pyvene.models.intervenable_base.IntervenableNdifModel

class IntervenableNdifModel(config, model, **kwargs)

Bases: BaseModel

Intervenable model via ndif backend.

__init__(config, model, **kwargs)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(config, model, **kwargs)	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
count_parameters([include_model])	Set device of interventions and the model
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
disable_intervention_gradients()	Disable gradient in the trainable intervention
disable_model_gradients()	Disable gradient in the model
double()	Casts all floating point parameters and buffers to double datatype.
enable_model_gradients()	Enable gradient in the model
eval()	Set the module in evaluation mode.
extra_repr()	Set the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(base[, sources, unit_locations, ...])	Define the computation performed at every call.
generate(base[, sources, unit_locations, ...])
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_cached_activations()	Return the cached activations with keys
get_cached_hot_activations()	Return the cached hot activations with linked keys
get_device()	Get device of interventions and the model
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
get_trainable_parameters()	Return trainable params as key value pairs
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load(load_directory, model[, ...])	Load interventions from disk or hub
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules()	Return an iterator over all modules in the network.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([recurse])	The above, but for HuggingFace.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post hook to be run after module's load_state_dict is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
save(save_directory[, save_to_hf_hub, ...])
set_device(device[, set_model])	Set device of interventions and the model
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_temperature(temp)	Set temperature if needed
set_zero_grad()	Set device of interventions and the model
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad()	The above, but for HuggingFace.

Attributes

BACKEND
T_destination
call_super_init
dump_patches
training

forward(base, sources: List | None = None, unit_locations: Dict | None = None, source_representations: Dict | None = None, subspaces: List | None = None, labels: LongTensor | None = None, output_original_output: bool | None = False, return_dict: bool | None = None, use_cache: bool | None = None)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static load(load_directory, model, local_directory=None, from_huggingface_hub=False)

Load interventions from disk or hub