[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
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//===-- ProfiledBinary.h - Binary decoder -----------------------*- C++ -*-===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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2021-07-13 19:48:58 -07:00
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[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
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#ifndef LLVM_TOOLS_LLVM_PROFGEN_PROFILEDBINARY_H
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#define LLVM_TOOLS_LLVM_PROFGEN_PROFILEDBINARY_H
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[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
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#include "CallContext.h"
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2021-11-08 23:05:16 -08:00
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#include "ErrorHandling.h"
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[PseudoProbe] Replace relocation with offset for entry probe.
Currently pseudo probe encoding for a function is like:
- For the first probe, a relocation from it to its physical position in the code body
- For subsequent probes, an incremental offset from the current probe to the previous probe
The relocation could potentially cause relocation overflow during link time. I'm now replacing it with an offset from the first probe to the function start address.
A source function could be lowered into multiple binary functions due to outlining (e.g, coro-split). Since those binary function have independent link-time layout, to really avoid relocations from .pseudo_probe sections to .text sections, the offset to replace with should really be the offset from the probe's enclosing binary function, rather than from the entry of the source function. This requires some changes to previous section-based emission scheme which now switches to be function-based. The assembly form of pseudo probe directive is also changed correspondingly, i.e, reflecting the binary function name.
Most of the source functions end up with only one binary function. For those don't, a sentinel probe is emitted for each of the binary functions with a different name from the source. The sentinel probe indicates the binary function name to differentiate subsequent probes from the ones from a different binary function. For examples, given source function
```
Foo() {
…
Probe 1
…
Probe 2
}
```
If it is transformed into two binary functions:
```
Foo:
…
Foo.outlined:
…
```
The encoding for the two binary functions will be separate:
```
GUID of Foo
Probe 1
GUID of Foo
Sentinel probe of Foo.outlined
Probe 2
```
Then probe1 will be decoded against binary `Foo`'s address, and Probe 2 will be decoded against `Foo.outlined`. The sentinel probe of `Foo.outlined` makes sure there's not accidental relocation from `Foo.outlined`'s probes to `Foo`'s entry address.
On the BOLT side, to be minimal intrusive, the pseudo probe re-encoding sticks with the old encoding format. This is fine since unlike linker, Bolt processes the pseudo probe section as a whole and it is free from relocation overflow issues.
The change is downwards compatible as long as there's no mixed use of the old encoding and the new encoding.
Reviewed By: wenlei, maksfb
Differential Revision: https://reviews.llvm.org/D135912
Differential Revision: https://reviews.llvm.org/D135914
Differential Revision: https://reviews.llvm.org/D136394
2022-10-17 10:07:18 -07:00
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#include "llvm/ADT/DenseMap.h"
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[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
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#include "llvm/ADT/StringRef.h"
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2022-02-23 22:18:20 +01:00
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#include "llvm/ADT/StringSet.h"
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2021-10-26 14:55:33 -07:00
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#include "llvm/DebugInfo/DWARF/DWARFContext.h"
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[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
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#include "llvm/DebugInfo/Symbolize/Symbolize.h"
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2020-10-19 10:02:05 -07:00
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#include "llvm/MC/MCAsmInfo.h"
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#include "llvm/MC/MCContext.h"
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#include "llvm/MC/MCDisassembler/MCDisassembler.h"
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#include "llvm/MC/MCInst.h"
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#include "llvm/MC/MCInstPrinter.h"
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#include "llvm/MC/MCInstrAnalysis.h"
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#include "llvm/MC/MCInstrInfo.h"
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#include "llvm/MC/MCObjectFileInfo.h"
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2021-08-04 08:50:28 -07:00
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#include "llvm/MC/MCPseudoProbe.h"
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2020-10-19 10:02:05 -07:00
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#include "llvm/MC/MCRegisterInfo.h"
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#include "llvm/MC/MCSubtargetInfo.h"
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#include "llvm/MC/MCTargetOptions.h"
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#include "llvm/Object/ELFObjectFile.h"
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[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
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#include "llvm/ProfileData/SampleProf.h"
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2021-07-28 15:07:40 -07:00
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#include "llvm/Support/CommandLine.h"
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[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
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#include "llvm/Support/Path.h"
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2021-08-16 18:29:07 -07:00
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#include "llvm/Transforms/IPO/SampleContextTracker.h"
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2021-09-23 22:53:12 -07:00
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#include <map>
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2020-10-19 10:02:05 -07:00
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#include <set>
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[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
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#include <sstream>
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2020-10-19 10:02:05 -07:00
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#include <string>
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#include <unordered_map>
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#include <unordered_set>
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#include <vector>
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2023-02-14 00:09:44 -08:00
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namespace llvm {
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2021-08-16 18:29:07 -07:00
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extern cl::opt<bool> EnableCSPreInliner;
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extern cl::opt<bool> UseContextCostForPreInliner;
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2023-02-14 00:09:44 -08:00
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} // namespace llvm
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2021-08-16 18:29:07 -07:00
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[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
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using namespace llvm;
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using namespace sampleprof;
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2020-10-19 10:02:05 -07:00
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using namespace llvm::object;
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[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
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namespace llvm {
|
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namespace sampleprof {
|
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[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
|
|
|
class ProfiledBinary;
|
2022-12-15 18:36:52 -08:00
|
|
|
class MissingFrameInferrer;
|
[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
|
|
|
|
|
|
|
struct InstructionPointer {
|
2021-08-31 13:27:42 -07:00
|
|
|
const ProfiledBinary *Binary;
|
2022-10-13 20:42:51 -07:00
|
|
|
// Address of the executable segment of the binary.
|
|
|
|
uint64_t Address;
|
[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
|
|
|
// Index to the sorted code address array of the binary.
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
uint64_t Index = 0;
|
2021-08-31 13:27:42 -07:00
|
|
|
InstructionPointer(const ProfiledBinary *Binary, uint64_t Address,
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
bool RoundToNext = false);
|
2021-11-04 20:51:04 -07:00
|
|
|
bool advance();
|
|
|
|
bool backward();
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
void update(uint64_t Addr);
|
|
|
|
};
|
|
|
|
|
[CSSPGO][llvm-profgen] Fix external address issues of perf reader (return to external addr part)
Before we have an issue with artificial LBR whose source is a return, recalling that "an internal code(A) can return to external address, then from the external address call a new internal code(B), making an artificial branch that looks like a return from A to B can confuse the unwinder". We just ignore the LBRs after this artificial LBR which can miss some samples. This change aims at fixing this by correctly unwinding them instead of ignoring them.
List some typical scenarios covered by this change.
1) multiple sequential call back happen in external address, e.g.
```
[ext, call, foo] [foo, return, ext] [ext, call, bar]
```
Unwinder should avoid having foo return from bar. Wrong call stack is like [foo, bar]
2) the call stack before and after external call should be correctly unwinded.
```
{call stack1} {call stack2}
[foo, call, ext] [ext, call, bar] [bar, return, ext] [ext, return, foo ]
```
call stack 1 should be the same to call stack2. Both shouldn't be truncated
3) call stack should be truncated after call into external code since we can't do inlining with external code.
```
[foo, call, ext] [ext, call, bar] [bar, call, baz] [baz, return, bar ] [bar, return, ext]
```
the call stack of code in baz should not include foo.
### Implementation:
We leverage artificial frame to fix #2 and #3: when we got a return artificial LBR, push an extra artificial frame to the stack. when we pop frame, check if the parent is an artificial frame to pop(fix #2). Therefore, call/ return artificial LBR is just the same as regular LBR which can keep the call stack.
While recording context on the trie, artificial frame is used as a tag indicating that we should truncate the call stack(fix #3).
To differentiate #1 and #2, we leverage `getCallAddrFromFrameAddr`. Normally the target of the return should be the next inst of a call inst and `getCallAddrFromFrameAddr` will return the address of call inst. Otherwise, getCallAddrFromFrameAddr will return to 0 which is the case of #1.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D115550
2021-12-13 20:33:33 -08:00
|
|
|
// The special frame addresses.
|
|
|
|
enum SpecialFrameAddr {
|
|
|
|
// Dummy root of frame trie.
|
|
|
|
DummyRoot = 0,
|
|
|
|
// Represent all the addresses outside of current binary.
|
|
|
|
// This's also used to indicate the call stack should be truncated since this
|
|
|
|
// isn't a real call context the compiler will see.
|
|
|
|
ExternalAddr = 1,
|
|
|
|
};
|
|
|
|
|
2021-10-26 14:55:33 -07:00
|
|
|
using RangesTy = std::vector<std::pair<uint64_t, uint64_t>>;
|
|
|
|
|
|
|
|
struct BinaryFunction {
|
|
|
|
StringRef FuncName;
|
2021-11-04 20:51:04 -07:00
|
|
|
// End of range is an exclusive bound.
|
2021-10-26 14:55:33 -07:00
|
|
|
RangesTy Ranges;
|
2021-11-28 18:42:09 -08:00
|
|
|
|
|
|
|
uint64_t getFuncSize() {
|
|
|
|
uint64_t Sum = 0;
|
|
|
|
for (auto &R : Ranges) {
|
|
|
|
Sum += R.second - R.first;
|
|
|
|
}
|
|
|
|
return Sum;
|
|
|
|
}
|
2021-10-26 14:55:33 -07:00
|
|
|
};
|
|
|
|
|
|
|
|
// Info about function range. A function can be split into multiple
|
|
|
|
// non-continuous ranges, each range corresponds to one FuncRange.
|
|
|
|
struct FuncRange {
|
2022-10-13 20:42:51 -07:00
|
|
|
uint64_t StartAddress;
|
|
|
|
// EndAddress is an exclusive bound.
|
|
|
|
uint64_t EndAddress;
|
2021-10-26 14:55:33 -07:00
|
|
|
// Function the range belongs to
|
|
|
|
BinaryFunction *Func;
|
2022-10-13 20:42:51 -07:00
|
|
|
// Whether the start address is the real entry of the function.
|
2021-10-26 14:55:33 -07:00
|
|
|
bool IsFuncEntry = false;
|
|
|
|
|
|
|
|
StringRef getFuncName() { return Func->FuncName; }
|
|
|
|
};
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
// PrologEpilog address tracker, used to filter out broken stack samples
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
// Currently we use a heuristic size (two) to infer prolog and epilog
|
|
|
|
// based on the start address and return address. In the future,
|
|
|
|
// we will switch to Dwarf CFI based tracker
|
|
|
|
struct PrologEpilogTracker {
|
2022-10-13 20:42:51 -07:00
|
|
|
// A set of prolog and epilog addresses. Used by virtual unwinding.
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
std::unordered_set<uint64_t> PrologEpilogSet;
|
|
|
|
ProfiledBinary *Binary;
|
|
|
|
PrologEpilogTracker(ProfiledBinary *Bin) : Binary(Bin){};
|
|
|
|
|
|
|
|
// Take the two addresses from the start of function as prolog
|
2022-10-13 20:42:51 -07:00
|
|
|
void
|
|
|
|
inferPrologAddresses(std::map<uint64_t, FuncRange> &FuncStartAddressMap) {
|
|
|
|
for (auto I : FuncStartAddressMap) {
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
PrologEpilogSet.insert(I.first);
|
|
|
|
InstructionPointer IP(Binary, I.first);
|
2021-11-04 20:51:04 -07:00
|
|
|
if (!IP.advance())
|
|
|
|
break;
|
2022-10-13 20:42:51 -07:00
|
|
|
PrologEpilogSet.insert(IP.Address);
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
}
|
[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
|
|
|
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
// Take the last two addresses before the return address as epilog
|
2022-10-13 20:42:51 -07:00
|
|
|
void inferEpilogAddresses(std::unordered_set<uint64_t> &RetAddrs) {
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
for (auto Addr : RetAddrs) {
|
|
|
|
PrologEpilogSet.insert(Addr);
|
|
|
|
InstructionPointer IP(Binary, Addr);
|
2021-11-04 20:51:04 -07:00
|
|
|
if (!IP.backward())
|
|
|
|
break;
|
2022-10-13 20:42:51 -07:00
|
|
|
PrologEpilogSet.insert(IP.Address);
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
|
|
|
}
|
|
|
|
};
|
|
|
|
|
2021-08-16 18:29:07 -07:00
|
|
|
// Track function byte size under different context (outlined version as well as
|
|
|
|
// various inlined versions). It also provides query support to get function
|
|
|
|
// size with the best matching context, which is used to help pre-inliner use
|
|
|
|
// accurate post-optimization size to make decisions.
|
|
|
|
// TODO: If an inlinee is completely optimized away, ideally we should have zero
|
|
|
|
// for its context size, currently we would misss such context since it doesn't
|
|
|
|
// have instructions. To fix this, we need to mark all inlinee with entry probe
|
|
|
|
// but without instructions as having zero size.
|
|
|
|
class BinarySizeContextTracker {
|
|
|
|
public:
|
|
|
|
// Add instruction with given size to a context
|
[CSSPGO] Split context string to deduplicate function name used in the context.
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
2021-08-25 11:40:34 -07:00
|
|
|
void addInstructionForContext(const SampleContextFrameVector &Context,
|
2021-08-16 18:29:07 -07:00
|
|
|
uint32_t InstrSize);
|
|
|
|
|
|
|
|
// Get function size with a specific context. When there's no exact match
|
|
|
|
// for the given context, try to retrieve the size of that function from
|
|
|
|
// closest matching context.
|
[CSSPGO][llvm-profgen] Reimplement SampleContextTracker using context trie
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
2022-06-27 23:00:05 -07:00
|
|
|
uint32_t getFuncSizeForContext(const ContextTrieNode *Context);
|
2021-08-16 18:29:07 -07:00
|
|
|
|
2021-08-18 21:09:49 -07:00
|
|
|
// For inlinees that are full optimized away, we can establish zero size using
|
|
|
|
// their remaining probes.
|
|
|
|
void trackInlineesOptimizedAway(MCPseudoProbeDecoder &ProbeDecoder);
|
|
|
|
|
|
|
|
using ProbeFrameStack = SmallVector<std::pair<StringRef, uint32_t>>;
|
2024-08-10 23:45:47 -07:00
|
|
|
void
|
|
|
|
trackInlineesOptimizedAway(MCPseudoProbeDecoder &ProbeDecoder,
|
|
|
|
const MCDecodedPseudoProbeInlineTree &ProbeNode,
|
|
|
|
ProbeFrameStack &Context);
|
2021-08-18 21:09:49 -07:00
|
|
|
|
2022-01-28 15:53:37 -08:00
|
|
|
void dump() { RootContext.dumpTree(); }
|
|
|
|
|
|
|
|
private:
|
2021-08-16 18:29:07 -07:00
|
|
|
// Root node for context trie tree, node that this is a reverse context trie
|
|
|
|
// with callee as parent and caller as child. This way we can traverse from
|
|
|
|
// root to find the best/longest matching context if an exact match does not
|
|
|
|
// exist. It gives us the best possible estimate for function's post-inline,
|
|
|
|
// post-optimization byte size.
|
|
|
|
ContextTrieNode RootContext;
|
|
|
|
};
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
using AddressRange = std::pair<uint64_t, uint64_t>;
|
2021-09-23 22:53:12 -07:00
|
|
|
|
[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
|
|
|
class ProfiledBinary {
|
2022-01-24 16:55:05 -08:00
|
|
|
// Absolute path of the executable binary.
|
[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
|
|
|
std::string Path;
|
2022-01-24 16:55:05 -08:00
|
|
|
// Path of the debug info binary.
|
|
|
|
std::string DebugBinaryPath;
|
2020-10-19 10:02:05 -07:00
|
|
|
// The target triple.
|
|
|
|
Triple TheTriple;
|
2023-05-25 08:11:40 -07:00
|
|
|
// Path of symbolizer path which should be pointed to binary with debug info.
|
|
|
|
StringRef SymbolizerPath;
|
|
|
|
// Options used to configure the symbolizer
|
|
|
|
symbolize::LLVMSymbolizer::Options SymbolizerOpts;
|
2021-07-12 09:47:05 -07:00
|
|
|
// The runtime base address that the first executable segment is loaded at.
|
2021-07-13 19:49:50 -07:00
|
|
|
uint64_t BaseAddress = 0;
|
2021-11-11 18:28:47 -08:00
|
|
|
// The runtime base address that the first loadabe segment is loaded at.
|
|
|
|
uint64_t FirstLoadableAddress = 0;
|
2021-07-12 09:47:05 -07:00
|
|
|
// The preferred load address of each executable segment.
|
|
|
|
std::vector<uint64_t> PreferredTextSegmentAddresses;
|
|
|
|
// The file offset of each executable segment.
|
|
|
|
std::vector<uint64_t> TextSegmentOffsets;
|
|
|
|
|
2020-10-19 10:02:05 -07:00
|
|
|
// Mutiple MC component info
|
|
|
|
std::unique_ptr<const MCRegisterInfo> MRI;
|
|
|
|
std::unique_ptr<const MCAsmInfo> AsmInfo;
|
|
|
|
std::unique_ptr<const MCSubtargetInfo> STI;
|
|
|
|
std::unique_ptr<const MCInstrInfo> MII;
|
|
|
|
std::unique_ptr<MCDisassembler> DisAsm;
|
|
|
|
std::unique_ptr<const MCInstrAnalysis> MIA;
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
std::unique_ptr<MCInstPrinter> IPrinter;
|
2020-10-19 10:02:05 -07:00
|
|
|
// A list of text sections sorted by start RVA and size. Used to check
|
|
|
|
// if a given RVA is a valid code address.
|
|
|
|
std::set<std::pair<uint64_t, uint64_t>> TextSections;
|
2021-10-26 14:55:33 -07:00
|
|
|
|
|
|
|
// A map of mapping function name to BinaryFunction info.
|
|
|
|
std::unordered_map<std::string, BinaryFunction> BinaryFunctions;
|
|
|
|
|
[llvm-profdata] Do not create numerical strings for MD5 function names read 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
2023-10-17 17:09:39 -04:00
|
|
|
// Lookup BinaryFunctions using the function name's MD5 hash. Needed if the
|
|
|
|
// profile is using MD5.
|
|
|
|
std::unordered_map<uint64_t, BinaryFunction *> HashBinaryFunctions;
|
|
|
|
|
2022-03-23 12:36:44 -07:00
|
|
|
// A list of binary functions that have samples.
|
|
|
|
std::unordered_set<const BinaryFunction *> ProfiledFunctions;
|
|
|
|
|
[PseudoProbe] Replace relocation with offset for entry probe.
Currently pseudo probe encoding for a function is like:
- For the first probe, a relocation from it to its physical position in the code body
- For subsequent probes, an incremental offset from the current probe to the previous probe
The relocation could potentially cause relocation overflow during link time. I'm now replacing it with an offset from the first probe to the function start address.
A source function could be lowered into multiple binary functions due to outlining (e.g, coro-split). Since those binary function have independent link-time layout, to really avoid relocations from .pseudo_probe sections to .text sections, the offset to replace with should really be the offset from the probe's enclosing binary function, rather than from the entry of the source function. This requires some changes to previous section-based emission scheme which now switches to be function-based. The assembly form of pseudo probe directive is also changed correspondingly, i.e, reflecting the binary function name.
Most of the source functions end up with only one binary function. For those don't, a sentinel probe is emitted for each of the binary functions with a different name from the source. The sentinel probe indicates the binary function name to differentiate subsequent probes from the ones from a different binary function. For examples, given source function
```
Foo() {
…
Probe 1
…
Probe 2
}
```
If it is transformed into two binary functions:
```
Foo:
…
Foo.outlined:
…
```
The encoding for the two binary functions will be separate:
```
GUID of Foo
Probe 1
GUID of Foo
Sentinel probe of Foo.outlined
Probe 2
```
Then probe1 will be decoded against binary `Foo`'s address, and Probe 2 will be decoded against `Foo.outlined`. The sentinel probe of `Foo.outlined` makes sure there's not accidental relocation from `Foo.outlined`'s probes to `Foo`'s entry address.
On the BOLT side, to be minimal intrusive, the pseudo probe re-encoding sticks with the old encoding format. This is fine since unlike linker, Bolt processes the pseudo probe section as a whole and it is free from relocation overflow issues.
The change is downwards compatible as long as there's no mixed use of the old encoding and the new encoding.
Reviewed By: wenlei, maksfb
Differential Revision: https://reviews.llvm.org/D135912
Differential Revision: https://reviews.llvm.org/D135914
Differential Revision: https://reviews.llvm.org/D136394
2022-10-17 10:07:18 -07:00
|
|
|
// GUID to Elf symbol start address map
|
|
|
|
DenseMap<uint64_t, uint64_t> SymbolStartAddrs;
|
|
|
|
|
2023-10-22 17:00:14 -07:00
|
|
|
// These maps are for temporary use of warning diagnosis.
|
|
|
|
DenseSet<int64_t> AddrsWithMultipleSymbols;
|
|
|
|
DenseSet<std::pair<uint64_t, uint64_t>> AddrsWithInvalidInstruction;
|
|
|
|
|
[PseudoProbe] Replace relocation with offset for entry probe.
Currently pseudo probe encoding for a function is like:
- For the first probe, a relocation from it to its physical position in the code body
- For subsequent probes, an incremental offset from the current probe to the previous probe
The relocation could potentially cause relocation overflow during link time. I'm now replacing it with an offset from the first probe to the function start address.
A source function could be lowered into multiple binary functions due to outlining (e.g, coro-split). Since those binary function have independent link-time layout, to really avoid relocations from .pseudo_probe sections to .text sections, the offset to replace with should really be the offset from the probe's enclosing binary function, rather than from the entry of the source function. This requires some changes to previous section-based emission scheme which now switches to be function-based. The assembly form of pseudo probe directive is also changed correspondingly, i.e, reflecting the binary function name.
Most of the source functions end up with only one binary function. For those don't, a sentinel probe is emitted for each of the binary functions with a different name from the source. The sentinel probe indicates the binary function name to differentiate subsequent probes from the ones from a different binary function. For examples, given source function
```
Foo() {
…
Probe 1
…
Probe 2
}
```
If it is transformed into two binary functions:
```
Foo:
…
Foo.outlined:
…
```
The encoding for the two binary functions will be separate:
```
GUID of Foo
Probe 1
GUID of Foo
Sentinel probe of Foo.outlined
Probe 2
```
Then probe1 will be decoded against binary `Foo`'s address, and Probe 2 will be decoded against `Foo.outlined`. The sentinel probe of `Foo.outlined` makes sure there's not accidental relocation from `Foo.outlined`'s probes to `Foo`'s entry address.
On the BOLT side, to be minimal intrusive, the pseudo probe re-encoding sticks with the old encoding format. This is fine since unlike linker, Bolt processes the pseudo probe section as a whole and it is free from relocation overflow issues.
The change is downwards compatible as long as there's no mixed use of the old encoding and the new encoding.
Reviewed By: wenlei, maksfb
Differential Revision: https://reviews.llvm.org/D135912
Differential Revision: https://reviews.llvm.org/D135914
Differential Revision: https://reviews.llvm.org/D136394
2022-10-17 10:07:18 -07:00
|
|
|
// Start address to Elf symbol GUID map
|
|
|
|
std::unordered_multimap<uint64_t, uint64_t> StartAddrToSymMap;
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
// An ordered map of mapping function's start address to function range
|
[PseudoProbe] Replace relocation with offset for entry probe.
Currently pseudo probe encoding for a function is like:
- For the first probe, a relocation from it to its physical position in the code body
- For subsequent probes, an incremental offset from the current probe to the previous probe
The relocation could potentially cause relocation overflow during link time. I'm now replacing it with an offset from the first probe to the function start address.
A source function could be lowered into multiple binary functions due to outlining (e.g, coro-split). Since those binary function have independent link-time layout, to really avoid relocations from .pseudo_probe sections to .text sections, the offset to replace with should really be the offset from the probe's enclosing binary function, rather than from the entry of the source function. This requires some changes to previous section-based emission scheme which now switches to be function-based. The assembly form of pseudo probe directive is also changed correspondingly, i.e, reflecting the binary function name.
Most of the source functions end up with only one binary function. For those don't, a sentinel probe is emitted for each of the binary functions with a different name from the source. The sentinel probe indicates the binary function name to differentiate subsequent probes from the ones from a different binary function. For examples, given source function
```
Foo() {
…
Probe 1
…
Probe 2
}
```
If it is transformed into two binary functions:
```
Foo:
…
Foo.outlined:
…
```
The encoding for the two binary functions will be separate:
```
GUID of Foo
Probe 1
GUID of Foo
Sentinel probe of Foo.outlined
Probe 2
```
Then probe1 will be decoded against binary `Foo`'s address, and Probe 2 will be decoded against `Foo.outlined`. The sentinel probe of `Foo.outlined` makes sure there's not accidental relocation from `Foo.outlined`'s probes to `Foo`'s entry address.
On the BOLT side, to be minimal intrusive, the pseudo probe re-encoding sticks with the old encoding format. This is fine since unlike linker, Bolt processes the pseudo probe section as a whole and it is free from relocation overflow issues.
The change is downwards compatible as long as there's no mixed use of the old encoding and the new encoding.
Reviewed By: wenlei, maksfb
Differential Revision: https://reviews.llvm.org/D135912
Differential Revision: https://reviews.llvm.org/D135914
Differential Revision: https://reviews.llvm.org/D136394
2022-10-17 10:07:18 -07:00
|
|
|
// relevant info. Currently to determine if the offset of ELF is the start of
|
2021-10-26 14:55:33 -07:00
|
|
|
// a real function, we leverage the function range info from DWARF.
|
2022-10-13 20:42:51 -07:00
|
|
|
std::map<uint64_t, FuncRange> StartAddrToFuncRangeMap;
|
2021-10-26 14:55:33 -07:00
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
// Address to context location map. Used to expand the context.
|
|
|
|
std::unordered_map<uint64_t, SampleContextFrameVector> AddressToLocStackMap;
|
2021-09-24 17:06:39 -07:00
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
// Address to instruction size map. Also used for quick Address lookup.
|
|
|
|
std::unordered_map<uint64_t, uint64_t> AddressToInstSizeMap;
|
2021-09-24 17:06:39 -07:00
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
// An array of Addresses of all instructions sorted in increasing order. The
|
2020-10-19 10:02:05 -07:00
|
|
|
// sorting is needed to fast advance to the next forward/backward instruction.
|
2022-10-13 20:42:51 -07:00
|
|
|
std::vector<uint64_t> CodeAddressVec;
|
|
|
|
// A set of call instruction addresses. Used by virtual unwinding.
|
|
|
|
std::unordered_set<uint64_t> CallAddressSet;
|
|
|
|
// A set of return instruction addresses. Used by virtual unwinding.
|
|
|
|
std::unordered_set<uint64_t> RetAddressSet;
|
|
|
|
// An ordered set of unconditional branch instruction addresses.
|
|
|
|
std::set<uint64_t> UncondBranchAddrSet;
|
|
|
|
// A set of branch instruction addresses.
|
|
|
|
std::unordered_set<uint64_t> BranchAddressSet;
|
2020-10-19 10:02:05 -07:00
|
|
|
|
2021-08-16 18:29:07 -07:00
|
|
|
// Estimate and track function prolog and epilog ranges.
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
PrologEpilogTracker ProEpilogTracker;
|
|
|
|
|
2022-12-15 18:36:52 -08:00
|
|
|
// Infer missing frames due to compiler optimizations such as tail call
|
|
|
|
// elimination.
|
|
|
|
std::unique_ptr<MissingFrameInferrer> MissingContextInferrer;
|
|
|
|
|
2021-08-16 18:29:07 -07:00
|
|
|
// Track function sizes under different context
|
|
|
|
BinarySizeContextTracker FuncSizeTracker;
|
|
|
|
|
[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
|
|
|
// The symbolizer used to get inline context for an instruction.
|
|
|
|
std::unique_ptr<symbolize::LLVMSymbolizer> Symbolizer;
|
[CSSPGO][llvm-profgen] Pseudo probe decoding and disassembling
This change implements pseudo probe decoding and disassembling for llvm-profgen/CSSPGO. Please see https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s and https://reviews.llvm.org/D89707 for more context about CSSPGO and llvm-profgen.
**ELF section format**
Please see the encoding patch(https://reviews.llvm.org/D91878) for more details of the format, just copy the example here:
Two section(`.pseudo_probe_desc` and `.pseudoprobe` ) is emitted in ELF to support pseudo probe.
The format of `.pseudo_probe_desc` section looks like:
```
.section .pseudo_probe_desc,"",@progbits
.quad 6309742469962978389 // Func GUID
.quad 4294967295 // Func Hash
.byte 9 // Length of func name
.ascii "_Z5funcAi" // Func name
.quad 7102633082150537521
.quad 138828622701
.byte 12
.ascii "_Z8funcLeafi"
.quad 446061515086924981
.quad 4294967295
.byte 9
.ascii "_Z5funcBi"
.quad -2016976694713209516
.quad 72617220756
.byte 7
.ascii "_Z3fibi"
```
For each `.pseudoprobe` section, the encoded binary data consists of a single function record corresponding to an outlined function (i.e, a function with a code entry in the `.text` section). A function record has the following format :
```
FUNCTION BODY (one for each outlined function present in the text section)
GUID (uint64)
GUID of the function
NPROBES (ULEB128)
Number of probes originating from this function.
NUM_INLINED_FUNCTIONS (ULEB128)
Number of callees inlined into this function, aka number of
first-level inlinees
PROBE RECORDS
A list of NPROBES entries. Each entry contains:
INDEX (ULEB128)
TYPE (uint4)
0 - block probe, 1 - indirect call, 2 - direct call
ATTRIBUTE (uint3)
reserved
ADDRESS_TYPE (uint1)
0 - code address, 1 - address delta
CODE_ADDRESS (uint64 or ULEB128)
code address or address delta, depending on ADDRESS_TYPE
INLINED FUNCTION RECORDS
A list of NUM_INLINED_FUNCTIONS entries describing each of the inlined
callees. Each record contains:
INLINE SITE
GUID of the inlinee (uint64)
ID of the callsite probe (ULEB128)
FUNCTION BODY
A FUNCTION BODY entry describing the inlined function.
```
**Disassembling**
A switch `--show-pseudo-probe` is added to use along with `--show-disassembly` to print disassembly code with pseudo probe directives.
For example:
```
00000000002011a0 <foo2>:
2011a0: 50 push rax
2011a1: 85 ff test edi,edi
[Probe]: FUNC: foo2 Index: 1 Type: Block
2011a3: 74 02 je 2011a7 <foo2+0x7>
[Probe]: FUNC: foo2 Index: 3 Type: Block
[Probe]: FUNC: foo2 Index: 4 Type: Block
[Probe]: FUNC: foo Index: 1 Type: Block Inlined: @ foo2:6
2011a5: 58 pop rax
2011a6: c3 ret
[Probe]: FUNC: foo2 Index: 2 Type: Block
2011a7: bf 01 00 00 00 mov edi,0x1
[Probe]: FUNC: foo2 Index: 5 Type: IndirectCall
2011ac: ff d6 call rsi
[Probe]: FUNC: foo2 Index: 4 Type: Block
2011ae: 58 pop rax
2011af: c3 ret
```
**Implementation**
- `PseudoProbeDecoder` is added in ProfiledBinary as an infra for the decoding. It decoded the two section and generate two map: `GUIDProbeFunctionMap` stores all the `PseudoProbeFunction` which is the abstraction of a general function. `AddressProbesMap` stores all the pseudo probe info indexed by its address.
- All the inline info is encoded into binary as a trie(`PseudoProbeInlineTree`) and will be constructed from the decoding. Each pseudo probe can get its inline context(`getInlineContext`) by traversing its inline tree node backwards.
Test Plan:
ninja & ninja check-llvm
Differential Revision: https://reviews.llvm.org/D92334
2020-11-23 20:33:23 -08:00
|
|
|
|
[CSSPGO] Split context string to deduplicate function name used in the context.
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
2021-08-25 11:40:34 -07:00
|
|
|
// String table owning function name strings created from the symbolizer.
|
|
|
|
std::unordered_set<std::string> NameStrings;
|
|
|
|
|
2021-09-22 09:11:53 -07:00
|
|
|
// A collection of functions to print disassembly for.
|
|
|
|
StringSet<> DisassembleFunctionSet;
|
|
|
|
|
[CSSPGO][llvm-profgen] Pseudo probe decoding and disassembling
This change implements pseudo probe decoding and disassembling for llvm-profgen/CSSPGO. Please see https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s and https://reviews.llvm.org/D89707 for more context about CSSPGO and llvm-profgen.
**ELF section format**
Please see the encoding patch(https://reviews.llvm.org/D91878) for more details of the format, just copy the example here:
Two section(`.pseudo_probe_desc` and `.pseudoprobe` ) is emitted in ELF to support pseudo probe.
The format of `.pseudo_probe_desc` section looks like:
```
.section .pseudo_probe_desc,"",@progbits
.quad 6309742469962978389 // Func GUID
.quad 4294967295 // Func Hash
.byte 9 // Length of func name
.ascii "_Z5funcAi" // Func name
.quad 7102633082150537521
.quad 138828622701
.byte 12
.ascii "_Z8funcLeafi"
.quad 446061515086924981
.quad 4294967295
.byte 9
.ascii "_Z5funcBi"
.quad -2016976694713209516
.quad 72617220756
.byte 7
.ascii "_Z3fibi"
```
For each `.pseudoprobe` section, the encoded binary data consists of a single function record corresponding to an outlined function (i.e, a function with a code entry in the `.text` section). A function record has the following format :
```
FUNCTION BODY (one for each outlined function present in the text section)
GUID (uint64)
GUID of the function
NPROBES (ULEB128)
Number of probes originating from this function.
NUM_INLINED_FUNCTIONS (ULEB128)
Number of callees inlined into this function, aka number of
first-level inlinees
PROBE RECORDS
A list of NPROBES entries. Each entry contains:
INDEX (ULEB128)
TYPE (uint4)
0 - block probe, 1 - indirect call, 2 - direct call
ATTRIBUTE (uint3)
reserved
ADDRESS_TYPE (uint1)
0 - code address, 1 - address delta
CODE_ADDRESS (uint64 or ULEB128)
code address or address delta, depending on ADDRESS_TYPE
INLINED FUNCTION RECORDS
A list of NUM_INLINED_FUNCTIONS entries describing each of the inlined
callees. Each record contains:
INLINE SITE
GUID of the inlinee (uint64)
ID of the callsite probe (ULEB128)
FUNCTION BODY
A FUNCTION BODY entry describing the inlined function.
```
**Disassembling**
A switch `--show-pseudo-probe` is added to use along with `--show-disassembly` to print disassembly code with pseudo probe directives.
For example:
```
00000000002011a0 <foo2>:
2011a0: 50 push rax
2011a1: 85 ff test edi,edi
[Probe]: FUNC: foo2 Index: 1 Type: Block
2011a3: 74 02 je 2011a7 <foo2+0x7>
[Probe]: FUNC: foo2 Index: 3 Type: Block
[Probe]: FUNC: foo2 Index: 4 Type: Block
[Probe]: FUNC: foo Index: 1 Type: Block Inlined: @ foo2:6
2011a5: 58 pop rax
2011a6: c3 ret
[Probe]: FUNC: foo2 Index: 2 Type: Block
2011a7: bf 01 00 00 00 mov edi,0x1
[Probe]: FUNC: foo2 Index: 5 Type: IndirectCall
2011ac: ff d6 call rsi
[Probe]: FUNC: foo2 Index: 4 Type: Block
2011ae: 58 pop rax
2011af: c3 ret
```
**Implementation**
- `PseudoProbeDecoder` is added in ProfiledBinary as an infra for the decoding. It decoded the two section and generate two map: `GUIDProbeFunctionMap` stores all the `PseudoProbeFunction` which is the abstraction of a general function. `AddressProbesMap` stores all the pseudo probe info indexed by its address.
- All the inline info is encoded into binary as a trie(`PseudoProbeInlineTree`) and will be constructed from the decoding. Each pseudo probe can get its inline context(`getInlineContext`) by traversing its inline tree node backwards.
Test Plan:
ninja & ninja check-llvm
Differential Revision: https://reviews.llvm.org/D92334
2020-11-23 20:33:23 -08:00
|
|
|
// Pseudo probe decoder
|
2021-08-04 08:50:28 -07:00
|
|
|
MCPseudoProbeDecoder ProbeDecoder;
|
[CSSPGO][llvm-profgen] Pseudo probe decoding and disassembling
This change implements pseudo probe decoding and disassembling for llvm-profgen/CSSPGO. Please see https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s and https://reviews.llvm.org/D89707 for more context about CSSPGO and llvm-profgen.
**ELF section format**
Please see the encoding patch(https://reviews.llvm.org/D91878) for more details of the format, just copy the example here:
Two section(`.pseudo_probe_desc` and `.pseudoprobe` ) is emitted in ELF to support pseudo probe.
The format of `.pseudo_probe_desc` section looks like:
```
.section .pseudo_probe_desc,"",@progbits
.quad 6309742469962978389 // Func GUID
.quad 4294967295 // Func Hash
.byte 9 // Length of func name
.ascii "_Z5funcAi" // Func name
.quad 7102633082150537521
.quad 138828622701
.byte 12
.ascii "_Z8funcLeafi"
.quad 446061515086924981
.quad 4294967295
.byte 9
.ascii "_Z5funcBi"
.quad -2016976694713209516
.quad 72617220756
.byte 7
.ascii "_Z3fibi"
```
For each `.pseudoprobe` section, the encoded binary data consists of a single function record corresponding to an outlined function (i.e, a function with a code entry in the `.text` section). A function record has the following format :
```
FUNCTION BODY (one for each outlined function present in the text section)
GUID (uint64)
GUID of the function
NPROBES (ULEB128)
Number of probes originating from this function.
NUM_INLINED_FUNCTIONS (ULEB128)
Number of callees inlined into this function, aka number of
first-level inlinees
PROBE RECORDS
A list of NPROBES entries. Each entry contains:
INDEX (ULEB128)
TYPE (uint4)
0 - block probe, 1 - indirect call, 2 - direct call
ATTRIBUTE (uint3)
reserved
ADDRESS_TYPE (uint1)
0 - code address, 1 - address delta
CODE_ADDRESS (uint64 or ULEB128)
code address or address delta, depending on ADDRESS_TYPE
INLINED FUNCTION RECORDS
A list of NUM_INLINED_FUNCTIONS entries describing each of the inlined
callees. Each record contains:
INLINE SITE
GUID of the inlinee (uint64)
ID of the callsite probe (ULEB128)
FUNCTION BODY
A FUNCTION BODY entry describing the inlined function.
```
**Disassembling**
A switch `--show-pseudo-probe` is added to use along with `--show-disassembly` to print disassembly code with pseudo probe directives.
For example:
```
00000000002011a0 <foo2>:
2011a0: 50 push rax
2011a1: 85 ff test edi,edi
[Probe]: FUNC: foo2 Index: 1 Type: Block
2011a3: 74 02 je 2011a7 <foo2+0x7>
[Probe]: FUNC: foo2 Index: 3 Type: Block
[Probe]: FUNC: foo2 Index: 4 Type: Block
[Probe]: FUNC: foo Index: 1 Type: Block Inlined: @ foo2:6
2011a5: 58 pop rax
2011a6: c3 ret
[Probe]: FUNC: foo2 Index: 2 Type: Block
2011a7: bf 01 00 00 00 mov edi,0x1
[Probe]: FUNC: foo2 Index: 5 Type: IndirectCall
2011ac: ff d6 call rsi
[Probe]: FUNC: foo2 Index: 4 Type: Block
2011ae: 58 pop rax
2011af: c3 ret
```
**Implementation**
- `PseudoProbeDecoder` is added in ProfiledBinary as an infra for the decoding. It decoded the two section and generate two map: `GUIDProbeFunctionMap` stores all the `PseudoProbeFunction` which is the abstraction of a general function. `AddressProbesMap` stores all the pseudo probe info indexed by its address.
- All the inline info is encoded into binary as a trie(`PseudoProbeInlineTree`) and will be constructed from the decoding. Each pseudo probe can get its inline context(`getInlineContext`) by traversing its inline tree node backwards.
Test Plan:
ninja & ninja check-llvm
Differential Revision: https://reviews.llvm.org/D92334
2020-11-23 20:33:23 -08:00
|
|
|
|
2022-01-28 15:53:37 -08:00
|
|
|
// Function name to probe frame map for top-level outlined functions.
|
|
|
|
StringMap<MCDecodedPseudoProbeInlineTree *> TopLevelProbeFrameMap;
|
|
|
|
|
2020-11-25 20:33:17 -08:00
|
|
|
bool UsePseudoProbes = false;
|
|
|
|
|
2021-11-04 00:08:37 -07:00
|
|
|
bool UseFSDiscriminator = false;
|
|
|
|
|
2021-08-16 18:29:07 -07:00
|
|
|
// Whether we need to symbolize all instructions to get function context size.
|
|
|
|
bool TrackFuncContextSize = false;
|
|
|
|
|
2024-06-13 10:21:46 -07:00
|
|
|
// Whether this is a kernel image;
|
|
|
|
bool IsKernel = false;
|
|
|
|
|
2021-07-29 11:42:55 -07:00
|
|
|
// Indicate if the base loading address is parsed from the mmap event or uses
|
|
|
|
// the preferred address
|
|
|
|
bool IsLoadedByMMap = false;
|
|
|
|
// Use to avoid redundant warning.
|
|
|
|
bool MissingMMapWarned = false;
|
|
|
|
|
2024-03-15 09:02:26 +08:00
|
|
|
bool IsCOFF = false;
|
|
|
|
|
|
|
|
void setPreferredTextSegmentAddresses(const ObjectFile *O);
|
2021-07-12 09:47:05 -07:00
|
|
|
|
|
|
|
template <class ELFT>
|
[PseudoProbe] Replace relocation with offset for entry probe.
Currently pseudo probe encoding for a function is like:
- For the first probe, a relocation from it to its physical position in the code body
- For subsequent probes, an incremental offset from the current probe to the previous probe
The relocation could potentially cause relocation overflow during link time. I'm now replacing it with an offset from the first probe to the function start address.
A source function could be lowered into multiple binary functions due to outlining (e.g, coro-split). Since those binary function have independent link-time layout, to really avoid relocations from .pseudo_probe sections to .text sections, the offset to replace with should really be the offset from the probe's enclosing binary function, rather than from the entry of the source function. This requires some changes to previous section-based emission scheme which now switches to be function-based. The assembly form of pseudo probe directive is also changed correspondingly, i.e, reflecting the binary function name.
Most of the source functions end up with only one binary function. For those don't, a sentinel probe is emitted for each of the binary functions with a different name from the source. The sentinel probe indicates the binary function name to differentiate subsequent probes from the ones from a different binary function. For examples, given source function
```
Foo() {
…
Probe 1
…
Probe 2
}
```
If it is transformed into two binary functions:
```
Foo:
…
Foo.outlined:
…
```
The encoding for the two binary functions will be separate:
```
GUID of Foo
Probe 1
GUID of Foo
Sentinel probe of Foo.outlined
Probe 2
```
Then probe1 will be decoded against binary `Foo`'s address, and Probe 2 will be decoded against `Foo.outlined`. The sentinel probe of `Foo.outlined` makes sure there's not accidental relocation from `Foo.outlined`'s probes to `Foo`'s entry address.
On the BOLT side, to be minimal intrusive, the pseudo probe re-encoding sticks with the old encoding format. This is fine since unlike linker, Bolt processes the pseudo probe section as a whole and it is free from relocation overflow issues.
The change is downwards compatible as long as there's no mixed use of the old encoding and the new encoding.
Reviewed By: wenlei, maksfb
Differential Revision: https://reviews.llvm.org/D135912
Differential Revision: https://reviews.llvm.org/D135914
Differential Revision: https://reviews.llvm.org/D136394
2022-10-17 10:07:18 -07:00
|
|
|
void setPreferredTextSegmentAddresses(const ELFFile<ELFT> &Obj,
|
|
|
|
StringRef FileName);
|
2024-03-15 09:02:26 +08:00
|
|
|
void setPreferredTextSegmentAddresses(const COFFObjectFile *Obj,
|
|
|
|
StringRef FileName);
|
2020-10-19 10:02:05 -07:00
|
|
|
|
2022-03-23 12:36:44 -07:00
|
|
|
void checkPseudoProbe(const ELFObjectFileBase *Obj);
|
|
|
|
|
[CSSPGO][llvm-profgen] Pseudo probe decoding and disassembling
This change implements pseudo probe decoding and disassembling for llvm-profgen/CSSPGO. Please see https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s and https://reviews.llvm.org/D89707 for more context about CSSPGO and llvm-profgen.
**ELF section format**
Please see the encoding patch(https://reviews.llvm.org/D91878) for more details of the format, just copy the example here:
Two section(`.pseudo_probe_desc` and `.pseudoprobe` ) is emitted in ELF to support pseudo probe.
The format of `.pseudo_probe_desc` section looks like:
```
.section .pseudo_probe_desc,"",@progbits
.quad 6309742469962978389 // Func GUID
.quad 4294967295 // Func Hash
.byte 9 // Length of func name
.ascii "_Z5funcAi" // Func name
.quad 7102633082150537521
.quad 138828622701
.byte 12
.ascii "_Z8funcLeafi"
.quad 446061515086924981
.quad 4294967295
.byte 9
.ascii "_Z5funcBi"
.quad -2016976694713209516
.quad 72617220756
.byte 7
.ascii "_Z3fibi"
```
For each `.pseudoprobe` section, the encoded binary data consists of a single function record corresponding to an outlined function (i.e, a function with a code entry in the `.text` section). A function record has the following format :
```
FUNCTION BODY (one for each outlined function present in the text section)
GUID (uint64)
GUID of the function
NPROBES (ULEB128)
Number of probes originating from this function.
NUM_INLINED_FUNCTIONS (ULEB128)
Number of callees inlined into this function, aka number of
first-level inlinees
PROBE RECORDS
A list of NPROBES entries. Each entry contains:
INDEX (ULEB128)
TYPE (uint4)
0 - block probe, 1 - indirect call, 2 - direct call
ATTRIBUTE (uint3)
reserved
ADDRESS_TYPE (uint1)
0 - code address, 1 - address delta
CODE_ADDRESS (uint64 or ULEB128)
code address or address delta, depending on ADDRESS_TYPE
INLINED FUNCTION RECORDS
A list of NUM_INLINED_FUNCTIONS entries describing each of the inlined
callees. Each record contains:
INLINE SITE
GUID of the inlinee (uint64)
ID of the callsite probe (ULEB128)
FUNCTION BODY
A FUNCTION BODY entry describing the inlined function.
```
**Disassembling**
A switch `--show-pseudo-probe` is added to use along with `--show-disassembly` to print disassembly code with pseudo probe directives.
For example:
```
00000000002011a0 <foo2>:
2011a0: 50 push rax
2011a1: 85 ff test edi,edi
[Probe]: FUNC: foo2 Index: 1 Type: Block
2011a3: 74 02 je 2011a7 <foo2+0x7>
[Probe]: FUNC: foo2 Index: 3 Type: Block
[Probe]: FUNC: foo2 Index: 4 Type: Block
[Probe]: FUNC: foo Index: 1 Type: Block Inlined: @ foo2:6
2011a5: 58 pop rax
2011a6: c3 ret
[Probe]: FUNC: foo2 Index: 2 Type: Block
2011a7: bf 01 00 00 00 mov edi,0x1
[Probe]: FUNC: foo2 Index: 5 Type: IndirectCall
2011ac: ff d6 call rsi
[Probe]: FUNC: foo2 Index: 4 Type: Block
2011ae: 58 pop rax
2011af: c3 ret
```
**Implementation**
- `PseudoProbeDecoder` is added in ProfiledBinary as an infra for the decoding. It decoded the two section and generate two map: `GUIDProbeFunctionMap` stores all the `PseudoProbeFunction` which is the abstraction of a general function. `AddressProbesMap` stores all the pseudo probe info indexed by its address.
- All the inline info is encoded into binary as a trie(`PseudoProbeInlineTree`) and will be constructed from the decoding. Each pseudo probe can get its inline context(`getInlineContext`) by traversing its inline tree node backwards.
Test Plan:
ninja & ninja check-llvm
Differential Revision: https://reviews.llvm.org/D92334
2020-11-23 20:33:23 -08:00
|
|
|
void decodePseudoProbe(const ELFObjectFileBase *Obj);
|
|
|
|
|
2021-11-04 00:08:37 -07:00
|
|
|
void
|
2024-03-15 09:02:26 +08:00
|
|
|
checkUseFSDiscriminator(const ObjectFile *Obj,
|
2021-11-04 00:08:37 -07:00
|
|
|
std::map<SectionRef, SectionSymbolsTy> &AllSymbols);
|
|
|
|
|
2020-10-19 10:02:05 -07:00
|
|
|
// Set up disassembler and related components.
|
2024-03-15 09:02:26 +08:00
|
|
|
void setUpDisassembler(const ObjectFile *Obj);
|
2023-05-25 08:11:40 -07:00
|
|
|
symbolize::LLVMSymbolizer::Options getSymbolizerOpts() const;
|
2020-10-19 10:02:05 -07:00
|
|
|
|
2021-10-26 14:55:33 -07:00
|
|
|
// Load debug info of subprograms from DWARF section.
|
|
|
|
void loadSymbolsFromDWARF(ObjectFile &Obj);
|
|
|
|
|
2022-02-22 12:09:01 -08:00
|
|
|
// Load debug info from DWARF unit.
|
|
|
|
void loadSymbolsFromDWARFUnit(DWARFUnit &CompilationUnit);
|
|
|
|
|
[PseudoProbe] Replace relocation with offset for entry probe.
Currently pseudo probe encoding for a function is like:
- For the first probe, a relocation from it to its physical position in the code body
- For subsequent probes, an incremental offset from the current probe to the previous probe
The relocation could potentially cause relocation overflow during link time. I'm now replacing it with an offset from the first probe to the function start address.
A source function could be lowered into multiple binary functions due to outlining (e.g, coro-split). Since those binary function have independent link-time layout, to really avoid relocations from .pseudo_probe sections to .text sections, the offset to replace with should really be the offset from the probe's enclosing binary function, rather than from the entry of the source function. This requires some changes to previous section-based emission scheme which now switches to be function-based. The assembly form of pseudo probe directive is also changed correspondingly, i.e, reflecting the binary function name.
Most of the source functions end up with only one binary function. For those don't, a sentinel probe is emitted for each of the binary functions with a different name from the source. The sentinel probe indicates the binary function name to differentiate subsequent probes from the ones from a different binary function. For examples, given source function
```
Foo() {
…
Probe 1
…
Probe 2
}
```
If it is transformed into two binary functions:
```
Foo:
…
Foo.outlined:
…
```
The encoding for the two binary functions will be separate:
```
GUID of Foo
Probe 1
GUID of Foo
Sentinel probe of Foo.outlined
Probe 2
```
Then probe1 will be decoded against binary `Foo`'s address, and Probe 2 will be decoded against `Foo.outlined`. The sentinel probe of `Foo.outlined` makes sure there's not accidental relocation from `Foo.outlined`'s probes to `Foo`'s entry address.
On the BOLT side, to be minimal intrusive, the pseudo probe re-encoding sticks with the old encoding format. This is fine since unlike linker, Bolt processes the pseudo probe section as a whole and it is free from relocation overflow issues.
The change is downwards compatible as long as there's no mixed use of the old encoding and the new encoding.
Reviewed By: wenlei, maksfb
Differential Revision: https://reviews.llvm.org/D135912
Differential Revision: https://reviews.llvm.org/D135914
Differential Revision: https://reviews.llvm.org/D136394
2022-10-17 10:07:18 -07:00
|
|
|
// Create elf symbol to its start address mapping.
|
|
|
|
void populateElfSymbolAddressList(const ELFObjectFileBase *O);
|
|
|
|
|
2021-10-26 14:55:33 -07:00
|
|
|
// A function may be spilt into multiple non-continuous address ranges. We use
|
2022-12-15 18:36:52 -08:00
|
|
|
// this to set whether start a function range is the real entry of the
|
2021-10-26 14:55:33 -07:00
|
|
|
// function and also set false to the non-function label.
|
2022-12-15 18:36:52 -08:00
|
|
|
void setIsFuncEntry(FuncRange *FRange, StringRef RangeSymName);
|
2021-10-26 14:55:33 -07:00
|
|
|
|
2021-11-08 23:05:16 -08:00
|
|
|
// Warn if no entry range exists in the function.
|
|
|
|
void warnNoFuncEntry();
|
|
|
|
|
2020-10-19 10:02:05 -07:00
|
|
|
/// Dissassemble the text section and build various address maps.
|
2024-03-15 09:02:26 +08:00
|
|
|
void disassemble(const ObjectFile *O);
|
2020-10-19 10:02:05 -07:00
|
|
|
|
|
|
|
/// Helper function to dissassemble the symbol and extract info for unwinding
|
|
|
|
bool dissassembleSymbol(std::size_t SI, ArrayRef<uint8_t> Bytes,
|
|
|
|
SectionSymbolsTy &Symbols, const SectionRef &Section);
|
[CSSPGO][llvm-profgen] Instruction symbolization
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 adds the support of instruction symbolization. Given the RVA on an instruction pointer, a full calling context can be printed side-by-side with the disassembly code.
E.g.
```
Disassembly of section .text [0x0, 0x4a]:
<funcA>:
0: mov eax, edi funcA:0
2: mov ecx, dword ptr [rip] funcLeaf:2 @ funcA:1
8: lea edx, [rcx + 3] fib:2 @ funcLeaf:2 @ funcA:1
b: cmp ecx, 3 fib:2 @ funcLeaf:2 @ funcA:1
e: cmovl edx, ecx fib:2 @ funcLeaf:2 @ funcA:1
11: sub eax, edx funcLeaf:2 @ funcA:1
13: ret funcA:2
14: nop word ptr cs:[rax + rax]
1e: nop
<funcLeaf>:
20: mov eax, edi funcLeaf:1
22: mov ecx, dword ptr [rip] funcLeaf:2
28: lea edx, [rcx + 3] fib:2 @ funcLeaf:2
2b: cmp ecx, 3 fib:2 @ funcLeaf:2
2e: cmovl edx, ecx fib:2 @ funcLeaf:2
31: sub eax, edx funcLeaf:2
33: ret funcLeaf:3
34: nop word ptr cs:[rax + rax]
3e: nop
<fib>:
40: lea eax, [rdi + 3] fib:2
43: cmp edi, 3 fib:2
46: cmovl eax, edi fib:2
49: ret fib:8
```
Test Plan:
ninja check-llvm
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D89715
2020-10-19 10:19:08 -07:00
|
|
|
/// Symbolize a given instruction pointer and return a full call context.
|
[CSSPGO] Split context string to deduplicate function name used in the context.
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
2021-08-25 11:40:34 -07:00
|
|
|
SampleContextFrameVector symbolize(const InstructionPointer &IP,
|
|
|
|
bool UseCanonicalFnName = false,
|
|
|
|
bool UseProbeDiscriminator = false);
|
2020-10-19 10:02:05 -07:00
|
|
|
/// Decode the interesting parts of the binary and build internal data
|
|
|
|
/// structures. On high level, the parts of interest are:
|
|
|
|
/// 1. Text sections, including the main code section and the PLT
|
|
|
|
/// entries that will be used to handle cross-module call transitions.
|
|
|
|
/// 2. The .debug_line section, used by Dwarf-based profile generation.
|
|
|
|
/// 3. Pseudo probe related sections, used by probe-based profile
|
|
|
|
/// generation.
|
|
|
|
void load();
|
[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
|
|
|
|
|
|
|
public:
|
2022-12-15 18:36:52 -08:00
|
|
|
ProfiledBinary(const StringRef ExeBinPath, const StringRef DebugBinPath);
|
|
|
|
~ProfiledBinary();
|
2022-03-23 12:36:44 -07:00
|
|
|
|
|
|
|
void decodePseudoProbe();
|
|
|
|
|
2021-01-31 10:23:43 -08:00
|
|
|
StringRef getPath() const { return Path; }
|
|
|
|
StringRef getName() const { return llvm::sys::path::filename(Path); }
|
[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
|
|
|
uint64_t getBaseAddress() const { return BaseAddress; }
|
|
|
|
void setBaseAddress(uint64_t Address) { BaseAddress = Address; }
|
2021-07-28 15:07:40 -07:00
|
|
|
|
2024-03-15 09:02:26 +08:00
|
|
|
bool isCOFF() const { return IsCOFF; }
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
// Canonicalize to use preferred load address as base address.
|
|
|
|
uint64_t canonicalizeVirtualAddress(uint64_t Address) {
|
|
|
|
return Address - BaseAddress + getPreferredBaseAddress();
|
|
|
|
}
|
2021-07-12 09:47:05 -07:00
|
|
|
// Return the preferred load address for the first executable segment.
|
[PseudoProbe] Replace relocation with offset for entry probe.
Currently pseudo probe encoding for a function is like:
- For the first probe, a relocation from it to its physical position in the code body
- For subsequent probes, an incremental offset from the current probe to the previous probe
The relocation could potentially cause relocation overflow during link time. I'm now replacing it with an offset from the first probe to the function start address.
A source function could be lowered into multiple binary functions due to outlining (e.g, coro-split). Since those binary function have independent link-time layout, to really avoid relocations from .pseudo_probe sections to .text sections, the offset to replace with should really be the offset from the probe's enclosing binary function, rather than from the entry of the source function. This requires some changes to previous section-based emission scheme which now switches to be function-based. The assembly form of pseudo probe directive is also changed correspondingly, i.e, reflecting the binary function name.
Most of the source functions end up with only one binary function. For those don't, a sentinel probe is emitted for each of the binary functions with a different name from the source. The sentinel probe indicates the binary function name to differentiate subsequent probes from the ones from a different binary function. For examples, given source function
```
Foo() {
…
Probe 1
…
Probe 2
}
```
If it is transformed into two binary functions:
```
Foo:
…
Foo.outlined:
…
```
The encoding for the two binary functions will be separate:
```
GUID of Foo
Probe 1
GUID of Foo
Sentinel probe of Foo.outlined
Probe 2
```
Then probe1 will be decoded against binary `Foo`'s address, and Probe 2 will be decoded against `Foo.outlined`. The sentinel probe of `Foo.outlined` makes sure there's not accidental relocation from `Foo.outlined`'s probes to `Foo`'s entry address.
On the BOLT side, to be minimal intrusive, the pseudo probe re-encoding sticks with the old encoding format. This is fine since unlike linker, Bolt processes the pseudo probe section as a whole and it is free from relocation overflow issues.
The change is downwards compatible as long as there's no mixed use of the old encoding and the new encoding.
Reviewed By: wenlei, maksfb
Differential Revision: https://reviews.llvm.org/D135912
Differential Revision: https://reviews.llvm.org/D135914
Differential Revision: https://reviews.llvm.org/D136394
2022-10-17 10:07:18 -07:00
|
|
|
uint64_t getPreferredBaseAddress() const {
|
|
|
|
return PreferredTextSegmentAddresses[0];
|
|
|
|
}
|
2021-11-11 18:28:47 -08:00
|
|
|
// Return the preferred load address for the first loadable segment.
|
|
|
|
uint64_t getFirstLoadableAddress() const { return FirstLoadableAddress; }
|
2021-07-12 09:47:05 -07:00
|
|
|
// Return the file offset for the first executable segment.
|
|
|
|
uint64_t getTextSegmentOffset() const { return TextSegmentOffsets[0]; }
|
|
|
|
const std::vector<uint64_t> &getPreferredTextSegmentAddresses() const {
|
|
|
|
return PreferredTextSegmentAddresses;
|
|
|
|
}
|
|
|
|
const std::vector<uint64_t> &getTextSegmentOffsets() const {
|
|
|
|
return TextSegmentOffsets;
|
|
|
|
}
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
uint64_t getInstSize(uint64_t Address) const {
|
|
|
|
auto I = AddressToInstSizeMap.find(Address);
|
|
|
|
if (I == AddressToInstSizeMap.end())
|
2021-12-02 16:51:42 -08:00
|
|
|
return 0;
|
|
|
|
return I->second;
|
|
|
|
}
|
|
|
|
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
bool addressIsCode(uint64_t Address) const {
|
2022-10-13 20:42:51 -07:00
|
|
|
return AddressToInstSizeMap.find(Address) != AddressToInstSizeMap.end();
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
2022-10-13 20:42:51 -07:00
|
|
|
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
bool addressIsCall(uint64_t Address) const {
|
2022-10-13 20:42:51 -07:00
|
|
|
return CallAddressSet.count(Address);
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
bool addressIsReturn(uint64_t Address) const {
|
2022-10-13 20:42:51 -07:00
|
|
|
return RetAddressSet.count(Address);
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
bool addressInPrologEpilog(uint64_t Address) const {
|
2022-10-13 20:42:51 -07:00
|
|
|
return ProEpilogTracker.PrologEpilogSet.count(Address);
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
bool addressIsTransfer(uint64_t Address) {
|
|
|
|
return BranchAddressSet.count(Address) || RetAddressSet.count(Address) ||
|
|
|
|
CallAddressSet.count(Address);
|
2021-10-25 19:20:28 -07:00
|
|
|
}
|
|
|
|
|
2022-05-11 21:33:09 -07:00
|
|
|
bool rangeCrossUncondBranch(uint64_t Start, uint64_t End) {
|
|
|
|
if (Start >= End)
|
|
|
|
return false;
|
2022-10-13 20:42:51 -07:00
|
|
|
auto R = UncondBranchAddrSet.lower_bound(Start);
|
|
|
|
return R != UncondBranchAddrSet.end() && *R < End;
|
2022-05-11 21:33:09 -07:00
|
|
|
}
|
|
|
|
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
uint64_t getAddressforIndex(uint64_t Index) const {
|
2022-10-13 20:42:51 -07:00
|
|
|
return CodeAddressVec[Index];
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
size_t getCodeAddrVecSize() const { return CodeAddressVec.size(); }
|
2021-11-04 20:51:04 -07:00
|
|
|
|
2020-11-25 20:33:17 -08:00
|
|
|
bool usePseudoProbes() const { return UsePseudoProbes; }
|
2021-11-04 00:08:37 -07:00
|
|
|
bool useFSDiscriminator() const { return UseFSDiscriminator; }
|
2024-06-13 10:21:46 -07:00
|
|
|
bool isKernel() const { return IsKernel; }
|
|
|
|
|
|
|
|
static bool isKernelImageName(StringRef BinaryName) {
|
|
|
|
return BinaryName == "[kernel.kallsyms]" ||
|
|
|
|
BinaryName == "[kernel.kallsyms]_stext" ||
|
|
|
|
BinaryName == "[kernel.kallsyms]_text";
|
|
|
|
}
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
// Get the index in CodeAddressVec for the address
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
// As we might get an address which is not the code
|
|
|
|
// here it would round to the next valid code address by
|
|
|
|
// using lower bound operation
|
|
|
|
uint32_t getIndexForAddr(uint64_t Address) const {
|
2022-10-13 20:42:51 -07:00
|
|
|
auto Low = llvm::lower_bound(CodeAddressVec, Address);
|
|
|
|
return Low - CodeAddressVec.begin();
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
|
|
|
|
uint64_t getCallAddrFromFrameAddr(uint64_t FrameAddr) const {
|
[CSSPGO][llvm-profgen] Fix external address issues of perf reader (return to external addr part)
Before we have an issue with artificial LBR whose source is a return, recalling that "an internal code(A) can return to external address, then from the external address call a new internal code(B), making an artificial branch that looks like a return from A to B can confuse the unwinder". We just ignore the LBRs after this artificial LBR which can miss some samples. This change aims at fixing this by correctly unwinding them instead of ignoring them.
List some typical scenarios covered by this change.
1) multiple sequential call back happen in external address, e.g.
```
[ext, call, foo] [foo, return, ext] [ext, call, bar]
```
Unwinder should avoid having foo return from bar. Wrong call stack is like [foo, bar]
2) the call stack before and after external call should be correctly unwinded.
```
{call stack1} {call stack2}
[foo, call, ext] [ext, call, bar] [bar, return, ext] [ext, return, foo ]
```
call stack 1 should be the same to call stack2. Both shouldn't be truncated
3) call stack should be truncated after call into external code since we can't do inlining with external code.
```
[foo, call, ext] [ext, call, bar] [bar, call, baz] [baz, return, bar ] [bar, return, ext]
```
the call stack of code in baz should not include foo.
### Implementation:
We leverage artificial frame to fix #2 and #3: when we got a return artificial LBR, push an extra artificial frame to the stack. when we pop frame, check if the parent is an artificial frame to pop(fix #2). Therefore, call/ return artificial LBR is just the same as regular LBR which can keep the call stack.
While recording context on the trie, artificial frame is used as a tag indicating that we should truncate the call stack(fix #3).
To differentiate #1 and #2, we leverage `getCallAddrFromFrameAddr`. Normally the target of the return should be the next inst of a call inst and `getCallAddrFromFrameAddr` will return the address of call inst. Otherwise, getCallAddrFromFrameAddr will return to 0 which is the case of #1.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D115550
2021-12-13 20:33:33 -08:00
|
|
|
if (FrameAddr == ExternalAddr)
|
|
|
|
return ExternalAddr;
|
2021-09-14 16:44:41 -07:00
|
|
|
auto I = getIndexForAddr(FrameAddr);
|
|
|
|
FrameAddr = I ? getAddressforIndex(I - 1) : 0;
|
|
|
|
if (FrameAddr && addressIsCall(FrameAddr))
|
|
|
|
return FrameAddr;
|
|
|
|
return 0;
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
FuncRange *findFuncRangeForStartAddr(uint64_t Address) {
|
|
|
|
auto I = StartAddrToFuncRangeMap.find(Address);
|
|
|
|
if (I == StartAddrToFuncRangeMap.end())
|
2021-10-26 14:55:33 -07:00
|
|
|
return nullptr;
|
|
|
|
return &I->second;
|
2021-09-23 22:53:12 -07:00
|
|
|
}
|
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
// Binary search the function range which includes the input address.
|
|
|
|
FuncRange *findFuncRange(uint64_t Address) {
|
|
|
|
auto I = StartAddrToFuncRangeMap.upper_bound(Address);
|
|
|
|
if (I == StartAddrToFuncRangeMap.begin())
|
2021-10-26 14:55:33 -07:00
|
|
|
return nullptr;
|
2021-09-23 22:53:12 -07:00
|
|
|
I--;
|
2021-10-26 14:55:33 -07:00
|
|
|
|
2022-10-13 20:42:51 -07:00
|
|
|
if (Address >= I->second.EndAddress)
|
2021-10-26 14:55:33 -07:00
|
|
|
return nullptr;
|
|
|
|
|
|
|
|
return &I->second;
|
|
|
|
}
|
|
|
|
|
|
|
|
// Get all ranges of one function.
|
2022-10-13 20:42:51 -07:00
|
|
|
RangesTy getRanges(uint64_t Address) {
|
|
|
|
auto *FRange = findFuncRange(Address);
|
2021-10-26 14:55:33 -07:00
|
|
|
// Ignore the range which falls into plt section or system lib.
|
|
|
|
if (!FRange)
|
|
|
|
return RangesTy();
|
|
|
|
|
|
|
|
return FRange->Func->Ranges;
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
|
2021-10-29 16:33:31 -07:00
|
|
|
const std::unordered_map<std::string, BinaryFunction> &
|
|
|
|
getAllBinaryFunctions() {
|
|
|
|
return BinaryFunctions;
|
|
|
|
}
|
|
|
|
|
2022-03-23 12:36:44 -07:00
|
|
|
std::unordered_set<const BinaryFunction *> &getProfiledFunctions() {
|
|
|
|
return ProfiledFunctions;
|
|
|
|
}
|
|
|
|
|
|
|
|
void setProfiledFunctions(std::unordered_set<const BinaryFunction *> &Funcs) {
|
|
|
|
ProfiledFunctions = Funcs;
|
|
|
|
}
|
[llvm-profdata] Do not create numerical strings for MD5 function names read 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
2023-10-17 17:09:39 -04:00
|
|
|
|
|
|
|
BinaryFunction *getBinaryFunction(FunctionId FName) {
|
|
|
|
if (FName.isStringRef()) {
|
|
|
|
auto I = BinaryFunctions.find(FName.str());
|
|
|
|
if (I == BinaryFunctions.end())
|
|
|
|
return nullptr;
|
|
|
|
return &I->second;
|
|
|
|
}
|
|
|
|
auto I = HashBinaryFunctions.find(FName.getHashCode());
|
|
|
|
if (I == HashBinaryFunctions.end())
|
2021-11-28 18:42:09 -08:00
|
|
|
return nullptr;
|
[llvm-profdata] Do not create numerical strings for MD5 function names read 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
2023-10-17 17:09:39 -04:00
|
|
|
return I->second;
|
2021-11-28 18:42:09 -08:00
|
|
|
}
|
|
|
|
|
[CSSPGO][llvm-profgen] Reimplement SampleContextTracker using context trie
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
2022-06-27 23:00:05 -07:00
|
|
|
uint32_t getFuncSizeForContext(const ContextTrieNode *ContextNode) {
|
|
|
|
return FuncSizeTracker.getFuncSizeForContext(ContextNode);
|
2021-08-16 18:29:07 -07:00
|
|
|
}
|
|
|
|
|
2022-12-15 18:36:52 -08:00
|
|
|
void inferMissingFrames(const SmallVectorImpl<uint64_t> &Context,
|
|
|
|
SmallVectorImpl<uint64_t> &NewContext);
|
|
|
|
|
2021-10-21 20:56:06 -07:00
|
|
|
// Load the symbols from debug table and populate into symbol list.
|
|
|
|
void populateSymbolListFromDWARF(ProfileSymbolList &SymbolList);
|
|
|
|
|
2022-10-25 21:07:55 -07:00
|
|
|
SampleContextFrameVector
|
2022-10-13 20:42:51 -07:00
|
|
|
getFrameLocationStack(uint64_t Address, bool UseProbeDiscriminator = false) {
|
2022-10-25 21:07:55 -07:00
|
|
|
InstructionPointer IP(this, Address);
|
2023-05-25 08:11:40 -07:00
|
|
|
return symbolize(IP, SymbolizerOpts.UseSymbolTable, UseProbeDiscriminator);
|
2022-10-25 21:07:55 -07:00
|
|
|
}
|
|
|
|
|
|
|
|
const SampleContextFrameVector &
|
|
|
|
getCachedFrameLocationStack(uint64_t Address,
|
|
|
|
bool UseProbeDiscriminator = false) {
|
2022-10-13 20:42:51 -07:00
|
|
|
auto I = AddressToLocStackMap.emplace(Address, SampleContextFrameVector());
|
2021-09-24 17:06:39 -07:00
|
|
|
if (I.second) {
|
2022-10-25 21:07:55 -07:00
|
|
|
I.first->second = getFrameLocationStack(Address, UseProbeDiscriminator);
|
2021-09-24 17:06:39 -07:00
|
|
|
}
|
|
|
|
return I.first->second;
|
2021-09-22 20:00:24 -07:00
|
|
|
}
|
|
|
|
|
2022-12-14 08:01:04 +00:00
|
|
|
std::optional<SampleContextFrame> getInlineLeafFrameLoc(uint64_t Address) {
|
2022-10-25 21:16:29 -07:00
|
|
|
const auto &Stack = getCachedFrameLocationStack(Address);
|
2021-02-10 10:04:39 -08:00
|
|
|
if (Stack.empty())
|
|
|
|
return {};
|
|
|
|
return Stack.back();
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
}
|
|
|
|
|
2022-01-28 15:53:37 -08:00
|
|
|
void flushSymbolizer() { Symbolizer.reset(); }
|
|
|
|
|
2023-10-22 17:00:14 -07:00
|
|
|
MissingFrameInferrer *getMissingContextInferrer() {
|
2022-12-15 18:36:52 -08:00
|
|
|
return MissingContextInferrer.get();
|
|
|
|
}
|
|
|
|
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
|
|
|
// Compare two addresses' inline context
|
2021-09-24 17:06:39 -07:00
|
|
|
bool inlineContextEqual(uint64_t Add1, uint64_t Add2);
|
[CSSPGO][llvm-profgen] Context-sensitive profile data generation
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
2020-10-19 12:55:59 -07:00
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[CSSPGO] Split context string to deduplicate function name used in the context.
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
2021-08-25 11:40:34 -07:00
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// Get the full context of the current stack with inline context filled in.
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2022-10-13 20:42:51 -07:00
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// It will search the disassembling info stored in AddressToLocStackMap. This
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// is used as the key of function sample map
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[CSSPGO] Split context string to deduplicate function name used in the context.
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
2021-08-25 11:40:34 -07:00
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SampleContextFrameVector
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getExpandedContext(const SmallVectorImpl<uint64_t> &Stack,
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2021-09-24 17:06:39 -07:00
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bool &WasLeafInlined);
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2021-09-24 18:16:36 -07:00
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// Go through instructions among the given range and record its size for the
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// inline context.
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2022-10-13 20:42:51 -07:00
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void computeInlinedContextSizeForRange(uint64_t StartAddress,
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uint64_t EndAddress);
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2020-11-25 20:33:17 -08:00
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2022-01-28 15:53:37 -08:00
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void computeInlinedContextSizeForFunc(const BinaryFunction *Func);
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2021-08-04 08:50:28 -07:00
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const MCDecodedPseudoProbe *getCallProbeForAddr(uint64_t Address) const {
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2020-11-25 20:33:17 -08:00
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return ProbeDecoder.getCallProbeForAddr(Address);
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}
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2021-08-04 08:50:28 -07:00
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[CSSPGO] Split context string to deduplicate function name used in the context.
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
2021-08-25 11:40:34 -07:00
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void getInlineContextForProbe(const MCDecodedPseudoProbe *Probe,
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SampleContextFrameVector &InlineContextStack,
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bool IncludeLeaf = false) const {
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2024-07-08 20:40:07 -07:00
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SmallVector<MCPseudoProbeFrameLocation, 16> ProbeInlineContext;
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[CSSPGO] Split context string to deduplicate function name used in the context.
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
2021-08-25 11:40:34 -07:00
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ProbeDecoder.getInlineContextForProbe(Probe, ProbeInlineContext,
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IncludeLeaf);
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2021-11-19 13:57:05 -08:00
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for (uint32_t I = 0; I < ProbeInlineContext.size(); I++) {
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auto &Callsite = ProbeInlineContext[I];
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// Clear the current context for an unknown probe.
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if (Callsite.second == 0 && I != ProbeInlineContext.size() - 1) {
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InlineContextStack.clear();
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continue;
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}
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[llvm-profdata] Do not create numerical strings for MD5 function names read 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
2023-10-17 17:09:39 -04:00
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InlineContextStack.emplace_back(FunctionId(Callsite.first),
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[CSSPGO] Split context string to deduplicate function name used in the context.
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
2021-08-25 11:40:34 -07:00
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LineLocation(Callsite.second, 0));
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}
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2020-11-25 20:33:17 -08:00
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}
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2021-01-11 09:08:39 -08:00
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const AddressProbesMap &getAddress2ProbesMap() const {
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return ProbeDecoder.getAddress2ProbesMap();
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}
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2021-08-04 08:50:28 -07:00
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const MCPseudoProbeFuncDesc *getFuncDescForGUID(uint64_t GUID) {
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2021-01-11 09:08:39 -08:00
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return ProbeDecoder.getFuncDescForGUID(GUID);
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}
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2021-08-04 08:50:28 -07:00
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const MCPseudoProbeFuncDesc *
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getInlinerDescForProbe(const MCDecodedPseudoProbe *Probe) {
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2021-01-11 09:08:39 -08:00
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return ProbeDecoder.getInlinerDescForProbe(Probe);
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}
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2021-07-29 11:42:55 -07:00
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2021-09-24 18:16:36 -07:00
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bool getTrackFuncContextSize() { return TrackFuncContextSize; }
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2021-07-29 11:42:55 -07:00
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bool getIsLoadedByMMap() { return IsLoadedByMMap; }
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void setIsLoadedByMMap(bool Value) { IsLoadedByMMap = Value; }
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bool getMissingMMapWarned() { return MissingMMapWarned; }
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void setMissingMMapWarned(bool Value) { MissingMMapWarned = Value; }
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[CSSPGO][llvm-profgen] Parse mmap events from perf script
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
2020-10-18 21:36:54 -07:00
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};
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} // end namespace sampleprof
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} // end namespace llvm
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#endif
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