diffusion_cloak

Module for Diffusion Cloak noise layers.

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
*args	Any	Positional arguments used to define the transformer parameter models.	()
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)

Changed in version v2.22.0: Removed deepspeed mixture of experts support from transformer cloak.

Methods:

Name	Description
__call__	Transform the input data.
__getstate__	Prepare a JSON-serializable copy of the noise layer's state.
__setstate__	Set the state of the object.
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.
tensor_parallel	Tensor parallelize the model across the given device mesh.

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 JSON-serializable copy of the noise layer's state.

Returns:

Type	Description
dict[str, Any]	A dictionary containing the configuration of the noise layer, including its type string, the state dict, and the generator
dict[str, Any]	states if they exist.

__setstate__

__setstate__(
    state: dict[str, Any],
    trust_remote_code: bool = False,
    third_party_model_path: (
        str | PathLike[str] | None
    ) = None,
) -> None

Set the state of the object.

state_dict and _generators are both optional keys, and will be restored if they exist in the state.

Parameters:

Name	Type	Description	Default
state	dict[str, Any]	The state to set.	required
trust_remote_code	bool	Whether to trust remote code when loading from HuggingFace Hub.	False
third_party_model_path	str | PathLike[str] | None	The path or huggingface reference to a third-party model to load. This is useful when loading SGTs whose internal structure depends on transformers which are not importable directly through transformers, but are present on the Hugging Face Hub.	None

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, rank_dependent: bool = True
) -> 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
rank_dependent	bool	Whether to add the distributed rank to the seed to ensure that each process samples different noise.	True

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.

tensor_parallel

tensor_parallel(mesh: DeviceMesh) -> None

Tensor parallelize the model across the given device mesh.

Parameters:

Name	Type	Description	Default
mesh	DeviceMesh	The tensor parallel device mesh.	required