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.
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
For profile generation, we need to filter raw perf samples for binary of interest. Sometimes binary name along isn't enough as we can have binary of the same name running in the system. This change adds a process id filter to allow users to further disambiguiate the input raw samples.
Differential Revision: https://reviews.llvm.org/D123869
Sometimes we would like to run post-processing repeatedly on the original sample profile for tuning. In order to avoid regenerating the original profile from scratch every time, this change adds the support of reading in the original profile (called symbolized profile) and running the post-processor on it.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D121655
For reducing binary size purpose, the binary's debug info and executable segment can be separated(like using objcopy --only-keep-debug). Here add support in llvm-profgen to use two binaries as input. The original one is executable binary and added for debug info only binary. Adding a flag `--debug-binary=file-path`, with this, the binary will load debug info from debug binary.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D115948
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
This change allows the unsymbolized profile as input. The unsymbolized profile is created by `llvm-profgen` with `--skip-symbolization` and it's after the sample aggregation but before symbolization , so it has much small file size. It can be used for sample merging and trimming, also is useful for debugging or adding test cases. A switch `--unsymbolized-profile=file-patch` is added for this.
Format of unsymbolized profile:
```
[context stack1] # If it's a CS profile
number of entries in RangeCounter
from_1-to_1:count_1
from_2-to_2:count_2
......
from_n-to_n:count_n
number of entries in BranchCounter
src_1->dst_1:count_1
src_2->dst_2:count_2
......
src_n->dst_n:count_n
[context stack2]
......
```
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D111750
This change enables llvm-profgen to take raw perf data as alternative input format. Sometimes we need to retrieve evenets for processes with matching binary. Using perf data as input allows us to retrieve process Ids from mmap events for matching binary, then filter by process id during perf script generation.
Differential Revision: https://reviews.llvm.org/D110793
This change contains diagnostics improvments, refactoring and preparation for consuming perf data directly.
Diagnostics:
- We now have more detailed diagnostics when no mmap is found.
- We also print warning for abnormal transition to external code.
Refactoring:
- Simplify input perf trace processing to only allow a single input file. This is because 1) using multiple input perf trace (perf script) is error prone because we may miss key mmap events. 2) the functionality is not really being used anyways.
- Make more functions private for Readers, move non-trivial definitions out of header. Cleanup some inconsistency.
- Prepare for consuming perf data as input directly.
Differential Revision: https://reviews.llvm.org/D110729
This patch introduces non-CS AutoFDO profile generation into LLVM. The profile is supposed to be well consumed by compiler using `-fprofile-sample-use=[profile]`.
After range and branch counters are extracted from the LBR sample, here we go through each addresses for symbolization, create FunctionSamples and populate its sub fields like TotalSamples, BodySamples and HeadSamples etc. For inlined code, as we need to map back to original code, so we always add body samples to the leaf frame's function sample.
Reviewed By: wenlei, hoy
Differential Revision: https://reviews.llvm.org/D109551
This change aims at supporting LBR only sample perf script which is used for regular(Non-CS) profile generation. A LBR perf script includes a batch of LBR sample which starts with a frame pointer and a group of 32 LBR entries is followed. The FROM/TO LBR pair and the range between two consecutive entries (the former entry's TO and the latter entry's FROM) will be used to infer function profile info.
An example of LBR perf script(created by `perf script -F ip,brstack -i perf.data`)
```
40062f 0x40062f/0x4005b0/P/-/-/9 0x400645/0x4005ff/P/-/-/1 0x400637/0x400645/P/-/-/1 ...
4005d7 0x4005d7/0x4005e5/P/-/-/8 0x40062f/0x4005b0/P/-/-/6 0x400645/0x4005ff/P/-/-/1 ...
...
```
For implementation:
- Extended a new child class `LBRPerfReader` for the sample parsing, reused all the functionalities in `extractLBRStack` except for an extension to parsing leading instruction pointer.
- `HybridSample` is reused(just leave the call stack empty) and the parsed samples is still aggregated in `AggregatedSamples`. After that, range samples, branch sample, address samples are computed and recorded.
- Reused `ContextSampleCounterMap` to store the raw profile, since it's no need to aggregation by context, here it just registered one sample counter with a fake context key.
- Unified to use `show-raw-profile` instead of `show-unwinder-output` to dump the intermediate raw profile, see the comments of the format of the raw profile. For CS profile, it remains to output the unwinder output.
Profile generation part will come soon.
Differential Revision: https://reviews.llvm.org/D108153
Change to use unique pointer of profiled binary to unblock asan.
At same time, I realized we can decouple to move the profiled binary loading out of PerfReader, so I made some other related refactors.
Reviewed By: hoy
Differential Revision: https://reviews.llvm.org/D108254
As we decided to support only one binary each time, this patch cleans up the related code dealing with multiple binaries. We can use `llvm-profdata` to merge profile from multiple binaries.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D108002
In order to support different types of perf scripts, this change tried to refactor `PerfReader` by adding the base class `PerfReaderBase` and current HybridPerfReader is derived from it for CS profile generation. Common functions like, passMM2PEvents, extract_lbrs, extract_callstack, etc. can be reused.
Next step is to add LBR only reader(for non-CS profile) and aggregated perf scripts reader(do a pre-aggregation of scripts).
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D107014
This include some changes related with PerfReader's the input check and command line change:
1) It appears there might be thousands of leading MMAP-Event line in the perfscript for large workload. For this case, the 4k threshold is not eligible to determine it's a hybrid sample. This change renovated the `isHybridPerfScript` by going through the script without threshold limitation checking whether there is a non-empty call stack immediately followed by a LBR sample. It will stop once it find a valid one.
2) Added several input validations for the command line switches in PerfReader.
3) Changed the command line `show-disassembly` to `show-disassembly-only`, it will print to stdout and exit early which leave an empty output profile.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D96387
I am experimenting with turning backends into loadable modules and in
that scenario, target specific command line arguments won't be available
until after the targets are initialized.
Also, most other tools initialize targets before parsing arguments.
Reviewed By: wlei
Differential Revision: https://reviews.llvm.org/D93348
This stack of changes introduces `llvm-profgen` utility which generates a profile data file from given perf script data files for sample-based PGO. It’s part of(not only) the CSSPGO work. Specifically to support context-sensitive with/without pseudo probe profile, it implements a series of functionalities including perf trace parsing, instruction symbolization, LBR stack/call frame stack unwinding, pseudo probe decoding, etc. Also high throughput is achieved by multiple levels of sample aggregation and compatible format with one stop is generated at the end. Please refer to: https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s for the CSSPGO RFC.
This change supports context-sensitive profile data generation into llvm-profgen. With simultaneous sampling for LBR and call stack, we can identify leaf of LBR sample with calling context from stack sample . During the process of deriving fall through path from LBR entries, we unwind LBR by replaying all the calls and returns (including implicit calls/returns due to inlining) backwards on top of the sampled call stack. Then the state of call stack as we unwind through LBR always represents the calling context of current fall through path.
we have two types of virtual unwinding 1) LBR unwinding and 2) linear range unwinding.
Specifically, for each LBR entry which can be classified into call, return, regular branch, LBR unwinding will replay the operation by pushing, popping or switching leaf frame towards the call stack and since the initial call stack is most recently sampled, the replay should be in anti-execution order, i.e. for the regular case, pop the call stack when LBR is call, push frame on call stack when LBR is return. After each LBR processed, it also needs to align with the next LBR by going through instructions from previous LBR's target to current LBR's source, which we named linear unwinding. As instruction from linear range can come from different function by inlining, linear unwinding will do the range splitting and record counters through the range with same inline context.
With each fall through path from LBR unwinding, we aggregate each sample into counters by the calling context and eventually generate full context sensitive profile (without relying on inlining) to driver compiler's PGO/FDO.
A breakdown of noteworthy changes:
- Added `HybridSample` class as the abstraction perf sample including LBR stack and call stack
* Extended `PerfReader` to implement auto-detect whether input perf script output contains CS profile, then do the parsing. Multiple `HybridSample` are extracted
* Speed up by aggregating `HybridSample` into `AggregatedSamples`
* Added VirtualUnwinder that consumes aggregated `HybridSample` and implements unwinding of calls, returns, and linear path that contains implicit call/return from inlining. Ranges and branches counters are aggregated by the calling context. Here calling context is string type, each context is a pair of function name and callsite location info, the whole context is like `main:1 @ foo:2 @ bar`.
* Added PorfileGenerater that accumulates counters by ranges unfolding or branch target mapping, then generates context-sensitive function profile including function body, inferring callee's head sample, callsite target samples, eventually records into ProfileMap.
* Leveraged LLVM build-in(`SampleProfWriter`) writer to support different serialization format with no stop
- `getCanonicalFnName` for callee name and name from ELF section
- Added regression test for both unwinding and profile generation
Test Plan:
ninja & ninja check-llvm
Reviewed By: hoy, wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89723
This stack of changes introduces `llvm-profgen` utility which generates a profile data file from given perf script data files for sample-based PGO. It’s part of(not only) the CSSPGO work. Specifically to support context-sensitive with/without pseudo probe profile, it implements a series of functionalities including perf trace parsing, instruction symbolization, LBR stack/call frame stack unwinding, pseudo probe decoding, etc. Also high throughput is achieved by multiple levels of sample aggregation and compatible format with one stop is generated at the end. Please refer to: https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s for the CSSPGO RFC.
This change enables disassembling the text sections to build various address maps that are potentially used by the virtual unwinder. A switch `--show-disassembly` is being added to print the disassembly code.
Like the llvm-objdump tool, this change leverages existing LLVM components to parse and disassemble ELF binary files. So far X86 is supported.
Test Plan:
ninja check-llvm
Reviewed By: wmi, wenlei
Differential Revision: https://reviews.llvm.org/D89712
This stack of changes introduces `llvm-profgen` utility which generates a profile data file from given perf script data files for sample-based PGO. It’s part of(not only) the CSSPGO work. Specifically to support context-sensitive with/without pseudo probe profile, it implements a series of functionalities including perf trace parsing, instruction symbolization, LBR stack/call frame stack unwinding, pseudo probe decoding, etc. Also high throughput is achieved by multiple levels of sample aggregation and compatible format with one stop is generated at the end. Please refer to: https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s for the CSSPGO RFC.
As a starter, this change sets up an entry point by introducing PerfReader to load profiled binaries and perf traces(including perf events and perf samples). For the event, here it parses the mmap2 events from perf script to build the loader snaps, which is used to retrieve the image load address in the subsequent perf tracing parsing.
As described in llvm-profgen.rst, the tool being built aims to support multiple input perf data (preprocessed by perf script) as well as multiple input binary images. It should also support dynamic reload/unload shared objects by leveraging the loader snaps being built by this change
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89707
Fangrui Song