gradnet.gradnet.GradNet

class gradnet.gradnet.GradNet(num_nodes, budget, mask=None, adj0=None, delta_sign='nonnegative', final_sign='free', directed=False, rand_init_weights=True, strict_budget=True, cost_matrix=None, cost_aggr_norm=1, *, device=None, dtype=None)

User-facing GradNet: learn a constrained delta over a base adjacency.

This thin wrapper owns the mask, cost matrix, and base adjacency adj0, and delegates the trainable parameters to either a dense or sparse parameterization depending on mask layout.

Construct a GradNet instance.

Parameters:

• num_nodes (int) – Number of nodes (matrix dimension).

• budget (float | None) – Target cost-weighted p-norm of the perturbation. If None, no budget normalization is enforced.

• mask (torch.Tensor | None, optional) – Active-entry mask. Dense masks result in a dense parameterization; sparse COO masks use the sparse backend. If None, defaults to all-ones off-diagonal.

• adj0 (torch.Tensor | None, optional) – Base adjacency. If None, uses a zero matrix matching the selected backend layout.

• delta_sign (str, optional) – Sign constraint for delta. One of {"free", "nonnegative", "nonpositive"}.

• final_sign (str, optional) – Sign constraint applied to the returned adjacency. One of {"free", "nonnegative", "nonpositive"}.

• directed (bool, optional) – If False, symmetrize delta and expect a symmetric cost matrix.

• rand_init_weights (bool | float, optional) – Initialization mix coefficient a. Cast to float and clamped to [0,1]. a = 1.0 or True yields fully random U(0,1); a = 0.0 or False yields uniform ones. Intermediate values yield interpolation.

• strict_budget (bool, optional) – If True, always scale up/down to the exact budget. If False, scale down only.

• cost_matrix (torch.Tensor | None, optional) – Per-entry costs for normalization; defaults to ones. In sparse backend mode, omitted costs remain implicit (unit costs) and no dense default matrix is materialized.

• cost_aggr_norm (int, optional) – Aggregation norm p for the cost-weighted p-norm.

• device (torch.device | str | None, optional) – Target device for buffers/parameters. If None, inferred from input tensors or defaults to CPU.

• dtype (torch.dtype | str | None, optional) – Target dtype for buffers/parameters. If None, inferred from input tensors or from PyTorch defaults.

__init__(num_nodes, budget, mask=None, adj0=None, delta_sign='nonnegative', final_sign='free', directed=False, rand_init_weights=True, strict_budget=True, cost_matrix=None, cost_aggr_norm=1, *, device=None, dtype=None)

Construct a GradNet instance.

Parameters:

• num_nodes (int) – Number of nodes (matrix dimension).

• budget (float | None) – Target cost-weighted p-norm of the perturbation. If None, no budget normalization is enforced.

• mask (torch.Tensor | None, optional) – Active-entry mask. Dense masks result in a dense parameterization; sparse COO masks use the sparse backend. If None, defaults to all-ones off-diagonal.

• adj0 (torch.Tensor | None, optional) – Base adjacency. If None, uses a zero matrix matching the selected backend layout.

• delta_sign (str, optional) – Sign constraint for delta. One of {"free", "nonnegative", "nonpositive"}.

• final_sign (str, optional) – Sign constraint applied to the returned adjacency. One of {"free", "nonnegative", "nonpositive"}.

• directed (bool, optional) – If False, symmetrize delta and expect a symmetric cost matrix.

• rand_init_weights (bool | float, optional) – Initialization mix coefficient a. Cast to float and clamped to [0,1]. a = 1.0 or True yields fully random U(0,1); a = 0.0 or False yields uniform ones. Intermediate values yield interpolation.

• strict_budget (bool, optional) – If True, always scale up/down to the exact budget. If False, scale down only.

• cost_matrix (torch.Tensor | None, optional) – Per-entry costs for normalization; defaults to ones. In sparse backend mode, omitted costs remain implicit (unit costs) and no dense default matrix is materialized.

• cost_aggr_norm (int, optional) – Aggregation norm p for the cost-weighted p-norm.

• device (torch.device | str | None, optional) – Target device for buffers/parameters. If None, inferred from input tensors or defaults to CPU.

• dtype (torch.dtype | str | None, optional) – Target dtype for buffers/parameters. If None, inferred from input tensors or from PyTorch defaults.

Methods

__init__(num_nodes, budget[, mask, adj0, ...])	Construct a GradNet instance.
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().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward([noise_amplitude])	Return the full adjacency A = adj0 + delta.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_delta_adj([noise_amplitude])	Return the normalized perturbation matrix delta from the backend.
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.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules([remove_duplicate])	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([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
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.
renorm_params()	Renormalize internal parameters using the backend's strategy.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_initial_state(delta_adj_raw_0)	Forward to the parameterization's set_initial_state and renormalize.
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([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
device
dtype
dump_patches
training