pyvene.models.modeling_utils

Functions

b_sd_to_bsd(tensor, s)	Convert a tensor of shape (b, s*d) back to (b, s, d).
bhsd_to_bs_hd(tensor)	Convert a tensor of shape (b, h, s, d) to (b, s, h*d).
bs_hd_to_bhsd(tensor, h)	Convert a tensor of shape (b, s, h*d) back to (b, h, s, d).
bsd_to_b_sd(tensor)	Convert a tensor of shape (b, s, d) to (b, s*d).
do_intervention(base_representation, ...)	Do the actual intervention.
gather_neurons(tensor_input, unit, ...[, device])	Gather intervening neurons.
get_dimension_by_component(model_type, ...)	Based on the representation, get the aligning dimension size.
get_internal_model_type(model)	Return the model type.
get_module_hook(model, representation[, backend])	Render the intervening module with a hook.
getattr_for_torch_module(model, parameter_name)	Recursively fetch the model based on the name.
is_gru(model)	Determine if this is a transformer model.
is_mlp(model)	Determine if this is a mlp model.
is_stateless(model)	Determine if the model is stateful (e.g., rnn) or stateless (e.g., transformer)
is_transformer(model)	Determine if this is a transformer model.
output_to_subcomponent(output, component, ...)	Split the raw output to subcomponents if specified in the config.
print_forward_hooks(main_module)	Function to print forward hooks of a module and its sub-modules.
remove_forward_hooks(main_module)	Function to remove all forward and pre-forward hooks from a module and
scatter_neurons(tensor_input, ...[, device])	Replace selected neurons in tensor_input by replacing_tensor_input.
simple_output_to_subcomponent(output, ...)	This is an oversimplied version for demo.
simple_scatter_intervention_output(...)	This is an oversimplied version for demo.
weighted_average(values, weights)

Classes

HandlerList(handlers)

General class to set hooks and set off hooks.