This allows the compiler to support more features than those supported by a
model. The only requirement (development mode only) is that the new
features must be appended at the end of the list of features requested
from the model. The support is transparent to compiler code: for
unsupported features, we provide a valid buffer to copy their values;
it's just that this buffer is disconnected from the model, so insofar
as the model is concerned (AOT or development mode), these features don't
exist. The buffers are allocated at setup - meaning, at steady state,
there is no extra allocation (maintaining the current invariant). These
buffers has 2 roles: one, keep the compiler code simple. Second, allow
logging their values in development mode. The latter allows retraining
a model supporting the larger feature set starting from traces produced
with the old model.
For release mode (AOT-ed models), this decouples compiler evolution from
model evolution, which we want in scenarios where the toolchain is
frequently rebuilt and redeployed: we can first deploy the new features,
and continue working with the older model, until a new model is made
available, which can then be picked up the next time the compiler is built.
Differential Revision: https://reviews.llvm.org/D124565
The tensorflow AOT compiler can cross-target, but it can't run on (for
example) arm64. We added earlier support where the AOT-ed header and object
would be built on a separate builder and then passed at build time to
a build host where the AOT compiler can't run, but clang can be otherwise
built.
To simplify such scenarios given we now support more than one AOT-able
case (regalloc and inliner), we make the AOT scenario centered on whether
files are generated, case by case (this includes the "passed from a
different builder" scenario).
This means we shouldn't need an 'umbrella' LLVM_HAVE_TF_AOT, in favor of
case by case control. A builder can opt out of an AOT case by passing that case's
model path as `none`. Note that the overrides still take precedence.
This patch controls conditional compilation with case-specific flags,
which can be enabled locally, for the component where those are
available. We still keep an overall flag for some tests.
The 'development/training' mode is unchanged, because there the model is
passed from the command line and interpreted.
Differential Revision: https://reviews.llvm.org/D117752
The global state refers to the number of the nodes currently in the
module, and the number of direct calls between nodes, across the
module.
Node counts are not a problem; edge counts are because we want strictly
the kind of edges that affect inlining (direct calls), and that is not
easily obtainable without iteration over the whole module.
This patch avoids relying on analysis invalidation because it turned out
to be too aggressive in some cases. It leverages the fact that Node
objects are stable - they do not get deleted while cgscc passes are
run over the module; and cgscc pass manager invariants.
Reviewed By: aeubanks
Differential Revision: https://reviews.llvm.org/D115847
This avoids the InlineAdvisor carrying the responsibility of deleting
Function objects. We use LazyCallGraph::Node objects instead, which are
stable in memory for the duration of the Module-wide performance of CGSCC
passes started under the same ModuleToPostOrderCGSCCPassAdaptor (which
is the case here)
Differential Revision: https://reviews.llvm.org/D116964
This prepares it for the regalloc work. Part of it is making model
evaluation accross 'development' and 'release' scenarios more reusable.
This patch:
- extends support to tensors of any shape (not just scalars, like we had
in the inliner -Oz case). While the tensor shape can be anything, we
assume row-major layout and expose the tensor as a buffer.
- exposes the NoInferenceModelRunner, which we use in the 'development'
mode to keep the evaluation code path consistent and simplify logging,
as we'll want to reuse it in the regalloc case.
Differential Revision: https://reviews.llvm.org/D115306
This avoids unnecessary re-calculation of module-wide features in the
MLInlineAdvisor. In cases where function passes don't invalidate
functions (and, thus, don't invalidate the module), but we re-process a
CGSCC, we currently refreshed module features unnecessarily. The
overhead of fetching cached results (albeit they weren't themselves
invalidated) was noticeable in certain modules' compilations.
We don't want to just invalidate the advisor object, though, via the
analysis manager, because we'd then need to re-create expensive state
(like the model evaluator in the ML 'development' mode).
Reviewed By: phosek
Differential Revision: https://reviews.llvm.org/D113644
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
This reverts commit d97f776be5.
The original problem was due to build failures in shared lib builds. D95079
moved ImportedFunctionsInliningStatistics under Analysis, unblocking
this.
When using 2 InlinePass instances in the same CGSCC - one for other
mandatory inlinings, the other for the heuristic-driven ones - the order
in which the ImportedFunctionStats would be output-ed would depend on
the destruction order of the inline passes, which is not deterministic.
This patch moves the ImportedFunctionStats responsibility to the
InlineAdvisor to address this problem.
Differential Revision: https://reviews.llvm.org/D94982
Expanding from D94808 - we ensure the same InlineAdvisor is used by both
InlinerPass instances. The notion of mandatory inlining is moved into
the core InlineAdvisor: advisors anyway have to handle that case, so
this change also factors out that a bit better.
Differential Revision: https://reviews.llvm.org/D94825
Enable performing mandatory inlinings upfront, by reusing the same logic
as the full inliner, instead of the AlwaysInliner. This has the
following benefits:
- reduce code duplication - one inliner codebase
- open the opportunity to help the full inliner by performing additional
function passes after the mandatory inlinings, but before th full
inliner. Performing the mandatory inlinings first simplifies the problem
the full inliner needs to solve: less call sites, more contextualization, and,
depending on the additional function optimization passes run between the
2 inliners, higher accuracy of cost models / decision policies.
Note that this patch does not yet enable much in terms of post-always
inline function optimization.
Differential Revision: https://reviews.llvm.org/D91567
(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
Mircea Trofin