This changes adds attribute field for metadata of context profile. Currently we have an inline attribute that indicates whether the leaf frame corresponding to a context profile was inlined in previous build.
This will be used to help estimating inlining and be taken into account when trimming context. Changes for that in llvm-profgen will follow. It will also help tuning.
Differential Revision: https://reviews.llvm.org/D98823
It appears some instructions doesn't have the debug location info and the symbolizer will return an empty call stack for them which will cause some crash later in profile unwinding. Actually we do not record the sample info for them, so this change just filter out those instruction.
As those instruction would appears at the begin and end of the instruction list, without them we need to add the boundary check for IP `advance` and `backward`.
Also for pseudo probe based profile, we actually don't need the symbolized location info, so here just change to use an empty stack for it. This could save half of the binary loading time.
Differential Revision: https://reviews.llvm.org/D96434
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
when we skip the call stack starting with an external address, we should also skip the bottom LBR entry, otherwise it will cause a truncated context issue.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D95480
For CS profile generation, the process of call stack unwinding is time-consuming since for each LBR entry we need linear time to generate the context( hash, compression, string concatenation). This change speeds up this by grouping all the call frame within one LBR sample into a trie and aggregating the result(sample counter) on it, deferring the context compression and string generation to the end of unwinding.
Specifically, it uses `StackLeaf` as the top frame on the stack and manipulates(pop or push a trie node) it dynamically during virtual unwinding so that the raw sample can just be recoded on the leaf node, the path(root to leaf) will represent its calling context. In the end, it traverses the trie and generates the context on the fly.
Results:
Our internal branch shows about 5X speed-up on some large workloads in SPEC06 benchmark.
Differential Revision: https://reviews.llvm.org/D94110
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
For CS profile generation, the process of call stack unwinding is time-consuming since for each LBR entry we need linear time to generate the context( hash, compression, string concatenation). This change speeds up this by grouping all the call frame within one LBR sample into a trie and aggregating the result(sample counter) on it, deferring the context compression and string generation to the end of unwinding.
Specifically, it uses `StackLeaf` as the top frame on the stack and manipulates(pop or push a trie node) it dynamically during virtual unwinding so that the raw sample can just be recoded on the leaf node, the path(root to leaf) will represent its calling context. In the end, it traverses the trie and generates the context on the fly.
Results:
Our internal branch shows about 5X speed-up on some large workloads in SPEC06 benchmark.
Differential Revision: https://reviews.llvm.org/D94110
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
This change implements profile generation infra for pseudo probe in llvm-profgen. During virtual unwinding, the raw profile is extracted into range counter and branch counter and aggregated to sample counter map indexed by the call stack context. This change introduces the last step and produces the eventual profile. Specifically, the body of function sample is recorded by going through each probe among the range and callsite target sample is recorded by extracting the callsite probe from branch's source.
Please refer https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s and https://reviews.llvm.org/D89707 for more context about CSSPGO and llvm-profgen.
**Implementation**
- Extended `PseudoProbeProfileGenerator` for pseudo probe based profile generation.
- `populateBodySamplesWithProbes` reading range counter is responsible for recording function body samples and inferring caller's body samples.
- `populateBoundarySamplesWithProbes` reading branch counter is responsible for recording call site target samples.
- Each sample is recorded with its calling context(named `ContextId`). Remind that the probe based context key doesn't include the leaf frame probe info, so the `ContextId` string is created from two part: one from the probe stack strings' concatenation and other one from the leaf frame probe.
- Added regression test
Test Plan:
ninja & ninja check-llvm
Differential Revision: https://reviews.llvm.org/D92998
This change extends virtual unwinder to support pseudo probe in llvm-profgen. Please refer https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s and https://reviews.llvm.org/D89707 for more context about CSSPGO and llvm-profgen.
**Implementation**
- Added `ProbeBasedCtxKey` derived from `ContextKey` for sample counter aggregation. As we need string splitting to infer the profile for callee function, string based context introduces more string handling overhead, here we just use probe pointer based context.
- For linear unwinding, as inline context is encoded in each pseudo probe, we don't need to go through each instruction to extract range sharing same inliner. So just record the range for the context.
- For probe based context, we should ignore the top frame probe since it will be extracted from the address range. we defer the extraction in `ProfileGeneration`.
- Added `PseudoProbeProfileGenerator` for pseudo probe based profile generation.
- Some helper function to get pseduo probe info(call probe, inline context) from profiled binary.
- Added regression test for unwinder's output
The pseudo probe based profile generation will be in the upcoming patch.
Test Plan:
ninja & ninja check-llvm
Differential Revision: https://reviews.llvm.org/D92896
As we plan to support both CSSPGO and AutoFDO for llvm-profgen, we will have different kinds of perf sample and different kinds of sample counter(cs/non-cs, with/without pseudo probe) which both need to do aggregation in hash map. This change implements the hashable interface(`Hashable`) and the unified base class for them to have better extensibility and reusability.
Currently perf trace sample and sample counter with context implemented this `Hashable` and the class hierarchy is like:
```
| Hashable
| PerfSample
| HybridSample
| LBRSample
| ContextKey
| StringBasedCtxKey
| ProbeBasedCtxKey
| CallsiteBasedCtxKey
| ...
```
- Class specifying `Hashable` should implement `getHashCode` and `isEqual`. Here we make `getHashCode` a non-virtual function to avoid vtable overhead, so derived class should calculate and assign the base class's HashCode manually. This also provides the flexibility for calculating the hash code incrementally(like rolling hash) during frame stack unwinding
- `isEqual` is a virtual function, which will have perf overhead. In the future, if we redesign a better hash function, then we can just skip this or switch to non-virtual function.
- Added `PerfSample` and `ContextKey` as base class for perf sample and counter context key, leveraging llvm-style RTTI for this.
- Added `StringBasedCtxKey` class extending `ContextKey` to use string as context id.
- Refactor `AggregationCounter` to take all kinds of `PerfSample` as key
- Refactor `ContextSampleCounter` to take all kinds of `ContextKey` as key
- Other refactoring work:
- Create a wrapper class `SampleCounter` to wrap `RangeCounter` and `BranchCounter`
- Hoist `ContextId` and `FunctionProfile` out of `populateFunctionBodySamples` and `populateFunctionBoundarySamples` to reuse them in ProfileGenerator
Differential Revision: https://reviews.llvm.org/D92584
Don't know why under Sanitizer build(asan/msan/ubsan), the `std::unordered_map<string, ...>`'s output order is reversed, make the regression test failed.
This change creates a workaround by using sorted container to make the output deterministic.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D92816
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
Wenlei He