compiler_gym.wrappers

The compiler_gym.wrappers module provides.

Base wrappers

class compiler_gym.wrappers.CompilerEnvWrapper(env: compiler_gym.envs.compiler_env.CompilerEnv)

Wraps a CompilerEnv environment to allow a modular transformation.

This class is the base class for all wrappers. This class must be used rather than gym.Wrapper to support the CompilerGym API extensions such as the fork() method.

__init__(env: compiler_gym.envs.compiler_env.CompilerEnv)

Constructor.

Parameters

env – The environment to wrap.

Raises

TypeError – If env is not a CompilerEnv.

class compiler_gym.wrappers.ActionWrapper(env: compiler_gym.envs.compiler_env.CompilerEnv)

Wraps a CompilerEnv environment to allow an action space transformation.

action(action)

Translate the action to the new space.

reverse_action(action)

Translate an action from the new space to the wrapped space.

class compiler_gym.wrappers.ObservationWrapper(env: compiler_gym.envs.compiler_env.CompilerEnv)

Wraps a CompilerEnv environment to allow an observation space transformation.

observation(observation)

Translate an observation to the new space.

class compiler_gym.wrappers.RewardWrapper(env: compiler_gym.envs.compiler_env.CompilerEnv)

Wraps a CompilerEnv environment to allow an reward space transformation.

reward(reward)

Translate a reward to the new space.

Action space wrappers

class compiler_gym.wrappers.CommandlineWithTerminalAction(env: compiler_gym.envs.compiler_env.CompilerEnv, terminal=CommandlineFlag(name='end-of-episode', flag='# end-of-episode', description='End the episode'))

Creates a new action space with a special “end of episode” terminal action at the start. If step() is called with it, the “done” flag is set.

__init__(env: compiler_gym.envs.compiler_env.CompilerEnv, terminal=CommandlineFlag(name='end-of-episode', flag='# end-of-episode', description='End the episode'))

Constructor.

Parameters

• env – The environment to wrap.

• terminal – The flag to use as the terminal action. Optional.

class compiler_gym.wrappers.ConstrainedCommandline(env: compiler_gym.envs.compiler_env.CompilerEnv, flags: Iterable[str], name: Optional[str] = None)

Constrains a Commandline action space to a subset of the original space’s flags.

__init__(env: compiler_gym.envs.compiler_env.CompilerEnv, flags: Iterable[str], name: Optional[str] = None)

Constructor.

Parameters

• env – The environment to wrap.

• flags – A list of entries from env.action_space.flags denoting flags that are available in this wrapped environment.

• name – The name of the new action space.

class compiler_gym.wrappers.TimeLimit(env: compiler_gym.envs.compiler_env.CompilerEnv, max_episode_steps: Optional[int] = None)

A step-limited wrapper that is compatible with CompilerGym.

Example usage:

>>> env = TimeLimit(env, max_episode_steps=3)
>>> env.reset()
>>> _, _, done, _ = env.step(0)
>>> _, _, done, _ = env.step(0)
>>> _, _, done, _ = env.step(0)
>>> done
True

Datasets wrappers

class compiler_gym.wrappers.IterateOverBenchmarks(env: compiler_gym.envs.compiler_env.CompilerEnv, benchmarks: Iterable[Union[str, compiler_gym.datasets.benchmark.Benchmark]], fork_shares_iterator: bool = False)

Iterate over a (possibly infinite) sequence of benchmarks on each call to reset(). Will raise StopIteration on reset() once the iterator is exhausted. Use CycleOverBenchmarks or RandomOrderBenchmarks for wrappers which will loop over the benchmarks.

__init__(env: compiler_gym.envs.compiler_env.CompilerEnv, benchmarks: Iterable[Union[str, compiler_gym.datasets.benchmark.Benchmark]], fork_shares_iterator: bool = False)

Constructor.

Parameters

• env – The environment to wrap.

• benchmarks – An iterable sequence of benchmarks.

• fork_shares_iterator – If True, the benchmarks iterator will bet shared by a forked environment created by env.fork(). This means that calling env.reset() with one environment will advance the iterator in the other. If False, forked environments will use itertools.tee() to create a copy of the iterator so that each iterator may advance independently. However, this requires shared buffers between the environments which can lead to memory overheads if env.reset() is called many times more in one environment than the other.

class compiler_gym.wrappers.CycleOverBenchmarks(env: compiler_gym.envs.compiler_env.CompilerEnv, benchmarks: Iterable[Union[str, compiler_gym.datasets.benchmark.Benchmark]], fork_shares_iterator: bool = False)

Cycle through a list of benchmarks on each call to reset(). Same as IterateOverBenchmarks except the list of benchmarks repeats once exhausted.

__init__(env: compiler_gym.envs.compiler_env.CompilerEnv, benchmarks: Iterable[Union[str, compiler_gym.datasets.benchmark.Benchmark]], fork_shares_iterator: bool = False)

Constructor.

Parameters

• env – The environment to wrap.

• benchmarks – An iterable sequence of benchmarks.

• fork_shares_iterator – If True, the benchmarks iterator will be shared by a forked environment created by env.fork(). This means that calling env.reset() with one environment will advance the iterator in the other. If False, forked environments will use itertools.tee() to create a copy of the iterator so that each iterator may advance independently. However, this requires shared buffers between the environments which can lead to memory overheads if env.reset() is called many times more in one environment than the other.

class compiler_gym.wrappers.RandomOrderBenchmarks(env: compiler_gym.envs.compiler_env.CompilerEnv, benchmarks: Iterable[Union[str, compiler_gym.datasets.benchmark.Benchmark]], rng: Optional[numpy.random._generator.Generator] = None)

Select randomly from a list of benchmarks on each call to reset().

Note

Uniform random selection is provided by evaluating the input benchmarks iterator into a list and sampling randomly from the list. This will not work for random iteration over infinite or very large iterables of benchmarks.

__init__(env: compiler_gym.envs.compiler_env.CompilerEnv, benchmarks: Iterable[Union[str, compiler_gym.datasets.benchmark.Benchmark]], rng: Optional[numpy.random._generator.Generator] = None)

Constructor.

Parameters

• env – The environment to wrap.

• benchmarks – An iterable sequence of benchmarks. The entirety of this input iterator is evaluated during construction.

• rng – A random number generator to use for random benchmark selection.