This helps training algorithms that may want to sometimes replicate the
default decision. The default decision is presented as an extra feature
called `inlining_default`. It's not normally exported to save
computation time.
This is only available in interactive mode.
Differential Revision: https://reviews.llvm.org/D147794
This reverts commit a772f0bb920a4957fb94dd8dbe45943809fd0ec3.
The main problem was related to how we handled `dbgs()` from the hosted
compiler. Using explicit `subprocess.communicate`, and not relying on
dbgs() being flushed until the end appears to address the problem.
Also some fixes due to some bots running older pythons, so we can't have
nice things like `int | float` and such.
This reverts commit a7354899d1a235a796b3a2ccb45f6596983c8672.
The way stdout/stderr get routed seems to work differently locally and
on the bots. Investigating.
This hooks up the interactive model runner to the passes that support
ml-based decisions. Because the interface to this runner is the exact
same as the one used during inference, we just reuse the exact same
setup we have for "release mode". This makes "release mode" a misnomer -
and that's something we needed to resolve sooner or later (e.g.
supporting more than one embedded model for the same problem was another
reason to drop that nomenclature). That will happen in a subsequent
change.
To use this evaluator, just enable the pass in (currently) "release"
mode, but also pass the base name for the 2 channel files via the
pass-specific flag.
The 2 files are the responsibilty of the hosting process. The added
tests use a minimal, toy such host, illustrating setup and
communication.
Differential Revision: https://reviews.llvm.org/D143218
This leverages the new logging format in that we don't need to buffer
the training data, we can just write it out.
Differential Revision: https://reviews.llvm.org/D142168
The dependency was due to the log format. This change switches to the
previously-introduced (D139370) "dependency-free" logger instead of the
protobuf-based one.
A subsequent change will clean out the unnecessary abstraction left
behind.
This change drops the logger unittest, we have sufficient test coverage
via lit tests, and a unit test would require adding, unnecesarily, a log
reader (the reader is expected to be python, for the ML side, and there
is a reader for that under Analysis/models, used for tests).
Differential Revision: https://reviews.llvm.org/D141720
We use LLVM_HAVE_TFLITE as the key to enable the mlgo work these days,
and LLVM_HAVE_TF_API is defined whenever LLVM_HAVE_TF_API is defined.
I'm posting this patch because it's purely mechanical.
I'll post a follow-up patch to remove LLVM_HAVE_TF_API in non-C++
files, and that will not be as mechanical as this one.
Differential Revision: https://reviews.llvm.org/D139863
It's an artifact very specific to using TFAgents during training, so it
belongs with ModelUnderTrainingRunner.
Differential Revision: https://reviews.llvm.org/D139031
This just shuffles implementations and declarations around. Now the
logger and the TF C API-based model evaluator are separate.
Differential Revision: https://reviews.llvm.org/D131116
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
LLVM Programmer’s Manual strongly discourages the use of `std::vector<bool>` and suggests `llvm::BitVector` as a possible replacement.
This patch does just that for llvm.
Reviewed By: dexonsmith
Differential Revision: https://reviews.llvm.org/D117121
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
When using final reward (which is now the default), we were skipping
logging decisions that were leading to callee deletion. This fixes that.
Differential Revision: https://reviews.llvm.org/D108587
Some files still contained the old University of Illinois Open Source
Licence header. This patch replaces that with the Apache 2 with LLVM
Exception licence.
Differential Revision: https://reviews.llvm.org/D107528
1) add some self-diagnosis (when asserts are enabled) to check that all
features have the same nr of entries
2) avoid storing pointers to mutable fields because the proto API
contract doesn't actually guarantee those stay fixed even if no further
mutation of the object occurs.
Differential Revision: https://reviews.llvm.org/D107594
Avoid buffering just to copy the buffered data, in 'development
mode', when logging. Instead, just populate the underlying protobuf.
Differential Revision: https://reviews.llvm.org/D106592
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
The lookup logic is also reusable.
Also refactored the API to return the loaded vector - this makes it more
clear what state it is in in the case of error (as it won't be
returned).
Differential Revision: https://reviews.llvm.org/D91759
If we use training algorithms that don't need partial rewards, we don't
need to worry about an ir2native model. In that case, training logs
won't contain a 'delta_size' feature either (since that's the partial
reward).
Differential Revision: https://reviews.llvm.org/D86481
Different training algorithms may produce models that, besides the main
policy output (i.e. inline/don't inline), produce additional outputs
that are necessary for the next training stage. To facilitate this, in
development mode, we require the training policy infrastructure produce
a description of the outputs that are interesting to it, in the form of
a JSON file. We special-case the first entry in the JSON file as the
inlining decision - we care about its value, so we can guide inlining
during training - but treat the rest as opaque data that we just copy
over to the training log.
Differential Revision: https://reviews.llvm.org/D85674
This allows us to subsequently configure the logger for the case when we
use a model evaluator and want to log additional outputs.
Differential Revision: https://reviews.llvm.org/D85577
We don't want mandatory events in the training log. We do want to handle
them, to keep the native size accounting accurate, but that's all.
Fixed the code, also expanded the test to capture this.
Differential Revision: https://reviews.llvm.org/D85373
Further abstracting the specification of a tensor, to more easily
support different types and shapes of tensor, and also to perform
initialization up-front, at TFModelEvaluator construction time.
Differential Revision: https://reviews.llvm.org/D84685
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
