Add Interactive Multi-Modal Attention Visualization for Vision-Language Models
Author: sisird864
Pull request: no
Labels: name: Feature request

### Feature request

Implement a comprehensive attention visualization toolkit specifically designed for multi-modal transformers that can:

- Visualize attention patterns between text tokens and image patches
- Show hierarchical attention across different transformer layers
- Support interactive exploration of attention heads
- Export visualizations for research papers and presentations

### Motivation

With the growing adoption of multi-modal models (CLIP, DALL-E, Flamingo, BLIP-2), understanding how these models attend to different modalities is crucial for debugging, interpretability, and research. Currently, Transformers library lacks comprehensive tools for visualizing cross-modal attention patterns.

### Your contribution

I would like to submit a PR for this issue.

Technical Specification
Core Components:

1. Attention Extraction Module (~250 lines)
```
class MultiModalAttentionExtractor:
    def __init__(self, model, layer_indices=None):
        self.model = model
        self.attention_maps = {}
        self._register_hooks()
    
    def extract_attention(self, text_inputs, image_inputs):
        # Extract and organize attention patterns
        pass
```
2. Visualization Engine (~400 lines)

Interactive heatmap generation for image-text attention
Token-level attention flow diagrams
Head-wise attention pattern comparison
Support for different color schemes and normalization methods


3. Interactive Dashboard (~300 lines)

Web-based interface using Gradio/Streamlit integration
Real-time attention exploration
Export functionality (PNG, SVG, JSON)
Comparative visualization for multiple inputs


4. Model Compatibility Layer (~200 lines)

Support for CLIP, BLIP, Flamingo, and other vision-language models
Automatic attention layer detection
Handling different attention mechanisms (self, cross, multi-head)



Implementation Example

```
from transformers import CLIPModel, CLIPProcessor
from transformers.visualization import MultiModalAttentionVisualizer

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
visualizer = MultiModalAttentionVisualizer(model)

# Process inputs
inputs = processor(
    text=["a photo of a cat"], 
    images=image, 
    return_tensors="pt"
)

# Generate visualization
attention_viz = visualizer.visualize(
    inputs,
    layer_indices=[6, 11],  # Visualize specific layers
    attention_type="cross",  # Focus on cross-modal attention
    interactive=True
)

# Launch interactive dashboard
attention_viz.show()
```
Key Features

Layer-wise Analysis: Examine how attention patterns evolve through the network
Head-wise Decomposition: Understand specialized roles of different attention heads
Attention Statistics: Compute entropy, sparsity, and other metrics
Comparative Mode: Compare attention patterns across different inputs or models
Research Export: Generate publication-ready visualizations

Use Cases

Research: Understanding model behavior and attention mechanisms
Debugging: Identifying attention-related issues in multi-modal models
Education: Teaching how vision-language models work
Model Development: Optimizing attention mechanisms

Implementation Timeline

Phase 1: Core attention extraction module and visualization engine
Phase 2: Interactive dashboard and model compatibility layer
Phase 3: Documentation, examples, and testing

Why This Feature Matters
As multi-modal models become increasingly prevalent in production applications, understanding their internal mechanics is crucial for debugging, optimization, and research. This tool would provide unprecedented insights into how vision-language models process and relate different modalities.
I'm excited to contribute this feature to enhance the interpretability of multi-modal models in the Transformers ecosystem. This tool would be valuable for both researchers and practitioners working with vision-language models.
Estimated Lines of Code: 1200-1500 lines (excluding tests and documentation)

Comments:
Cool idea, but I think it makes more sense if you develop the tool first in an independent repo! We can consider integrating it into Transformers afterwards if it works well

Thank you for the feedback! That makes sense - developing it as a standalone tool first would allow for faster iteration and community feedback.

I'll create a separate repository (thinking `transformers-attention-viz` or similar) and develop it as a pip-installable package that works seamlessly with Transformers models. This approach would let me:
- Iterate quickly without affecting the main library
- Get community feedback early
- Ensure the API design is solid before any integration

Once I have a working version with good adoption/feedback, I'll circle back for potential integration into Transformers core.

I'll make sure to:
- Keep it compatible with Transformers' design patterns
- Document integration examples clearly  
- Post updates in the Transformers forum for visibility

Thanks again for the guidance! I'll share the repo link here once I have an initial version.

Cool idea!

Hello @Rocketknight1 and @Player256,
I have completed the initial version of the standalone attention visualization toolkit as suggested:

Repository: https://github.com/sisird864/transformers-attention-viz

PyPI: pip install transformers-attention-viz

Demo: [Colab Notebook](https://colab.research.google.com/github/sisird864/transformers-attention-viz/blob/master/demo.ipynb)

Implemented Features:
- Cross-modal attention visualization (BLIP - text→image)
- Self-attention visualization (CLIP, BLIP - vision & text)
- Multi-layer attention analysis
- Interactive Gradio dashboard
- Export to publication-ready formats (PNG/PDF/SVG)
- Attention statistics (entropy, concentration, top-attended regions)
Currently Supported Models:
- BLIP (full cross-modal support)
- CLIP (self-attention only)
- Architecture ready for BLIP-2, Flamingo, etc.
Example Use Case:
```
from transformers_attention_viz import AttentionVisualizer
from transformers import BlipProcessor, BlipForConditionalGeneration

model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")

visualizer = AttentionVisualizer(model, processor)
viz = visualizer.visualize(image="cat.jpg", text="a fluffy cat", attention_type="cross")
viz.show()
```

The tool is designed to integrate seamlessly with HuggingFace Transformers while remaining independent. I'd love to get community feedback and iterate based on real-world usage. Once it's battle-tested and refined, I'd be happy to discuss potential integration into Transformers core.
Thanks you.


Good idea! I'm looking forward to this feature