7.10. Notes on Custom Pipelines¶

For library developers looking for a way to extend or modify the compiler behavior, you can do so by defining a custom compiler pipeline by inheriting from numba.compiler.BasePipeline. The default numba pipeline is defined as numba.compiler.Pipeline, implementing the .define_pipelines() method, which adds the nopython-mode, object-mode and interpreted-mode pipelines. These three pipelines are defined in BasePipeline by the methods .define_nopython_pipeline, .define_objectmode_pipeline and .define_interpreted_pipeline, respectively..

To use a custom subclass of BasePipeline, supply it as the pipeline_class keyword argument to the @jit and @generated_jit decorators. By doing so, the effect of the custom pipeline is limited to the function being decorated.

Below are the common methods available to implementors of the BasePipeline class:

class numba.compiler.BasePipeline(typingctx, targetctx, library, args, return_type, flags, locals)¶

Stores and manages states for the compiler pipeline

add_cleanup_stage(pm)¶

Add the clean-up stage to remove intermediate results.

add_lowering_stage(pm)¶

Add the lowering (code-generation) stage for nopython-mode

add_optimization_stage(pm)¶

Add optimization stages.

add_pre_typing_stage(pm)¶

Add any stages that go before type-inference. The current stages contain type-agnostic rewrite passes.

add_preprocessing_stage(pm)¶

Add the preprocessing stage that analyzes the bytecode to prepare the Numba IR.

add_typing_stage(pm)¶

Add the type-inference stage necessary for nopython mode.

define_interpreted_pipeline(pm, name='interp')¶

Add the interpreted-mode (fallback) pipeline to the pipeline manager

define_nopython_pipeline(pm, name='nopython')¶

Add the nopython-mode pipeline to the pipeline manager

define_objectmode_pipeline(pm, name='object')¶

Add the object-mode pipeline to the pipeline manager