diffusion_cloak

Classes:

Name	Description
DiffusionCloak	Applies a stochastic transformation to a causal language model input embedding Tensor using TransformerBlockEstimator,

DiffusionCloak

Bases: TransformerCloak

Applies a stochastic transformation to a causal language model input embedding Tensor using TransformerBlockEstimator, with standard deviations parameterized by either CloakStandardDeviationParameterization or DirectStandardDeviationParameterization.

Uses diffusion to explore the input embedding space more deeply for stronger obfuscations. Approximating solutions to stochastic differential equations allows DiffusionCloak to learn a more complicated distribution of inputs that the causal language model treats similarly to the original, untransformed inputs.

Parameters:

Name	Type	Description	Default
scale	tuple[float, float]	The range of standard deviations of the noise.	required
transformer_type	type[TransformerT]	The type of the transformer to build a single layer estimator from.	required
config_path	str	Path to transformer config.	required
percent_to_mask	float | None	The percentage of the input to mask.	None
shallow	float	A fixed temperature like parameter which alters the scale of the standard deviation of the noise.	1.0
seed	int | None	Seed for the random number generator used to generate noise.	None
rho_init	float	Initial values for rhos.	-3.0
std_dropout	float	Dropout ratio for std parameter model.	0.0
mean_dropout	float	Dropout ratio for mean parameter model.	0.0
directly_learn_stds	bool	Whether or not the rhos estimator is used to learn rhos (values in R) or standard deviations directly (values in R^+).	False
mean_num_experts	int	The number of experts to use for the multilayer perceptron after the attention layer for mean_estimator. The value zero corresponds to not using mixture of experts.	0
std_num_experts	int	The number of experts to use for the multilayer perceptron after the attention layer for std_estimator. The value zero corresponds to not using mixture of experts.	0
use_causal_mask	bool	Whether to use a causal or a non-causal attention mask in the llama estimator.	True
num_diffusion_steps	int	The number of steps to use in generating the transformation.	11
stopping_time	float	The final time of the diffusion.	1.0
**kwargs	Any	Keyword arguments used to define the transformer parameter models.	{}

Raises:

Type	Description
NotImplementedError	If percent_to_mask is not None.
ValueError	If num_diffusion_steps is not positive.
ValueError	If stopping_time is not positive.

Note

For more information on SDE SGT, Ito diffusion, and the numerical approximation of SDE's see: * https://en.wikipedia.org/wiki/Stochastic_differential_equation * https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_method_(SDE)

Methods:

Name	Description
__call__	Transform the input data.
__getstate__	Prepare a serializable copy of self.__dict__.
__init_subclass__	Set the default dtype to torch.float32 inside all subclass __init__ methods.
__setstate__	Restore from a serialized copy of self.__dict__.
forward	Transform the input data.
get_applied_transform_components_factory	Create a function that returns the elements of the transform components ('mean' and 'std') applied during the most recent
get_transformed_output_factory	Create a function that returns the transformed output from the most recent forward pass.
initial_seed	Return the initial seed of the CPU device's random number generator.
manual_seed	Seed each of the random number generators.
reset_parameters	Reinitialize parameters and buffers.
seed	Seed each of the random number generators using a non-deterministic random number.

Attributes:

Name	Type	Description
num_diffusion_steps	int	The number of steps to use in generating the transformation.
stopping_time	float	The final time of the diffusion.

num_diffusion_steps property

num_diffusion_steps: int

The number of steps to use in generating the transformation.

stopping_time property

stopping_time: float

The final time of the diffusion.

__call__

__call__(
    input: Tensor,
    noise_mask: Tensor | None = None,
    **kwargs: Any,
) -> torch.Tensor

Transform the input data.

Parameters:

Name	Type	Description	Default
input	Tensor	The input to transform.	required
noise_mask	Tensor | None	An optional mask that selects the elements of input to transform. Where the mask is False, the original input value is returned. Also used to select the elements of the sampled standard deviations to use to mask the input. If None, the entire input is transformed.	None
**kwargs	Any	Additional keyword arguments to the estimator modules.	required

__getstate__

__getstate__() -> dict[str, Any]

Prepare a serializable copy of self.__dict__.

__init_subclass__

__init_subclass__() -> None

Set the default dtype to torch.float32 inside all subclass __init__ methods.

__setstate__

__setstate__(state: dict[str, Any]) -> None

Restore from a serialized copy of self.__dict__.

forward

forward(
    input: Tensor,
    noise_mask: Tensor | None = None,
    **kwargs: Any,
) -> torch.Tensor

Transform the input data.

Parameters:

Name	Type	Description	Default
input	Tensor	The input to transform.	required
noise_mask	Tensor | None	An optional mask that selects the elements of input to transform. Where the mask is 0, the original input value is returned. Also used to select the elements of the sampled standard deviations to use to mask the input. If None, the entire input is transformed.	None
**kwargs	Any	Additional keyword arguments to the estimator modules.	required

Returns:

Type	Description
torch.Tensor	The transformed input data.

Raises:

Type	Description
ValueError	If noise_mask is None.

get_applied_transform_components_factory

get_applied_transform_components_factory() -> Callable[
    [], dict[str, torch.Tensor]
]

Create a function that returns the elements of the transform components ('mean' and 'std') applied during the most recent forward pass.

Specifically, the applied elements are those selected by the noise mask (if supplied) and standard deviation mask (if std_estimator.masker is not None). If no masks are used, all elements are returned.

The applied transform components are returned flattened.

This function is intended to be used to log histograms of the transform components.

Returns:

Type	Description
Callable[[], dict[str, torch.Tensor]]	A function that returns the the elements of the transform components applied during the most recent forward pass.

Examples:

>>> from torch import nn
>>> from stainedglass_core import model as sg_model, noise_layer as sg_noise_layer
>>> base_model = nn.Linear(20, 2)
>>> noisy_model = sg_model.NoisyModel(
...     sg_noise_layer.CloakNoiseLayer1,
...     base_model,
...     target_parameter="input",
... )
>>> get_applied_transform_components = (
...     noisy_model.noise_layer.get_applied_transform_components_factory()
... )
>>> input = torch.ones(1, 20)
>>> noise_mask = torch.tensor(5 * [False] + 15 * [True])
>>> output = noisy_model(input, noise_mask=noise_mask)
>>> applied_transform_components = get_applied_transform_components()
>>> applied_transform_components
{'mean': tensor(...), 'std': tensor(...)}
>>> {
...     component_name: component.shape
...     for component_name, component in applied_transform_components.items()
... }
{'mean': torch.Size([15]), 'std': torch.Size([15])}

get_transformed_output_factory

get_transformed_output_factory() -> Callable[
    [], torch.Tensor
]

Create a function that returns the transformed output from the most recent forward pass.

If super batching is active, only the transformed half of the super batch output is returned.

Returns:

Type	Description
Callable[[], torch.Tensor]	A function that returns the transformed output from the most recent forward pass.

Examples:

>>> from stainedglass_core import noise_layer as sg_noise_layer
>>> noise_layer = sg_noise_layer.CloakNoiseLayer1()
>>> get_transformed_output = noise_layer.get_transformed_output_factory()
>>> input = torch.ones(2, 3, 32, 32)
>>> output = noise_layer(input)
>>> transformed_output = get_transformed_output()
>>> assert output.equal(transformed_output)

initial_seed

initial_seed() -> int

Return the initial seed of the CPU device's random number generator.

manual_seed

manual_seed(seed: int | None) -> None

Seed each of the random number generators.

Setting seed to None will destroy any existing generators.

Parameters:

Name	Type	Description	Default
seed	int | None	The seed to set.	required

reset_parameters

reset_parameters() -> None

Reinitialize parameters and buffers.

This method is useful for initializing tensors created on the meta device.

seed

seed() -> None

Seed each of the random number generators using a non-deterministic random number.