tiny-dashboard 0.7.3

A tool for visualizing and exploring feature activations in neural language models.

Tiny Activation Dashboard

A tiny easily hackable implementation of a feature dashboard.

Installation

pip install tiny-dashboard

Overview

This repository provides minimal implementations of activations visualization with:

• An online feature dashboard, where you compute and display activations on some custom text
• An offline feature dashboard, which can display precomputed activation examples.

To get an overview of all the features you can check the demo on colab!

Online dashboard demo:

Offline dashboard demo:

Motivation

There are some other good feature activations dashboard tools out there, but I found them very hard to hack on when I wanted to add support for Crosscoders. This implementation is not as complete as https://github.com/jbloomAus/SAEDashboard or even the simplier https://github.com/callummcdougall/sae_vis but in my honest non-biased-at-all opinion, this implementation seems easier to hack on?

If you're looking for a quick and easy to setup tool for feature analysis, this might be the one for you.

Key Features

Both the offline and online dashboards include:

• Token-level activation highlighting
• Hover tooltips showing token details
• Responsive design
• Save HTML reports

1. Offline Feature Exploration

• Analyze pre-computed feature activations
• Visualize max activation examples for specific features
• Expandable text views
• Generate interactive HTML reports

You can either store the max activation examples in a database file, or in a python dictionary.

A. Using a python dictionary

from tiny_dashboard.feature_centric_dashboards import OfflineFeatureCentricDashboard

# Create dashboard with pre-computed activations
max_activation_examples: dict[int, list[tuple[float, list[str], list[float]]]] = ...
# max_activation_examples is a dictionary where the keys are feature indices and the values are lists of tuples. Each tuple contains a float (max activation value), a list of strings (the text of the example), and a list of floats (the activation values for each token in the example).

dashboard = OfflineFeatureCentricDashboard(max_activation_examples, tokenizer)
dashboard.display()

# Export to HTML for sharing
feature_to_export = 0
dashboard.export_to_html("feature_analysis.html", feature_to_export)

B. Using a database file

For larger datasets, you can store your max activation examples in a sqlite3 database. This allows you to avoid loading all the examples into memory. The database should contain a table with:

• A primary key column of type INTEGER
• A column storing lists of examples as a JSON string, where each example is a tuple containing:
  • max_activation_value (float): The highest activation value
  • tokens (list[str]): The sequence of tokens
  • activation_values (list[float]): The activation value for each token

dashboard = OfflineFeatureCentricDashboard.from_db("path/to/db.db", tokenizer, column_name="column_name_of_examples")
dashboard.display()

Check demo.ipynb for an example on how to build such a database from a python dictionary.

2. Online Feature Exploration

The online dashboard allows you to analyze the activations of a model in real-time. This is useful for quickly exploring the activations of a model on your custom prompts.

The online dashboard supports chat_template formatting: just include <eot> in your input text to separate your chat turns. E.g:

What is the capital of France?<eot>The capital of France is Paris.<eot>Good bing

will be interpreted as:

[
    {"role": "user", "content": "What is the capital of France?"},
    {"role": "assistant", "content": "The capital of France is Paris."},
    {"role": "user", "content": "Good bing"}
]

and formated using the tokenizer's chat template.

Two approaches to build your real-time feature analysis dashboard:

A. Class-based Method

Create a class that implements the AbstractOnlineFeatureCentricDashboard class and implements the get_feature_activation function. This function should take a string and a tuple of feature indices and return a tensor of activation values of shape (seq_len, num_features) containing the activations of the specified features for the input text.

from tiny_dashboard.feature_centric_dashboards import AbstractOnlineFeatureCentricDashboard
class DummyOnlineFeatureCentricDashboard(AbstractOnlineFeatureCentricDashboard):
    def get_feature_activation(self, text: str, feature_indices: tuple[int, ...]) -> th.Tensor:
        # Custom activation computation logic
        tok_len = len(self.tokenizer.encode(text))
        activations = th.randn((tok_len, len(feature_indices))).exp()
        return activations
    
    # Optional: override generate_model_response to change the model's response generation

online_dashboards = DummyOnlineFeatureCentricDashboard(tokenizer)
online_dashboards.display()

B. Function-based Method

If you hate classes for some reason, you can also use the function-based method:

from tiny_dashboard.feature_centric_dashboards import OnlineFeatureCentricDashboard
def get_feature_activation(text, feature_indices):
    return th.randn((len(tokenizer.encode(text)), len(feature_indices))).exp()

online_dashboards = OnlineFeatureCentricDashboard(
    get_feature_activation, 
    tokenizer,
    generate_model_response = None,  # Optional: override the model's response generation function
    model = None,  # Optional: pass in a model to use the model's response generation function
    call_with_self = False,  # Whether to call the functions with self as the first argument, defaults to Falses
)
online_dashboards.display()

Specialized Implementations

The package includes several specialized dashboard implementations in dashboard_implementations.py:

CrosscoderOnlineFeatureDashboard

For analyzing features using a crosscoder model that combines base and instruct model activations:

from tiny_dashboard.dashboard_implementations import CrosscoderOnlineFeatureDashboard

base_model, instruct_model, crosscoder = ...
collect_layer = 12

dashboard = CrosscoderOnlineFeatureDashboard(
    base_model=base_model,
    instruct_model=instruct_model,
    crosscoder=crosscoder,
    collect_layer=collect_layer,
    crosscoder_device="cuda"  # optional, use it if the crosscoder is on a different device than the base and instruct models
)
dashboard.display()

Additional specialized implementations can be found in the dashboard_implementations.py file. Feel free to contribute new implementations!

Repository Structure

The repository is organized as follows:

• demo.ipynb: A Jupyter notebook containing minimal examples demonstrating how to use both offline and online dashboards
• src/: Main package directory
  • feature_centric_dashboards.py: Core implementation of the dashboard classes (OfflineFeatureCentricDashboard, OnlineFeatureCentricDashboard, and AbstractOnlineFeatureCentricDashboard)
  • dashboard_implementations.py: Collection of specialized dashboard implementations (e.g., CrosscoderOnlineFeatureDashboard)
  • visualization_utils.py: Utility functions for visualizing activations, without the need to use the dashboard classes
  • html_utils.py: Utility functions for generating HTML elements using templates
  • utils.py: General utility functions for text processing and HTML sanitization
  • templates/: HTML, CSS, and JavaScript templates
    • HTML templates for different components (base layout, feature sections, examples, etc.)
    • styles.css: CSS styling for the dashboard
    • listeners.js: JavaScript for interactive features (tooltips, expandable text)

Contributing

Contributions are welcome! Please feel free to improve the minimal design and add some usage examples.