vision

Module for visualizing transformed images.

Classes:

Name	Description
`StainedGlassTransformForVision`	A client for producing protected image embeddings using Stained Glass Transforms.
`TransformedImageVisualizationManager`	Captures `NoisyModel` input images and intermediate activations and creates visualizations, formatted as a grid of images where

ActivationImages ¶

Bases: TypedDict

A dictionary of images, comprised of a grid of input images and their corresponding activations.

Attributes:

Name	Type	Description
`activation`	`Tensor`	An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer output.
`mask`	`NotRequired[Tensor]`	An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer mask.
`masked_std`	`NotRequired[Tensor]`	An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer mask applied to the
`mean`	`Tensor`	An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer mean.
`std`	`Tensor`	An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer standard deviation.

activation `instance-attribute` ¶

activation: Tensor

An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer output.

mask `instance-attribute` ¶

mask: NotRequired[Tensor]

An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer mask.

masked_std `instance-attribute` ¶

masked_std: NotRequired[Tensor]

An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer mask applied to the standard deviation.

mean `instance-attribute` ¶

mean: Tensor

An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer mean.

std `instance-attribute` ¶

std: Tensor

An image tensor, formatted as a grid, where each row contains an input image and its corresponding noise layer standard deviation.

InputInfo `dataclass` ¶

Information about an input tensor for the transform, which can be used to infer the minimal submodules necessary for the forward pass.

Attributes:

Name	Type	Description
`input_dtype`	`dtype`	The data type of the input tensor.
`input_shape`	`tuple[int \| None, ...]`	The shape of the input tensor for the transform, with `None` a dimension where the size can vary. This is usually

input_dtype `instance-attribute` ¶

input_dtype: dtype

The data type of the input tensor.

input_shape `instance-attribute` ¶

input_shape: tuple[int | None, ...]

The shape of the input tensor for the transform, with None a dimension where the size can vary. This is usually (None, 3, None, None) for a batch of 3-channel images of varying height and width.

StainedGlassTransformForVision ¶

Bases: StainedGlassTransform[NoisyModelT, NoiseLayerT]

A client for producing protected image embeddings using Stained Glass Transforms.

This class can be used to package and distribute Stained Glass Transforms designed for vision models.

Because it inherits from StainedGlassTransform, it can also use any functionality provided by that base class, including serialization/deserialization, saving/loading from the Hugging Face Hub, etc...

Note

Instances of this class simply wrap the noisy model passed into their constructor. This means that changes made to the noisy model after the client's creation will affect the client's behavior and vice versa. If you need an independent copy of the client, you should either serialize/deserialize it or use copy.deepcopy.

Warning

Inferring the minimal parameters of the client requires a forward pass through the model. This assumes that the model has a static computational graph, i.e. the forward pass will require the same parameters for any valid input. This inference is done implicitly and automatically at initialization on an arbitrary input. To override the inferred parameters with parameters of your choosing, you can pass in the parameter_names argument to the constructor. Note, however, that you must specify all of the parameters necessary to calculate the protected input to the base model.

Warning

Calls to forward or __call__ are not guaranteed to be thread-safe or reentrant.

Attributes:

Name	Type	Description
`truncated_module`		The truncated module that wraps the noisy model. This module acts like the noisy model, but its forward method will return early as soon as the noise layer is applied, i.e. its output will be the transformed input embeddings, without calling any unnecessary layers of the noisy/base model. Generally, there is little need for users to interact with this attribute directly. If you need to access the noisy model, you can do so via the `self.noisy_model` attribute.
`parameter_names_relative_to_base_model`		Parameters of the base model to be saved and loaded during serialization and deserialization. This should be the minimal list of parameters necessary to get the base model's input embeddings. Each of these parameter names should be relative to the base model. I.e. if the base model's input embedding submodule can be accessed by `base_model.model.embed_tokens`, then the parameter name for its weight matrix should be `model.embed_tokens.weight`. Note that this attribute is only used for serialization and deserialization. Although non-minimal parameters will be included in the state_dict and when serializing, they will not be used to calculate the protected input embeddings.
`name`		The name of the StainedGlassTransformForVision. This is used to identify the transform when saving and loading.
`model_card_data`		Optional model card data to associate with the Stained Glass Transform. Useful for providing metadata when sharing the transform on the Hugging Face Hub. Follow the documentation on Model Cards and `ModelCardData` for more information on how to fill out the model card data. Useful for providing context about the model's intended use, training data, and other relevant information.
`input_info`		Optional information about the input tensor for the transform, which can be used to infer the minimal submodules necessary for the forward pass. This is required if neither `parameter_names` nor `include_all_base_model_params` is set, and if either of those are set, this information is ignored, except for being stored as metadata.
`noise_layer_type`		The type of noise layer used in the transform. This is used for serialization and deserialization to ensure that the noise layer is correctly reconstructed when loading the transform. This should be the actual type of the noise layer, not a string or the name of the type.

Examples:

Preparing the Model. It is important that the base model passed into the Noisy Model has implemented getstate and setstate methods that save/load a JSON-serializable dictionary of the state. This does not need to include the model's parameters/buffers since, those are handled by the SGT's state_dict and load_state_dict methods, but it should include any information necessary to reconstruct the architecture of the model prior to loading the state dict.

>>> import torch
>>> from torch import nn
>>> class BaseModel(nn.Module):
...     def __init__(self, num_classes: int = 10) -> None:
...         super().__init__()
...         self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
...         self.relu = nn.ReLU()
...         self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
...         self.flatten = nn.Flatten()
...         self.fc = nn.Linear(32 * 32 * 32, num_classes)
...
...     def forward(self, x):
...         x = self.conv1(x)
...         x = self.relu(x)
...         x = self.conv2(x)
...         x = self.flatten(x)
...         x = self.fc(x)
...         return x
...
...     def __getstate__(self) -> dict:
...         return {
...             "num_classes": self.fc.out_features,
...         }
...
...     def __setstate__(self, state: dict) -> None:
...         self.__init__(num_classes=state["num_classes"])

>>> import stainedglass_core.transform.vision as vision_module
>>> BaseModel.__module__ = vision_module.__name__
>>> setattr(vision_module, "BaseModel", BaseModel)
>>> base_model = BaseModel()
>>> from stainedglass_core import noise_layer as sg_noise_layer
>>> from stainedglass_core import model as sg_model
>>> noisy_model = sg_model.NoisyModel(
...     noise_layer_class=sg_noise_layer.CloakNoiseLayer,
...     base_model=base_model,
...     target_parameter="x",
... )

Creating the StainedGlassTransformForVision

>>> from stainedglass_core.transform.vision import StainedGlassTransformForVision
>>> sgt_vision = StainedGlassTransformForVision(
...     model=noisy_model,
...     input_info=None,  # Not needed since target_parameter is the input
...     noise_layer_type=sg_noise_layer.CloakNoiseLayer,
...     name="sample-sgt-for-vision",
... )

Inference with the StainedGlassTransformForVision

>>> random_image = torch.rand(
...     (1, 3, 64, 64)
... )  # A random image tensor with shape (batch_size, color_channels, height, width)
>>> transformed_image = sgt_vision(
...     random_image
... )  # Pass the image through the transform
>>> transformed_image
tensor(...)

Saving the client:

>>> import tempfile
>>> temp_file = tempfile.NamedTemporaryFile(suffix=".sgt")
>>> FILE_PATH = temp_file.name
>>> sgt_vision.save_pretrained(FILE_PATH)

Loading the client:

>>> loaded_sgt_vision = StainedGlassTransformForVision.from_pretrained(FILE_PATH)
>>> transformed_image = loaded_sgt_vision(
...     random_image
... )  # Pass the image through the loaded client
>>> transformed_image
tensor(...)

If no part of the base model is required for the inference pass of the Stained Glass Transform (i.e. the noise layer targets the input and does not depend on the base model), then it can be useful to not save the base model's class information. This might help avoid a situation where the base model's class cannot be found during loading (for example if the base model's class is defined in a third party library that your users do not need to depend on). It also avoids the need to define custom __getstate__ and __setstate__ methods on the base model. PlaceholderBaseModel can be a helpful tool for this.

>>> from stainedglass_core.transform import base
>>> import copy
>>> no_base_model_noisy_model = copy.copy(noisy_model)
>>> no_base_model_noisy_model.base_model = base.PlaceholderBaseModel()
>>> sgt_vision_no_base_model = StainedGlassTransformForVision(
...     model=no_base_model_noisy_model,
...     input_info=None,  # Not needed since target_parameter is the input
...     noise_layer_type=sg_noise_layer.CloakNoiseLayer,
...     name="sgt-for-vision-without-base-model",
... )
>>> transformed_image = sgt_vision_no_base_model(
...     random_image
... )  # Pass the image through the transform without a base model
>>> transformed_image
tensor(...)

Added in version v3.24.0.

Methods:

Name	Description
`__init__`	Initialize the Stained Glass Transform for Vision.
`__repr__`	Safe string representation of the Stained Glass Transform module.
`__setstate__`	Deserialize the Stained Glass Transform for Vision.
`forward`	Run the forward pass of the Stained Glass Transform for Vision.
`from_pretrained`	Load the client from the given path.
`generate_model_card`	Generate model card from instance model card metadata and class templates.
`infer_minimal_parameters`	Infer the minimal parameters of the client, excluding parameters not needed for the client.
`infer_minimal_submodules`	Infer the minimal submodules necessary for the forward pass of the Stained Glass Transform for Vision.
`manual_seed`	Set seed to enable/disable reproducible behavior.
`override_runtime_config_during_load`	Override the runtime configuration during the loading of the Stained Glass Transform for Vision.
`push_to_hub`	Upload model checkpoint to the Hub.
`save_pretrained`	Save the client to the given path.
`state_dict`	Get the state dictionary of the client, excluding parameters not needed for the client.

is_input_targeted_sgt `property` ¶

is_input_targeted_sgt: bool

Whether this Stained Glass Transform's noise layer targets the input of the model.

If True, then no part of the base model is involved in the forward pass of the transform, and therefore inferring the minimal submodules can be bypassed.

noise_layer `property` ¶

noise_layer: NoiseLayerT

Alias for the contained noise layer.

noisy_model `property` ¶

noisy_model: NoisyModelT

Alias for the contained NoisyModel.

Warning

A deserialized Stained Glass Transform may not include the complete base-model parameters. Calling the underlying noisy model referenced in this property can fail.

parameter_names_relative_to_client `property` ¶

parameter_names_relative_to_client: list[str]

Get the minimal parameters of the client, excluding parameters not needed for the client.

This property will first check if self.parameter_names_relative_to_base_model is set (this is usually set via the parameter_names argument in the __init__ method). If it is, then it will return the parameters defined there, but with the submodule names changed to be relative to the client.

If self.parameter_names_relative_to_base_model is not set, then it will return the parameters inferred by the infer_minimal_parameters method's most recent call. This requires that the infer_minimal_parameters method has been called at least once before accessing this property.

Note

self.parameter_names_relative_to_base_model, if specified, will override the inferred parameters in calculating this property.

Returns:

Type	Description
`list[str]`	The minimal parameters of the client, excluding parameters not needed for the client.

Raises:

Type	Description
`ValueError`	If the minimal parameters of the base model have not been specified manually or inferred automatically.

parameter_names_to_remove_relative_to_client `property` ¶

parameter_names_to_remove_relative_to_client: list[str]

Get the parameters to ignore when saving the client, excluding parameters not needed for the client.

This is effectively the set of all parameters in the client that are not in parameter_names_relative_to_client, considering duplicate parameters shared by multiple modules (and thus can be accessed by multiple names).

Returns:

Type	Description
`list[str]`	The parameters to ignore when saving the client, excluding parameters not needed for the client.

stainedglass_core_version `property` ¶

stainedglass_core_version: str | None

Get the version of Stained Glass Core used to save the Stained Glass Transform.

Returns:

Type	Description
`str \| None`	The version of Stained Glass Core used to save the Stained Glass Transform.

init ¶

__init__(
    model: NoisyModelT,
    input_info: InputInfo | None,
    noise_layer_type: type[NoiseLayerT],
    parameter_names: list[str] | None = None,
    include_all_base_model_params: bool = False,
    name: str | None = None,
    model_card_data: ModelCardData | None = None,
) -> None

Initialize the Stained Glass Transform for Vision.

Parameters:

Name	Type	Description	Default
`model` ¶	`NoisyModelT`	The base model to be transformed.	required
`input_info` ¶	`InputInfo \| None`	Information about an input tensor for the transform, which can be used to infer the minimal submodules necessary for the forward pass. This is required if neither `parameter_names` nor `include_all_base_model_params` is set, and if either of those are set, this information is ignored, except for being stored as metadata when serializing. This is also unnecessary if the `target_parameter` of the `NoisyModel` is `"input"`, as no part of the base model is involved in the forward pass of the transform.	required
`noise_layer_type` ¶	`type[NoiseLayerT]`	The type of noise layer to be used.	required
`parameter_names` ¶	`list[str] \| None`	Optional list of parameter names to include.	`None`
`include_all_base_model_params` ¶	`bool`	Whether to include all base model parameters.	`False`
`name` ¶	`str \| None`	Optional name for the transform.	`None`
`model_card_data` ¶	`ModelCardData \| None`	Optional model card data.	`None`

Raises:

Type	Description
`ValueError`	If none of `input_info`, `parameter_names`, or `include_all_base_model_params` are provided and the `target_parameter` of the `NoisyModel` is not `"input"`. In this case, the constructor would not have enough information to infer the minimal submodules.

repr ¶

__repr__() -> <class 'str'>

Safe string representation of the Stained Glass Transform module.

The Stained Glass Transform operates with None weight valued submodules which are not needed for the SGT computation. Standard nn.Module.__repr__ calls extra_repr on each submodule, which may attempt to access weight attributes and raise an AttributeError. This override temporarily patches nn.Module.__repr__ to gracefully handle those cases for the duration of the call.

Returns:

Type	Description
`<class 'str'>`	A string representation of the Stained Glass Transform module.

setstate ¶

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

Deserialize the Stained Glass Transform for Vision.

Parameters:

Name	Type	Description	Default
`state` ¶	`dict[str, Any]`	The serialized state.	required
`trust_remote_code` ¶	`bool`	Whether to trust remote code when loading from the Hugging Face 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`
`**kwargs` ¶	`Any`	Unused. This is to allow for compatibility with the parent class which can pass in kwargs to its children.	required

Raises:

Type	Description
`ValueError`	If any unexpected `kwargs` are passed in, as they are not supported.

forward ¶

forward(*args: Any, **kwargs: Any) -> Any

Run the forward pass of the Stained Glass Transform for Vision.

Parameters:

Name	Type	Description	Default
`*args` ¶	`Any`	Positional arguments to pass to the truncated module's forward method.	required
`**kwargs` ¶	`Any`	Keyword arguments to pass to the truncated module's forward method.	required

Returns:

Type	Description
`Any`	The output of the forward pass.

from_pretrained `classmethod` ¶

from_pretrained(
    pretrained_model_name_or_path: str | Path,
    map_location: device | str | None = None,
    index_file_name: str | None = None,
    dtype: str | dtype | None = None,
    noise_layer_attention: (
        Literal[
            "sdpa",
            "flash_attention_2",
            "flex_attention",
            "transformers_default",
            "flash_attention_4",
        ]
        | None
    ) = None,
    third_party_model_path: (
        str | PathLike[str] | None
    ) = None,
    *,
    force_download: bool = False,
    resume_download: bool | None = None,
    proxies: bool | dict[Any, Any] | None = None,
    token: str | bool | None = None,
    cache_dir: str | Path | None = None,
    local_files_only: bool = False,
    revision: str | None = None,
    trust_remote_code: bool = False,
    **model_kwargs: Any
) -> typing.Self

Load the client from the given path.

Parameters:

Name	Type	Description	Default
`pretrained_model_name_or_path` ¶	`str \| Path`	The path to load the client from. This can be a path to a `.sgt` zipfile or a model name on the Hugging Face Hub (such as `Protopia/SGT-for-llama-3.1-8b-instruct-rare-rain-bfloat16`). Passing a local directory is not supported.	required
`map_location` ¶	`device \| str \| None`	The location to map the client to. See torch.device for more information.	`None`
`index_file_name` ¶	`str \| None`	The name of the index file to use within the zipfile. If None, the default index file name will be used.	`None`
`dtype` ¶	`str \| dtype \| None`	The dtype, either as a string or a `torch.dtype`, to use for the noise layer and embedding weights. If None, the default dtype will be used. When passed as a string, it should be formatted as "torch.", e.g. "torch.float32" or "torch.bfloat16".	`None`
`noise_layer_attention` ¶	`Literal['sdpa', 'flash_attention_2', 'flex_attention', 'transformers_default', 'flash_attention_4'] \| None`	The attention type to use for the noise layer. If None, the default attention type will be used.	`None`
`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`
`force_download` ¶	`bool`	Whether to force the download of the client. If False, the client will be downloaded if it is not already present in the cache.	`False`
`resume_download` ¶	`bool \| None`	Unused. Required for compatibility with the Hugging Face Hub API.	`None`
`proxies` ¶	`bool \| dict[Any, Any] \| None`	Unused. Required for compatibility with the Hugging Face Hub API.	`None`
`token` ¶	`str \| bool \| None`	The token to use for authentication with the Hugging Face Hub API.	`None`
`cache_dir` ¶	`str \| Path \| None`	The directory to use for caching the client. If None, the default cache directory will be used.	`None`
`local_files_only` ¶	`bool`	Whether to only use local files and not attempt to download the client. If True, an error will be raised if the client is not present in the cache.	`False`
`revision` ¶	`str \| None`	The revision of the client to use. This can be a branch name, tag name, or commit hash. If None, the default revision will be used.	`None`
`trust_remote_code` ¶	`bool`	Whether to trust remote code when loading from HuggingFace Hub.	`False`
`model_kwargs` ¶	`Any`	Unused. Required for compatibility with the Hugging Face Hub API.	required

Note

When using a third party model path, loading may fail with an error indicating that the provided model type does not match the expected transformer model type. If you trust that the third-party model layout matches the serialized SGT, you can set SGC_IGNORE_MODEL_TYPE_MISMATCH_ON_SGT_LOAD=true to disable this safety check during load.

Returns:

Type	Description
`typing.Self`	The loaded client.

Raises:

Type	Description
`ValueError`	If any `model_kwargs` are passed in, as they are not supported
`IsADirectoryError`	If the specified path is a directory, but a .sgt file path is required.

generate_model_card ¶

generate_model_card(*args: Any, **kwargs: Any) -> <class 'huggingface_hub.repocard.ModelCard'>

Generate model card from instance model card metadata and class templates.

Parameters:

Name	Type	Description	Default
`*args` ¶	`Any`	Positional arguments to huggingface_hub.ModelCard.from_template. Unused (because all arguments are passed by keyword).	required
`**kwargs` ¶	`Any`	Keyword arguments to the template_str passed to huggingface_hub.ModelCard.from_template.	required

Returns:

Type	Description
`<class 'huggingface_hub.repocard.ModelCard'>`	Generated ModelCard object.

Changed in version v2.8.0: Automatically generated model card files now respect instance model card metadata.

infer_minimal_parameters ¶

infer_minimal_parameters() -> None

Infer the minimal parameters of the client, excluding parameters not needed for the client.

This method will infer the minimal parameters of the client by tracing a forward pass through the model. This is useful when the minimal parameters are not known ahead of time.

Raises:

Type	Description
`ValueError`	If the minimal parameters of the client have been specified

infer_minimal_submodules ¶

infer_minimal_submodules() -> list[str]

Infer the minimal submodules necessary for the forward pass of the Stained Glass Transform for Vision.

Returns:

Type	Description
`list[str]`	A list of submodule names, relative to the base model, that are necessary for the forward pass of the transform.

manual_seed ¶

manual_seed(
    seed: int | None, rank_dependent: bool = True
) -> None

Set seed to enable/disable reproducible behavior.

Setting seed to None will disable reproducible behavior.

Parameters:

Name	Type	Description	Default
`seed` ¶	`int \| None`	Value to seed into the random number generator.	required
`rank_dependent` ¶	`bool`	Whether to add the distributed rank to the seed to ensure that each process samples different noise.	`True`

override_runtime_config_during_load `staticmethod` ¶

override_runtime_config_during_load(
    runtime_config: Mapping[str, Any],
    noise_layer_attention: SupportedAttentionImplementationsType = None,
    dtype: str | dtype | None = None,
    **kwargs: Any
) -> dict[str, Any]

Override the runtime configuration during the loading of the Stained Glass Transform for Vision.

Parameters:

Name	Type	Description	Default
`runtime_config` ¶	`Mapping[str, Any]`	The runtime configuration to override.	required
`noise_layer_attention` ¶	`SupportedAttentionImplementationsType`	Optional attention implementation for the noise layer. Unsupported for vision transforms.	`None`
`dtype` ¶	`str \| dtype \| None`	Optional data type for the noise layer. Unsupported for vision transforms.	`None`
`**kwargs` ¶	`Any`	Additional keyword arguments.	required

Returns:

Type	Description
`dict[str, Any]`	The overridden runtime configuration.

Raises:

Type	Description
`ValueError`	If any unexpected `kwargs` are passed in, as they are not supported.

push_to_hub ¶

push_to_hub(repo_id: str, *, config: dict | DataclassInstance | None = None, commit_message: str = 'Upload using stainedglass_core.', private: bool | None = None, token: str | None = None, branch: str | None = None, create_pr: bool | None = None, allow_patterns: list[str] | str | None = None, ignore_patterns: list[str] | str | None = None, delete_patterns: list[str] | str | None = None, model_card_kwargs: dict[str, Any] | None = None) -> <class 'str'>

Upload model checkpoint to the Hub.

Warning

This method is currently not supported on StainedGlassTransform. Instead use save_pretrained with push_to_hub=True.

Use allow_patterns and ignore_patterns to precisely filter which files should be pushed to the hub. Use delete_patterns to delete existing remote files in the same commit. See [upload_folder] reference for more details.

Parameters:

Name	Type	Description	Default
`repo_id` ¶	`str`	ID of the repository to push to (example: `"username/my-model"`).	required
`config` ¶	`dict \| DataclassInstance \| None`	Model configuration specified as a key/value dictionary or a dataclass instance.	`None`
`commit_message` ¶	`str`	Message to commit while pushing.	`'Upload using stainedglass_core.'`
`private` ¶	`bool \| None`	Whether the repository created should be private. If `None` (default), the repo will be public unless the organization's default is private.	`None`
`token` ¶	`str \| None`	The token to use as HTTP bearer authorization for remote files. By default, it will use the token cached when running `hf auth login`.	`None`
`branch` ¶	`str \| None`	The git branch on which to push the model. This defaults to `"main"`.	`None`
`create_pr` ¶	`bool \| None`	Whether or not to create a Pull Request from `branch` with that commit.	`None`
`allow_patterns` ¶	`list[str] \| str \| None`	If provided, only files matching at least one pattern are pushed.	`None`
`ignore_patterns` ¶	`list[str] \| str \| None`	If provided, files matching any of the patterns are not pushed.	`None`
`delete_patterns` ¶	`list[str] \| str \| None`	If provided, remote files matching any of the patterns will be deleted from the repo.	`None`
`model_card_kwargs` ¶	`dict[str, Any] \| None`	Additional arguments passed to the model card template to customize the model card.	`None`

Returns:

Type	Description
`<class 'str'>`	The url of the commit of your model in the given repository.

Raises:

Type	Description
`NotImplementedError`	This method is not implemented.

save_pretrained ¶

save_pretrained(
    save_directory: str | Path,
    *,
    compression: int = 8,
    push_to_hub: bool = False,
    repo_id: str | None = None,
    private: bool = True,
    config: dict | DataclassInstance | None = None,
    model_card_kwargs: dict[str, Any] | None = None,
    **push_to_hub_kwargs: Any
) -> None

Save the client to the given path.

Parameters:

Name	Type	Description	Default
`save_directory` ¶	`str \| Path`	The path to save the client to. Although this is called `save_directory` for compatibility with `ModelHubMixin.save_pretrained`, passing in a directory name is not supported. A `.sgt` zipfile will be generated at the path provided (even if the path provided does not use the `.sgt` file extension)	required
`compression` ¶	`int`	The compression method to use for the ZIP file. Defaults to zipfile.ZIP_DEFLATED, but this can cause very slow serialization times. If serialization times are a problem, use zipfile.ZIP_STORED instead.	`8`
`push_to_hub` ¶	`bool`	Whether to push the client to the Hugging Face Hub.	`False`
`repo_id` ¶	`str \| None`	The repository ID to push the client to. This is required if `push_to_hub` is `True`.	`None`
`private` ¶	`bool`	Whether to make the repository private. This is only used if `push_to_hub` is `True`.	`True`
`config` ¶	`dict \| DataclassInstance \| None`	Unused. Required for compatibility with the Hugging Face Hub API.	`None`
`model_card_kwargs` ¶	`dict[str, Any] \| None`	The kwargs to pass to the model card generator. This is only used if `push_to_hub` is `True`.	`None`
`push_to_hub_kwargs` ¶	`Any`	The kwargs to pass to the `HfApi.upload_folder` method. This is only used if `push_to_hub` is `True`.	required

Raises:

Type	Description
`IsADirectoryError`	If a directory is passed in.
`ValueError`	If `push_to_hub` is `True` and `repo_id` is `None`.
`UserWarning`	If `push_to_hub` is `True` and `private` is `False`.
`compression`	The compression method to use for the ZIP file. Defaults to zipfile.ZIP_DEFLATED, but this can cause very slow serialization times. If serialization times are a problem, use zipfile.ZIP_STORED instead.

Examples:

Uploading a Stained Glass Transform zipfile to the Hugging Face Hub (note that this will also create a local copy of the SGT zipfile):

>>> from stainedglass_core.transform import text
>>> sgt = text.StainedGlassTransformForText.from_pretrained(
...     "path/to/sgt_file.sgt"
... )
>>> sgt.save_pretrained(
...     "new-sgt-zipfile.sgt",
...     push_to_hub=True,
...     repo_id="username/new-sgt-repo",
... )

Optionally, you can override any model card metadata before uploading to the Hub. This can be useful for specifying the base model and datasets used for training Stained Glass Transform. You can also specify additional metadata such as eval_results. See huggingface_hub.ModelCardData for more details on the available fields.

>>> sgt.model_card_data.base_model = (
...     "meta-llama/Llama-3.1-8B-Instruct"
... )
>>> sgt.model_card_data.__dict__["base_model_relation"] = (
...     "adapter"
... )
>>> sgt.model_card_data.datasets = ["Open-Orca/OpenOrca"]
>>> sgt.save_pretrained(
...     "new-sgt-zipfile.sgt",
...     push_to_hub=True,
...     repo_id="username/new-sgt-repo",
... )

Changed in version v2.8.0: Added ability to push Stained Glass Transform to the Hugging Face Hub. BREAKING CHANGE: Argument `path` was renamed `save_directory` for compatibility with ModelHubMixin.save_pretrained

Changed in version v2.20.3: The model safetensors filename was changed for better compatibility with the Hugging Face Hub. This has no practical effect on saving or loading.

state_dict ¶

state_dict(
    *, prefix: str = "", keep_vars: bool = False
) -> dict[str, typing.Any]

Get the state dictionary of the client, excluding parameters not needed for the client.

The parameters considered necessary for the client are those passed into the constructor as parameter_names.

Parameters:

Name	Type	Description	Default
`prefix` ¶	`str`	A prefix added to parameter and buffer names to compose the keys in state_dict.	`''`
`keep_vars` ¶	`bool`	By default the `torch.Tensor`s returned in the state dict are detached from autograd. If it's set to `True`, detaching will not be performed.	`False`

Returns:

Type	Description
`dict[str, typing.Any]`	The state dictionary of the client, excluding parameters not needed for the client.

TensorsToVisualize ¶

Bases: TypedDict

A dictionary of tensors to process into a visualization.

Attributes:

Name	Type	Description
`activation`	`Tensor`	The noise layer activation.
`input`	`Tensor`	The input to the model.
`mask`	`NotRequired[Tensor]`	The noise layer mask.
`masked_std`	`NotRequired[Tensor]`	The masked noise layer standard deviation.
`mean`	`Tensor`	The noise layer mean.
`std`	`Tensor`	The noise layer standard deviation.

activation `instance-attribute` ¶

activation: Tensor

The noise layer activation.

input `instance-attribute` ¶

input: Tensor

The input to the model.

mask `instance-attribute` ¶

mask: NotRequired[Tensor]

The noise layer mask.

masked_std `instance-attribute` ¶

masked_std: NotRequired[Tensor]

The masked noise layer standard deviation.

mean `instance-attribute` ¶

mean: Tensor

The noise layer mean.

std `instance-attribute` ¶

std: Tensor

The noise layer standard deviation.

TransformedImageVisualizationManager ¶

Captures NoisyModel input images and intermediate activations and creates visualizations, formatted as a grid of images where each row contains an input image and its corresponding activation.

Methods:

Name	Description
`__init__`	Construct a new `TransformedImageVisualizationManager`.
`prepare_activation_images`	Collect the input and activation tensors from the most recent forward pass and populate them into a grid of images for each

Attributes:

Name	Type	Description
`grids`	`ActivationImages`	The tensors representing the grid of images to visualize.

grids `property` ¶

grids: ActivationImages

The tensors representing the grid of images to visualize.

init ¶

__init__(
    noisy_model: NoisyModel[ModuleT, ..., NoiseLayerT],
    input_name: str | None = None,
    max_examples: int = 4,
    max_color_channels: int = 3,
) -> None

Construct a new TransformedImageVisualizationManager.

Parameters:

Name	Type	Description	Default
`noisy_model` ¶	`NoisyModel[ModuleT, ..., NoiseLayerT]`	The model to visualize input images and activations for.	required
`input_name` ¶	`str \| None`	The name of the `noisy_model` input `Tensor` argument. If `noisy_model`'s `target_parameter` is set, it will be used by default, otherwise, this argument is required.	`None`
`max_examples` ¶	`int`	The maximum number of rows to display in the visualizations.	`4`
`max_color_channels` ¶	`int`	The maximum number of individual color channels to additionally display in each visualization. If the target layer has more than 3 color channels, these will be displayed in grayscale.	`3`

Raises:

Type	Description
`ValueError`	If `noisy_model` has no `target_parameter` and `input_name` is not provided.
`ValueError`	If `noisy_model` has a `target_parameter` and `input_name` is provided but does not match. In this case, you do not need to provide `input_name`.

prepare_activation_images ¶

prepare_activation_images() -> ActivationImages

Collect the input and activation tensors from the most recent forward pass and populate them into a grid of images for each activation.

Returns:

Type	Description
`ActivationImages`	A dictionary of image tensors, each formatted as a grid of images, where each row corresponds to a specific example.

vision

ActivationImages ¶

activation instance-attribute ¶

mask instance-attribute ¶

masked_std instance-attribute ¶

mean instance-attribute ¶

std instance-attribute ¶

InputInfo dataclass ¶

input_dtype instance-attribute ¶

input_shape instance-attribute ¶

StainedGlassTransformForVision ¶

is_input_targeted_sgt property ¶

noise_layer property ¶

noisy_model property ¶

parameter_names_relative_to_client property ¶

parameter_names_to_remove_relative_to_client property ¶

stainedglass_core_version property ¶

__init__ ¶

model ¶

input_info ¶

noise_layer_type ¶

parameter_names ¶

include_all_base_model_params ¶

name ¶

model_card_data ¶

__repr__ ¶

__setstate__ ¶

state ¶

trust_remote_code ¶

third_party_model_path ¶

**kwargs ¶

forward ¶

*args ¶

**kwargs ¶

from_pretrained classmethod ¶

pretrained_model_name_or_path ¶

map_location ¶

index_file_name ¶

dtype ¶

noise_layer_attention ¶

third_party_model_path ¶

force_download ¶

resume_download ¶

proxies ¶

token ¶

cache_dir ¶

local_files_only ¶

revision ¶

trust_remote_code ¶

model_kwargs ¶

generate_model_card ¶

*args ¶

**kwargs ¶

infer_minimal_parameters ¶

infer_minimal_submodules ¶

manual_seed ¶

seed ¶

rank_dependent ¶

override_runtime_config_during_load staticmethod ¶

runtime_config ¶

noise_layer_attention ¶

dtype ¶

**kwargs ¶

push_to_hub ¶

repo_id ¶

config ¶

commit_message ¶

private ¶

token ¶

branch ¶

create_pr ¶

allow_patterns ¶

ignore_patterns ¶

delete_patterns ¶

model_card_kwargs ¶

save_pretrained ¶

save_directory ¶

compression ¶

push_to_hub ¶

repo_id ¶

activation `instance-attribute` ¶

mask `instance-attribute` ¶

masked_std `instance-attribute` ¶

mean `instance-attribute` ¶

std `instance-attribute` ¶

InputInfo `dataclass` ¶

input_dtype `instance-attribute` ¶

input_shape `instance-attribute` ¶

is_input_targeted_sgt `property` ¶

noise_layer `property` ¶

noisy_model `property` ¶

parameter_names_relative_to_client `property` ¶

parameter_names_to_remove_relative_to_client `property` ¶

stainedglass_core_version `property` ¶

init ¶

`model` ¶

`input_info` ¶

`noise_layer_type` ¶

`parameter_names` ¶

`include_all_base_model_params` ¶

`name` ¶

`model_card_data` ¶

repr ¶

setstate ¶

`state` ¶

`trust_remote_code` ¶

`third_party_model_path` ¶

`**kwargs` ¶

`*args` ¶

`**kwargs` ¶

from_pretrained `classmethod` ¶

`pretrained_model_name_or_path` ¶

`map_location` ¶

`index_file_name` ¶

`dtype` ¶

`noise_layer_attention` ¶

`third_party_model_path` ¶

`force_download` ¶

`resume_download` ¶

`proxies` ¶

`token` ¶

`cache_dir` ¶

`local_files_only` ¶

`revision` ¶

`trust_remote_code` ¶

`model_kwargs` ¶

`*args` ¶

`**kwargs` ¶

`seed` ¶

`rank_dependent` ¶

override_runtime_config_during_load `staticmethod` ¶

`runtime_config` ¶

`noise_layer_attention` ¶

`dtype` ¶

`**kwargs` ¶

`repo_id` ¶

`config` ¶

`commit_message` ¶

`private` ¶

`token` ¶

`branch` ¶

`create_pr` ¶

`allow_patterns` ¶

`ignore_patterns` ¶

`delete_patterns` ¶

`model_card_kwargs` ¶

`save_directory` ¶

`compression` ¶

`push_to_hub` ¶

`repo_id` ¶

`private` ¶

`config` ¶

`model_card_kwargs` ¶

`push_to_hub_kwargs` ¶

`prefix` ¶

`keep_vars` ¶

activation `instance-attribute` ¶

input `instance-attribute` ¶

mask `instance-attribute` ¶

masked_std `instance-attribute` ¶