Transformer Visualization Concepts
Experimenting with Claude’s understanding of transformer models and how it imagines semantically accurate visualizations of what they do internally.
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
└─────────────────┘
- 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
- Use collapsible/expandable views:
[COLLAPSED] [EXPANDED]
┌──┐ ┌────────┐
│L1│ → │ ▂▃▅▂▇▁ │
└──┘ └────────┘
- Provide different viewing angles:
[Front] [Side] [Top]
▣ ▤▤▤ ┌─┐
▣ └─┘ │▣│
▣ └─┘
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
Hopefully I will be able to share the fruits of these labors in the near future.
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