With https://reviews.llvm.org/D136627, now we have the metrics for profile staleness based on profile statistics, monitoring the profile staleness in real-time can help user quickly identify performance issues. For a production scenario, the build is usually incremental and if we want the real-time metrics, we should store/cache all the old object's metrics somewhere and pull them in a post-build time. To make it more convenient, this patch add an option to persist them into the object binary, the metrics can be reported right away by decoding the binary rather than polling the previous stdout/stderrs from a cache system.
For implementation, it writes the statistics first into a new metadata section(llvm.stats) then encode into a special ELF `.llvm_stats` section. The section data is formatted as a list of key/value pair so that future statistics can be easily extended. This is also under a new switch(`-persist-profile-staleness`)
In terms of size overhead, the metrics are computed at module level, so the size overhead should be small, measured on one of our internal service, it costs less than < 1MB for a 10GB+ binary.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D136698
When a profile is stale and profile mismatch could happen, the mismatched samples are discarded, so we'd like to compute the mismatch metrics to quantify how stale the profile is, which will suggest user to refresh the profile if the number is high.
Two sets of metrics are introduced here:
- (Num_of_mismatched_funchash/Total_profiled_funchash), (Samples_of_mismached_func_hash / Samples_of_profiled_function) : Here it leverages the FunctionSamples's checksums attribute which is a feature of pseudo probe. When the source code CFG changes, the function checksums will be different, later sample loader will discard the whole functions' samples, this metrics can show the percentage of samples are discarded due to this.
- (Num_of_mismatched_callsite/Total_profiled_callsite), (Samples_of_mismached_callsite / Samples_of_profiled_callsite) : This shows how many mismatching for the callsite location as callsite location mismatch will affect the inlining which is highly correlated with the performance. It goes through all the callsite location in the IR and profile, use the call target name to match, report the num of samples in the profile that doesn't match a IR callsite.
This is implemented in a new class(SampleProfileMatcher) and under a switch("--report-profile-staleness"), we plan to extend it with a fuzzy profile matching feature in the future.
Reviewed By: hoy, wenlei, davidxl
Differential Revision: https://reviews.llvm.org/D136627
With the recent addition of new parameter MergeAttributes (D134117),
callers need to specify several default parameters before getting to
specify the new parameter.
This patch reorders the parameters so that callers do not have to
specify as many default parameters.
Differential Revision: https://reviews.llvm.org/D134125
In the past, we've had a bug resulting in a compiler crash after
forgetting to merge function attributes (D105729).
This patch teaches InlineFunction to merge function attributes. This
way, we minimize the "time" when the IR is valid, but the function
attributes are not.
Differential Revision: https://reviews.llvm.org/D134117
MisExpect was occasionally crashing under SampleProfiling, due to a division by zero.
We worked around that in D124302 by changing the assert to an early return.
This patch is intended to add a test case for the crashing scenario and
re-enable MisExpect for SampleProfiling.
Reviewed By: tejohnson
Differential Revision: https://reviews.llvm.org/D124481
We're seeing non-determinism with loading sample profiles. It seems to
be related to the order in which we merge FunctionSamples in
promoteMergeNotInlinedContextSamples(). Use a MapVector to iterate over
NonInlinedCallSites in the order entries were inserted.
Reviewed By: wenlei, davidxl
Differential Revision: https://reviews.llvm.org/D131592
The name `getEntrySamples` was misleading for 2 reasons. One, it's
close in name to `Function::getEntryCount`, but the equivalent here is
`getHeadSamples`; second, as opposed to the other get* APIs in
`FunctionSamples`, it performs an estimate/heuristic rather than just
retrieving raw data (or a non-heuristic derivate off that data, like
`getMaxCountInside`)
The new name should more clearly communicate its intent; and, being
close (in name) to `getHeadSamples`, it should allow the reader discover
the relation between them.
Also updated the doc comments for both `getHeadSamples[Estimate]` so a
reader may better understand the relation between them.
Differential Revision: https://reviews.llvm.org/D130281
This is the followup patch to https://reviews.llvm.org/D125246 for the `SampleContextTracker` part. Before the promotion and merging of the context is based on the SampleContext(the array of frame), this causes a lot of cost to the memory. This patch detaches the tracker from using the array ref instead to use the context trie itself. This can save a lot of memory usage and benefit both the compiler's CS inliner and llvm-profgen's pre-inliner.
One structure needs to be specially treated is the `FuncToCtxtProfiles`, this is used to get all the functionSamples for one function to do the merging and promoting. Before it search each functions' context and traverse the trie to get the node of the context. Now we don't have the context inside the profile, instead we directly use an auxiliary map `ProfileToNodeMap` for profile , it initialize to create the FunctionSamples to TrieNode relations and keep updating it during promoting and merging the node.
Moreover, I was expecting the results before and after remain the same, but I found that the order of FuncToCtxtProfiles matter and affect the results. This can happen on recursive context case, but the difference should be small. Now we don't have the context, so I just used a vector for the order, the result is still deterministic.
Measured on one huge size(12GB) profile from one of our internal service. The profile similarity difference is 99.999%, and the running time is improved by 3X(debug mode) and the memory is reduced from 170GB to 90GB.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D127031
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.
As a follow-up to D124632, I'm turning on unlimited size caps for inlining with preinlined profile. It should be safe as a preinlined profile has "bounded" inline contexts.
No noticeable size or perf delta was seen with two of our internal large services, but I think this is still a good change to be consistent with the other case.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D124793
The two fields have the same meaning. Their values come from the reader. Therefore I'm removing one.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D124788
We have seen that the prioirty inliner delivered on-par performance with the old inliner for probe-only CSSPGO profile, as long as without a size budget. I'm turning on the priority inliner for probe-only profile by default.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D124632
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
MisExpect diagnostics should not prevent compilation from succeeding, and the
assertion is insufficient to prevent division by zero in release builds.
This patch addresses that by replacing the assert with an early return.
Additionally, it disables MisExpect diagnostics when using sample profiling,
since this is the only known case where this error has manifested.
Reviewed By: tejohnson
Differential Revision: https://reviews.llvm.org/D124302
Reimplements MisExpect diagnostics from D66324 to reconstruct its
original checking methodology only using MD_prof branch_weights
metadata.
New checks rely on 2 invariants:
1) For frontend instrumentation, MD_prof branch_weights will always be
populated before llvm.expect intrinsics are lowered.
2) for IR and sample profiling, llvm.expect intrinsics will always be
lowered before branch_weights are populated from the IR profiles.
These invariants allow the checking to assume how the existing branch
weights are populated depending on the profiling method used, and emit
the correct diagnostics. If these invariants are ever invalidated, the
MisExpect related checks would need to be updated, potentially by
re-introducing MD_misexpect metadata, and ensuring it always will be
transformed the same way as branch_weights in other optimization passes.
Frontend based profiling is now enabled without using LLVM Args, by
introducing a new CodeGen option, and checking if the -Wmisexpect flag
has been passed on the command line.
Reviewed By: tejohnson
Differential Revision: https://reviews.llvm.org/D115907
Reimplements MisExpect diagnostics from D66324 to reconstruct its
original checking methodology only using MD_prof branch_weights
metadata.
New checks rely on 2 invariants:
1) For frontend instrumentation, MD_prof branch_weights will always be
populated before llvm.expect intrinsics are lowered.
2) for IR and sample profiling, llvm.expect intrinsics will always be
lowered before branch_weights are populated from the IR profiles.
These invariants allow the checking to assume how the existing branch
weights are populated depending on the profiling method used, and emit
the correct diagnostics. If these invariants are ever invalidated, the
MisExpect related checks would need to be updated, potentially by
re-introducing MD_misexpect metadata, and ensuring it always will be
transformed the same way as branch_weights in other optimization passes.
Frontend based profiling is now enabled without using LLVM Args, by
introducing a new CodeGen option, and checking if the -Wmisexpect flag
has been passed on the command line.
Reviewed By: tejohnson
Differential Revision: https://reviews.llvm.org/D115907
Reimplements MisExpect diagnostics from D66324 to reconstruct its
original checking methodology only using MD_prof branch_weights
metadata.
New checks rely on 2 invariants:
1) For frontend instrumentation, MD_prof branch_weights will always be
populated before llvm.expect intrinsics are lowered.
2) for IR and sample profiling, llvm.expect intrinsics will always be
lowered before branch_weights are populated from the IR profiles.
These invariants allow the checking to assume how the existing branch
weights are populated depending on the profiling method used, and emit
the correct diagnostics. If these invariants are ever invalidated, the
MisExpect related checks would need to be updated, potentially by
re-introducing MD_misexpect metadata, and ensuring it always will be
transformed the same way as branch_weights in other optimization passes.
Frontend based profiling is now enabled without using LLVM Args, by
introducing a new CodeGen option, and checking if the -Wmisexpect flag
has been passed on the command line.
Reviewed By: tejohnson
Differential Revision: https://reviews.llvm.org/D115907
Probe-based profile leads to a better performance when combined with profi and ext-tsp block layout. I'm turning them on by default.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D122442
When --disable-sample-loader-inlining is true, skip inline transformation, but merge profiles of inlined instances to outlining versions.
Differential Revision: https://reviews.llvm.org/D121862
Reimplements MisExpect diagnostics from D66324 to reconstruct its
original checking methodology only using MD_prof branch_weights
metadata.
New checks rely on 2 invariants:
1) For frontend instrumentation, MD_prof branch_weights will always be
populated before llvm.expect intrinsics are lowered.
2) for IR and sample profiling, llvm.expect intrinsics will always be
lowered before branch_weights are populated from the IR profiles.
These invariants allow the checking to assume how the existing branch
weights are populated depending on the profiling method used, and emit
the correct diagnostics. If these invariants are ever invalidated, the
MisExpect related checks would need to be updated, potentially by
re-introducing MD_misexpect metadata, and ensuring it always will be
transformed the same way as branch_weights in other optimization passes.
Frontend based profiling is now enabled without using LLVM Args, by
introducing a new CodeGen option, and checking if the -Wmisexpect flag
has been passed on the command line.
Differential Revision: https://reviews.llvm.org/D115907
The priority-based inliner currenlty uses block count combined with callee entry count to drive callsite inlining. This doesn't work well with LTO where postlink inlining is driven by prelink-annotated block count which could be based on the merge of all context profiles. I'm fixing it by using callee profile entry count only which should be context-sensitive.
I'm seeing 0.2% perf improvment for one of our internal large benchmarks with probe-based non-CS profile.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D120784
In places where `MaxNumPromotions` is used to allocated an array, bail out early to prevent allocating an array of length 0.
Differential Revision: https://reviews.llvm.org/D120295
Do not merge a context that is already duplicated into the base profile.
Also fixing a typo caused by previous refactoring.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D119735
I'm seeing ext-tsp helps CSSPGO for our intern large benchmarks so I'm turning on it for CSSPGO. For non-CS AutoFDO, ext-tsp doesn't seem to help, probably because of lower profile counts quality.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D119048
Modules that are not compiled with pseudo probe enabled can still be compiled with a sample profile input, such as in LTO postlink where other modules are probed. Since the profile is unrelated to the current modules, we should warn instead of error out the compilation.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D115642
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
Fangrui Song