llvm dev - Aug 2020 - [RFC] Context-sensitive Sample PGO with Pseudo-Instrumentation

Hi All,
Our team at Facebook is building a new context-sensitive Sample PGO as an
alternative to the existing AutoFDO. We’d like to share our motivation, propose
a new design, and reveal preliminary results on benchmarks. We will refer to the
proposed design as CSSPGO in this RFC.

The new CSSPGO leverages simultaneous LBR and stack sampling to construct a full
context-sensitive profile. It doesn’t rely on previous inlining like today’s
AutoFDO to get context-sensitive profile, and it also doesn’t need a separate
post-inline context-sensitive profile like CSPGO. In addition, we introduced
pseudo-instrumentation for more accurate mapping from binary samples back to IR,
similar to instrumentation PGO, but without any measure-able runtime overhead
that is usually associated with instrumentation.

We have a functioning implementation for the new CSSPGO now. Initial results on
SPEC2006 shows ~2% geomean performance win on top of AutoFDO (with MonoLTO and
NewPM) and ~4% .text size reduction at the same time.


Motivation
AutoFDO is a big success as it lowers the entry barrier for PGO significantly
while still delivering substantial performance boost. However, there’s still a
gap between AutoFDO and its instrumentation counterpart. From several failed
internal attempts to improve AutoFDO, we realized that the bottleneck of AutoFDO
lies in its profile quality. With the current level of profile quality, it’s
difficult to reap more performance win because good heuristics are often limited
by inferior profile. That prompted a systemic effort to investigate and improve
AutoFDO framework. Specifically, we’re trying to handle the two biggest sources
of profile quality issues:


  1.  AutoFDO relies on a limited context-sensitive profile collected based on
previous inlining. Therefore it can only replay or prune the previous inlining.
With the main CGSCC inliner, post-inline counts are not accurate due to scaling
of context-less profile, which affects the effectiveness of later passes such as
profile-guided code layout.
  2.  Dwarf line and discriminator info aren’t always well-maintained throughout
the compilation, thus using them as anchors to map binary samples back to the IR
can sometimes be inaccurate, which leads to inferior profile quality and limits
PGO performance.
To lift the above limitations, we’d like to propose an alternative design that
consists of two components: 1) Context-sensitive sample PGO, 2) Sample to IR
mapping using pseudo probes. The goal is to further improve sample PGO
performance while maintaining usability and keeping training runtime overhead at
zero. In addition, we hope the CSSPGO framework can also open up opportunities
for new optimizations with more stringent requirements on profile quality.


Context-sensitive Sample PGO
The effectiveness of BOLT, Propeller and CSPGO all demonstrated the importance
of context-sensitive profile for PGO. However there are two limitations with the
existing approaches.

  1.  The current solutions focus on leveraging a context-sensitive profile to
attain an accurate post-inline profile that helps achieve a better code layout,
but do not use the context-sensitive profile to drive better inlining.
  2.  The current solutions involve multiple training processes and profiles
(e.g. a post-inline profile for CSPGO, or a post-link profile for BOLT and
Propeller), which incurs higher operational cost and complicates the build and
release workflow.
We propose a full context-sensitive sample profiling infrastructure that
utilizes both LBR and call stack samples at the same time to synthesize a
profile with a full context sensitivity. The key advantage is that rather than
relying on previous inlining or a separate profile, the profile collected with
the new approach will have full calling contexts recovered from both inlined and
not inlined call sites. To achieve an accurate post-inline profile, a separate
profile is no longer needed. Instead, the post-inline profile can be directly
derived from adjusting the input profile based on all inline decisions. The
richer context-sensitive profile also enables better inline decisions. The
infrastructure has two key components listed below.

Synthesizing context-sensitive LBR with a virtual unwinder
To make sample PGO’s input profile context aware, we need to know the call stack
of each LBR fall through path. That is done by sampling LBR and call stack
simultaneously. With that, each sample will contain a call stack in addition to
LBR entries. We use level 2 PEBS to control sampling skid so that the leaf frame
from stack sample aligns with leaf frame from LBR. The raw call stack sample
describes the calling context for the leaf LBR entry. In addition, by unwinding
“call” and “return” (including implicit ones from inlinee) from LBR entries
backwards on top of raw stack samples, we can recover the calling context for
each of the LBR entries from the sample, thus synthesizing context-sensitive LBR
profile.

We can then generate context-sensitive sample PGO profile using the
context-sensitive LBR profile. In the new profile, a function’s profile becomes
a collection of profiles, each representing a profile for a given calling
context.

Context-sensitive FDO/PGO framework in LLVM
In order to leverage context-sensitive profile for inlining, and to maintain
accurate post-inline counts, we introduced SampleContextTracker which is a layer
sitting in between input profile and the profile used to annotate CFG for
optimizations. We also introduced the notion of base profile which is the merged
profile for function’s profiles from any outstanding (not inlined) context, and
context profile which is a function's profile for a given calling context.
The framework includes four simple APIs for updating and query profiles:

Query API:

  *   getBaseSamplesFor: Query base profile by function name.
  *   getContextSamplesFor: Query context profile by calling context and
function name.
Update API:

  *   MarkContextSamplesInlined: When a function is inlined for a given calling
context, we need to mark the context profile for that context as inlined. This
is to make sure we don't include inlined context profile when synthesizing
the base profile.
  *   PromoteMergeContextSamplesTree: When a function is not inlined for a given
calling context, we need to promote the context profile tree to be top-level
context. This preserves the child context under that function so later inline
decisions for calls originating from that not inlined function will still be
driven by an accurate context profile.
These APIs are used by SampleProfileLoader’s inlining, for better inline
decisions and better post-inline counts. For optimal results, the new
infrastructure needs to work with a top-down FDO inliner. We added top-down FDO
inlining under a switch, and the switch is turned on by default in upstream
recently. There’re a few other improvements for the FDO inliner that we plan to
upstream soon.


Pseudo-instrumentation for sample to IR mapping
Being able to profile production binaries is a key advantage of AutoFDO over
Instrumentation PGO, but it also comes with a big challenge. While using line
number and discriminator as anchor for profile mapping incurs zero run time
overhead for AutoFDO, it’s not as accurate as instrumented probes. This is
because the instrumented probes are part of the IR, rather than metadata
attached to the IR like !dbg. That has two implications: 1) it’s easier to
maintain IR than metadata for optimization passes; 2) probe blocks some CFG
transformations that can mess up profile correlation.

With the proposed pseudo instrumentation, we can achieve most of the benefit of
instrumentation PGO in little runtime overhead. We instrument each basic block
with a pseudo probe associated with the block Id. Unlike in PGO instrumentation
where a counter is implemented as a persisting operation such as atomic
read/write or runtime helper call, a pseudo probe is implemented as a dedicated
intrinsic call with IntrInaccessibleMemOnly flag. The intrinsic comes with most
of the semantics of a PGO counter but is much less optimization-intrusive.

The pseudo probe intrinsic calls are on the IR throughout the optimization and
code generation pipeline and are materialized as a piece of binary data stored
in a separate .pseudo_probe data section. The section is then used to map binary
samples back to blocks of CFG during profile generation. There are also no real
machine instructions generated for a pseudo probe and the.pseudo_probe section
won’t be loaded into memory at runtime, therefore they should incur very little
runtime overhead. As a fact, we see no measure-able performance impact from
pseudo-instrumentation itself on SPEC2006 or big internal workload.

Pseudo-instrumentation integration and Pass Ordering
One implication from pseudo-probe instrumentation is that the profile is now
sensitive to CFG changes. We now defect stale profiles for sample PGO via CFG
checksum, instead of just using it. However, the potential downside is that CFG
may change between different versions of the compiler even if the source code is
unchanged. To solve that problem, we perform the pseudo instrumentation very
early in the pre-LTO pipeline, before any CFG transformation. This ensures that
the CFG instrumented and annotated is stable. We added SampleProfileProber that
performs the pseudo instrumentation and runs independent of profile annotation.

A new switch -fpseudo-probe-for-profiling is added to enable sample PGO with
pseudo instrumentation, similar to -fdebug-info-for-profiling for AutoFDO. Input
profile is still provided through the same switch used by today’s AutoFDO,
namely -fprofile-sample-use, and the profile loader will automatically decide
how to load and annotate profile depending on whether input profile is
dwarf-based or pseudo-probe based.


New profile format and profile generation
We extend current profile format in order to be able to represent a full
context-sensitive profile and also encode pseudo-probe info. This is done
without drastically diverging from today’s AutoFDO profile format so that
existing tools and libraries can be reused with minor changes (e.g.
llvm-profdata, profiler reader and writer).

For a context-sensitive profile, we extend the profile format by changing the
function profile header line to include calling context in addition to function
name. With today’s AutoFDO, we have a single profile header for each function to
represent its accumulative profile. E.g. This is the profile header for foo,
with 1290 total samples, and 74 header samples.

foo:1290:74

For CSSPGO, we will have multiple profile headers for a single function’s
profile, each representing profile for a specific calling context as shown
below. CSSPGO profile header is bracketed to differentiate from today’s AutoFDO.

[main:12 @ bar:3 @ foo]:279:54
[main:19 @ zoo:7 @ foo]:1011:20

With calling context encoded in the function header, we no longer need a nested
function profile for inlinees. Instead, a context profile will be represented
uniformly using context strings in the function profile header, regardless of
whether the calls in the context are inlined or not. The flat structure makes
sure that context profile is easily indexable. The change is mostly transparent
to tools like llvm-profdata. Support for binary profile format has not been
added yet, but should be easy to do.

For pseudo-probe, we repurposed the line to count map of AutoFDO profile to be
block Id to count map. This only changes the interpretation of profile content
rather than the representation, hence all reader/writer helpers can be reused.

There's a new profile generation tool, llvm-profgen, with the virtual winder
implemented for context-sensitive profiling, and uses the .pseudo_probe section
to map binary profile to pre-opt CFG profile. Since profile generation is a
critical piece of the workflow, we’d like to propose to include the tool as part
of LLVM, alongside with llvm-profdata.


Preliminary Results
To quantitatively assess profile quality improvement brought by
pseudo-instrumentation, we introduce a profile quality metric. We measure the
metric by first annotating an optimized binary with the MIR block execution
counts derived from a profile. The binary is then sampled and the counts are
compared against the annotation. The weighted relative delta is used as an
indicator for profile quality (lower is better).

Table below shows the profile quality metric for SPEC2006. We can see from the
numbers that the profile quality of pseudo-instrumentation sample PGO is much
better than AutoFDO and close to instrumentation PGO.

Profile quality metric
Baseline AutoFDO
Instrumentation PGO
Sample PGO w/ Pseudo Instrumentation
SPEC2006
24.58%
15.70%
16.21%

We also measured performance and code size on SPEC2006 with CSSPGO. The
measurement was done with MonoLTO and new pass manager, with tuning for FDO
inliner to accommodate context-sensitive profile, and used training dataset for
both pass1 and pass2. The result shows ~2% performance win on top of today’s
AutoFDO, with ~4% .text reduction, see table below.

SPEC2006
Performance
Code Size
AutoFDO over LTO
CSSPGO
Over LTO
CSSPGO over AutoFDO
AutoFDO over LTO
CSSPGO
Over LTO
CSSPGO over AutoFDO
Geomean Delta %
6.80%
8.70%
2.04%
11.17%
6.66%
4.06%

While the SPEC2006 benchmark suite is different from large workloads, we think
the results demonstrated the potential of CSSPGO and served its purpose for
proof of concept. We plan to continue tuning and start to evaluate larger
internal workloads soon, and we’d like to upstream our work. Feedbacks are
welcomed!


Thanks,
Wenlei & Hongtao


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Wenlei, Thanks for the interesting proposal! please see my replies inline
below.

On Fri, Aug 7, 2020 at 11:28 AM Wenlei He <wenlei at fb.com> wrote:
> Hi All,
>
> Our team at Facebook is building a new context-sensitive Sample PGO as an
> alternative to the existing AutoFDO. We’d like to share our motivation,
> propose a new design, and reveal preliminary results on benchmarks. We will
> refer to the proposed design as CSSPGO in this RFC.
>
>
>
> The new CSSPGO leverages simultaneous LBR and stack sampling to construct
> a full context-sensitive profile.
>

Can you share more details on this? LBR only has 32 entries, so it won't
give you full call context, so stack unwinding is needed. What is the
overhead you see in production environment?


> It doesn’t rely on previous inlining like today’s AutoFDO to get
> context-sensitive profile, and it also doesn’t need a separate post-inline
> context-sensitive profile like CSPGO.
>
What is the sample profile data size impact with the full context
information?


> In addition, we introduced pseudo-instrumentation for more accurate
> mapping from binary samples back to IR, similar to instrumentation PGO, but
> without any measure-able runtime overhead that is usually associated with
> instrumentation.
>
>
>
Is CSSPGO inherently dependent upon pseudo-probe or is it orthogonal? I
hope that it is the latter :)


> We have a functioning implementation for the new CSSPGO now. Initial
> results on SPEC2006 shows ~2% geomean performance win on top of AutoFDO
> (with MonoLTO and NewPM) and ~4% .text size reduction at the same time.
>
>
>
>
>
> *Motivation*
>
> AutoFDO is a big success as it lowers the entry barrier for PGO
> significantly while still delivering substantial performance boost.
> However, there’s still a gap between AutoFDO and its instrumentation
> counterpart. From several failed internal attempts to improve AutoFDO, we
> realized that the bottleneck of AutoFDO lies in its profile quality. With
> the current level of profile quality, it’s difficult to reap more
> performance win because good heuristics are often limited by inferior
> profile. That prompted a systemic effort to investigate and improve AutoFDO
> framework. Specifically, we’re trying to handle the two biggest sources of
> profile quality issues:
>
>
>
>    1. AutoFDO relies on a limited context-sensitive profile collected
>    based on previous inlining. Therefore it can only replay or prune the
>    previous inlining. With the main CGSCC inliner, post-inline counts are
not
>    accurate due to scaling of context-less profile, which affects the
>    effectiveness of later passes such as profile-guided code layout.
>
>
Acknowledge of the limitation here.
>
>    1.
>    2. Dwarf line and discriminator info aren’t always well-maintained
>    throughout the compilation, thus using them as anchors to map binary
>    samples back to the IR can sometimes be inaccurate, which leads to
inferior
>    profile quality and limits PGO performance.
>
>
I think we need more quantification of the impact of using debug
information for matching purposes:  How much performance are left on the
table due to this, and are they fixable issues or not.


>
>    1.
>
> To lift the above limitations, we’d like to propose an alternative design
> that consists of two components: 1) Context-sensitive sample PGO, 2) Sample
> to IR mapping using pseudo probes. The goal is to further improve sample
> PGO performance while maintaining usability and keeping training runtime
> overhead at zero. In addition, we hope the CSSPGO framework can also open
> up opportunities for new optimizations with more stringent requirements on
> profile quality.
>
>
>
>
>
CSSPGO is a very attractive optimization by itself.  Can you provide more
motivation for the pseudo probes?




> *Context-sensitive Sample PGO*
>
> The effectiveness of BOLT, Propeller and CSPGO all demonstrated the
> importance of context-sensitive profile for PGO. However there are two
> limitations with the existing approaches.
>
>    1. The current solutions focus on leveraging a context-sensitive
>    profile to attain an accurate post-inline profile that helps achieve a
>    better code layout, but do not use the context-sensitive profile to
drive
>    better inlining.
>    2. The current solutions involve multiple training processes and
>    profiles (e.g. a post-inline profile for CSPGO, or a post-link profile
for
>    BOLT and Propeller), which incurs higher operational cost and
complicates
>    the build and release workflow.
>
> We propose a full context-sensitive sample profiling infrastructure that
> utilizes both LBR and call stack samples at the same time to synthesize a
> profile with a full context sensitivity. The key advantage is that rather
> than relying on previous inlining or a separate profile, the profile
> collected with the new approach will have full calling contexts recovered
> from both inlined and not inlined call sites. To achieve an accurate
> post-inline profile, a separate profile is no longer needed. Instead, the
> post-inline profile can be directly derived from adjusting the input
> profile based on all inline decisions. The richer context-sensitive profile
> also enables better inline decisions. The infrastructure has two key
> components listed below.
>
>
>
> *Synthesizing context-sensitive LBR with a virtual unwinder*
>
> To make sample PGO’s input profile context aware, we need to know the call
> stack of each LBR fall through path. That is done by sampling LBR and call
> stack simultaneously. With that, each sample will contain a call stack in
> addition to LBR entries. We use level 2 PEBS to control sampling skid so
> that the leaf frame from stack sample aligns with leaf frame from LBR. The
> raw call stack sample describes the calling context for the leaf LBR entry.
> In addition, by unwinding “call” and “return” (including implicit ones from
> inlinee) from LBR entries backwards on top of raw stack samples, we can
> recover the calling context for each of the LBR entries from the sample,
> thus synthesizing context-sensitive LBR profile.
>
>
>
> We can then generate context-sensitive sample PGO profile using the
> context-sensitive LBR profile. In the new profile, a function’s profile
> becomes a collection of profiles, each representing a profile for a given
> calling context.
>
>
>
Sounds good -- see the overhead question posted at the beginning.



> *Context-sensitive FDO/PGO framework in LLVM*
>
> In order to leverage context-sensitive profile for inlining, and to
> maintain accurate post-inline counts, we introduced SampleContextTracker
which
> is a layer sitting in between input profile and the profile used to
> annotate CFG for optimizations. We also introduced the notion of base
> profile which is the merged profile for function’s profiles from any
> outstanding (not inlined) context, and context profile which is a
> function's profile for a given calling context. The framework includes
four
> simple APIs for updating and query profiles:
>
>
>
> Query API:
>
>    - getBaseSamplesFor: Query base profile by function name.
>    - getContextSamplesFor: Query context profile by calling context and
>    function name.
>
> Update API:
>
>    - MarkContextSamplesInlined: When a function is inlined for a given
>    calling context, we need to mark the context profile for that context as
>    inlined. This is to make sure we don't include inlined context
profile when
>    synthesizing the base profile.
>    - PromoteMergeContextSamplesTree: When a function is not inlined for a
>    given calling context, we need to promote the context profile tree to be
>    top-level context. This preserves the child context under that function
so
>    later inline decisions for calls originating from that not inlined
function
>    will still be driven by an accurate context profile.
>
> These APIs are used by SampleProfileLoader’s inlining, for better inline
> decisions and better post-inline counts. For optimal results, the new
> infrastructure needs to work with a top-down FDO inliner. We added top-down
> FDO inlining under a switch, and the switch is turned on by default in
> upstream recently. There’re a few other improvements for the FDO inliner
> that we plan to upstream soon.
>
The profile data should be usable by the SCC inliner as well. In the bottom
up inlining, as the function gets inline further up in the call chain, the
inline instance has few incoming contexts to merge.



>
>
>
>
> *Pseudo-instrumentation for sample to IR mapping*
>
> Being able to profile production binaries is a key advantage of AutoFDO
> over Instrumentation PGO, but it also comes with a big challenge. While
> using line number and discriminator as anchor for profile mapping incurs
> zero run time overhead for AutoFDO, it’s not as accurate as instrumented
> probes. This is because the instrumented probes are part of the IR, rather
> than metadata attached to the IR like !dbg. That has two implications: 1)
> it’s easier to maintain IR than metadata for optimization passes; 2) probe
> blocks some CFG transformations that can mess up profile correlation.
>
>
>
> With the proposed pseudo instrumentation, we can achieve most of the
> benefit of instrumentation PGO in little runtime overhead. We instrument
> each basic block with a pseudo probe associated with the block Id. Unlike
> in PGO instrumentation where a counter is implemented as a persisting
> operation such as atomic read/write or runtime helper call, a pseudo probe
> is implemented as a dedicated intrinsic call with IntrInaccessibleMemOnly
flag.
> The intrinsic comes with most of the semantics of a PGO counter but is
> much less optimization-intrusive.
>
>
>
> The pseudo probe intrinsic calls are on the IR throughout the optimization
> and code generation pipeline and are materialized as a piece of binary data
> stored in a separate .pseudo_probe data section.
>
How are these information maintained? Blocks can be eliminated or cloned in
many optimization passes: jump threading, taildup, unrolling, peeling etc.
For instance, how to handle the block that is merged into another? Does it
lose samples because of this?



The section is then used to map binary samples back to blocks of CFG
during
> profile generation. There are also no real machine instructions generated
> for a pseudo probe and the.pseudo_probe section won’t be loaded into
> memory at runtime, therefore they should incur very little runtime
> overhead. As a fact, we see no measure-able performance impact from
> pseudo-instrumentation itself on SPEC2006 or big internal workload.
>
>
How large are the probe sections?

>
>
> *Pseudo-instrumentation integration and Pass Ordering*
>
> One implication from pseudo-probe instrumentation is that the profile is
> now sensitive to CFG changes. We now defect stale profiles for sample PGO
> via CFG checksum, instead of just using it. However, the potential downside
> is that CFG may change between different versions of the compiler even if
> the source code is unchanged. To solve that problem, we perform the pseudo
> instrumentation very early in the pre-LTO pipeline, before any CFG
> transformation. This ensures that the CFG instrumented and annotated is
> stable. We added SampleProfileProber that performs the pseudo
> instrumentation and runs independent of profile annotation.
>
>
>
> A new switch -fpseudo-probe-for-profiling is added to enable sample PGO
> with pseudo instrumentation, similar to -fdebug-info-for-profiling for
> AutoFDO. Input profile is still provided through the same switch used by
> today’s AutoFDO, namely -fprofile-sample-use, and the profile loader will
> automatically decide how to load and annotate profile depending on whether
> input profile is dwarf-based or pseudo-probe based.
>

Can you compare the source change tolerance of pseudo probe based approach
vs debug info based approach?

>
>
>
> *New profile format and profile generation*
>
> We extend current profile format in order to be able to represent a full
> context-sensitive profile and also encode pseudo-probe info. This is done
> without drastically diverging from today’s AutoFDO profile format so that
> existing tools and libraries can be reused with minor changes (e.g.
> llvm-profdata, profiler reader and writer).
>
>
>
> For a context-sensitive profile, we extend the profile format by changing
> the function profile header line to include calling context in addition to
> function name. With today’s AutoFDO, we have a single profile header for
> each function to represent its accumulative profile. E.g. This is the
> profile header for foo, with 1290 total samples, and 74 header samples.
>
>
>
> foo:1290:74
>
>
>
> For CSSPGO, we will have multiple profile headers for a single function’s
> profile, each representing profile for a specific calling context as shown
> below. CSSPGO profile header is bracketed to differentiate from today’s
> AutoFDO.
>
>
>
> [main:12 @ bar:3 @ foo]:279:54
>
> [main:19 @ zoo:7 @ foo]:1011:20
>
>
sounds good.


>
>
> With calling context encoded in the function header, we no longer need a
> nested function profile for inlinees. Instead, a context profile will be
> represented uniformly using context strings in the function profile header,
> regardless of whether the calls in the context are inlined or not. The flat
> structure makes sure that context profile is easily indexable. The change
> is mostly transparent to tools like llvm-profdata. Support for binary
> profile format has not been added yet, but should be easy to do.
>
>
>

It is still useful to annotate (as least with comment line) that a profile
is for top level function or inline instance.


> For pseudo-probe, we repurposed the line to count map of AutoFDO profile
> to be block Id to count map. This only changes the interpretation of
> profile content rather than the representation, hence all reader/writer
> helpers can be reused.
>
>
>
> There's a new profile generation tool, llvm-profgen, with the virtual
> winder implemented for context-sensitive profiling, and uses the
> .pseudo_probe section to map binary profile to pre-opt CFG profile. Since
> profile generation is a critical piece of the workflow, we’d like to
> propose to include the tool as part of LLVM, alongside with llvm-profdata.
>
>
>
>
>
> *Preliminary Results*
>
> To quantitatively assess profile quality improvement brought by
> pseudo-instrumentation, we introduce a profile quality metric. We measure
> the metric by first annotating an optimized binary with the MIR block
> execution counts derived from a profile. The binary is then sampled and the
> counts are compared against the annotation. The weighted relative delta is
> used as an indicator for profile quality (lower is better).
>
>
>
> Table below shows the profile quality metric for SPEC2006. We can see from
> the numbers that the profile quality of pseudo-instrumentation sample PGO
> is much better than AutoFDO and close to instrumentation PGO.
>
>
>
> Profile quality metric
>
> Baseline AutoFDO
>
> Instrumentation PGO
>
> Sample PGO w/ Pseudo Instrumentation
>
> SPEC2006
>
> 24.58%
>
> 15.70%
>
> 16.21%
>
>
>
Instrumentation PGO does not have context sensitivity, so I would expect it
scores worse than CSSPGO. Do you know why it is better here?



> We also measured performance and code size on SPEC2006 with CSSPGO. The
> measurement was done with MonoLTO and new pass manager, with tuning for FDO
> inliner to accommodate context-sensitive profile, and used training dataset
> for both pass1 and pass2. The result shows ~2% performance win on top of
> today’s AutoFDO, with ~4% .text reduction, see table below.
>
>
>
> SPEC2006
>
> Performance
>
> Code Size
>
> AutoFDO over LTO
>
> CSSPGO
>
> Over LTO
>
> CSSPGO over AutoFDO
>
> AutoFDO over LTO
>
> CSSPGO
>
> Over LTO
>
> CSSPGO over AutoFDO
>
> Geomean Delta %
>
> 6.80%
>
> 8.70%
>
> 2.04%
>
> 11.17%
>
> 6.66%
>
> 4.06%
>
>
>
> While the SPEC2006 benchmark suite is different from large workloads, we
> think the results demonstrated the potential of CSSPGO and served its
> purpose for proof of concept. We plan to continue tuning and start to
> evaluate larger internal workloads soon, and we’d like to upstream our
> work. Feedbacks are welcomed!
>
>
>

What is the performance win with peudo-probe alone?

thanks,

David


>
>
> Thanks,
>
> Wenlei & Hongtao
>
>
>
>
>
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Thanks for the thoughtful questions, David. See my answers inline.

Thanks,
Wenlei

From: Xinliang David Li <davidxl at google.com>
Date: Friday, August 7, 2020 at 1:24 PM
To: Wenlei He <wenlei at fb.com>
Cc: "llvm-dev at lists.llvm.org" <llvm-dev at lists.llvm.org>,
Wei Mi <wmi at google.com>, Hongtao Yu <hoy at fb.com>
Subject: Re: [RFC] Context-sensitive Sample PGO with Pseudo-Instrumentation

Wenlei, Thanks for the interesting proposal! please see my replies inline below.

On Fri, Aug 7, 2020 at 11:28 AM Wenlei He <wenlei at fb.com<mailto:wenlei
at fb.com>> wrote:
Hi All,
Our team at Facebook is building a new context-sensitive Sample PGO as an
alternative to the existing AutoFDO. We’d like to share our motivation, propose
a new design, and reveal preliminary results on benchmarks. We will refer to the
proposed design as CSSPGO in this RFC.

The new CSSPGO leverages simultaneous LBR and stack sampling to construct a full
context-sensitive profile.


Can you share more details on this? LBR only has 32 entries, so it won't
give you full call context, so stack unwinding is needed. What is the overhead
you see in production environment?

[wenlei] We are not worried about overhead in production environment as the
sampling rate there is extremely low. We did measure locally however, for stack
sampling and level 2 PEBS on top of regular LBR sampling, the overheads isn’t
very noticeable still, but I don’t have numbers at hand.

It doesn’t rely on previous inlining like today’s AutoFDO to get
context-sensitive profile, and it also doesn’t need a separate post-inline
context-sensitive profile like CSPGO.

What is the sample profile data size impact with the full context information?

[wenlei] Text CS profile is normally around 1x-10x of regular profile size, with
all live context included. We plan to trim cold context, which we expect to
bring the size down in a meaningful way. Another source of size increase is the
context string, which could contain duplicated mangle names (can be very long
for C++ templated code), but should be very compressible with the built-in
compression support from extended binary profile. We will move to extended
binary format, and leverage the compression support if needed. We can also
consider more efficient fixed-length integer context representation (similar to
rolling hash).


In addition, we introduced pseudo-instrumentation for more accurate mapping from
binary samples back to IR, similar to instrumentation PGO, but without any
measure-able runtime overhead that is usually associated with instrumentation.


Is CSSPGO inherently dependent upon pseudo-probe or is it orthogonal? I hope
that it is the latter :)

[wenlei] They’re orthogonal. Context-sensitive SPGO can work without
pseudo-probe and still use dwarf. Our plan is to keep context-sensitive SPGO
working w/ and w/o pseudo-probe functionality-wise, but we only look at perf and
tune with the two combined.


We have a functioning implementation for the new CSSPGO now. Initial results on
SPEC2006 shows ~2% geomean performance win on top of AutoFDO (with MonoLTO and
NewPM) and ~4% .text size reduction at the same time.


Motivation
AutoFDO is a big success as it lowers the entry barrier for PGO significantly
while still delivering substantial performance boost. However, there’s still a
gap between AutoFDO and its instrumentation counterpart. From several failed
internal attempts to improve AutoFDO, we realized that the bottleneck of AutoFDO
lies in its profile quality. With the current level of profile quality, it’s
difficult to reap more performance win because good heuristics are often limited
by inferior profile. That prompted a systemic effort to investigate and improve
AutoFDO framework. Specifically, we’re trying to handle the two biggest sources
of profile quality issues:


  1.  AutoFDO relies on a limited context-sensitive profile collected based on
previous inlining. Therefore it can only replay or prune the previous inlining.
With the main CGSCC inliner, post-inline counts are not accurate due to scaling
of context-less profile, which affects the effectiveness of later passes such as
profile-guided code layout.

Acknowledge of the limitation here.


  1.
  2.  Dwarf line and discriminator info aren’t always well-maintained throughout
the compilation, thus using them as anchors to map binary samples back to the IR
can sometimes be inaccurate, which leads to inferior profile quality and limits
PGO performance.

I think we need more quantification of the impact of using debug information for
matching purposes:  How much performance are left on the table due to this, and
are they fixable issues or not.

[wenlei] The first table in the result section is comparing pseudo-probe with
AutoFDO and Instr. PGO, all with inlining turned off. So that’s a quantitative
assessment of the effectiveness of pseudo-probe. It’s hard to assess performance
benefit though, because PGO performance is a function of profile quality and
heuristic. Currently heuristics are tuned to cope with the profile quality we
have, so it may not do justice for profile quality improvements that
pseudo-probe brings us.

One example is how FDO inliner evaluates call site. It checks callee’s total
sample count instead of callee’s entry count. This is less than ideal, but we
couldn’t fix it due to profile quality issues – we can’t reliably get inlinee’s
entry count with dwarf based approach, see discussion in
https://reviews.llvm.org/D60086. That problem is solved with pseudo-probe, but
until we change the inliner, we won’t see perf win from that particular profile
quality improvement. There are other similar cases too, and that’s why we used
profile quality metric instead of performance to assess pseudo-probe.

Some of the issues may be fixable with dwarf info maintenance, but the
engineering cost to find and fix all issues are non-trivial. We think
maintaining anchor as IR is a more sustainable alternative than maintaining
anchor as metadata.



  1.
To lift the above limitations, we’d like to propose an alternative design that
consists of two components: 1) Context-sensitive sample PGO, 2) Sample to IR
mapping using pseudo probes. The goal is to further improve sample PGO
performance while maintaining usability and keeping training runtime overhead at
zero. In addition, we hope the CSSPGO framework can also open up opportunities
for new optimizations with more stringent requirements on profile quality.



CSSPGO is a very attractive optimization by itself.  Can you provide more
motivation for the pseudo probes?

[wenlei] One way to look at the combination of pseudo-probe and
context-sensitive sample PGO is that, the former brings sample PGO closer to
instrumentation PGO, and the latter to sample PGO is like the two-stage CSPGO,
or even post-link optimizer to instrumentation PGO. These are two orthogonal
problems that needs separate solutions.

There’re other secondary motivations for pseudo-probe as well beyond its profile
quality benefits that I didn’t mention earlier:
1). Stale profile detection. With line numbers, it’s hard to detect and react to
stale profile. Pseudo-probes are tied to blocks so it’s effectively using CFG as
carrier for profile, which makes stale profile detection easier.
2). Resilience to source changes. We’ve seen cases where deleting a single line
of comment caused a 8% perf regression for a large service because it completely
messed up profile annotation for a critical path. That will not happen with
pseudo-probe – any source change not altering CFG will be tolerated without perf
impact.
3). Possibility of offline count inference. We have an experiment that encodes
edges alongside with probes (blocks), so more sophisticated offline count
inference algorithm is possible to further improve profile quality. Our
algorithm researchers are working on new profile inference solution now.



Context-sensitive Sample PGO
The effectiveness of BOLT, Propeller and CSPGO all demonstrated the importance
of context-sensitive profile for PGO. However there are two limitations with the
existing approaches.

  1.  The current solutions focus on leveraging a context-sensitive profile to
attain an accurate post-inline profile that helps achieve a better code layout,
but do not use the context-sensitive profile to drive better inlining.
  2.  The current solutions involve multiple training processes and profiles
(e.g. a post-inline profile for CSPGO, or a post-link profile for BOLT and
Propeller), which incurs higher operational cost and complicates the build and
release workflow.
We propose a full context-sensitive sample profiling infrastructure that
utilizes both LBR and call stack samples at the same time to synthesize a
profile with a full context sensitivity. The key advantage is that rather than
relying on previous inlining or a separate profile, the profile collected with
the new approach will have full calling contexts recovered from both inlined and
not inlined call sites. To achieve an accurate post-inline profile, a separate
profile is no longer needed. Instead, the post-inline profile can be directly
derived from adjusting the input profile based on all inline decisions. The
richer context-sensitive profile also enables better inline decisions. The
infrastructure has two key components listed below.

Synthesizing context-sensitive LBR with a virtual unwinder
To make sample PGO’s input profile context aware, we need to know the call stack
of each LBR fall through path. That is done by sampling LBR and call stack
simultaneously. With that, each sample will contain a call stack in addition to
LBR entries. We use level 2 PEBS to control sampling skid so that the leaf frame
from stack sample aligns with leaf frame from LBR. The raw call stack sample
describes the calling context for the leaf LBR entry. In addition, by unwinding
“call” and “return” (including implicit ones from inlinee) from LBR entries
backwards on top of raw stack samples, we can recover the calling context for
each of the LBR entries from the sample, thus synthesizing context-sensitive LBR
profile.

We can then generate context-sensitive sample PGO profile using the
context-sensitive LBR profile. In the new profile, a function’s profile becomes
a collection of profiles, each representing a profile for a given calling
context.


Sounds good -- see the overhead question posted at the beginning.



Context-sensitive FDO/PGO framework in LLVM
In order to leverage context-sensitive profile for inlining, and to maintain
accurate post-inline counts, we introduced SampleContextTracker which is a layer
sitting in between input profile and the profile used to annotate CFG for
optimizations. We also introduced the notion of base profile which is the merged
profile for function’s profiles from any outstanding (not inlined) context, and
context profile which is a function's profile for a given calling context.
The framework includes four simple APIs for updating and query profiles:

Query API:

  *   getBaseSamplesFor: Query base profile by function name.
  *   getContextSamplesFor: Query context profile by calling context and
function name.
Update API:

  *   MarkContextSamplesInlined: When a function is inlined for a given calling
context, we need to mark the context profile for that context as inlined. This
is to make sure we don't include inlined context profile when synthesizing
the base profile.
  *   PromoteMergeContextSamplesTree: When a function is not inlined for a given
calling context, we need to promote the context profile tree to be top-level
context. This preserves the child context under that function so later inline
decisions for calls originating from that not inlined function will still be
driven by an accurate context profile.
These APIs are used by SampleProfileLoader’s inlining, for better inline
decisions and better post-inline counts. For optimal results, the new
infrastructure needs to work with a top-down FDO inliner. We added top-down FDO
inlining under a switch, and the switch is turned on by default in upstream
recently. There’re a few other improvements for the FDO inliner that we plan to
upstream soon.

The profile data should be usable by the SCC inliner as well. In the bottom up
inlining, as the function gets inline further up in the call chain, the inline
instance has few incoming contexts to merge.

[wenlei] Yes, we intentionally introduced the SampleContextTracker abstraction
that is decoupled from SampleProfileLoader, so it can work with both FDO inliner
and SCC inliner. But we expect FDO inliner to take over more inlining for CSSPGO
because the FDO inliner is no longer a replay inliner now. And it’s good as
top-down inline helps with specialization which is important for
context-sensitive inlining.



Pseudo-instrumentation for sample to IR mapping
Being able to profile production binaries is a key advantage of AutoFDO over
Instrumentation PGO, but it also comes with a big challenge. While using line
number and discriminator as anchor for profile mapping incurs zero run time
overhead for AutoFDO, it’s not as accurate as instrumented probes. This is
because the instrumented probes are part of the IR, rather than metadata
attached to the IR like !dbg. That has two implications: 1) it’s easier to
maintain IR than metadata for optimization passes; 2) probe blocks some CFG
transformations that can mess up profile correlation.

With the proposed pseudo instrumentation, we can achieve most of the benefit of
instrumentation PGO in little runtime overhead. We instrument each basic block
with a pseudo probe associated with the block Id. Unlike in PGO instrumentation
where a counter is implemented as a persisting operation such as atomic
read/write or runtime helper call, a pseudo probe is implemented as a dedicated
intrinsic call with IntrInaccessibleMemOnly flag. The intrinsic comes with most
of the semantics of a PGO counter but is much less optimization-intrusive.

The pseudo probe intrinsic calls are on the IR throughout the optimization and
code generation pipeline and are materialized as a piece of binary data stored
in a separate .pseudo_probe data section.

How are these information maintained? Blocks can be eliminated or cloned in many
optimization passes: jump threading, taildup, unrolling, peeling etc.  For
instance, how to handle the block that is merged into another? Does it lose
samples because of this?

[wenlei] They are just maintained as part of IR, like any other instructions,
without special care. The key difference is they’re part of IR instead of
metadata attached to IR. We can categorize relevant CFG transformations into 1)
duplication, 2) merge and removal.
For any duplication, tail/head dup, unrolling, probe will be duplicated along
with other instructions, and we don’t need duplication factor that was used by
dwarf-based approach, because counts from duplicated probes will be added
together naturally. For merge and removal, IntrInaccessibleMemOnly flag will
block it, similar to real probes.

Pseudo-probe is a framework that is tunable. Depending on the semantic we put on
the intrinsic, it can be as heavy as real probe, or as light as a label.
IntrInaccessibleMemOnly is a carefully chosen semantic based on our experiments
that balances run time overhead and profile quality – it doesn’t incur
measure-able overhead even though it still blocks merging and removal, we didn’t
see measure-able overhead from SPEC or a large internal workload. But the
profile quality improvement is measure-able as the 1st table in result section
shows.



The section is then used to map binary samples back to blocks of CFG during
profile generation. There are also no real machine instructions generated for a
pseudo probe and the.pseudo_probe section won’t be loaded into memory at
runtime, therefore they should incur very little runtime overhead. As a fact, we
see no measure-able performance impact from pseudo-instrumentation itself on
SPEC2006 or big internal workload.

How large are the probe sections?

[wenlei] About 10% of binary size, another 2% if we encode CFG edges in addition
to probes/blocks.


Pseudo-instrumentation integration and Pass Ordering
One implication from pseudo-probe instrumentation is that the profile is now
sensitive to CFG changes. We now defect stale profiles for sample PGO via CFG
checksum, instead of just using it. However, the potential downside is that CFG
may change between different versions of the compiler even if the source code is
unchanged. To solve that problem, we perform the pseudo instrumentation very
early in the pre-LTO pipeline, before any CFG transformation. This ensures that
the CFG instrumented and annotated is stable. We added SampleProfileProber that
performs the pseudo instrumentation and runs independent of profile annotation.

A new switch -fpseudo-probe-for-profiling is added to enable sample PGO with
pseudo instrumentation, similar to -fdebug-info-for-profiling for AutoFDO. Input
profile is still provided through the same switch used by today’s AutoFDO,
namely -fprofile-sample-use, and the profile loader will automatically decide
how to load and annotate profile depending on whether input profile is
dwarf-based or pseudo-probe based.


Can you compare the source change tolerance of pseudo probe based approach vs
debug info based approach?

[wenlei] Pseudo-probe should be more resilient to source changes. See my reply
for motivation of pseudo-probe. Pseudo-probe will be able to tolerate source
changes as long as they don’t alter CFG. On the contrary, changes that delete a
comment and shift line offset can cause perf churn with line-based approach.
We've been bitten by this a few times – people making comment only change
during holiday freeze only to find surprising perf regression due to AutoFDO 😊.
It also opens up possibility of fuzzy CFG matching when there’s a CFG mutation
due to source change to make it even more resilient.



New profile format and profile generation
We extend current profile format in order to be able to represent a full
context-sensitive profile and also encode pseudo-probe info. This is done
without drastically diverging from today’s AutoFDO profile format so that
existing tools and libraries can be reused with minor changes (e.g.
llvm-profdata, profiler reader and writer).

For a context-sensitive profile, we extend the profile format by changing the
function profile header line to include calling context in addition to function
name. With today’s AutoFDO, we have a single profile header for each function to
represent its accumulative profile. E.g. This is the profile header for foo,
with 1290 total samples, and 74 header samples.

foo:1290:74

For CSSPGO, we will have multiple profile headers for a single function’s
profile, each representing profile for a specific calling context as shown
below. CSSPGO profile header is bracketed to differentiate from today’s AutoFDO.

[main:12 @ bar:3 @ foo]:279:54
[main:19 @ zoo:7 @ foo]:1011:20


sounds good.



With calling context encoded in the function header, we no longer need a nested
function profile for inlinees. Instead, a context profile will be represented
uniformly using context strings in the function profile header, regardless of
whether the calls in the context are inlined or not. The flat structure makes
sure that context profile is easily indexable. The change is mostly transparent
to tools like llvm-profdata. Support for binary profile format has not been
added yet, but should be easy to do.



It is still useful to annotate (as least with comment line) that a profile is
for top level function or inline instance.

[wenlei] Agreed, and that’s in our plan too - we need that for tuning purpose.


For pseudo-probe, we repurposed the line to count map of AutoFDO profile to be
block Id to count map. This only changes the interpretation of profile content
rather than the representation, hence all reader/writer helpers can be reused.

There's a new profile generation tool, llvm-profgen, with the virtual winder
implemented for context-sensitive profiling, and uses the .pseudo_probe section
to map binary profile to pre-opt CFG profile. Since profile generation is a
critical piece of the workflow, we’d like to propose to include the tool as part
of LLVM, alongside with llvm-profdata.


Preliminary Results
To quantitatively assess profile quality improvement brought by
pseudo-instrumentation, we introduce a profile quality metric. We measure the
metric by first annotating an optimized binary with the MIR block execution
counts derived from a profile. The binary is then sampled and the counts are
compared against the annotation. The weighted relative delta is used as an
indicator for profile quality (lower is better).

Table below shows the profile quality metric for SPEC2006. We can see from the
numbers that the profile quality of pseudo-instrumentation sample PGO is much
better than AutoFDO and close to instrumentation PGO.

Profile quality metric
Baseline AutoFDO
Instrumentation PGO
Sample PGO w/ Pseudo Instrumentation
SPEC2006
24.58%
15.70%
16.21%


Instrumentation PGO does not have context sensitivity, so I would expect it
scores worse than CSSPGO. Do you know why it is better here?

[wenlei] This is for evaluating effectiveness of pseudo-probe exclusively. We
have all inlining turned off for this experiment, and this is without
context-sensitive profile for Sample PGO either, so the comparison should be
fair, and Instrumentation PGO should be the upper bound.



We also measured performance and code size on SPEC2006 with CSSPGO. The
measurement was done with MonoLTO and new pass manager, with tuning for FDO
inliner to accommodate context-sensitive profile, and used training dataset for
both pass1 and pass2. The result shows ~2% performance win on top of today’s
AutoFDO, with ~4% .text reduction, see table below.

SPEC2006
Performance
Code Size
AutoFDO over LTO
CSSPGO
Over LTO
CSSPGO over AutoFDO
AutoFDO over LTO
CSSPGO
Over LTO
CSSPGO over AutoFDO
Geomean Delta %
6.80%
8.70%
2.04%
11.17%
6.66%
4.06%

While the SPEC2006 benchmark suite is different from large workloads, we think
the results demonstrated the potential of CSSPGO and served its purpose for
proof of concept. We plan to continue tuning and start to evaluate larger
internal workloads soon, and we’d like to upstream our work. Feedbacks are
welcomed!



What is the performance win with peudo-probe alone?

[wenlei] We don’t have numbers for pseudo-probe along. As I mentioned earlier,
profile quality improvement may not translate directly to perf win without
heuristic changes. That’s why we evaluate pseudo-probe exclusively with profile
quality metric. The hope is that it will open up opportunity for better
optimizations. E.g. it could potentially help the Machine Function Splitting
pass too. That said, pseudo-probe does bring extra win for CSSPGO comparing to
line-based CSSPGO for some benchmarks, but we didn’t tune CSSPGO with line-based
profile, so we didn’t aggregate numbers as the comparison isn’t fair either.


thanks,

David



Thanks,
Wenlei & Hongtao


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Thanks for the proposal and the performance improvement over existing
AutoFDO is impressive.

On Fri, Aug 7, 2020 at 11:28 AM Wenlei He <wenlei at fb.com> wrote:
> Hi All,
>
> Our team at Facebook is building a new context-sensitive Sample PGO as an
> alternative to the existing AutoFDO. We’d like to share our motivation,
> propose a new design, and reveal preliminary results on benchmarks. We will
> refer to the proposed design as CSSPGO in this RFC.
>
>
>
> The new CSSPGO leverages simultaneous LBR and stack sampling to construct
> a full context-sensitive profile. It doesn’t rely on previous inlining like
> today’s AutoFDO to get context-sensitive profile, and it also doesn’t need
> a separate post-inline context-sensitive profile like CSPGO. In addition,
> we introduced pseudo-instrumentation for more accurate mapping from binary
> samples back to IR, similar to instrumentation PGO, but without any
> measure-able runtime overhead that is usually associated with
> instrumentation.
>
>
>
> We have a functioning implementation for the new CSSPGO now. Initial
> results on SPEC2006 shows ~2% geomean performance win on top of AutoFDO
> (with MonoLTO and NewPM) and ~4% .text size reduction at the same time.
>
>
>
>
>
> *Motivation*
>
> AutoFDO is a big success as it lowers the entry barrier for PGO
> significantly while still delivering substantial performance boost.
> However, there’s still a gap between AutoFDO and its instrumentation
> counterpart. From several failed internal attempts to improve AutoFDO, we
> realized that the bottleneck of AutoFDO lies in its profile quality. With
> the current level of profile quality, it’s difficult to reap more
> performance win because good heuristics are often limited by inferior
> profile. That prompted a systemic effort to investigate and improve AutoFDO
> framework. Specifically, we’re trying to handle the two biggest sources of
> profile quality issues:
>
>
>
>    1. AutoFDO relies on a limited context-sensitive profile collected
>    based on previous inlining. Therefore it can only replay or prune the
>    previous inlining. With the main CGSCC inliner, post-inline counts are
not
>    accurate due to scaling of context-less profile, which affects the
>    effectiveness of later passes such as profile-guided code layout.
>    2. Dwarf line and discriminator info aren’t always well-maintained
>    throughout the compilation, thus using them as anchors to map binary
>    samples back to the IR can sometimes be inaccurate, which leads to
inferior
>    profile quality and limits PGO performance.
>
> Acknowledge to issues. We also found an issue that current AFDO profile
doesn't keep edge information and that leads to nonoptimal profile in some
cases. Since profile format is needed to be redesigned for component 1, I
am thinking whether it is possible to extend the profile format in a way so
it can incorporate edge information as well.

About pseudo probe, seemly you doesn't mention in this proposal but does it
still provides the ability to solve the source drift issue you mentioned
before? If it does, how it is achieved?

>
>    1.
>
> To lift the above limitations, we’d like to propose an alternative design
> that consists of two components: 1) Context-sensitive sample PGO, 2) Sample
> to IR mapping using pseudo probes. The goal is to further improve sample
> PGO performance while maintaining usability and keeping training runtime
> overhead at zero. In addition, we hope the CSSPGO framework can also open
> up opportunities for new optimizations with more stringent requirements on
> profile quality.
>
I like both ideas, and those two components can be orthogonal? For the
first component, I hope the existing debug information based AutoFDO can be
benefited from it as well, with some extension to the current profile
format.

>
>
>
>
> *Context-sensitive Sample PGO*
>
> The effectiveness of BOLT, Propeller and CSPGO all demonstrated the
> importance of context-sensitive profile for PGO. However there are two
> limitations with the existing approaches.
>
>    1. The current solutions focus on leveraging a context-sensitive
>    profile to attain an accurate post-inline profile that helps achieve a
>    better code layout, but do not use the context-sensitive profile to
drive
>    better inlining.
>    2. The current solutions involve multiple training processes and
>    profiles (e.g. a post-inline profile for CSPGO, or a post-link profile
for
>    BOLT and Propeller), which incurs higher operational cost and
complicates
>    the build and release workflow.
>
> We propose a full context-sensitive sample profiling infrastructure that
> utilizes both LBR and call stack samples at the same time to synthesize a
> profile with a full context sensitivity. The key advantage is that rather
> than relying on previous inlining or a separate profile, the profile
> collected with the new approach will have full calling contexts recovered
> from both inlined and not inlined call sites. To achieve an accurate
> post-inline profile, a separate profile is no longer needed. Instead, the
> post-inline profile can be directly derived from adjusting the input
> profile based on all inline decisions. The richer context-sensitive profile
> also enables better inline decisions. The infrastructure has two key
> components listed below.
>
>
>
> *Synthesizing context-sensitive LBR with a virtual unwinder*
>
> To make sample PGO’s input profile context aware, we need to know the call
> stack of each LBR fall through path. That is done by sampling LBR and call
> stack simultaneously. With that, each sample will contain a call stack in
> addition to LBR entries. We use level 2 PEBS to control sampling skid so
> that the leaf frame from stack sample aligns with leaf frame from LBR. The
> raw call stack sample describes the calling context for the leaf LBR entry.
> In addition, by unwinding “call” and “return” (including implicit ones from
> inlinee) from LBR entries backwards on top of raw stack samples, we can
> recover the calling context for each of the LBR entries from the sample,
> thus synthesizing context-sensitive LBR profile.
>
>
>
What if the stack unwinding is not intact? For example, tail call
optimization may cause unwinding issue currently in perf. framepointer or
call frame information may not be properly maintained.

> We can then generate context-sensitive sample PGO profile using the
> context-sensitive LBR profile. In the new profile, a function’s profile
> becomes a collection of profiles, each representing a profile for a given
> calling context.
>
Will the profile size be significantly larger?

>
>
> *Context-sensitive FDO/PGO framework in LLVM*
>
> In order to leverage context-sensitive profile for inlining, and to
> maintain accurate post-inline counts, we introduced SampleContextTracker
which
> is a layer sitting in between input profile and the profile used to
> annotate CFG for optimizations. We also introduced the notion of base
> profile which is the merged profile for function’s profiles from any
> outstanding (not inlined) context, and context profile which is a
> function's profile for a given calling context. The framework includes
four
> simple APIs for updating and query profiles:
>
>
>
> Query API:
>
>    - getBaseSamplesFor: Query base profile by function name.
>    - getContextSamplesFor: Query context profile by calling context and
>    function name.
>
> Update API:
>
>    - MarkContextSamplesInlined: When a function is inlined for a given
>    calling context, we need to mark the context profile for that context as
>    inlined. This is to make sure we don't include inlined context
profile when
>    synthesizing the base profile.
>    - PromoteMergeContextSamplesTree: When a function is not inlined for a
>    given calling context, we need to promote the context profile tree to be
>    top-level context. This preserves the child context under that function
so
>    later inline decisions for calls originating from that not inlined
function
>    will still be driven by an accurate context profile.
>
> These APIs are used by SampleProfileLoader’s inlining, for better inline
> decisions and better post-inline counts. For optimal results, the new
> infrastructure needs to work with a top-down FDO inliner. We added top-down
> FDO inlining under a switch, and the switch is turned on by default in
> upstream recently. There’re a few other improvements for the FDO inliner
> that we plan to upstream soon.
>
>
>
>
>
> *Pseudo-instrumentation for sample to IR mapping*
>
> Being able to profile production binaries is a key advantage of AutoFDO
> over Instrumentation PGO, but it also comes with a big challenge. While
> using line number and discriminator as anchor for profile mapping incurs
> zero run time overhead for AutoFDO, it’s not as accurate as instrumented
> probes. This is because the instrumented probes are part of the IR, rather
> than metadata attached to the IR like !dbg. That has two implications: 1)
> it’s easier to maintain IR than metadata for optimization passes; 2) probe
> blocks some CFG transformations that can mess up profile correlation.
>
>
>
> With the proposed pseudo instrumentation, we can achieve most of the
> benefit of instrumentation PGO in little runtime overhead. We instrument
> each basic block with a pseudo probe associated with the block Id. Unlike
> in PGO instrumentation where a counter is implemented as a persisting
> operation such as atomic read/write or runtime helper call, a pseudo probe
> is implemented as a dedicated intrinsic call with IntrInaccessibleMemOnly
flag.
> The intrinsic comes with most of the semantics of a PGO counter but is
> much less optimization-intrusive.
>
>
>
> The pseudo probe intrinsic calls are on the IR throughout the optimization
> and code generation pipeline and are materialized as a piece of binary data
> stored in a separate .pseudo_probe data section. The section is then used
> to map binary samples back to blocks of CFG during profile generation.
> There are also no real machine instructions generated for a pseudo probe
> and the.pseudo_probe section won’t be loaded into memory at runtime,
> therefore they should incur very little runtime overhead. As a fact, we see
> no measure-able performance impact from pseudo-instrumentation itself on
> SPEC2006 or big internal workload.
>
>
> *Pseudo-instrumentation integration and Pass Ordering*
>
> One implication from pseudo-probe instrumentation is that the profile is
> now sensitive to CFG changes. We now defect stale profiles for sample PGO
> via CFG checksum, instead of just using it. However, the potential downside
> is that CFG may change between different versions of the compiler even if
> the source code is unchanged. To solve that problem, we perform the pseudo
> instrumentation very early in the pre-LTO pipeline, before any CFG
> transformation. This ensures that the CFG instrumented and annotated is
> stable. We added SampleProfileProber that performs the pseudo
> instrumentation and runs independent of profile annotation.
>
>
>
> A new switch -fpseudo-probe-for-profiling is added to enable sample PGO
> with pseudo instrumentation, similar to -fdebug-info-for-profiling for
> AutoFDO. Input profile is still provided through the same switch used by
> today’s AutoFDO, namely -fprofile-sample-use, and the profile loader will
> automatically decide how to load and annotate profile depending on whether
> input profile is dwarf-based or pseudo-probe based.
>
>
>
>
>
> *New profile format and profile generation*
>
> We extend current profile format in order to be able to represent a full
> context-sensitive profile and also encode pseudo-probe info. This is done
> without drastically diverging from today’s AutoFDO profile format so that
> existing tools and libraries can be reused with minor changes (e.g.
> llvm-profdata, profiler reader and writer).
>
>
>
> For a context-sensitive profile, we extend the profile format by changing
> the function profile header line to include calling context in addition to
> function name. With today’s AutoFDO, we have a single profile header for
> each function to represent its accumulative profile. E.g. This is the
> profile header for foo, with 1290 total samples, and 74 header samples.
>
>
>
> foo:1290:74
>
>
>
> For CSSPGO, we will have multiple profile headers for a single function’s
> profile, each representing profile for a specific calling context as shown
> below. CSSPGO profile header is bracketed to differentiate from today’s
> AutoFDO.
>
>
>
> [main:12 @ bar:3 @ foo]:279:54
>
> [main:19 @ zoo:7 @ foo]:1011:20
>
>
>
> With calling context encoded in the function header, we no longer need a
> nested function profile for inlinees. Instead, a context profile will be
> represented uniformly using context strings in the function profile header,
> regardless of whether the calls in the context are inlined or not. The flat
> structure makes sure that context profile is easily indexable. The change
> is mostly transparent to tools like llvm-profdata. Support for binary
> profile format has not been added yet, but should be easy to do.
>
>
>
> For pseudo-probe, we repurposed the line to count map of AutoFDO profile
> to be block Id to count map. This only changes the interpretation of
> profile content rather than the representation, hence all reader/writer
> helpers can be reused.
>
>
>
> There's a new profile generation tool, llvm-profgen, with the virtual
> winder implemented for context-sensitive profiling, and uses the
> .pseudo_probe section to map binary profile to pre-opt CFG profile. Since
> profile generation is a critical piece of the workflow, we’d like to
> propose to include the tool as part of LLVM, alongside with llvm-profdata.
>
>
>
>
>
> *Preliminary Results*
>
> To quantitatively assess profile quality improvement brought by
> pseudo-instrumentation, we introduce a profile quality metric. We measure
> the metric by first annotating an optimized binary with the MIR block
> execution counts derived from a profile. The binary is then sampled and the
> counts are compared against the annotation. The weighted relative delta is
> used as an indicator for profile quality (lower is better).
>
>
>
> Table below shows the profile quality metric for SPEC2006. We can see from
> the numbers that the profile quality of pseudo-instrumentation sample PGO
> is much better than AutoFDO and close to instrumentation PGO.
>
>
>
> Profile quality metric
>
> Baseline AutoFDO
>
> Instrumentation PGO
>
> Sample PGO w/ Pseudo Instrumentation
>
> SPEC2006
>
> 24.58%
>
> 15.70%
>
> 16.21%
>
>
>
> We also measured performance and code size on SPEC2006 with CSSPGO. The
> measurement was done with MonoLTO and new pass manager, with tuning for FDO
> inliner to accommodate context-sensitive profile, and used training dataset
> for both pass1 and pass2. The result shows ~2% performance win on top of
> today’s AutoFDO, with ~4% .text reduction, see table below.
>
>
>
> SPEC2006
>
> Performance
>
> Code Size
>
> AutoFDO over LTO
>
> CSSPGO
>
> Over LTO
>
> CSSPGO over AutoFDO
>
> AutoFDO over LTO
>
> CSSPGO
>
> Over LTO
>
> CSSPGO over AutoFDO
>
> Geomean Delta %
>
> 6.80%
>
> 8.70%
>
> 2.04%
>
> 11.17%
>
> 6.66%
>
> 4.06%
>
>
>
> While the SPEC2006 benchmark suite is different from large workloads, we
> think the results demonstrated the potential of CSSPGO and served its
> purpose for proof of concept. We plan to continue tuning and start to
> evaluate larger internal workloads soon, and we’d like to upstream our
> work. Feedbacks are welcomed!
>
>
>
>
>
> Thanks,
>
> Wenlei & Hongtao
>
>
>
>
>
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Thanks for the feedbacks and questions, Wei. See my replies inline.

From: Wei Mi <wmi at google.com>
Date: Friday, August 7, 2020 at 5:32 PM
To: Wenlei He <wenlei at fb.com>
Cc: "llvm-dev at lists.llvm.org" <llvm-dev at lists.llvm.org>,
Xinliang David Li <davidxl at google.com>, Hongtao Yu <hoy at
fb.com>
Subject: Re: [RFC] Context-sensitive Sample PGO with Pseudo-Instrumentation

Thanks for the proposal and the performance improvement over existing AutoFDO is
impressive.

On Fri, Aug 7, 2020 at 11:28 AM Wenlei He <wenlei at fb.com<mailto:wenlei
at fb.com>> wrote:
Hi All,
Our team at Facebook is building a new context-sensitive Sample PGO as an
alternative to the existing AutoFDO. We’d like to share our motivation, propose
a new design, and reveal preliminary results on benchmarks. We will refer to the
proposed design as CSSPGO in this RFC.

The new CSSPGO leverages simultaneous LBR and stack sampling to construct a full
context-sensitive profile. It doesn’t rely on previous inlining like today’s
AutoFDO to get context-sensitive profile, and it also doesn’t need a separate
post-inline context-sensitive profile like CSPGO. In addition, we introduced
pseudo-instrumentation for more accurate mapping from binary samples back to IR,
similar to instrumentation PGO, but without any measure-able runtime overhead
that is usually associated with instrumentation.


We have a functioning implementation for the new CSSPGO now. Initial results on
SPEC2006 shows ~2% geomean performance win on top of AutoFDO (with MonoLTO and
NewPM) and ~4% .text size reduction at the same time.


Motivation
AutoFDO is a big success as it lowers the entry barrier for PGO significantly
while still delivering substantial performance boost. However, there’s still a
gap between AutoFDO and its instrumentation counterpart. From several failed
internal attempts to improve AutoFDO, we realized that the bottleneck of AutoFDO
lies in its profile quality. With the current level of profile quality, it’s
difficult to reap more performance win because good heuristics are often limited
by inferior profile. That prompted a systemic effort to investigate and improve
AutoFDO framework. Specifically, we’re trying to handle the two biggest sources
of profile quality issues:


  1.  AutoFDO relies on a limited context-sensitive profile collected based on
previous inlining. Therefore it can only replay or prune the previous inlining.
With the main CGSCC inliner, post-inline counts are not accurate due to scaling
of context-less profile, which affects the effectiveness of later passes such as
profile-guided code layout.
  2.  Dwarf line and discriminator info aren’t always well-maintained throughout
the compilation, thus using them as anchors to map binary samples back to the IR
can sometimes be inaccurate, which leads to inferior profile quality and limits
PGO performance.
Acknowledge to issues. We also found an issue that current AFDO profile
doesn't keep edge information and that leads to nonoptimal profile in some
cases. Since profile format is needed to be redesigned for component 1, I am
thinking whether it is possible to extend the profile format in a way so it can
incorporate edge information as well.

[wenlei] Yes, we have implemented an “add-on” that could encode edges in
addition to probes/blocks in .pseudo_probe section, and we also have a way to
represent edges in new profile. But that’s not critical for the framework and
initial evaluation, which is why it’s not mentioned in this RFC. We did that
mostly for enabling offline count inference algorithm experiments. We will share
more details on that later. Curious what is the issue you saw due to lack of
edge info?

About pseudo probe, seemly you doesn't mention in this proposal but does it
still provides the ability to solve the source drift issue you mentioned before?
If it does, how it is achieved?

[wenlei] Pseudo-probe handles source drift reasonably well, and has good
resilience against source changes. It can tolerate any source changes that
doesn’t alter CFG, so the issues we ran into with line-based approach where
deleting a comment lead to big regression isn’t going to happen with
pseudo-probe. For changes that does alter CFG, we could also employ fuzzy CFG
matching in future. Bottom line is using probe and CFG as profile carrier
inherently has richer info, so it’s easier for PGO to see through the source
changes and can still make sense of a stale profile. (We didn’t expand on the
source drift issue in initial RFC, but I just mentioned that part in my reply to
David, as secondary motivation for pseudo-probe.)



  1.
To lift the above limitations, we’d like to propose an alternative design that
consists of two components: 1) Context-sensitive sample PGO, 2) Sample to IR
mapping using pseudo probes. The goal is to further improve sample PGO
performance while maintaining usability and keeping training runtime overhead at
zero. In addition, we hope the CSSPGO framework can also open up opportunities
for new optimizations with more stringent requirements on profile quality.

I like both ideas, and those two components can be orthogonal? For the first
component, I hope the existing debug information based AutoFDO can be benefited
from it as well, with some extension to the current profile format.

[wenlei] Thanks. Yes, they’re orthogonal. But we need both for peak performance,
and we want to focus tuning effort on the combination. Also see my reply to
David’s questions.



Context-sensitive Sample PGO
The effectiveness of BOLT, Propeller and CSPGO all demonstrated the importance
of context-sensitive profile for PGO. However there are two limitations with the
existing approaches.

  1.  The current solutions focus on leveraging a context-sensitive profile to
attain an accurate post-inline profile that helps achieve a better code layout,
but do not use the context-sensitive profile to drive better inlining.
  2.  The current solutions involve multiple training processes and profiles
(e.g. a post-inline profile for CSPGO, or a post-link profile for BOLT and
Propeller), which incurs higher operational cost and complicates the build and
release workflow.
We propose a full context-sensitive sample profiling infrastructure that
utilizes both LBR and call stack samples at the same time to synthesize a
profile with a full context sensitivity. The key advantage is that rather than
relying on previous inlining or a separate profile, the profile collected with
the new approach will have full calling contexts recovered from both inlined and
not inlined call sites. To achieve an accurate post-inline profile, a separate
profile is no longer needed. Instead, the post-inline profile can be directly
derived from adjusting the input profile based on all inline decisions. The
richer context-sensitive profile also enables better inline decisions. The
infrastructure has two key components listed below.

Synthesizing context-sensitive LBR with a virtual unwinder
To make sample PGO’s input profile context aware, we need to know the call stack
of each LBR fall through path. That is done by sampling LBR and call stack
simultaneously. With that, each sample will contain a call stack in addition to
LBR entries. We use level 2 PEBS to control sampling skid so that the leaf frame
from stack sample aligns with leaf frame from LBR. The raw call stack sample
describes the calling context for the leaf LBR entry. In addition, by unwinding
“call” and “return” (including implicit ones from inlinee) from LBR entries
backwards on top of raw stack samples, we can recover the calling context for
each of the LBR entries from the sample, thus synthesizing context-sensitive LBR
profile.


What if the stack unwinding is not intact? For example, tail call optimization
may cause unwinding issue currently in perf. framepointer or call frame
information may not be properly maintained.

[wenlei] That’s a good question. Currently, we have frame pointer optimization
(FPO) and tail call optimization disabled for experiments. FPO is disabled for
our production builds as well, so it’s not a problem for us. For tail call,
we’ll need to evaluate the cost-benefit and see what we can do. We know there’s
heuristic to recover single missing frame due to tail call, which we haven’t
implemented yet; beyond that, perhaps we can revisit leveraging dwarf unwinding,
or live with either imperfect profile or tail call disabled. We also implemented
special case for sample that lands in prolog and epilog where frame chain isn’t
ready. However, even with both FPO and tail call disabled, we still see
truncated stack samples, which we’re investigating. But the perf results are
with profiles containing truncated/imperfect stack samples, so it looks like a
small portion of imperfect profile doesn’t impact the effectiveness of CSSPGO
too much.

We can then generate context-sensitive sample PGO profile using the
context-sensitive LBR profile. In the new profile, a function’s profile becomes
a collection of profiles, each representing a profile for a given calling
context.

Will the profile size be significantly larger?

[wenlei] Currently text CS profile is 1-10x larger. But there’re ways to bring
them it and we’re working on it: 1) trim cold context, 2) leverage compression
from extended binary (should be effective for context strings that has
duplicated long C++ mangle names), 3) consider fixed-length integer context
presentation, e.g. rolling hash. Also see my replies to David’s question on
this.

Context-sensitive FDO/PGO framework in LLVM
In order to leverage context-sensitive profile for inlining, and to maintain
accurate post-inline counts, we introduced SampleContextTracker which is a layer
sitting in between input profile and the profile used to annotate CFG for
optimizations. We also introduced the notion of base profile which is the merged
profile for function’s profiles from any outstanding (not inlined) context, and
context profile which is a function's profile for a given calling context.
The framework includes four simple APIs for updating and query profiles:

Query API:

  *   getBaseSamplesFor: Query base profile by function name.
  *   getContextSamplesFor: Query context profile by calling context and
function name.
Update API:

  *   MarkContextSamplesInlined: When a function is inlined for a given calling
context, we need to mark the context profile for that context as inlined. This
is to make sure we don't include inlined context profile when synthesizing
the base profile.
  *   PromoteMergeContextSamplesTree: When a function is not inlined for a given
calling context, we need to promote the context profile tree to be top-level
context. This preserves the child context under that function so later inline
decisions for calls originating from that not inlined function will still be
driven by an accurate context profile.
These APIs are used by SampleProfileLoader’s inlining, for better inline
decisions and better post-inline counts. For optimal results, the new
infrastructure needs to work with a top-down FDO inliner. We added top-down FDO
inlining under a switch, and the switch is turned on by default in upstream
recently. There’re a few other improvements for the FDO inliner that we plan to
upstream soon.


Pseudo-instrumentation for sample to IR mapping
Being able to profile production binaries is a key advantage of AutoFDO over
Instrumentation PGO, but it also comes with a big challenge. While using line
number and discriminator as anchor for profile mapping incurs zero run time
overhead for AutoFDO, it’s not as accurate as instrumented probes. This is
because the instrumented probes are part of the IR, rather than metadata
attached to the IR like !dbg. That has two implications: 1) it’s easier to
maintain IR than metadata for optimization passes; 2) probe blocks some CFG
transformations that can mess up profile correlation.

With the proposed pseudo instrumentation, we can achieve most of the benefit of
instrumentation PGO in little runtime overhead. We instrument each basic block
with a pseudo probe associated with the block Id. Unlike in PGO instrumentation
where a counter is implemented as a persisting operation such as atomic
read/write or runtime helper call, a pseudo probe is implemented as a dedicated
intrinsic call with IntrInaccessibleMemOnly flag. The intrinsic comes with most
of the semantics of a PGO counter but is much less optimization-intrusive.

The pseudo probe intrinsic calls are on the IR throughout the optimization and
code generation pipeline and are materialized as a piece of binary data stored
in a separate .pseudo_probe data section. The section is then used to map binary
samples back to blocks of CFG during profile generation. There are also no real
machine instructions generated for a pseudo probe and the.pseudo_probe section
won’t be loaded into memory at runtime, therefore they should incur very little
runtime overhead. As a fact, we see no measure-able performance impact from
pseudo-instrumentation itself on SPEC2006 or big internal workload.

Pseudo-instrumentation integration and Pass Ordering
One implication from pseudo-probe instrumentation is that the profile is now
sensitive to CFG changes. We now defect stale profiles for sample PGO via CFG
checksum, instead of just using it. However, the potential downside is that CFG
may change between different versions of the compiler even if the source code is
unchanged. To solve that problem, we perform the pseudo instrumentation very
early in the pre-LTO pipeline, before any CFG transformation. This ensures that
the CFG instrumented and annotated is stable. We added SampleProfileProber that
performs the pseudo instrumentation and runs independent of profile annotation.

A new switch -fpseudo-probe-for-profiling is added to enable sample PGO with
pseudo instrumentation, similar to -fdebug-info-for-profiling for AutoFDO. Input
profile is still provided through the same switch used by today’s AutoFDO,
namely -fprofile-sample-use, and the profile loader will automatically decide
how to load and annotate profile depending on whether input profile is
dwarf-based or pseudo-probe based.


New profile format and profile generation
We extend current profile format in order to be able to represent a full
context-sensitive profile and also encode pseudo-probe info. This is done
without drastically diverging from today’s AutoFDO profile format so that
existing tools and libraries can be reused with minor changes (e.g.
llvm-profdata, profiler reader and writer).

For a context-sensitive profile, we extend the profile format by changing the
function profile header line to include calling context in addition to function
name. With today’s AutoFDO, we have a single profile header for each function to
represent its accumulative profile. E.g. This is the profile header for foo,
with 1290 total samples, and 74 header samples.

foo:1290:74

For CSSPGO, we will have multiple profile headers for a single function’s
profile, each representing profile for a specific calling context as shown
below. CSSPGO profile header is bracketed to differentiate from today’s AutoFDO.

[main:12 @ bar:3 @ foo]:279:54
[main:19 @ zoo:7 @ foo]:1011:20

With calling context encoded in the function header, we no longer need a nested
function profile for inlinees. Instead, a context profile will be represented
uniformly using context strings in the function profile header, regardless of
whether the calls in the context are inlined or not. The flat structure makes
sure that context profile is easily indexable. The change is mostly transparent
to tools like llvm-profdata. Support for binary profile format has not been
added yet, but should be easy to do.

For pseudo-probe, we repurposed the line to count map of AutoFDO profile to be
block Id to count map. This only changes the interpretation of profile content
rather than the representation, hence all reader/writer helpers can be reused.

There's a new profile generation tool, llvm-profgen, with the virtual winder
implemented for context-sensitive profiling, and uses the .pseudo_probe section
to map binary profile to pre-opt CFG profile. Since profile generation is a
critical piece of the workflow, we’d like to propose to include the tool as part
of LLVM, alongside with llvm-profdata.


Preliminary Results
To quantitatively assess profile quality improvement brought by
pseudo-instrumentation, we introduce a profile quality metric. We measure the
metric by first annotating an optimized binary with the MIR block execution
counts derived from a profile. The binary is then sampled and the counts are
compared against the annotation. The weighted relative delta is used as an
indicator for profile quality (lower is better).

Table below shows the profile quality metric for SPEC2006. We can see from the
numbers that the profile quality of pseudo-instrumentation sample PGO is much
better than AutoFDO and close to instrumentation PGO.

Profile quality metric
Baseline AutoFDO
Instrumentation PGO
Sample PGO w/ Pseudo Instrumentation
SPEC2006
24.58%
15.70%
16.21%

We also measured performance and code size on SPEC2006 with CSSPGO. The
measurement was done with MonoLTO and new pass manager, with tuning for FDO
inliner to accommodate context-sensitive profile, and used training dataset for
both pass1 and pass2. The result shows ~2% performance win on top of today’s
AutoFDO, with ~4% .text reduction, see table below.

SPEC2006
Performance
Code Size
AutoFDO over LTO
CSSPGO
Over LTO
CSSPGO over AutoFDO
AutoFDO over LTO
CSSPGO
Over LTO
CSSPGO over AutoFDO
Geomean Delta %
6.80%
8.70%
2.04%
11.17%
6.66%
4.06%

While the SPEC2006 benchmark suite is different from large workloads, we think
the results demonstrated the potential of CSSPGO and served its purpose for
proof of concept. We plan to continue tuning and start to evaluate larger
internal workloads soon, and we’d like to upstream our work. Feedbacks are
welcomed!


Thanks,
Wenlei & Hongtao


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