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dtch1997 / sae-eap

Edge attribution patching with SAEs
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feat: attribute #2

Closed dtch1997 closed 1 week ago

dtch1997 commented 1 week ago

Summary by CodeRabbit

coderabbitai[bot] commented 1 week ago

Walkthrough

These updates to sae_eap introduce functionality for calculating attribution scores in neural networks using integrated gradients. Key additions include methods to compute activations and gradients, cache values during forward and backward passes, and compute attribution scores. Changes also include indexing nodes in graph structures and computing positional encodings and input lengths within the model.

Changes

File Summary of Changes
sae_eap/attribute.py Added methods for computing activations, gradients, and attribution scores. Introduced hooks to cache values.
sae_eap/graph/index.py Introduced GraphIndexer class with methods to build indices for node outputs and inputs, and retrieve these indices.
sae_eap/utils.py Imported HookedTransformer and added get_npos_and_input_lengths function to compute positional encodings and lengths.

Sequence Diagrams

sequenceDiagram
    participant User
    participant Model
    participant Graph
    participant Cache

    User ->> Model: call attribute(...)
    activate Model
    Model ->> Graph: build graph and index nodes
    activate Graph
    Graph ->> Model: provide nodes and indices
    deactivate Graph

    Model ->> Cache: initialize cache tensors
    activate Cache
    Cache -->> Model: return cached tensors
    deactivate Cache

    Model ->> Model: compute activations and gradients
    Model ->> Model: compute attribution scores using integrated gradients
    deactivate Model

    Model -->> User: return attribution scores

Poem

Amid the bytes, the code does dance,
With gradients flowing in a graceful prance.
Nodes indexed, graphs aligned,
Through tensors cached, connections bind.
A rabbit's work, algorithms entwine,
Attribution scores illuminate, divine.
🐰✨ Debugging’s charm, oh so fine!

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