[BOLT] using jump weights in profi

We want to use profile inference (profi) in BOLT for stale profile matching.
This is the second change for existing usages of profi (e.g., CSSPGO):

(i) Added the

[BOLT] using jump weights in profi

We want to use profile inference (profi) in BOLT for stale profile matching.
This is the second change for existing usages of profi (e.g., CSSPGO):

(i) Added the ability to provide (estimated) jump weights for the algorithm. The
goal of the algorithm is to create a valid control flow for a given function
(that is, one in which incoming counts equal outgoing counts for every basic
block while minimally modifying the original input block and jump weights). The
input jump weights will be provided based on collected LBR profiles in BOLT.

(ii) Added the corresponding options to ProfiParams.

(iii) Slightly modified / simplified the construction of the flow network in profi
so as it utilizes fewer auxiliary nodes. This is done by introducing parallel
edges to the network (which is supported by MMF) and reduces the size of the
network from 3*|V| to 2*|V|, where |V| is the number of basic blocks in the
function.

**Inference (profile quality) impact:**
The diff is supposed to be a no-op for the inferred counts. However, our
implementation of MCF is not fully deterministic and might return different
results depending on the input network model. Since we changed the model
construction, there are a few differences in comparison to the original
implementation. I checked manually on an internal benchmark and see a minor
difference (+/- 1 count for certain basic blocks) in just a dozen of instances
(out of 10000+ input functions). Hence, the diff is highly unlikely to have an
impact for existing prod workloads.

**Runtime impact:**
I measure up to 10% speedup for block-only (ie CSSPGO/AutoFDO) inference and up
to 50% speedup for block+jump inference (ie BOLT) in comparison to the original
unoptimized version.

Reviewed By: hoy

Differential Revision: https://reviews.llvm.org/D139870

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[BOLT] introducing profi params

We want to use profile inference (**profi**) in BOLT for stale profile matching.
To this end, I am making a few changes modifying the interface of the algorithm.
This

[BOLT] introducing profi params

We want to use profile inference (**profi**) in BOLT for stale profile matching.
To this end, I am making a few changes modifying the interface of the algorithm.
This is the first change for existing usages of profi (e.g., CSSPGO):
- introducing an object holding the algorithmic parameters;
- some renaming of existing options;
- dropped unused option, SampleProfileInferEntryCount, as we don't plan to change its default value;
- no changes in the output / tests.

Reviewed By: hoy

Differential Revision: https://reviews.llvm.org/D134756

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[test] Change test/SampleProfile to use opaque pointers

[CSSPGO] Even flow distribution

Differential Revision: https://reviews.llvm.org/D118640

[SampleProf][Inliner] Add an option to turn off inliner in sample-profile pass.

Use case is offline evaluation (for inliner effectiveness) or debugging.

Differential Revision: https://reviews.llvm.

[SampleProf][Inliner] Add an option to turn off inliner in sample-profile pass.

Use case is offline evaluation (for inliner effectiveness) or debugging.

Differential Revision: https://reviews.llvm.org/D120344

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[CSSPGO] Turn on Profi by default

As titled.

Reviewed By: wenlei, wlei

Differential Revision: https://reviews.llvm.org/D115011

Revert "[NFC] remove explicit default value for strboolattr attribute in tests"

This reverts commit bda6e5bee04c75b1f1332b4fd1ac4e8ef6c3c247.

See https://lab.llvm.org/buildbot/#/builders/109/builds

Revert "[NFC] remove explicit default value for strboolattr attribute in tests"

This reverts commit bda6e5bee04c75b1f1332b4fd1ac4e8ef6c3c247.

See https://lab.llvm.org/buildbot/#/builders/109/builds/15424 for instance

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[NFC] remove explicit default value for strboolattr attribute in tests

Since d6de1e1a71406c75a4ea4d5a2fe84289f07ea3a1, no attributes is quivalent to
setting attribute to false.

This is a preliminar

[NFC] remove explicit default value for strboolattr attribute in tests

Since d6de1e1a71406c75a4ea4d5a2fe84289f07ea3a1, no attributes is quivalent to
setting attribute to false.

This is a preliminary commit for https://reviews.llvm.org/D99080

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[CSSPGO] Call site prioritized inlining for sample PGO

This change implemented call site prioritized BFS profile guided inlining for sample profile loader. The new inlining strategy maximize the ben

[CSSPGO] Call site prioritized inlining for sample PGO

This change implemented call site prioritized BFS profile guided inlining for sample profile loader. The new inlining strategy maximize the benefit of context-sensitive profile as mentioned in the follow up discussion of CSSPGO RFC. The change will not affect today's AutoFDO as it's opt-in. CSSPGO now defaults to the new FDO inliner, but can fall back to today's replay inliner using a switch (`-sample-profile-prioritized-inline=0`).

Motivation

With baseline AutoFDO, the inliner in sample profile loader only replays previous inlining, and the use of profile is only for pruning previous inlining that turned out to be cold. Due to the nature of replay, the FDO inliner is simple with hotness being the only decision factor. It has the following limitations that we're improving now for CSSPGO.
- It doesn't take inline candidate size into account. Since it's doing replay, the size growth is bounded by previous CGSCC inlining. With context-sensitive profile, FDO inliner is no longer limited by previous inlining, so we need to take size into account to avoid significant size bloat.
- The way it looks at hotness is not accurate. It uses total samples in an inlinee as proxy for hotness, while what really matters for an inline decision is the call site count. This is an unfortunate fall back because call site count and callee entry count are not reliable due to dwarf based correlation, especially for inlinees. Now paired with pseudo-probe, we have accurate call site count and callee's entry count, so we can use that to gauge hotness more accurately.
- It treats all call sites from a block as hot as long as there's one call site considered hot. This is normally true, but since total samples is used as hotness proxy, this transitiveness within block magnifies the inacurate hotness heuristic. With pseduo-probe and the change above, this is no longer an issue for CSSPGO.

New FDO Inliner

Putting all the requirement for CSSPGO together, we need a top-down call site prioritized BFS inliner. Here're reasons why each component is needed.
- Top-down: We need a top-down inliner to better leverage context-sensitive profile, so inlining is driven by accurate context profile, and post-inline is also accurate. This is already implemented in https://reviews.llvm.org/D70655.
- Size Cap: For top-down inliner, taking function size into account for inline decision alone isn't sufficient to control size growth. We also need to explicitly cap size growth because with top-down inlining, we can grow inliner size significantly with large number of smaller inlinees even if each individually passes the cost/size check.
- Prioritize call sites: With size cap, inlining order also becomes important, because if we stop inlining due to size budget limit, we'd want to use budget towards the most beneficial call sites.
- BFS inline: Same as call site prioritization, if we stop inlining due to size budget limit, we want a balanced inline tree, rather than going deep on one call path.

Note that the new inliner avoids repeatedly evaluating same set of call site, so it should help with compile time too. For this reason, we could transition today's FDO inliner to use a queue with equal priority to avoid wasted reevaluation of same call site (TODO).

Speculative indirect call promotion and inlining is also supported now with CSSPGO just like baseline AutoFDO.

Tunings and knobs

I created tuning knobs for size growth/cap control, and for hot threshold separate from CGSCC inliner. The default values are selected based on initial tuning with CSSPGO.

Results

Evaluated with an internal LLVM fork couple months ago, plus another change to adjust hot-threshold cutoff for context profile (will send up after this one), the new inliner show ~1% geomean perf win on spec2006 with CSSPGO, while reducing code size too. The measurement was done using train-train setup, MonoLTO w/ new pass manager and pseudo-probe. Note that this is just a starting point - we hope that the new inliner will open up more opportunity with CSSPGO, but it will certainly take more time and effort to make it fully calibrated and ready for bigger workloads (we're working on it).

Differential Revision: https://reviews.llvm.org/D94001

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[CSSPGO] Support of CS profiles in extended binary format.

This change brings up support of context-sensitive profiles in the format of extended binary. Existing sample profile reader/writer/merger

[CSSPGO] Support of CS profiles in extended binary format.

This change brings up support of context-sensitive profiles in the format of extended binary. Existing sample profile reader/writer/merger code is being tweaked to reflect the fact of bracketed input contexts, like (`[...]`). The paired brackets are also needed in extbinary profiles because we don't yet have an otherwise good way to tell calling contexts apart from regular function names since the context delimiter `@` can somehow serve as a part of the C++ mangled names.

Reviewed By: wmi, wenlei

Differential Revision: https://reviews.llvm.org/D95547

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[CSSPGO] Infrastructure for context-sensitive Sample PGO and Inlining

This change adds the context-senstive sample PGO infracture described in CSSPGO RFC (https://groups.google.com/g/llvm-dev/c/1p1r

[CSSPGO] Infrastructure for context-sensitive Sample PGO and Inlining

This change adds the context-senstive sample PGO infracture described in CSSPGO RFC (https://groups.google.com/g/llvm-dev/c/1p1rdYbL93s). It introduced an abstraction between input profile and profile loader that queries input profile for functions. Specifically, there's now the notion of base profile and context profile, and they are managed by the new SampleContextTracker for adjusting and merging profiles based on inline decisions. It works with top-down profiled guided inliner in profile loader (https://reviews.llvm.org/D70655) for better inlining with specialization and better post-inline profile fidelity. In the future, we can also expose this infrastructure to CGSCC inliner in order for it to take advantage of context-sensitive profile. This change is the consumption part of context-sensitive profile (The generation part is in this stack: https://reviews.llvm.org/D89707). We've seen good results internally in conjunction with Pseudo-probe (https://reviews.llvm.org/D86193). Pacthes for integration with Pseudo-probe coming up soon.

Currently the new infrastructure kick in when input profile contains the new context-sensitive profile; otherwise it's no-op and does not affect existing AutoFDO.

**Interface**

There're two sets of interfaces for query and tracking respectively exposed from SampleContextTracker. For query, now instead of simply getting a profile from input for a function, we can explicitly query base profile or context profile for given call path of a function. For tracking, there're separate APIs for marking context profile as inlined, or promoting and merging not inlined context profile.

- Query base profile (`getBaseSamplesFor`)
Base profile is the merged synthetic profile for function's CFG profile from any outstanding (not inlined) context. We can query base profile by function.

- Query context profile (`getContextSamplesFor`)
Context profile is a function's CFG profile for a given calling context. We can query context profile by context string.

- Track inlined context profile (`markContextSamplesInlined`)
When a function is inlined for 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 base profile for that inlined function.

- Track not-inlined context profile (`promoteMergeContextSamplesTree`)
When a function is not inlined for given calling context, we need to promote the context profile tree so the not inlined context becomes top-level context. This preserve the sub-context under that function so later inline decision for that not inlined function will still have context profile for its call tree. Note that profile will be merged if needed when promoting a context profile tree if any of the node already exists at its promoted destination.

**Implementation**

Implementation-wise, `SampleContext` is created as abstraction for context. Currently it's a string for call path, and we can later optimize it to something more efficient, e.g. context id. Each `SampleContext` also has a `ContextState` indicating whether it's raw context profile from input, whether it's inlined or merged, whether it's synthetic profile created by compiler. Each `FunctionSamples` now has a `SampleContext` that tells whether it's base profile or context profile, and for context profile what is the context and state.

On top of the above context representation, a custom trie tree is implemented to track and manager context profiles. Specifically, `SampleContextTracker` is implemented that encapsulates a trie tree with `ContextTireNode` as node. Each node of the trie tree represents a frame in calling context, thus the path from root to a node represents a valid calling context. We also track `FunctionSamples` for each node, so this trie tree can serve efficient query for context profile. Accordingly, context profile tree promotion now becomes moving a subtree to be under the root of entire tree, and merge nodes for subtree if this move encounters existing nodes.

**Integration**

`SampleContextTracker` is now also integrated with AutoFDO, `SampleProfileReader` and `SampleProfileLoader`. When we detected input profile contains context-sensitive profile, `SampleContextTracker` will be used to track profiles, and all profile query will go to `SampleContextTracker` instead of `SampleProfileReader` automatically. Tracking APIs are called automatically for each inline decision from `SampleProfileLoader`.

Differential Revision: https://reviews.llvm.org/D90125

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