thunder.recipes.BaseRecipe

class thunder.recipes.BaseRecipe(show_progress=False, fuser='nvfuser', interpreter='thunder.jit', plugins=None)

Bases: Recipe

Compilation recipe with Thunder defaults. The recipe wires a set of executors, transforms and debug options, while providing a single switch to pick the fusion backend (“nvfuser” or “torch.compile”). Should be used as a template to extend.

Parameters:

• show_progress – bool, default False Print interpreter-side progress bars.

• fuser – {“nvfuser”, “torch.compile”}, default “nvfuser” Fusion backend to register. Adjusts self.executor_names so the chosen backend is present and any mutually-exclusive one is removed.

• interpreter – str, default “thunder.jit” Interpreter identifier forwarded to Recipe.

• plugins – Iterable | None Extra Thunder plugins to enable.

__init__(show_progress=False, fuser='nvfuser', interpreter='thunder.jit', plugins=None)

Methods

__init__([show_progress, fuser, ...])
add_plugins(plugins)
apply(model)
get_for_model(model)
register(key)
setup_config()	Build the per-run configuration dictionary.
setup_executors()	Resolves executor names to concrete Executor objects.
setup_fuser()	Reconciles the requested fusion backend with self.executor_names.
setup_lookasides()	rtype: Optional[list[Lookaside]]
setup_transforms()	Constructs the list of graph-level transforms.
validate(model)

setup_config()

Build the per-run configuration dictionary.

Returns:

{} when show_progress is False; otherwise {"debug_options": DebugOptions(show_interpreter_progress=True)}.

Return type:

dict[str, Any]

setup_executors()

Resolves executor names to concrete Executor objects.

Returns:

Instantiated executors in the order given by self.executor_names.

Return type:

list[Executor]

Raises:

• TypeError – If self.executor_names is not a list.

• ValueError – If a non-nvfuser executor cannot be found.

setup_fuser()

Reconciles the requested fusion backend with self.executor_names.

Raises:

NotImplementedError – If fuser is not "nvfuser" or "torch.compile".

Return type:

None

setup_transforms()

Constructs the list of graph-level transforms.

Returns:

Currently [PrunePrologueChecks()]; extend as needed.

Return type:

list[Transform]