CompilerGym v0.2.1

Highlights of this release include:

• [Complex and composite action spaces] Added a new schema for describing action spaces (#369). This complete overhaul enables a much richer set of actions to be exposed, such as composite actions spaces, dictionaries, and continuous actions.
• [State Transition Dataset] We have released the first iteration of the state transition dataset, a large collection of (state,action,reward) tuples for the LLVM environments, suitable for large-scale supervised learning. We have added an example learned cost model using a graph neural network in examples/gnn_cost_model (#484, thanks @bcui19!).
• [New examples] We have added several new examples to the examples/ directory, including a new loop unrolling demo based on LLVM (#477, thanks @mostafaelhoushi!), a loop tool demo (#457, thanks @bwasti!), micro-benchmarks for operations, and example reinforcement learning scripts (#484). See examples/README.md for details. We also overhauled the example compiler gym service (#467).
• [New logo] Thanks Christy for designing a great new logo for CompilerGym! (#471)
• [llvm] Added a new Bitcode observation space (#442).
• Numerous bug fixes and improvements.

Deprecations and breaking changes:

• [Breaking change] Out-of-tree compiler services will require updating to the new action space API (#369).
• The env.observation.add_derived_space() method has been deprecated and will be removed in a future release. Please use the new derived_observation_spaces argument to the CompilerEnv constructor (#463).
• The compiler_gym.utils.logs module has been deprecated. Use compiler_gym.utils.runfiles_path instead (#453).
• The compiler_gym.replay_search module has been deprecated and merged into the compiler_gym.random_search (#453).

Full Changelog: v0.2.0...v0.2.1