[llvm-dev] RFC: a practical mechanism for applying Machine Learning for optimization policies in LLVM

[Mircea Trofin via llvm-dev]

TL;DR; We can improve compiler optimizations driven by heuristics by
replacing those heuristics with machine-learned policies (ML models).
Policies are trained offline and ship as part of the compiler. Determinism
is maintained because models are fixed when the compiler is operating in
production. Fine-tuning or regressions may be handled by incorporating the
interesting cases in the ML training set, retraining the compiler, and
redeploying it.

For a first milestone, we chose inlining for size (-Oz) on X86-64. We were
able to train an ML model to produce binaries 1.5-6% smaller than -Oz of
tip-of-tree. The trained model appears to generalize well over a diverse
set of binaries. Compile time is increased marginally (under 5%). The model
also happens to produce slightly better - performing code under SPEC2006 -
total score improvement by 1.75%. As we only wanted to verify there is no
significant regression in SPEC, and given the milestone goals, we haven’t
dug any deeper into the speed results.

We see these results as promising, and as a reasonable point for
contributing our current work as a build-time opt-in to LLVM to benefit the
community, in the hope of fostering collaboration and learning from the
community’s feedback, as we try to better understand the trade-offs such an
approach entails, and as we work on expanding the depth and breadth of
applying these techniques to compiler optimization problems.

https://reviews.llvm.org/D77752
Motivation

Optimization problems, such as inlining or register allocation, are hard:
we don’t know of algorithms that are guaranteed to produce the optimum
solution in all cases in a timely fashion. Instead, we use heuristics:
algorithms that we expect to produce some approximation of the optimum,
with some expected degree of generality, in a practical amount of time.

Heuristics have some common characteristics. Taking inlining as a case
study, it traverses the problem space in some way (bottom-up traversal of
the SCC graph), extracts some properties (let’s call them “features”) of
the program being optimized, and combine them with some weights (“tune”),
to produce a cost (InlineCost), which allows for trade-off analysis. We
validate the effectiveness of a heuristic over some accepted set of
benchmarks. Over time, we react to regressions or pathological cases
observed in the field, by manually analyzing such cases, figuring out an
enhancement to the heuristic, and then re-validating over that set of
benchmarks (maybe augmented by adding the newly found cases).

Because heuristics are code that needs to be maintained, there is pressure
to reduce complexity: adding more features means we need to reason about
the interactions between the old and new features, which scales
combinatorially. Re-tuning because of the new features adds a similar kind
of complexity. Potentially, we miss out on optimization improvements as a
result.

Because tuning is manual, there is pressure to keep the number of
benchmarks that can be studied in depth to a humanly-manageable size, which
potentially affects the generality of a heuristic or heuristic tuning.

The main advantage of manual heuristics is arguably their relatively lower
overhead: no additional dependencies and more transparent to human analysis
and improvement.

Machine learning, in particular reinforcement learning, can address the
tensions found in manual heuristics: once features are extracted from the
program, the way they are combined and tuned can easily be scaled up
through automation, improving effectiveness and generality. A major
drawback, at least at this point in time, of machine learning, is that we
don’t yet have a fully developed systematic approach for improving policy
effectiveness.
High level design

We identify two scenarios for a compiler using ML policies: development and
release.

The release scenario is equivalent to the regular compilation we have today
- the only difference is that it uses a pre-trained model (trained in the
development scenario beforehand) to make decisions instead of the
heuristics. Determinism is guaranteed since the model in the release
scenario is fixed. We imagine teams wishing to fine tune the effectiveness
of the optimization to their scenarios would train a different model.

The decision previously evaluated using a human-crafted heuristic is
optionally replaced by:

   -

   a compiler-specific component, extracting features from IR (i.e. a
   vector of values)
   -

   an evaluation of an ML model using those features, to obtain a result.
   In ML nomenclature, this is referred to using the model for inference (as
   opposed to training it)

For example, when we replaced the decision of whether to inline a callsite,
the ML model produces a boolean (inline/don’t inline) based on a features
vector characterizing the call site and some broader module-wide context.

Training/development is more complicated, and happens offline - akin to
how, today, attempts to improve an optimizing pass also happen offline. A
description of the high level design and the specifics we used for the
current scope are given in Appendix.
Current Scope

The goal of our first milestone was to evaluate end to end an integration
of ML with LLVM, and get a first promising result. To that end, we chose
inlining for size (-Oz) as a stepping stone, as we perceived it to be more
likely to require a simpler evaluation setup than performance-oriented
optimizations might. At this point, we only train whether a call site may
be inlined or not, leaving the SCC traversal order as-is.

We are proposing an initial change demonstrating the inference mechanism
using a pre-trained model, as a build-time opt-in to llvm. The compiler
components needed to perform training are also included in this first
change. Subsequent changes would include more training-related components.

At a high level, the changes we are proposing consist of:

   1.

   a new module analysis pass, InliningAdvisor. By default, its
   implementation does nothing.
   2.

   minimal hooks into Inliner.cpp.
   3.

   the implementation of InliningAdvisor, activated when we opt-in ML. This
   is available in Analysis/ML, together with:
   1.

      Rel/Dev specific ML model handing, also under Analysis/ML
      2.

      a pre-trained model for inlining for size
      (Analysis/ML/models/inlining)
      3.

      a pre-trained model for predicting native size from IR
      (Analysis/ML/models/ir_2_native_x86_64), used in Dev mode only.
      4.

   Some refactorings in PassBuilder, to allow opting into running mandatory
   inlining first - some compilation speedup for the ML case, minimal,
   noise-like size effect. Also simplifies testing (these would be introduced
   as a preliminary patch).

We discuss ‘rel’ mode here, and ‘dev’ mode in the Appendix, as it is more
involved.
Inference Opt-In Mechanism

The feature is primarily controlled by the cmake flag
LLVM_USE_ML_POLICY={“Rel”|”Dev”}. Each has different dependencies. The
“Rel”ease case requires specifying the location of the pip tensorflow
package (currently, that’s tf_nightly, and it should soon be available in
tensorflow)

To opt in the ‘Rel’ case:

   1.

   install tensorflow pip package

pip3 install tf_nightly --user

   1.

   configure llvm build

cmake ../llvm -DLLVM_USE_ML_POLICY=Rel \

-DLLVM_TF_AOT_RUNTIME=~/.local/lib/python3.7/site-packages/tensorflow \

{-DLLVM_TF_AOT_COMPILER=<path to saved_model_cli tool, if needed - it’s
usually in the path>}



   1.

   build llvm as usual.
   2.

   pass -mllvm -enable-ml-inliner -mllvm -mandatory-inlinings-first to
   clang.

Details

The ML model is captured as a TensorFlow ‘saved model’. When building llvm,
we use TensorFlow’s  XLA native compiler (saved_model_cli) to compile the
saved model into a native static library and a header file. Insofar as LLVM
is concerned, there are minimal additional runtime requirements, packaged
as source within the pip package: C++ wrappers around the compiled model.
These will also be statically linked in the LLVM target. The compiled code
is otherwise just a complex arithmetical computation, with no special
requirements - it is single threaded and runs natively on the targeted
architecture. Together with the aforementioned runtime dependencies, it
adds ~115KB to the clang binary (0.08% increase)

Runtime-wise, we observed a ~10% increase in the time spent in the inliner,
for a large (33MB) binary IR module; inlining typically consumes ~10-15% of
total compilation time, so the overall compile time overhead of the
approach is arguably negligible. This cost is almost in entirety
attributable to feature extraction.

Memory-wise, the precompiled model has a fixed size buffer for its inputs,
and performs a fixed amount of computations, so the memory overhead
inherent to our approach is independent from the program being optimized.
Using a small example to avoid effects such as memory use differences due
to different inlinings, we observed an 300KB increase in the maximum
resident size.

A table showing effect on -Oz compiled binaries’ size is given in Appendix.
Next directions

Our next milestone has two main high level goals: detailing a systematic
approach to driving policy effectiveness; and exploring in depth the type
of features and training algorithms most appropriate for compiler problems,
or at least problems like inlining. For the latter, we expect embedding
more of the call graph structure to play an important role, as well as,
potentially, delegating the problem space traversal to the ML model.

We plan to include inlining for speed as part of our work on these goals.
AppendixTraining - High Level

We use Reinforcement Learning (RL) to train the Inline-for-size model. At a
high level, it is composed of 3 parts: training data collection, model
training, and iterative data collection/model training. We use TensorFlow
as our ML framework.

Related, we also needed to learn a separate model to evaluate the native
size of a function, given its IR, in order to calculate a more precise
reward for the reinforcement learning algorithm (“IR2Native”). We evaluated
‘just counting IR’ and TargetTransformInfo, but they appeared to provide
too noisy of a signal for the reward, insofar as the RL training algorithm
for the inlining model was concerned. This model is only used during
training.

RL - Training data collection: the training data we need to feed into a
reinforcement learning algorithm are sequences consisting of: state of the
problem (i.e. features); action (inline/not inline), and reward (native
size shrinkage after inline/not inline, using ir2native). To collect the
sequences, we hook the logging infrastructure into LLVM Inliner that is
able to produce logs after the inline optimization pass.

RL - Model training: We use DQN (Deep Q-Network) to train our
inlining-for-size ML policy. On a high level, the DQN algorithm trains a
neural network to predict the value of different actions --- the DQN policy
then chooses to take the action with the highest predicted value. In our
scenario, we have two actions: 1) inline; 2) not inline, so the neural
network predicts the size reduction of these two actions based on features,
and then decides to conduct inlining if the neural network believes doing
inlining leads to higher size reduction. After the training finishes, it
produces a TensorFlow SavedModel that takes features as input and generates
inline decisions (whether to inline or not) as output.

The choice of the features and reward are essential in model training. The
features are chosen with the consideration of being helpful in making the
decision --- the input to the cost model is a good starting point. Ideally,
the reward in the inline-for-size problem is the native size shrinkage
after inline/not inline. It is difficult to obtain precisely, so we use the
estimate as an alternative. This means that, for training, we also need a
model (not necessarily machine learned) for estimating rewards.

RL - Iterative data collection/model training: Reinforcement learning is
ideally an iterative model/policy improvement process that: 1) the trained
model is deployed to the field to collect new data; 2) newly collected data
are used to update the model. Thus, we need to do iterative data
collection/model training. To facilitate data collection (avoid complex
build dependencies and time spent before/after the pass being trained), we
isolate out IR modules captured right before the optimization we are
interested in, and iterate on them with opt. We bootstrap the training from
the current heuristic, and stop the process once we are happy with the
outcome.

IR2Native: We collect IR features (different from the ones used for
inlining) for each function at the end of inlining, right before we perform
function simplification passes, and right after. This means we have two IR
‘shapes’ of the same function, and we know no further inlinings will be
performed, so whatever changes happen are based on that IR. We then extract
the native size at the end of compilation. Together, this data forms two
records per function that can be used in supervised learning - the features
are those extracted from IR, and the label is the native size. Training
IR2Native happens independently from the training of the inliner model.
Training support for the current scope

The initial change includes the logging mechanism, an ir2native model
trained for x86-64, and the means to rapidly iterate over development ML
models. For the components that will be included in subsequent changes, the
rest of this section describes the mechanisms we employed. These components
are detailed further below.

To build LLVM with the ML policy in ‘Dev’ mode, we need the tensorflow C
API library <https://www.tensorflow.org/install/lang_c>. We then configure
the build:

cmake .. -DLLVM_USE_ML_POLICY=Dev \

-DLLVM_TF_C_LIB=<path to unarchived package> \

{-DCMAKE_INSTALL_RPATH_USE_LINK_PATH=True, to copy tensorflow shared
library over, if it’s not on LD_LIBRARY_PATH}


To extract IR right before inlining, we hacked on top of the ThinLTO
infrastructure, interrupting its pre-link pipeline right before inlining.
This means clang produces binary IR files captured at that stage. We then
built a large target, obtaining a corpus of ~25K modules. We intend to
provide a clean mechanism in a subsequent change.

To obtain features/labels for training this “IR to Native Size” model, we
had to make some changes to the AsmPrinter (to get real native sizes) and
llvm-readobj, as well as add an analysis pass for extracting the IR
features for this model. We plan on upstreaming these changes subsequently.

Finally, the infrastructure driving the policy training is currently built
on proprietary APIs, as it benefits from a distributed computing
infrastructure. We are currently evaluating options for open sourcing it.
In the meantime, we are presenting the high level implementation details.

To train a new model, the infrastructure performs 2 steps: extracting the
logs, and using them in a training algorithm.

Log extraction is highly parallelizable: for each IR module in the training
corpus, we need to run opt once (or a few times, when we explore
improvements). Specifically, each run is this invocation:

opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=<path to
ir2native> -training-log=<path to training log output> -enable-ml-inliner
-mandatory-inlinings-first -o <output> <module.o>

Then collect the logs, and pass them to the next step.


Experimental results

Experimental results are available as follows:


   -

   SPEC2006
   <https://docs.google.com/spreadsheets/d/e/2PACX-1vQv0bAsUlgnG114zMYy_zKR6x-lTjcXVNt7VEeSwq2-pDr5oTxdASCscPRRg6L7iYLu2AVJ44G2xEkp/pubhtml?gid=1870752756&single=true>
   binary sizes (-Oz) and ‘test run’ scores.
   -

   Size report
   <https://docs.google.com/spreadsheets/d/e/2PACX-1vQv0bAsUlgnG114zMYy_zKR6x-lTjcXVNt7VEeSwq2-pDr5oTxdASCscPRRg6L7iYLu2AVJ44G2xEkp/pubhtml?gid=935959003&single=true>
   from some internal benchmarks and binaries, including opt and clang
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