[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 21:55:59 +02:00
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//===-- ProfileGenerator.h - Profile Generator -----------------*- 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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#ifndef LLVM_TOOLS_LLVM_PROGEN_PROFILEGENERATOR_H
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#define LLVM_TOOLS_LLVM_PROGEN_PROFILEGENERATOR_H
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[CSSPGO][llvm-profgen] Context-sensitive global pre-inliner
This change sets up a framework in llvm-profgen to estimate inline decision and adjust context-sensitive profile based on that. We call it a global pre-inliner in llvm-profgen.
It will serve two purposes:
1) Since context profile for not inlined context will be merged into base profile, if we estimate a context will not be inlined, we can merge the context profile in the output to save profile size.
2) For thinLTO, when a context involving functions from different modules is not inined, we can't merge functions profiles across modules, leading to suboptimal post-inline count quality. By estimating some inline decisions, we would be able to adjust/merge context profiles beforehand as a mitigation.
Compiler inline heuristic uses inline cost which is not available in llvm-profgen. But since inline cost is closely related to size, we could get an estimate through function size from debug info. Because the size we have in llvm-profgen is the final size, it could also be more accurate than the inline cost estimation in the compiler.
This change only has the framework, with a few TODOs left for follow up patches for a complete implementation:
1) We need to retrieve size for funciton//inlinee from debug info for inlining estimation. Currently we use number of samples in a profile as place holder for size estimation.
2) Currently the thresholds are using the values used by sample loader inliner. But they need to be tuned since the size here is fully optimized machine code size, instead of inline cost based on not yet fully optimized IR.
Differential Revision: https://reviews.llvm.org/D99146
2021-03-05 16:50:36 +01:00
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#include "CSPreInliner.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 21:55:59 +02:00
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#include "ErrorHandling.h"
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#include "PerfReader.h"
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#include "ProfiledBinary.h"
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2021-03-18 17:45:07 +01:00
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#include "llvm/Analysis/ProfileSummaryInfo.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 21:55:59 +02:00
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#include "llvm/ProfileData/SampleProfWriter.h"
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2021-03-18 17:45:07 +01:00
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#include <memory>
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2021-04-08 08:06:39 +02:00
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#include <unordered_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 21:55:59 +02:00
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using namespace llvm;
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using namespace sampleprof;
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namespace llvm {
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namespace sampleprof {
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class ProfileGenerator {
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public:
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ProfileGenerator(){};
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virtual ~ProfileGenerator() = default;
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static std::unique_ptr<ProfileGenerator>
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2021-01-11 18:08:39 +01:00
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create(const BinarySampleCounterMap &BinarySampleCounters,
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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 21:55:59 +02:00
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enum PerfScriptType SampleType);
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virtual void generateProfile() = 0;
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// Use SampleProfileWriter to serialize profile map
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2021-02-03 23:13:06 +01:00
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virtual void write(std::unique_ptr<SampleProfileWriter> Writer,
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StringMap<FunctionSamples> &ProfileMap);
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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 21:55:59 +02:00
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void write();
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protected:
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/*
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For each region boundary point, mark if it is begin or end (or both) of
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the region. Boundary points are inclusive. Log the sample count as well
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so we can use it when we compute the sample count of each disjoint region
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later. Note that there might be multiple ranges with different sample
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count that share same begin/end point. We need to accumulate the sample
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count for the boundary point for such case, because for the example
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below,
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|<--100-->|
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|<------200------>|
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A B C
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sample count for disjoint region [A,B] would be 300.
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*/
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void findDisjointRanges(RangeSample &DisjointRanges,
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const RangeSample &Ranges);
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[CSSPGO][llvm-profgen] Compress recursive cycles in calling context
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
2021-01-30 00:00:08 +01:00
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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 21:55:59 +02:00
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|
|
// Used by SampleProfileWriter
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|
StringMap<FunctionSamples> ProfileMap;
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|
};
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class CSProfileGenerator : public ProfileGenerator {
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2020-11-26 05:33:17 +01:00
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protected:
|
[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 21:55:59 +02:00
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|
|
const BinarySampleCounterMap &BinarySampleCounters;
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public:
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CSProfileGenerator(const BinarySampleCounterMap &Counters)
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: BinarySampleCounters(Counters){};
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|
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public:
|
[CSSPGO] Load context profile for external functions in PreLink and populate ThinLTO import list
For ThinLTO's prelink compilation, we need to put external inline candidates into an import list attached to function's entry count metadata. This enables ThinLink to treat such cross module callee as hot in summary index, and later helps postlink to import them for profile guided cross module inlining.
For AutoFDO, the import list is retrieved by traversing the nested inlinee functions. For CSSPGO, since profile is flatterned, a few things need to happen for it to work:
- When loading input profile in extended binary format, we need to load all child context profile whose parent is in current module, so context trie for current module includes potential cross module inlinee.
- In order to make the above happen, we need to know whether input profile is CSSPGO profile before start reading function profile, hence a flag for profile summary section is added.
- When searching for cross module inline candidate, we need to walk through the context trie instead of nested inlinee profile (callsite sample of AutoFDO profile).
- Now that we have more accurate counts with CSSPGO, we swtiched to use entry count instead of total count to decided if an external callee is potentially beneficial to inline. This make it consistent with how we determine whether call tagert is potential inline candidate.
Differential Revision: https://reviews.llvm.org/D98590
2021-03-13 22:55:28 +01:00
|
|
|
void generateProfile() override;
|
[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 21:55:59 +02:00
|
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[CSSPGO][llvm-profgen] Compress recursive cycles in calling context
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
2021-01-30 00:00:08 +01:00
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// Remove adjacent repeated context sequences up to a given sequence length,
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// -1 means no size limit. Note that repeated sequences are identified based
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// on the exact call site, this is finer granularity than function recursion.
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template <typename T>
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static void compressRecursionContext(SmallVectorImpl<T> &Context,
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int32_t CSize = MaxCompressionSize) {
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uint32_t I = 1;
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uint32_t HS = static_cast<uint32_t>(Context.size() / 2);
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uint32_t MaxDedupSize =
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CSize == -1 ? HS : std::min(static_cast<uint32_t>(CSize), HS);
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auto BeginIter = Context.begin();
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// Use an in-place algorithm to save memory copy
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// End indicates the end location of current iteration's data
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uint32_t End = 0;
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// Deduplicate from length 1 to the max possible size of a repeated
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// sequence.
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while (I <= MaxDedupSize) {
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// This is a linear algorithm that deduplicates adjacent repeated
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// sequences of size I. The deduplication detection runs on a sliding
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// window whose size is 2*I and it keeps sliding the window to deduplicate
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// the data inside. Once duplication is detected, deduplicate it by
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// skipping the right half part of the window, otherwise just copy back
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// the new one by appending them at the back of End pointer(for the next
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// iteration).
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//
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// For example:
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// Input: [a1, a2, b1, b2]
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// (Added index to distinguish the same char, the origin is [a, a, b,
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// b], the size of the dedup window is 2(I = 1) at the beginning)
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//
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// 1) The initial status is a dummy window[null, a1], then just copy the
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// right half of the window(End = 0), then slide the window.
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// Result: [a1], a2, b1, b2 (End points to the element right before ],
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// after ] is the data of the previous iteration)
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//
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// 2) Next window is [a1, a2]. Since a1 == a2, then skip the right half of
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// the window i.e the duplication happen. Only slide the window.
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// Result: [a1], a2, b1, b2
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//
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// 3) Next window is [a2, b1], copy the right half of the window(b1 is
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// new) to the End and slide the window.
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// Result: [a1, b1], b1, b2
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//
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// 4) Next window is [b1, b2], same to 2), skip b2.
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// Result: [a1, b1], b1, b2
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// After resize, it will be [a, b]
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// Use pointers like below to do comparison inside the window
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// [a b c a b c]
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// | | | | |
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// LeftBoundary Left Right Left+I Right+I
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// A duplication found if Left < LeftBoundry.
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int32_t Right = I - 1;
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End = I;
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|
int32_t LeftBoundary = 0;
|
|
|
|
while (Right + I < Context.size()) {
|
|
|
|
// To avoids scanning a part of a sequence repeatedly, it finds out
|
|
|
|
// the common suffix of two hald in the window. The common suffix will
|
|
|
|
// serve as the common prefix of next possible pair of duplicate
|
|
|
|
// sequences. The non-common part will be ignored and never scanned
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|
|
|
// again.
|
|
|
|
|
|
|
|
// For example.
|
|
|
|
// Input: [a, b1], c1, b2, c2
|
|
|
|
// I = 2
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|
|
|
//
|
|
|
|
// 1) For the window [a, b1, c1, b2], non-common-suffix for the right
|
|
|
|
// part is 'c1', copy it and only slide the window 1 step.
|
|
|
|
// Result: [a, b1, c1], b2, c2
|
|
|
|
//
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|
|
|
// 2) Next window is [b1, c1, b2, c2], so duplication happen.
|
|
|
|
// Result after resize: [a, b, c]
|
|
|
|
|
|
|
|
int32_t Left = Right;
|
|
|
|
while (Left >= LeftBoundary && Context[Left] == Context[Left + I]) {
|
|
|
|
// Find the longest suffix inside the window. When stops, Left points
|
|
|
|
// at the diverging point in the current sequence.
|
|
|
|
Left--;
|
|
|
|
}
|
|
|
|
|
|
|
|
bool DuplicationFound = (Left < LeftBoundary);
|
|
|
|
// Don't need to recheck the data before Right
|
|
|
|
LeftBoundary = Right + 1;
|
|
|
|
if (DuplicationFound) {
|
|
|
|
// Duplication found, skip right half of the window.
|
|
|
|
Right += I;
|
|
|
|
} else {
|
|
|
|
// Copy the non-common-suffix part of the adjacent sequence.
|
|
|
|
std::copy(BeginIter + Right + 1, BeginIter + Left + I + 1,
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|
|
|
BeginIter + End);
|
|
|
|
End += Left + I - Right;
|
|
|
|
// Only slide the window by the size of non-common-suffix
|
|
|
|
Right = Left + I;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
// Don't forget the remaining part that's not scanned.
|
|
|
|
std::copy(BeginIter + Right + 1, Context.end(), BeginIter + End);
|
|
|
|
End += Context.size() - Right - 1;
|
|
|
|
I++;
|
|
|
|
Context.resize(End);
|
|
|
|
MaxDedupSize = std::min(static_cast<uint32_t>(End / 2), MaxDedupSize);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2021-01-11 18:08:39 +01:00
|
|
|
protected:
|
|
|
|
// Lookup or create FunctionSamples for the context
|
2021-02-20 07:46:30 +01:00
|
|
|
FunctionSamples &getFunctionProfileForContext(StringRef ContextId,
|
|
|
|
bool WasLeafInlined = false);
|
[CSSPGO][llvm-profgen] Context-sensitive global pre-inliner
This change sets up a framework in llvm-profgen to estimate inline decision and adjust context-sensitive profile based on that. We call it a global pre-inliner in llvm-profgen.
It will serve two purposes:
1) Since context profile for not inlined context will be merged into base profile, if we estimate a context will not be inlined, we can merge the context profile in the output to save profile size.
2) For thinLTO, when a context involving functions from different modules is not inined, we can't merge functions profiles across modules, leading to suboptimal post-inline count quality. By estimating some inline decisions, we would be able to adjust/merge context profiles beforehand as a mitigation.
Compiler inline heuristic uses inline cost which is not available in llvm-profgen. But since inline cost is closely related to size, we could get an estimate through function size from debug info. Because the size we have in llvm-profgen is the final size, it could also be more accurate than the inline cost estimation in the compiler.
This change only has the framework, with a few TODOs left for follow up patches for a complete implementation:
1) We need to retrieve size for funciton//inlinee from debug info for inlining estimation. Currently we use number of samples in a profile as place holder for size estimation.
2) Currently the thresholds are using the values used by sample loader inliner. But they need to be tuned since the size here is fully optimized machine code size, instead of inline cost based on not yet fully optimized IR.
Differential Revision: https://reviews.llvm.org/D99146
2021-03-05 16:50:36 +01:00
|
|
|
// Post processing for profiles before writing out, such as mermining
|
|
|
|
// and trimming cold profiles, running preinliner on profiles.
|
|
|
|
void postProcessProfiles();
|
2021-03-18 17:45:07 +01:00
|
|
|
void computeSummaryAndThreshold();
|
2021-02-03 23:13:06 +01:00
|
|
|
void write(std::unique_ptr<SampleProfileWriter> Writer,
|
|
|
|
StringMap<FunctionSamples> &ProfileMap) override;
|
2021-01-11 18:08:39 +01:00
|
|
|
|
[CSSPGO][llvm-profgen] Context-sensitive global pre-inliner
This change sets up a framework in llvm-profgen to estimate inline decision and adjust context-sensitive profile based on that. We call it a global pre-inliner in llvm-profgen.
It will serve two purposes:
1) Since context profile for not inlined context will be merged into base profile, if we estimate a context will not be inlined, we can merge the context profile in the output to save profile size.
2) For thinLTO, when a context involving functions from different modules is not inined, we can't merge functions profiles across modules, leading to suboptimal post-inline count quality. By estimating some inline decisions, we would be able to adjust/merge context profiles beforehand as a mitigation.
Compiler inline heuristic uses inline cost which is not available in llvm-profgen. But since inline cost is closely related to size, we could get an estimate through function size from debug info. Because the size we have in llvm-profgen is the final size, it could also be more accurate than the inline cost estimation in the compiler.
This change only has the framework, with a few TODOs left for follow up patches for a complete implementation:
1) We need to retrieve size for funciton//inlinee from debug info for inlining estimation. Currently we use number of samples in a profile as place holder for size estimation.
2) Currently the thresholds are using the values used by sample loader inliner. But they need to be tuned since the size here is fully optimized machine code size, instead of inline cost based on not yet fully optimized IR.
Differential Revision: https://reviews.llvm.org/D99146
2021-03-05 16:50:36 +01:00
|
|
|
// Profile summary to answer isHotCount and isColdCount queries.
|
|
|
|
std::unique_ptr<ProfileSummaryInfo> PSI;
|
|
|
|
|
2021-04-08 08:06:39 +02:00
|
|
|
// String table owning context strings created from profile generation.
|
|
|
|
std::unordered_set<std::string> ContextStrings;
|
|
|
|
|
[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 21:55:59 +02:00
|
|
|
private:
|
|
|
|
// Helper function for updating body sample for a leaf location in
|
|
|
|
// FunctionProfile
|
|
|
|
void updateBodySamplesforFunctionProfile(FunctionSamples &FunctionProfile,
|
|
|
|
const FrameLocation &LeafLoc,
|
|
|
|
uint64_t Count);
|
2020-12-03 08:10:11 +01:00
|
|
|
void populateFunctionBodySamples(FunctionSamples &FunctionProfile,
|
|
|
|
const RangeSample &RangeCounters,
|
|
|
|
ProfiledBinary *Binary);
|
|
|
|
void populateFunctionBoundarySamples(StringRef ContextId,
|
|
|
|
FunctionSamples &FunctionProfile,
|
|
|
|
const BranchSample &BranchCounters,
|
|
|
|
ProfiledBinary *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 21:55:59 +02:00
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void populateInferredFunctionSamples();
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[CSSPGO][llvm-profgen] Compress recursive cycles in calling context
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
2021-01-30 00:00:08 +01:00
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public:
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// Deduplicate adjacent repeated context sequences up to a given sequence
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// length. -1 means no size limit.
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static int32_t MaxCompressionSize;
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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 21:55:59 +02:00
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};
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2021-01-11 18:08:39 +01:00
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using ProbeCounterMap = std::unordered_map<const PseudoProbe *, uint64_t>;
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2020-11-26 05:33:17 +01:00
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class PseudoProbeCSProfileGenerator : public CSProfileGenerator {
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public:
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PseudoProbeCSProfileGenerator(const BinarySampleCounterMap &Counters)
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: CSProfileGenerator(Counters) {}
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2021-01-11 18:08:39 +01:00
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void generateProfile() override;
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private:
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// Go through each address from range to extract the top frame probe by
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// looking up in the Address2ProbeMap
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void extractProbesFromRange(const RangeSample &RangeCounter,
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ProbeCounterMap &ProbeCounter,
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ProfiledBinary *Binary);
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// Fill in function body samples from probes
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[CSSPGO][llvm-profgen] Compress recursive cycles in calling context
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
2021-01-30 00:00:08 +01:00
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void
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populateBodySamplesWithProbes(const RangeSample &RangeCounter,
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SmallVectorImpl<std::string> &ContextStrStack,
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ProfiledBinary *Binary);
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2021-01-11 18:08:39 +01:00
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// Fill in boundary samples for a call probe
|
[CSSPGO][llvm-profgen] Compress recursive cycles in calling context
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
2021-01-30 00:00:08 +01:00
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void populateBoundarySamplesWithProbes(
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const BranchSample &BranchCounter,
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SmallVectorImpl<std::string> &ContextStrStack, ProfiledBinary *Binary);
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2021-01-11 18:08:39 +01:00
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// Helper function to get FunctionSamples for the leaf inlined context
|
[CSSPGO][llvm-profgen] Compress recursive cycles in calling context
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
2021-01-30 00:00:08 +01:00
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FunctionSamples &
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getFunctionProfileForLeafProbe(SmallVectorImpl<std::string> &ContextStrStack,
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2021-02-20 07:46:30 +01:00
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const PseudoProbeFuncDesc *LeafFuncDesc,
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bool WasLeafInlined);
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2021-01-11 18:08:39 +01:00
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// Helper function to get FunctionSamples for the leaf probe
|
[CSSPGO][llvm-profgen] Compress recursive cycles in calling context
This change compresses the context string by removing cycles due to recursive function for CS profile generation. Removing recursion cycles is a way to normalize the calling context which will be better for the sample aggregation and also make the context promoting deterministic.
Specifically for implementation, we recognize adjacent repeated frames as cycles and deduplicated them through multiple round of iteration.
For example:
Considering a input context string stack:
[“a”, “a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For first iteration,, it removed all adjacent repeated frames of size 1:
[“a”, “b”, “c”, “a”, “b”, “c”, “b”, “c”, “d”]
For second iteration, it removed all adjacent repeated frames of size 2:
[“a”, “b”, “c”, “a”, “b”, “c”, “d”]
So in the end, we get compressed output:
[“a”, “b”, “c”, “d”]
Compression will be called in two place: one for sample's context key right after unwinding, one is for the eventual context string id in the ProfileGenerator.
Added a switch `compress-recursion` to control the size of duplicated frames, default -1 means no size limit.
Added unit tests and regression test for this.
Differential Revision: https://reviews.llvm.org/D93556
2021-01-30 00:00:08 +01:00
|
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|
FunctionSamples &
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getFunctionProfileForLeafProbe(SmallVectorImpl<std::string> &ContextStrStack,
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const PseudoProbe *LeafProbe,
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ProfiledBinary *Binary);
|
2020-11-26 05:33:17 +01:00
|
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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 21:55:59 +02:00
|
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|
} // end namespace sampleprof
|
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} // end namespace llvm
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#endif
|