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

Mircea Trofin via llvm-dev llvm-dev at lists.llvm.org
Sun Apr 12 09:10:25 PDT 2020

On Sun, Apr 12, 2020 at 12:45 AM Owen Anderson <resistor at mac.com> wrote:

> Hi Mircea,
> A few questions:
> - Why is a code size estimator needed? Can't you roll out the compilation
> process to get an accurate final size.

The training algorithm (DQN) is such that it wants a partial reward after
each action. Yundi can provide a deeper answer here. We are considering
alternatives where we could do away with it, though.

> - Given that you have a trained code size estimator, how effective would
> MCTS be compared to the Q-network?

I assume you mean doing MCTS 'live', at compilation time? For release mode,
we'd be concerned with timeliness and determinism. We are considering doing
that for training, though, since that happens offline, and we don't have
those concerns either.

> - Can you provide more information on the feature engineering needed to
> get this to work well?

This initial set was chosen mostly for expedience, with the goal of getting
an end to end system working. We also chose to be conservative with our
features - we wanted to explore the limits of hand-picked features, before
going into embedding IR or the SCC graph (for example)

First we wanted to check if some hand-crafted feature set can be used to
train a policy for behavioral cloning - i.e. can we teach a model to
mimic what the manual heuristic does.

The initial set was: total number of functions and call edges;
caller/callee block counts, ir size, users, and conditionally-executed
blocks; number of constant parameters; and the cost estimate of the call
site (i.e. today's cost model, without using thresholds, which gives an
analytical signal of the "inlinability" of the site).

We learned these features weren't good enough - I forget the training
adherence (== the probability the learned model makes the same decision as
the manual policy), Yundi may remember, in any case, it was low.

We added as a result the 'height' feature - the position in the DAG of a
call site, which we chose to be the longest path from the SCC containing
that call site, to a leaf SCC. This proved critical - we got to over 99%
training adherence, and also a modest (~0.4) size reduction.

We also determined capturing the IR sizes of the caller/callee introduced
noise in the further training, so we eliminated them.

> - Can you comment on the time / data set requirements to retrain this
> model from scratch?

We use a set of about 25K modules, peeled off an internal app. The
exploration time takes ~15 minutes on what is roughly equivalent to a few
hundred hardware threads (a distributed environment). Training happens
locally, and one iteration is ~30 min (36 core, 72 thread machine). Note
that 'from scratch' is really 'starting from what can be learned from the
manual heuristic'. So far, we got no additional benefit from more than one
iteration. We believe embedding global information more thoroughly, for
example, will very likely change that; improvements in the ir2native model,
which seems to be more noisy after the first iteration; and/or using
training algorithm alternatives where we can do away with the ir2native
model altogether, like you suggested.

> --Owen
> On Apr 8, 2020, at 2:04 PM, Mircea Trofin via llvm-dev <
> llvm-dev at lists.llvm.org> wrote:
> 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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