It turns out that during training, the time required to parse the
textual protobuf of a training log is about the same as the time it
takes to compile the module generating that log. Using binary protobufs
instead elides that cost almost completely.
Differential Revision: https://reviews.llvm.org/D106157
This change yields an additional 2% size reduction on an internal search
binary, and an additional 0.5% size reduction on fuchsia.
Differential Revision: https://reviews.llvm.org/D104751
They are not conducive to being stored in git. Instead, we autogenerate
mock model artifacts for use in tests. Production models can be
specified with the cmake flag LLVM_INLINER_MODEL_PATH.
LLVM_INLINER_MODEL_PATH has two sentinel values:
- download, which will download the most recent compatible model.
- autogenerate, which will autogenerate a "fake" model for testing the
model uptake infrastructure.
Differential Revision: https://reviews.llvm.org/D104251
This was prompted by D95727, which had the side-effect to break the
'release' mode build bot for ML-driven policies. The problem is that now
the pre-compiled object files don't get transitively carried through as
'source' anymore; that being said, the previous way of consuming them
was problematic, because it was only working for static builds; in
dynamic builds, the whole tf_xla_runtime was linked, which is
undesirable.
The alternative is to treat tf_xla_runtime as an archive, which then
leads to the desired effect.
Differential Revision: https://reviews.llvm.org/D99829
This is related to D94982. We want to call these APIs from the Analysis
component, so we can't leave them under Transforms.
Differential Revision: https://reviews.llvm.org/D95079
This is being recommitted to try and address the MSVC complaint.
This patch implements a DDG printer pass that generates a graph in
the DOT description language, providing a more visually appealing
representation of the DDG. Similar to the CFG DOT printer, this
functionality is provided under an option called -dot-ddg and can
be generated in a less verbose mode under -dot-ddg-only option.
Reviewed By: Meinersbur
Differential Revision: https://reviews.llvm.org/D90159
This patch implements a DDG printer pass that generates a graph in
the DOT description language, providing a more visually appealing
representation of the DDG. Similar to the CFG DOT printer, this
functionality is provided under an option called -dot-ddg and can
be generated in a less verbose mode under -dot-ddg-only option.
Differential Revision: https://reviews.llvm.org/D90159
No longer rely on an external tool to build the llvm component layout.
Instead, leverage the existing `add_llvm_componentlibrary` cmake function and
introduce `add_llvm_component_group` to accurately describe component behavior.
These function store extra properties in the created targets. These properties
are processed once all components are defined to resolve library dependencies
and produce the header expected by llvm-config.
Differential Revision: https://reviews.llvm.org/D90848
This introduces the IRInstructionMapper, and the associated wrapper for
instructions, IRInstructionData, that maps IR level Instructions to
unsigned integers.
Mapping is done mainly by using the "isSameOperationAs" comparison
between two instructions. If they return true, the opcode, result type,
and operand types of the instruction are used to hash the instruction
with an unsigned integer. The mapper accepts instruction ranges, and
adds each resulting integer to a list, and each wrapped instruction to
a separate list.
At present, branches, phi nodes are not mapping and exception handling
is illegal. Debug instructions are not considered.
The different mapping schemes are tested in
unittests/Analysis/IRSimilarityIdentifierTest.cpp
Recommit of: b04c1a9d31
Differential Revision: https://reviews.llvm.org/D86968
This introduces the IRInstructionMapper, and the associated wrapper for
instructions, IRInstructionData, that maps IR level Instructions to
unsigned integers.
Mapping is done mainly by using the "isSameOperationAs" comparison
between two instructions. If they return true, the opcode, result type,
and operand types of the instruction are used to hash the instruction
with an unsigned integer. The mapper accepts instruction ranges, and
adds each resulting integer to a list, and each wrapped instruction to
a separate list.
At present, branches, phi nodes are not mapping and exception handling
is illegal. Debug instructions are not considered.
The different mapping schemes are tested in
unittests/Analysis/IRSimilarityIdentifierTest.cpp
Differential Revision: https://reviews.llvm.org/D86968
This patch recommits "[ConstraintSystem] Add helpers to deal with linear constraints."
(it reverts the revert commit 8da6ae4ce1).
The reason for the revert was using __builtin_multiply_overflow, which
is not available for all compilers. The patch has been updated to use
MulOverflow from MathExtras.h
This patch introduces a new ConstraintSystem class, that maintains a set
of linear constraints and uses Fourier–Motzkin elimination to eliminate
constraints to check if there are solutions for the system.
It also adds a convert-constraint-log-to-z3.py script, which can parse
the debug output of the constraint system and convert it to a python
script that feeds the constraints into Z3 and checks if it produces the
same result as the LLVM implementation. This is for verification
purposes.
Reviewed By: spatel
Differential Revision: https://reviews.llvm.org/D84544
This also changes -lint from an analysis to a pass. It's similar to
-verify, and that is a normal pass, and lives in llvm/IR.
Reviewed By: ychen
Differential Revision: https://reviews.llvm.org/D87057
This change added a new inline advisor that takes optimization remarks from previous inlining as input, and provides the decision as advice so current inlining can replay inline decisions of a different compilation. Dwarf inline stack with line and discriminator is used as anchor for call sites including call context. The change can be useful for Inliner tuning as it provides a channel to allow external input for tweaking inline decisions. Existing alternatives like alwaysinline attribute is per-function, not per-callsite. Per-callsite inline intrinsic can be another solution (not yet existing), but it's intrusive to implement and also does not differentiate call context.
A switch -sample-profile-inline-replay=<inline_remarks_file> is added to hook up the new inline advisor with SampleProfileLoader's inline decision for replay. Since SampleProfileLoader does top-down inlining, inline decision can be specialized for each call context, hence we should be able to replay inlining accurately. However with a bottom-up inliner like CGSCC inlining, the replay can be limited due to lack of specialization for different call context. Apart from that limitation, the new inline advisor can still be used by regular CGSCC inliner later if needed for tuning purpose.
This is a resubmit of https://reviews.llvm.org/D83743
(This reverts commit a5e0194709, and
corrects author).
Rename the pass to be able to extend it to function properties other than inliner features.
Reviewed By: mtrofin
Differential Revision: https://reviews.llvm.org/D82044
Rename the pass to be able to extend it to function properties other than inliner features.
Reviewed By: mtrofin
Differential Revision: https://reviews.llvm.org/D82044
Outside of compiler-rt (where it's arguably an anti-pattern too),
LLVM tries to keep its build files as simple as possible. See e.g.
llvm/docs/SupportLibrary.rst, "Code Organization".
Differential Revision: https://reviews.llvm.org/D84243
Summary:
This is the InlineAdvisor used in 'development' mode. It enables two
scenarios:
- loading models via a command-line parameter, thus allowing for rapid
training iteration, where models can be used for the next exploration
phase without requiring recompiling the compiler. This trades off some
compilation speed for the added flexibility.
- collecting training logs, in the form of tensorflow.SequenceExample
protobufs. We generate these as textual protobufs, which simplifies
generation and testing. The protobufs may then be readily consumed by a
tensorflow-based training algorithm.
To speed up training, training logs may also be collected from the
'default' training policy. In that case, this InlineAdvisor does not
use a model.
RFC: http://lists.llvm.org/pipermail/llvm-dev/2020-April/140763.html
Reviewers: jdoerfert, davidxl
Subscribers: mgorny, hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D83733
Summary:
This change added a new inline advisor that takes optimization remarks from previous inlining as input, and provides the decision as advice so current inlining can replay inline decisions of a different compilation. Dwarf inline stack with line and discriminator is used as anchor for call sites including call context. The change can be useful for Inliner tuning as it provides a channel to allow external input for tweaking inline decisions. Existing alternatives like alwaysinline attribute is per-function, not per-callsite. Per-callsite inline intrinsic can be another solution (not yet existing), but it's intrusive to implement and also does not differentiate call context.
A switch -sample-profile-inline-replay=<inline_remarks_file> is added to hook up the new inline advisor with SampleProfileLoader's inline decision for replay. Since SampleProfileLoader does top-down inlining, inline decision can be specialized for each call context, hence we should be able to replay inlining accurately. However with a bottom-up inliner like CGSCC inlining, the replay can be limited due to lack of specialization for different call context. Apart from that limitation, the new inline advisor can still be used by regular CGSCC inliner later if needed for tuning purpose.
Subscribers: mgorny, aprantl, hiraditya, llvm-commits
Tags: #llvm
Resubmit for https://reviews.llvm.org/D84086
Summary:
This change added a new inline advisor that takes optimization remarks for previous inlining as input, and provide the decision as advice so current inlining can replay inline decision of a different compilation. Dwarf inline stack with line and discriminator is used as anchor for call sites. The change can be useful for Inliner tuning.
A switch -sample-profile-inline-replay=<inline_remarks_file> is added to hook up the new inliner advisor with SampleProfileLoader's inline decision for replay. The new inline advisor can also be used by regular CGSCC inliner later if needed.
Reviewers: davidxl, mtrofin, wmi, hoy
Subscribers: aprantl, hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D83743
This reverts commit 9908a3b9f5.
The fix was to exclude the content of TFUtils.h (automatically
included in the LLVM_Analysis module, when LLVM_ENABLE_MODULES is enabled).
Differential Revision: https://reviews.llvm.org/D82817
Summary:
This is an experimental ML-based native size estimator, necessary for
computing partial rewards during -Oz inliner policy training. Data
extraction for model training will be provided in a separate patch.
RFC: http://lists.llvm.org/pipermail/llvm-dev/2020-April/140763.html
Reviewers: davidxl, jdoerfert
Subscribers: mgorny, hiraditya, mgrang, arphaman, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D82817
Summary: This patch moves OrderedInstructions to CodeMoverUtils as It was
the only place where OrderedInstructions is required.
Authored By: RithikSharma
Reviewer: Whitney, bmahjour, etiotto, fhahn, nikic
Reviewed By: Whitney, nikic
Subscribers: mgorny, hiraditya, llvm-commits
Tag: LLVM
Differential Revision: https://reviews.llvm.org/D80643
This makes it usable from outside of SCEV,
while previously it was internal to the ScalarEvolution.cpp
In particular, i want to use it in an WIP alloca promotion helper pass,
to analyze if some SCEV is a multiple of some other SCEV.
Summary:
This code is going to be used in StackSafety.
This patch is file move with minimal changes. Identifiers
will be fixed in the followup patch.
Reviewers: eugenis, pcc
Reviewed By: eugenis
Subscribers: mgorny, hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D81831
The initial intent was to organize ML stuff in its own directory, but
it turns out that conflicts with llvm component layering policies: it
is not a component, because subsequent changes want to rely on other
analyses, which would create a cycle; and we don't have a reliable,
cross-platform mechanism to compile files in a subdirectory, and fit in
the existing LLVM build structure.
This change moves the files into Analysis, and subsequent changes will
leverage conditional compilation for those that have optional
dependencies.
Summary:
Currently, add_llvm_library would create an OBJECT library alongside
of a STATIC / SHARED library, but losing the link interface (its
elements would become dependencies instead). To support scenarios
where linking an object library also brings in its usage
requirements, this patch adds support for 'stand-alone' OBJECT
libraries - i.e. without an accompanying SHARED/STATIC library, and
maintaining the link interface defined by the user.
The support is via a new option, OBJECT_ONLY, to avoid breaking changes
- since just specifying "OBJECT" would currently imply also STATIC or
SHARED, depending on BUILD_SHARED_LIBS.
This is useful for cases where, for example, we want to build a part
of a component separately. Using a STATIC target would incur the risk
that symbols not referenced in the consumer would be dropped (which may
be undesirable).
The current application is the ML part of Analysis. It should be part
of the Analysis component, so it may reference other analyses; and (in
upcoming changes) it has dependencies on optional libraries.
Reviewers: karies, davidxl
Subscribers: mgorny, hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D81447
SCEVExpander modifies the underlying function so it is more suitable in
Transforms/Utils, rather than Analysis. This allows using other
transform utils in SCEVExpander.
This patch was originally committed as b8a3c34eee, but broke the
modules build, as LoopAccessAnalysis was using the Expander.
The code-gen part of LAA was moved to lib/Transforms recently, so this
patch can be landed again.
Reviewers: sanjoy.google, efriedma, reames
Reviewed By: sanjoy.google
Differential Revision: https://reviews.llvm.org/D71537
ModuleSummaryAnalysis is the only file in libAnalysis that brings a
dependency on the CodeGen layer from libAnalysis, moving it breaks this
dependency.
Differential Revision: https://reviews.llvm.org/D77994
This patch introduces the heat coloring of the Control Flow Graph which is based
on the relative "hotness" of each BB. The patch is a part of sequence of three
patches, related to graphs Heat Coloring.
Reviewers: rcorcs, apilipenko, davidxl, sfertile, fedor.sergeev, eraman, bollu
Differential Revision: https://reviews.llvm.org/D77161
Summary:
Splitting Knowledge retention into Queries in Analysis and Builder into Transform/Utils
allows Queries and Transform/Utils to use Analysis.
Reviewers: jdoerfert, sstefan1
Reviewed By: jdoerfert
Subscribers: mgorny, hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D77171
Jacob Hegna