transformer-attention-hooker 0.1.1a1

A lightweight, robust utility for extracting and visualizing attention weights from PyTorch Transformer models.

Transformer-Attention-Hooker

A lightweight, robust utility for extracting and visualizing attention weights from PyTorch Transformer models.

This tool simplifies the process of debugging and analyzing Transformer internals by automatically hooking into nn.MultiheadAttention modules, handling the need_weights=True flag, and managing multiple forward passes (e.g., in generation loops or shared layers).

Features

• Automatic Hooking: Automatically detects nn.MultiheadAttention layers using regex.
• Force Weights: Automatically sets need_weights=True during the forward pass so you don't have to modify your model code.
• Layer Reuse Support: Correctly captures attention weights even if a layer is called multiple times (e.g., in a loop or with shared weights).
• Cross-Attention Support: Works with both square self-attention and rectangular cross-attention matrices.
• Visualization Tools: Includes a built-in visualizer to plot attention heads as heatmaps.

Requirements

• Python 3.6+
• PyTorch
• Matplotlib

pip install torch matplotlib

Quick Start

1. Extracting Attention Weights

Wrap your model with TransformerAttentionHooker before running the forward pass.

import torch
import torch.nn as nn
from transformer_attention_hooker import TransformerAttentionHooker

# 1. Define or load your model
model = nn.TransformerEncoder(
    nn.TransformerEncoderLayer(d_model=32, nhead=4, batch_first=True),
    num_layers=2
)

# 2. Setup the hooker
# By default, it hooks layers ending with 'self_attn'
hooker = TransformerAttentionHooker(model, layer_regex=r"self_attn$").setup()

# 3. Run a forward pass
x = torch.randn(1, 10, 32) # (Batch, Seq, Feature)
output = model(x)

# 4. Access the captured attention weights
# hooker.values is a dict: {layer_name: [tensor_call_1, tensor_call_2, ...]}
print("Captured layers:", list(hooker.values.keys()))

for name, attn_list in hooker.values.items():
    print(f"Layer: {name}")
    # Get the tensor from the first call
    attn_tensor = attn_list[0]
    print(f"  Shape: {attn_tensor.shape}") # (Batch, Heads, Seq, Seq)

# 5. Cleanup
hooker.remove_hooks()

2. Visualizing Attention

Use the included plot_attention_grid function to generate heatmaps for all heads in a layer.

from transformer_attention_hooker import plot_attention_grid

# Assuming 'attn_tensor' is captured from the example above
layer_name = "layers.0.self_attn"
attn_tensor = hooker.values[layer_name][0]

plot_attention_grid(
    attn_tensor,
    tokens=[f"Token_{i}" for i in range(10)], # Optional: Add labels
    layer_name=layer_name,
    save_path=f"plots/{layer_name}.png"
)

Advanced Usage

Custom Layer Selection

If your model names its attention layers differently (e.g., attn1, cross_attention), you can pass a custom regex pattern.

# Hook all layers containing "attn"
hooker = TransformerAttentionHooker(model, layer_regex=r".*attn.*").setup()

Handling Loops (Generation / Shared Layers)

If a layer is used multiple times during a forward pass (common in recurrent-style generation or weight sharing), hooker.values[layer_name] will contain a list of tensors, one for each call.

# Example: A layer called 3 times
output = model(x)

attn_calls = hooker.values['my_layer']
print(len(attn_calls)) # 3
print(attn_calls[0].shape) # Attention from 1st pass
print(attn_calls[1].shape) # Attention from 2nd pass

Project Structure

• src/attention_hooker.py: Core hooking logic.
• src/visualizer.py: Matplotlib plotting utilities.
• demo_viz.py: Runnable demo script.
• test_edge_cases.py: Tests ensuring robustness for loops and cross-attention.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (c) 2025 Donghwee Yoon