tdhook 0.1.2

Interpretability with tensordict and torch hooks.

tdhook 🤖🪝

Interpretability with tensordict and torch hooks.

Getting Started

Most methods should work with minimal configuration. Here's a basic example of running Integrated Gradients on a VGG16 model (full example available here):

from tdhook.attribution import IntegratedGradients

# Define attribution target (e.g., zebra class = 340)
def init_attr_targets(targets, _):
    zebra_logit = targets["output"][..., 340]
    return TensorDict(out=zebra_logit, batch_size=targets.batch_size)

# Compute attribution
with IntegratedGradients(init_attr_targets=init_attr_targets).prepare(model) as hooked_model:
    td = TensorDict({
        "input": image_tensor,
        ("baseline", "input"): torch.zeros_like(image_tensor) # required for integrated gradients
    }).unsqueeze(0)
    td = hooked_model(td) # Access attribution with td.get(("attr", "input"))

To dig deeper, see the documentation.

Features

• Integrated Gradients:
• Steering Vectors:

Python Config

Using uv to manage python dependencies and run scripts.

Scripts

This project uses Just to manage scripts, refer to their instructions for installation.