is dropped by 25%.
Differential Revision: https://reviews.llvm.org/D107299
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Sample profiles are stored in a string map which is basically an unordered map. Printing out profiles by simply walking the string map doesn't enforce an order. I'm sorting the map in the decreasing order of total samples to enable a more stable dump, which is good for comparing two dumps.
Reviewed By: wenlei, wlei
Differential Revision: https://reviews.llvm.org/D108147
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We were using a `StringMap` object to store all profiles to be emitted. The object is basically an unordered hash table, therefore updating it in the process of trasvering it may cause issue since the underlying bucket array could change.
I'm also moving the `csspgo-preinliner` switch around so that no context tri will be constructed (by the constructor of `CSPreInliner`) when the switch is off.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D104267
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Previously dangling samples were represented by INT64_MAX in sample profile while probes never executed were not reported. This was based on an observation that dangling probes were only at a smaller portion than zero-count probes. However, with compiler optimizations, dangling probes end up becoming at large portion of all probes in general and reporting them does not make sense from profile size point of view. This change flips sample reporting by reporting zero-count probes instead. This enabled dangling probe to be represented by none (missing entry in profile). This has a couple benefits:
1. Reducing sample profile size in optimize mode, even when the number of non-executed probes outperform the number of dangling probes, since INT64_MAX takes more space over 0 to encode.
2. Binary size savings. No need to encode dangling probe anymore, since missing probes are treated as dangling in the profile reader.
3. Reducing compiler work to track dangling probes. However, for probes that are real dead and removed, we still need the compiler to identify them so that they can be reported as zero-count, instead of mistreated as dangling probes.
4. Improving counts quality by respecting the counts already collected on the non-dangling copy of a probe. A probe, when duplicated, gets two copies at runtime. If one of them is dangling while the other is not, merging the two probes at profile generation time will cause the real samples collected on the non-dangling one to be discarded. Not reporting the dangling counterpart will keep the real samples.
5. Better readability.
6. Be consistent with non-CS dwarf line number based profile. Zero counts are trusted by the compiler counts inferencer while missing counts will be inferred by the compiler.
Note that the current patch does include any work for #3. There will be follow-up changes.
For #1, I've seen for a large Facebook service, the text profile is reduced by 7%. For extbinary profile, the size of LBRProfileSection is reduced by 35%.
For #4, I have seen general counts quality for SPEC2017 is improved by 10%.
Reviewed By: wenlei, wlei, wmi
Differential Revision: https://reviews.llvm.org/D104129
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This change provides the option to merge and aggregate cold context by the last k frames instead of context-less name. By default K = 1 means the context-less one.
This is for better perf tuning. The more selective merging and trimming will rely on llvm-profgen's preinliner.
Reviewed By: wenlei, hoy
Differential Revision: https://reviews.llvm.org/D104131
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This patch was split from https://reviews.llvm.org/D102246
[SampleFDO] New hierarchical discriminator for Flow Sensitive SampleFDO
This is mainly for ProfileData part of change. It will load
FS Profile when such profile is detected. For an extbinary format profile,
create_llvm_prof tool will add a flag to profile summary section.
For other format profiles, the users need to use an internal option
(-profile-isfs) to tell the compiler that the profile uses FS discriminators.
This patch also simplified the bit API used by FS discriminators.
Differential Revision: https://reviews.llvm.org/D103041
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and error handling
This patch fixed the following issues along side with some refactoring:
1. Fix bugs where StringRef for context string out live the underlying std::string. We now keep string table in profile generator to hold std::strings. We also do the same for bracketed context strings in profile writer.
2. Make sure profile output strictly follow (total sample, name) order. Previously, there's inconsistency between ProfileMap's key and FunctionSamples's name, leading to inconsistent ordering. This is now fixed by introducing context profile canonicalization. Assertions are also added to make sure ProfileMap's key and FunctionSamples's name are always consistent.
3. Enhanced error handling for profile writing to make sure we bubble up errors properly for both llvm-profgen and llvm-profdata when string table is not populated correctly for extended binary profile.
4. Keep all internal context representation bracket free. This avoids creating new strings for context trimming, merging and preinline. getNameWithContext API is now simplied accordingly.
5. Factor out the code for context trimming and merging into SampleContextTrimmer in SampleProf.cpp. This enables llvm-profdata to use the trimmer when merging profiles. Changes in llvm-profgen will be in separate patch.
Differential Revision: https://reviews.llvm.org/D100090
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now -funique-internal-linkage-name flag is available, and we want to flip
it on by default since it is beneficial to have separate sample profiles
for different internal symbols with the same name. As a preparation, we
want to avoid regression caused by the flip.
When we flip -funique-internal-linkage-name on, the profile is collected
from binary built without -funique-internal-linkage-name so it has no uniq
suffix, but the IR in the optimized build contains the suffix. This kind of
mismatch may introduce transient regression.
To avoid such mismatch, we introduce a NameTable section flag indicating
whether there is any name in the profile containing uniq suffix. Compiler
will decide whether to keep uniq suffix during name canonicalization
depending on the NameTable section flag. The flag is only available for
extbinary format. For other formats, by default compiler will keep uniq
suffix so they will only experience transient regression when
-funique-internal-linkage-name is just flipped.
Another type of regression is caused by places where we miss to call
getCanonicalFnName. Those places are fixed.
Differential Revision: https://reviews.llvm.org/D96932
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Dangling probes are the probes associated to an empty block. This usually happens when all real instructions are optimized away from the block. There is a problem with dangling probes during the offline counts processing. The way the sample profiler works is that samples collected on the first physical instruction following a probe will be counted towards the probe. This logically equals to treating the instruction next to a probe as if it is from the same block of the probe. In the dangling probe case, the real instruction following a dangling probe actually starts a new block, and samples collected on the new block may cause issues when counted towards the empty block.
To mitigate this issue, we first try to move around a dangling probe inside its owning block. If there are still native instructions preceding the probe in the same block, we can then use them as a place holder to collect samples for the probe. A pass is added to walk each block backwards looking for probes not followed by any real instruction and moving them before the first real instruction. This is done right before the object emission.
If we are unlucky to find such in-block preceding instructions for a probe, the solution we are taking is to tag such probe as dangling so that the samples reported for them will not be trusted by the compiler. We leave it up to the counts inference algorithm to get such probes a reasonable count. The number `UINT64_MAX` is used to mark sample count as collected for a dangling probe.
Reviewed By: wmi
Differential Revision: https://reviews.llvm.org/D95962
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into profile symbol list.
When test is unrepresentative to production behavior, sample profile
collected from production can cause unexpected performance behavior
in test. To triage such issue, it is useful to have a cutoff flag
to control how many symbols will be included into profile symbol list
in order to do binary search.
Differential Revision: https://reviews.llvm.org/D97623
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This change enables pseudo-probe-based sample counts to be consumed by the sample profile loader under the regular `-fprofile-sample-use` switch with minimal adjustments to the existing sample file formats. After the counts are imported, a probe helper, aka, a `PseudoProbeManager` object, is automatically launched to verify the CFG checksum of every function in the current compilation against the corresponding checksum from the profile. Mismatched checksums will cause a function profile to be slipped. A `SampleProfileProber` pass is scheduled before any of the `SampleProfileLoader` instances so that the CFG checksums as well as probe mappings are available during the profile loading time. The `PseudoProbeManager` object is set up right after the profile reading is done. In the future a CFG-based fuzzy matching could be done in `PseudoProbeManager`.
Samples will be applied only to pseudo probe instructions as well as probed callsites once the checksum verification goes through. Those instructions are processed in the same way that regular instructions would be processed in the line-number-based scenario. In other words, a function is processed in a regular way as if it was reduced to just containing pseudo probes (block probes and callsites).
**Adjustment to profile format **
A CFG checksum field is being added to the existing AutoFDO profile formats. So far only the text format and the extended binary format are supported. For the text format, a new line like
```
!CFGChecksum: 12345
```
is added to the end of the body sample lines. For the extended binary profile format, we introduce a metadata section to store the checksum map from function names to their CFG checksums.
Differential Revision: https://reviews.llvm.org/D92347
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This change adds the context-senstive sample PGO infracture described in CSSPGO RFC (https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s). It introduced an abstraction between input profile and profile loader that queries input profile for functions. Specifically, there's now the notion of base profile and context profile, and they are managed by the new SampleContextTracker for adjusting and merging profiles based on inline decisions. It works with top-down profiled guided inliner in profile loader (https://reviews.llvm.org/D70655) for better inlining with specialization and better post-inline profile fidelity. In the future, we can also expose this infrastructure to CGSCC inliner in order for it to take advantage of context-sensitive profile. This change is the consumption part of context-sensitive profile (The generation part is in this stack: https://reviews.llvm.org/D89707). We've seen good results internally in conjunction with Pseudo-probe (https://reviews.llvm.org/D86193). Pacthes for integration with Pseudo-probe coming up soon.
Currently the new infrastructure kick in when input profile contains the new context-sensitive profile; otherwise it's no-op and does not affect existing AutoFDO.
**Interface**
There're two sets of interfaces for query and tracking respectively exposed from SampleContextTracker. For query, now instead of simply getting a profile from input for a function, we can explicitly query base profile or context profile for given call path of a function. For tracking, there're separate APIs for marking context profile as inlined, or promoting and merging not inlined context profile.
- Query base profile (`getBaseSamplesFor`)
Base profile is the merged synthetic profile for function's CFG profile from any outstanding (not inlined) context. We can query base profile by function.
- Query context profile (`getContextSamplesFor`)
Context profile is a function's CFG profile for a given calling context. We can query context profile by context string.
- Track inlined context profile (`markContextSamplesInlined`)
When a function is inlined for given calling context, we need to mark the context profile for that context as inlined. This is to make sure we don't include inlined context profile when synthesizing base profile for that inlined function.
- Track not-inlined context profile (`promoteMergeContextSamplesTree`)
When a function is not inlined for given calling context, we need to promote the context profile tree so the not inlined context becomes top-level context. This preserve the sub-context under that function so later inline decision for that not inlined function will still have context profile for its call tree. Note that profile will be merged if needed when promoting a context profile tree if any of the node already exists at its promoted destination.
**Implementation**
Implementation-wise, `SampleContext` is created as abstraction for context. Currently it's a string for call path, and we can later optimize it to something more efficient, e.g. context id. Each `SampleContext` also has a `ContextState` indicating whether it's raw context profile from input, whether it's inlined or merged, whether it's synthetic profile created by compiler. Each `FunctionSamples` now has a `SampleContext` that tells whether it's base profile or context profile, and for context profile what is the context and state.
On top of the above context representation, a custom trie tree is implemented to track and manager context profiles. Specifically, `SampleContextTracker` is implemented that encapsulates a trie tree with `ContextTireNode` as node. Each node of the trie tree represents a frame in calling context, thus the path from root to a node represents a valid calling context. We also track `FunctionSamples` for each node, so this trie tree can serve efficient query for context profile. Accordingly, context profile tree promotion now becomes moving a subtree to be under the root of entire tree, and merge nodes for subtree if this move encounters existing nodes.
**Integration**
`SampleContextTracker` is now also integrated with AutoFDO, `SampleProfileReader` and `SampleProfileLoader`. When we detected input profile contains context-sensitive profile, `SampleContextTracker` will be used to track profiles, and all profile query will go to `SampleContextTracker` instead of `SampleProfileReader` automatically. Tracking APIs are called automatically for each inline decision from `SampleProfileLoader`.
Differential Revision: https://reviews.llvm.org/D90125
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and indirect call promotion candidate.
Profile remapping is a feature to match a function in the module with its
profile in sample profile if the function name and the name in profile look
different but are equivalent using given remapping rules. This is a useful
feature to keep the performance stable by specifying some remapping rules
when sampleFDO targets are going through some large scale function signature
change.
However, currently profile remapping support is only valid for outline
function profile in SampleFDO. It cannot match a callee with an inline
instance profile if they have different but equivalent names. We found
that without the support for inline instance profile, remapping is less
effective for some large scale change.
To add that support, before any remapping lookup happens, all the names
in the profile will be inserted into remapper and the Key to the name
mapping will be recorded in a map called NameMap in the remapper. During
name lookup, a Key will be returned for the given name and it will be used
to extract an equivalent name in the profile from NameMap. So with the help
of the NameMap, we can translate any given name to an equivalent name in
the profile if it exists. Whenever we try to match a name in the module to
a name in the profile, we will try the match with the original name first,
and if it doesn't match, we will use the equivalent name got from remapper
to try the match for another time. In this way, the patch can enhance the
profile remapping support for searching inline instance and searching
indirect call promotion candidate.
In a planned large scale change of int64 type (long long) to int64_t (long),
we found the performance of a google internal benchmark degraded by 2% if
nothing was done. If existing profile remapping was enabled, the performance
degradation dropped to 1.2%. If the profile remapping with the current patch
was enabled, the performance degradation further dropped to 0.14% (Note the
experiment was done before searching indirect call promotion candidate was
added. We hope with the remapping support of searching indirect call promotion
candidate, the degradation can drop to 0% in the end. It will be evaluated
post commit).
Differential Revision: https://reviews.llvm.org/D86332
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Compbinary format uses MD5 to represent strings in name table. That gives smaller profile without the need of compression/decompression when writing/reading the profile. The patch adds the support in extbinary format. It is off by default but user can choose to enable it.
Note the feature of using MD5 in name table can bring very small chance of name conflict leading to profile mismatch. Besides, profile using the feature won't have the profile remapping support.
Differential Revision: https://reviews.llvm.org/D76255
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Summary: Minor format fix for output of "llvm-profdata -show"
Reviewers: wmi
Subscribers: hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D68440
llvm-svn: 373917
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Previously ExtBinary profile format only supports compression using zlib for
profile symbol list. In this patch, we extend the compression support to any
section. User can select some or all of the sections to compress. In an
experiment, for a 45M profile in ExtBinary format, compressing name table
reduced its size to 24M, and compressing all the sections reduced its size
to 11M.
Differential Revision: https://reviews.llvm.org/D68253
llvm-svn: 373914
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machine.
1. zlib::compress accept &size_t but the param is an uint64_t.
2. Some systems don't have zlib installed. Don't use compression by default.
llvm-svn: 370564
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