[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
Thu Apr 9 10:01:20 PDT 2020
Sorry, I wasn't aware of that.
I can make the google doc view-only, keeping the current comments. I'll
wait a bit (few hrs) to see if there's any pushback to that.
On Thu, Apr 9, 2020 at 9:57 AM Xinliang David Li <xinliangli at gmail.com>
wrote:
> One suggestion : should we consolidate the discussion into the main
> thread? I know some folks are not willing to comment in Google docs.
>
> David
>
> On Wed, Apr 8, 2020 at 7:06 PM Mircea Trofin via llvm-dev <
> llvm-dev at lists.llvm.org> wrote:
>
>> +Yundi Qian <yundi at google.com> +Eugene Brevdo <ebrevdo at google.com> , our
>> team members from the ML side.
>>
>> To avoid formatting issues, here is a link to the RFC
>> <https://docs.google.com/document/d/1BoSGQlmgAh-yUZMn4sCDoWuY6KWed2tV58P4_472mDE/edit?usp=sharing>,
>> open to comments.
>>
>> Thanks!
>>
>> On Wed, Apr 8, 2020 at 2:34 PM Mircea Trofin <mtrofin at google.com> wrote:
>>
>>> Unfortunately I needed to update the links. Here they are, hopefully
>>> these work correctly.
>>>
>>> SPEC2006
>>> <https://docs.google.com/spreadsheets/d/e/2PACX-1vQNAcTDfyQvh6Jq7IRdCvK_fuluUFrzrsGL_75Ile29hX3caBSfT6_jHulxeCJ5MXIHp5SB--A_goEi/pubhtml?gid=987260531&single=true>
>>>
>>> Internal benchmarks, clang, opt
>>> <https://docs.google.com/spreadsheets/d/e/2PACX-1vQNAcTDfyQvh6Jq7IRdCvK_fuluUFrzrsGL_75Ile29hX3caBSfT6_jHulxeCJ5MXIHp5SB--A_goEi/pubhtml?gid=0&single=true>
>>>
>>> Thanks!
>>>
>>> On Wed, Apr 8, 2020 at 2:23 PM Mircea Trofin <mtrofin at google.com> wrote:
>>>
>>>> It turns out it's me, sorry. Let me see how I can sort this out. In the
>>>> meantime, here is the csv:
>>>>
>>>> SPEC2006 data:
>>>>
>>>> binary,base -Oz size,ML -Oz size,ML size shrink by,,perf: base -Oz
>>>> scores,perf: ML -Oz scores,ML improvement by
>>>> 400.perlbench,2054200,2086776,-1.59%,,2.9,2.9,0.00%
>>>> 401.bzip2,1129976,1095544,3.05%,,6.4,6.2,-3.13%
>>>> 403.gcc,4078488,4130840,-1.28%,,11.6,11.7,0.86%
>>>> 429.mcf,1089368,1054552,3.20%,,2.3,2.4,4.35%
>>>> 433.milc,1161336,1129592,2.73%,,0.5,0.5,0.00%
>>>> 444.namd,1324824,1290968,2.56%,,4.4,4.3,-2.27%
>>>> 445.gobmk,5003096,4992472,0.21%,,3.9,4.1,5.13%
>>>> 447.dealII,1003376,975024,2.83%,,162.1,197.8,22.02%
>>>> 450.soplex,1359416,1326008,2.46%,,0.1,0.1,0.00%
>>>> 453.povray,1921432,1952280,-1.61%,,32.9,32.7,-0.61%
>>>> 456.hmmer,1210744,1184632,2.16%,,1.5,1.5,0.00%
>>>> 458.sjeng,1185976,1155320,2.58%,,812.9,840.2,3.36%
>>>> 462.libquantum,1101144,1066712,3.13%,,12.3,12.2,-0.81%
>>>> 464.h264ref,1557176,1593272,-2.32%,,0.3,0.3,0.00%
>>>> 470.lbm,1091352,1056664,3.18%,,2.3,2.5,8.70%
>>>> 471.omnetpp,1045496,1046664,-0.11%,,27.9,28.4,1.79%
>>>> 473.astar,1106680,1071992,3.13%,,2.4,2.6,8.33%
>>>> 482.sphinx3,1216600,1182680,2.79%,,16.5,16.3,-1.21%
>>>> 483.xalancbmk,4666936,4669112,-0.05%,,9.7,9.6,-1.03%
>>>> ,,,,,,,
>>>> SIZE SUM,34307616,34061104,0.72%,TOTAL SCORES,5.7,5.8,1.75%
>>>>
>>>>
>>>> various benchmarks:
>>>>
>>>> binary,base -Oz size,ML -Oz size,ML shrink over base
>>>> llvm:opt,66318624,62124256,6.32%
>>>> math_1,6181552,5884144,4.81%
>>>> event_management_1,4998728,4802696,3.92%
>>>> llvm:lcalsBRaw,1270168,1222168,3.78%
>>>> llvm:lcalsBLambda,1270232,1222232,3.78%
>>>> llvm:lcalsARaw,1276248,1228248,3.76%
>>>> llvm:lcalsALambda,1276440,1228504,3.76%
>>>> llvm:lcalsCRaw,1278936,1231000,3.75%
>>>> llvm:lcalsCLambda,1279064,1231256,3.74%
>>>> llvm:Blur,1236888,1191192,3.69%
>>>> image_processing_diffusion,1236120,1190488,3.69%
>>>> llvm:Interpolation,1237208,1191576,3.69%
>>>> llvm:harris,1237208,1191768,3.67%
>>>> llvm:Dither,1238232,1192792,3.67%
>>>> event_management_2,4741096,4568584,3.64%
>>>> hashing_1,5189360,5004176,3.57%
>>>> compression_2,5329392,5140496,3.54%
>>>> math_2,6072336,5858992,3.51%
>>>> crypto_1,4721576,4556008,3.51%
>>>> math_4,27720208,26755568,3.48%
>>>> math_3,27732496,26767920,3.48%
>>>> infra_1,30673232,29630464,3.40%
>>>> hashing_2,5834544,5637168,3.38%
>>>> image_processing_entropy,5854960,5657008,3.38%
>>>> hashing_3,5831088,5634288,3.38%
>>>> memory_management_1,4957960,4790632,3.37%
>>>> hasing_4,5816048,5619888,3.37%
>>>> data_storage_2,5852976,5655792,3.37%
>>>> compression_1,5971984,5771120,3.36%
>>>> arena_unittest,6672944,6453584,3.29%
>>>> data_storage_3,44113232,42710960,3.18%
>>>> data_storage_1,22858992,22132960,3.18%
>>>> datastructures_1,6362032,6161264,3.16%
>>>> infra_2,56828720,55053496,3.12%
>>>> datastructures_2,164558704,160790320,2.29%
>>>> llvm:clang,77027416,75672088,1.76%
>>>> benchmark_infra,20221424,19944432,1.37%
>>>> search_1,169475360,167645632,1.08%
>>>> protobuf_3,24071504,23895440,0.73%
>>>> protobuf_1,24071504,23895504,0.73%
>>>> protobuf_2,24071504,23895504,0.73%
>>>> protobuf_4,24071504,23895504,0.73%
>>>> protobuf_5,24071504,23895504,0.73%
>>>> protobuf_6,23982800,23809424,0.72%
>>>>
>>>> On Wed, Apr 8, 2020 at 2:11 PM Johannes Doerfert <
>>>> johannesdoerfert at gmail.com> wrote:
>>>>
>>>>>
>>>>> Cool!
>>>>>
>>>>> I skipped to the end and tried to access the gdoc and the spreadsheet
>>>>> but it did tell me I need permissions. Can you make them accessible or
>>>>> am I the problem?
>>>>>
>>>>> Thanks,
>>>>> Johannes
>>>>>
>>>>> On 4/8/20 4:04 PM, Mircea Trofin via llvm-dev 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
>>>>> >
>>>>> >
>>>>> > _______________________________________________
>>>>> > LLVM Developers mailing list
>>>>> > llvm-dev at lists.llvm.org
>>>>> > https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
>>>>>
>>>>> _______________________________________________
>> LLVM Developers mailing list
>> llvm-dev at lists.llvm.org
>> https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
>>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20200409/20353ada/attachment.html>
More information about the llvm-dev
mailing list