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Release 0.2.0 (2021-09-28)

This release adds two new compiler optimization problems to CompilerGym: GCC command line flag optimization and CUDA loop nest optimization.

  • [GCC] A new gcc-v0 environment, authored by @hughleat, exposes the command line flags of GCC as a reinforcement learning environment. GCC is a production-grade compiler for C and C++ used throughout industry. The environment provides several datasets and a large, high dimensional action space that works on several GCC versions. For further details check out the reference documentation.
  • [loop_tool] A new loop_tool-v0 environment, authored by @bwasti, provides an experimental intermediate representation of n-dimensional data computation that can be lowered to both CPU and GPU backends. This provides a reinforcement learning environment for manipulating nests of loop computations to maximize throughput. For further details check out the reference documentation.

Other highlights of this release include:

  • [Docker] Published a chriscummins/compiler_gym docker image that can be used to run CompilerGym services in standalone isolated containers (#424).
  • [LLVM] Fixed a bug in the experimental Runtime observation space that caused observations to slow down over time (#398).
  • [LLVM] Added a new utility module to compute observations from bitcodes (#405).
  • Overhauled the continuous integration services to reduce computational requirements by 59.4% while increasing test coverage (#392).
  • Improved error reporting if computing an observation fails (#380).
  • Changed the return type of compiler_gym.random_search() to a CompilerEnv (#387).
  • Numerous other bug fixes and improvements.

Many thanks to code contributors: @thecoblack, @bwasti, @hughleat, and @sahirgomez1!

Release 0.1.10 (2021-09-08)

This release lays the foundation for several new exciting additions to CompilerGym:

  • [LLVM] Added experimental support for optimizing for runtime and compile time (#307). This is still proof of concept and is not yet stable. For now, only the benchmark://cbench-v1 and generator://csmith-v0 datasets are supported.
  • [CompilerGym Explorer] Started development of a web frontend for the LLVM environments. The work-in-progress Flask API and React website can be found in the www directory.
  • [New Backend API] Added a mechanism for sending arbitrary data payloads to the compiler service backends (#313). This allows ad-hoc parameters that do not conform to the usual action space to be set for the duration of an episode. Add support for these parameters in the backend by implementing the optional handle_session_parameter() method, and then send parameters using the send_params() method.

Other highlights of this release include:

  • [LLVM] The Csmith program generator is now shipped as part of the CompilerGym binary release, removing the need to compile it locally (#348).
  • [LLVM] A new ProgramlJson observation space provides the JSON node-link data of a ProGraML graph without parsing to a nx.MultiDiGraph (#332).
  • [LLVM] Added a leaderboard submission for a DQN agent (#292, thanks @phesse001!).
  • [Backend API Update] The Reward.reset() method now receives an observation view that can be used to compute initial states (#341, thanks @bwasti!).
  • [Datasets API] The size of infinite datasets has been changed from float("inf") to 0 (#347). This is a compatibility fix for __len__() which requires integers values.
  • Prevent excessive growth of in-memory caches (#299).
  • Multiple compatibility fixes for compiler_gym.wrappers.
  • Numerous other bug fixes and improvements.

Release 0.1.9 (2021-06-03)

This release of CompilerGym focuses on backend extensibility and adds a bunch of new features to make it easier to add support for new compilers:

  • Adds a new CompilationSession class encapsulates a single incremental compilation session (#261).
  • Adds a common runtime for CompilerGym services that takes a CompilationSession subclass and handles all the RPC wrangling for you (#270).
  • Ports the LLVM service and example services to the new runtime (#277). This provides a net performance win with fewer lines of code.

Other highlights of this release include:

  • [Core API] Adds a new compiler_gym.wrappers module that makes it easy to apply modular transformations to CompilerGym environments without modifying the environment code (#272).
  • [Core API] Adds a new Datasets.random_benchmark() method for selecting a uniform random benchmark from one or more datasets (#247).
  • [Core API] Adds a new compiler_gym.make() function, equivalent to gym.make() (#257).
  • [LLVM] Adds a new IrSha1 observation space that uses a fast, service-side C++ implementation to compute a checksum of the environment state (#267).
  • [LLVM] Adds 12 new C programs from the CHStone benchmark suite (#284).
  • [LLVM] Adds the anghabench-v1 dataset and deprecated anghabench-v0 (#242).
  • Numerous bug fixes and improvements.

Release 0.1.8 (2021-04-30)

This release introduces some significant changes to the way that benchmarks are managed, introducing a new dataset API. This enabled us to add support for millions of new benchmarks and a more efficient implementation for the LLVM environment, but this will require some migrating of old code to the new interfaces (see "Migration Checklist" below). Some of the key changes of this release are:

  • [Core API change] We have added a Python Benchmark class (#190). The env.benchmark attribute is now an instance of this class rather than a string (#222).
  • [Core behavior change] Environments will no longer select benchmarks randomly. Now env.reset() will now always select the last-used benchmark, unless the benchmark argument is provided or env.benchmark has been set. If no benchmark is specified, a default is used.
  • [API deprecations] We have added a new Dataset class hierarchy (#191, #192). All datasets are now available without needing to be downloaded first, and a new Datasets class can be used to iterate over them (#200). We have deprecated the old dataset management operations, the compiler_gym.bin.datasets script, and removed the --dataset and --ls_benchmark flags from the command line tools.
  • [RPC interface change] The StartSession RPC endpoint now accepts a list of initial observations to compute. This removes the need for an immediate call to Step, reducing environment reset time by 15-21% (#189).
  • [LLVM] We have added several new datasets of benchmarks, including the Csmith and llvm-stress program generators (#207), a dataset of OpenCL kernels (#208), and a dataset of compilable C functions (#210). See the docs for an overview.
  • CompilerEnv now takes an optional Logger instance at construction time for fine-grained control over logging output (#187).
  • [LLVM] The ModuleID and source_filename of LLVM-IR modules are now anonymized to prevent unintentional overfitting to benchmarks by name (#171).
  • [docs] We have added a Feature Stability section to the documentation (#196).
  • Numerous bug fixes and improvements.

Please use this checklist when updating code for the previous CompilerGym release:

  • Review code that accesses the env.benchmark property and update to env.benchmark.uri if a string name is required. Setting this attribute by string (env.benchmark = "benchmark://a-v0/b") and comparison to string types (env.benchmark == "benchmark://a-v0/b") still work.
  • Review code that calls env.reset() without first setting a benchmark. Previously, calling env.reset() would select a random benchmark. Now, env.reset() always selects the last used benchmark, or a predetermined default if none is specified.
  • Review code that relies on env.benchmark being None to select benchmarks randomly. Now, env.benchmark is always set to the previously used benchmark, or a predetermined default benchmark if none has been specified. Setting env.benchmark = None will raise an error. Select a benchmark randomly by sampling from the env.datasets.benchmark_uris() iterator.
  • Remove calls to env.require_dataset() and related operations. These are no longer required.
  • Remove accesses to env.benchmarks. An iterator over available benchmark URIs is now available at env.datasets.benchmark_uris(), but the list of URIs cannot be relied on to be fully enumerable (the LLVM environments have over 2^32 URIs).
  • Review code that accesses env.observation_space and update to env.observation_space_spec where necessary (#228).
  • Update compiler service implementations to support the updated RPC interface by removing the deprecated GetBenchmarks RPC endpoint and replacing it with Dataset classes. See the example service for details.
  • [LLVM] Update references to the poj104-v0 dataset to poj104-v1.
  • [LLVM] Update references to the cBench-v1 dataset to cbench-v1.

Release 0.1.7 (2021-04-01)

This release introduces public leaderboards to track the performance of user-submitted algorithms on compiler optimization tasks.

  • Added a new compiler_gym.leaderboard package which contains utilities for preparing leaderboard submissions (#161).
  • Added a LLVM instruction count leaderboard and seeded it with a random search baseline (#117).
  • Added support for Python 3.9, extending the set of supported python versions to 3.6, 3.7, 3.8, and 3.9 (#160).
  • [llvm] Added a new InstCount observation space that contains the counts of each type of instruction (#159).

Build dependencies update notice: If you are building from source and upgrading from an older version of CompilerGym, your build environment will need to be updated. The easiest way to do that is to remove your existing conda environment using conda remove --name compiler_gym --all and to repeat the steps in building from source.

Release 0.1.6 (2021-03-22)

This release focuses on hardening the LLVM environments, providing improved semantics validation, and improving the datasets. Many thanks to @JD-at-work, @bwasti, and @mostafaelhoushi for code contributions.

  • [llvm] Added a new cBench-v1 dataset which changes the function attributes of the IR to permit inlining. cBench-v0 is deprecated and will be removed no earlier than v0.1.6.
  • [llvm] Removed 15 passes from the LLVM action space: -bounds-checking, -chr, -extract-blocks, -gvn-sink, -loop-extract-single, -loop-extract, -objc-arc-apelim, -objc-arc-contract, -objc-arc-expand, -objc-arc, -place-safepoints, -rewrite-symbols, -strip-dead-debug-info, -strip-nonlinetable-debuginfo, -structurizecfg. Passes are removed if they are: irrelevant (e.g. used only debugging), if they change the program semantics (e.g. inserting runtimes bound checking), or if they have been found to have nondeterministic behavior between runs.
  • Extended env.step() so that it can take a list of actions that are all performed in a single batch. This improve efficiency.
  • Added default reward spaces for CompilerEnv that are derived from scalar observations (thanks @bwasti!)
  • Added a new Q learning example (thanks @JD-at-work!).
  • Deprecation: The v0.1.8 release will introduce a new datasets API that is easier to use and more flexible. In preparation for this, the Dataset class has been renamed to LegacyDataset, the following dataset operations have been marked deprecated: activate(), deactivate(), and delete(). The GetBenchmarks() RPC interface method has also been marked deprecated.
  • [llvm] Improved semantics validation using LLVM's memory, thread, address, and undefined behavior sanitizers.
  • Numerous bug fixes and improvements.

Release 0.1.3 (2021-02-25)

This release adds numerous enhancements aimed at improving ease-of-use. Thanks to @broune, @hughleat, and @JD-ETH for contributions.

  • Added a new env.validate() API for validating the state of an environment. Added semantics validation for some LLVM benchmarks.
  • Added a env.fork() method to efficiently duplicate an environment state.
  • The manual_env environment has been improved with new features such as hill climbing search and tab completion.
  • Ease of use improvements for string observation space and reward space names: Added new getter methods such as env.observation.Autophase() and generated constants such as llvm.observation_spaces.autophase.
  • Breaking change: Calculation of environment reward has been moved to Python. Reward functions have been removed from backend service implementations and replaced with equivalent Python classes.
  • Various bug fixes and improvements.

Release 0.1.2 (2021-01-25)

  • Add a new compiler_gym.views.ObservationView.add_derived_space(...) API for constructing derived observation spaces.
  • Added default reward and observation values for env.step() in case of service failure.
  • Extended the public compiler_gym.datasets API for managing datasets.
  • [llvm] Adds -Norm-suffixed rewards that are normalized to unoptimized cost.
  • Extended documentation and example codes.
  • Numerous bug fixes and improvements.

Release 0.1.1 (2020-12-28)

  • Expose the package version through compiler_gym.__version__, and the compiler version through CompilerEnv.compiler_version.
  • Add a notebook version of the "Getting Started" guide that can be run in colab.
  • [llvm] Reformulate reward signals to be cumulative.
  • [llvm] Add a new reward signal based on the size of the .text section of compiled object files.
  • [llvm] Add a LlvmEnv.make_benchmark() API for easily constructing custom benchmarks for use in environments.
  • Numerous bug fixes and improvements.

Release 0.1.0 (2020-12-21)

Initial release.