Transformer Visualization Concepts

Prompt

So I am working on designing a 3-D interface that would somehow accurately represent what the neural network is doing with a visual model. I am able to stream the activations of each layer with the output tokens. How would you go about visualizing this in a way that Helps make clear what the network is doing? Feel free to use more ascii art to help

Additional context came before this, where I asked Claude 3.5 Sonnet to explain how transformers work on a technical level. The following concepts are all continuations of the above prompt, generated by Claude.

Concept 01

This is a fascinating design challenge! Here are a few visualization approaches you could consider:

APPROACH 1: FLOWING ACTIVATION STREAMS
(Side view of layers flowing down)

        Layer 1                 ▲ 
   [Token vectors]             │
    ╱  │  │  ╲                │
   ○   ○  ○   ○               │
    \__|__|__/                 │
        │                      │
        ▼                      │   User can rotate
Layer 2 Attention             │   and zoom through
    ╭─────────╮               │   the 3D space
    │ ◂─●─▸   │               │
    │ ◂─●─▸   │               │
    ╰─────────╯               │
        │                      │
        ▼                      │
   Feed Forward                │
    ╭─────╮                   │
    │ ███ │                   │
    ╰─────╯                   ▼

APPROACH 2: INTERACTIVE TENSOR BLOCKS
(Top-down view of one layer)

     Attention Patterns
    ┌─────────────────┐
    │     ╭─┬─┬─╮     │
    │     ├─┼─┼─┤     │
    │     ├─┼─┼─┤     │    → Brighter colors = 
    │     ╰─┴─┴─╯     │      higher activations
    └─────────────────┘

APPROACH 3: FLOWING PARTICLE SYSTEM
(Showing token movement through layers)

Layer N
   ∙∙∙∙∙∙∙    Token 1 particles
    ╲│╱
     ●        Attention blend
    ╱│╲
   ∙∙∙∙∙∙∙    Updated state

Key visualization principles I’d recommend:

Use depth and layers:
- Stack transformer layers vertically
- Allow camera to move between layers
- Show how vectors transform through depth
Show attention clearly:
- Use lines/particles to show attention connections
- Brightness/thickness for attention strength
- Allow focusing on single attention heads
Represent activations:
- Color intensity for activation strength
- Size/scale for magnitude
- Particle density for activation patterns
Interactive elements:

Controls:
┌─────────────────┐
│ ▶ Play         │  - Step through tokens
│ ◀ Layer focus  │  - Zoom into layer
│ ↻ Rotate      │  - Move through space
└─────────────────┘

5. Multiple views:

┌────────────┬────────────┐
│            │            │
│ 3D View    │ Layer View │
│            │            │
├────────────┼────────────┤
│            │            │
│ Attention  │ Token      │
│ Patterns   │ States     │
│            │            │
└────────────┴────────────┘

Some specific features to consider:

Token Tracing:
- Follow specific tokens through the network
- Show how their representations change
- Highlight attention patterns they participate in
Activation Patterns:
- Use color gradients for activation strength
- Show how patterns evolve through layers
- Highlight important feature detectors
Interactive Elements:
- Allow zooming into specific layers
- Isolate attention heads
- Follow specific tokens
- Pause and step through processing
Performance Considerations:
- Use LOD (Level of Detail) for distant layers
- Batch similar visualizations
- Allow toggling different visual elements

The key is balancing:

Information density
Visual clarity
Interactive performance
Intuitive understanding

Concept 02

I’ll propose a 3D visualization concept and use ASCII art to illustrate different views. The key is to make the visualization both accurate and intuitive.

TOP VIEW (looking down at one layer):
                        ATTENTION PATTERNS
              token1    token2    token3    token4
token1   ┌─────┬─────┬─────┬─────┐
         │ ███ │ ░░░ │ ▒▒▒ │ ░░░ │
token2   │ ░░░ │ ███ │ ▒▒▒ │ ░░░ │
         │ ▒▒▒ │ ░░░ │ ███ │ ░░░ │
token3   │ ░░░ │ ░░░ │ ░░░ │ ███ │
token4   └─────┴─────┴─────┴─────┘
         (darker = stronger attention)

SIDE VIEW (showing layers stacked):
Layer N+1    ┌────────────────────┐
             │    ▲ Vector        │
             │    │ Transform     │
Layer N      ├────┼───────────────┤
             │    │               │
             │    │               │
Layer N-1    └────┼───────────────┘
                  │

3D INTERACTIVE CONCEPT:
   Layer N+1    ╱╲       z-axis = layers
   Layer N     ╱──╲     x-axis = sequence position
   Layer N-1  ╱────╲    y-axis = hidden dimensions
             ╱──────╲
            └────────┘

ACTIVATION VISUALIZATION (one token through layers):
L3   [▁█▅▂▃▅█▁] →  Feature importance
L2   [▂▅█▁▂█▃▄] →  by dimension
L1   [▄▂▅█▃▁▅▂] →  (higher = more activated)

Key visualization features I’d recommend:

Interactive Elements:
- Ability to rotate the 3D structure
- Zoom in/out of specific layers
- Click to expand attention patterns
- Highlight specific activation paths
Color Coding:
- Use color intensity for activation strength
- Different colors for different types of features
- Highlight attention patterns with heat maps
Layer Organization:
- Stack layers vertically
- Show connections between layers
- Make attention patterns visible as heat maps
- Display residual connections as curved lines
Dynamic Features:
- Show activations changing in real-time
- Animate information flow through layers
- Allow toggling between different views
- Filter to show specific circuits/features
Important Metrics to Display:
- Attention scores between tokens
- Activation values per dimension
- Layer normalization statistics
- Residual stream contributions

Example interaction flow:

User clicks token "cat" →

     ┌── Token: "cat" ──┐
     │ Layer 3 [█▅▂▃▅] │
     │   ↑             │
     │ Layer 2 [▅█▁▂█] │──→ Attention to "sat"
     │   ↑             │    ╭────────────╮
     │ Layer 1 [▂▅█▃▁] │    │ Score: 0.8 │
     └─────────────────┘    ╰────────────╯

The key is to make it:

Intuitive enough for newcomers
Detailed enough for experts
Interactive to explore different aspects
Clear about what information is being shown
Able to show both high-level patterns and detailed features

Concept 03

Here’s my suggested approach for a 3D visualization, broken down into key elements:

[SIDE VIEW]              [TOP-DOWN VIEW OF ONE LAYER]
   z                        y
   ↑                        ↑
   │     Layer N           │
   □──────────────┐        │   ┌─────────────────┐
   │   Layer 3    │        │   │     Attn1       │
   │   Layer 2    │        └───┼─► Attn2   Attn8 │
   │   Layer 1    │            │     ....        │
   └──────────────┘      x →   └─────────────────┘


[DETAILED LAYER VIEW - STREAMING ACTIVATIONS]

   4096 dimensions
    ┌──────────┐
    │ ▂▃▅▂▇▁▄▆ │ Token1 "The"      Activation strength:
    │ ▄▅▁▇▂▆▃▄ │ Token2 "cat"      █ High
    │ ▅▂▆▃▄▇▁▄ │ Token3 "sat"      ▄ Medium
    └──────────┘                    ▁ Low

[ATTENTION VISUALIZATION]
                   
Token1   Token2   Token3
  ●━━━━━━━●━━━━━━━●      Line thickness = attention strength
  ┃       ┃       ┃      Color intensity = activation value
  ●━━━━━━━●━━━━━━━●
  ┃       ┃       ┃
  ●━━━━━━━●━━━━━━━●

Key visualization elements I’d recommend:

Macro Structure:
- Layer stack visible in 3D space
- Ability to “zoom” into individual layers
- Clear separation between attention and feed-forward components
Per-Layer View:
- Show activation vectors as heat maps/bar graphs
- Highlight strongest activations
- Allow filtering to show only most active features
Attention Visualization:
- Interactive attention graph showing token relationships
- Line thickness/color showing attention strength
- Ability to filter weak attention connections
Interactive Elements:
- Click to expand/investigate specific layers
- Hover for detailed activation values
- Time slider to watch activations change as tokens process
Color Coding:
- Use color intensity for activation strength
- Different colors for different types of features
- Highlight specific circuits when detected

Implementation suggestions:

Make it explorable at multiple scales:

[ZOOM LEVELS]

Level 1:   □ Whole Model
           ↓
Level 2:   □ Layer
           ↓
Level 3:   □ Component (Attention/FF)
           ↓
Level 4:   □ Individual Activations

2. Provide different viewing angles:

[COLLAPSED]         [EXPANDED]
   ┌──┐              ┌────────┐
   │L1│      →       │ ▂▃▅▂▇▁ │
   └──┘              └────────┘

This kind of interface would allow users to:

See the big picture of information flow
Dive deep into specific mechanisms
Watch the network “think” in real-time
Identify patterns and important features

Concept 04

Let me propose a 3D visualization concept with some ASCII sketches to explain the key ideas:

TOP VIEW (looking down at layers stacked vertically):
   Layer N
    ┌────────────────┐
    │  t₁   t₂   t₃  │    Each token position gets a
    │  ○    ○    ○   │    "column" in 3D space
    │  │    │    │   │
    └──┼────┼────┼───┘
       │    │    │
    ┌──┼────┼────┼───┐
    │  ○    ○    ○   │    Attention shown as arcs
    │  │╭───╮│    │   │    between tokens
    └──┼────┼────┼───┘
    Layer N-1

SIDE VIEW (showing activation patterns):
                   HIGH
    Layer N     ▁▂█▇▃▁    Activation intensity shown
    Layer N-1   ▁█▇▂▁▁    as height/color/brightness
    Layer N-2   ▂▇█▅▂▁    
    Layer N-3   ▁▂▇█▅▂    
                   LOW    

3D COMPOSITE VIEW:
    Layer N      ╱|    Each layer is a "floor" with:
     ▄▟██▛▄    ╱ |    - Token positions as columns
    ▟████████ ╱  |    - Activation values as height/color
    ████████▛╱   |    - Attention as arcs between tokens
    ▜██████╱    ╱     - Previous layers visible below
     ▀██▀╱    ╱
       ╱    ╱

Key visualization principles I’d recommend:

Spatial Organization:
- Layers stacked vertically like floors of a building
- Each token position gets a vertical “column” through the layers
- Preserves the spatial relationship between tokens
Activation Visualization:
- Use height/color/brightness to show activation strengths
- Could use particle effects or glowing intensity
- Allows seeing patterns of activation across layers
Attention Visualization:
- Show attention as arcs or beams between token positions
- Thickness/brightness indicates attention strength
- Could be toggleable to reduce visual complexity
Interactive Features:
- Allow rotating/zooming the 3D view
- Enable showing/hiding different components
- Let users “slice” through layers
Time Dimension:
- Animate how activations change as tokens are processed
- Show the flow of information through the network
- Visualize attention patterns shifting

Think of it like a “neural city” where:

Buildings are token columns
Floors are layers
Light intensity shows activation
Beams between buildings show attention
The whole city “pulses” as it processes text

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Hopefully I will be able to share the fruits of these labors in the near future.

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