🔊 AttentionEcho

Cross-request attention pattern reuse for LLM inference optimization

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🎯 What is AttentionEcho?

AttentionEcho is a novel inference optimization technique that reuses attention patterns across semantically similar requests. Unlike traditional prefix caching (which caches KV tensors), AttentionEcho caches the actual attention weights and adjusts them for new queries.

Key Innovation

Standard Inference:
  Request 1: "You are helpful. What is Python?"  → Compute Q @ K.T (expensive)
  Request 2: "You are helpful. What is Java?"    → Compute Q @ K.T (expensive)
  
With AttentionEcho:
  Request 1: "You are helpful. What is Python?"  → Compute Q @ K.T → Cache pattern
  Request 2: "You are helpful. What is Java?"    → Reuse pattern (fast!) ✓

✨ Features

• Semantic Matching: Uses embedding similarity (not exact token match)
• Pattern Adjustment: First-order Taylor expansion for query differences
• Cross-Request Sharing: One user's cached pattern helps another
• Framework Agnostic: Works with PyTorch, NumPy, or any tensor library
• Production Ready: Thread-safe, LRU eviction, comprehensive stats

📦 Installation

pip install attention-echo

# With PyTorch support
pip install attention-echo[torch]

# For development
pip install attention-echo[dev]

🚀 Quick Start

Basic Usage (NumPy)

from attention_echo import AttentionEchoCache, EchoConfig

# Create cache
config = EchoConfig(
    capacity=1000,
    similarity_threshold=0.85
)
cache = AttentionEchoCache(config)

# First request - computes and caches
output1, meta1 = cache.attention_with_echo(
    query=q1, key=k1, value=v1,
    prefix_length=10,
    prefix_embeddings=embeddings1
)
print(meta1)  # {'echo_hit': False, 'tokens_computed': 15}

# Second request with similar prefix - reuses pattern!
output2, meta2 = cache.attention_with_echo(
    query=q2, key=k2, value=v2,
    prefix_length=10,
    prefix_embeddings=embeddings2  # Similar to embeddings1
)
print(meta2)  # {'echo_hit': True, 'similarity': 0.95, 'tokens_echoed': 10}

PyTorch Integration

import torch
from attention_echo.torch import EchoAttention

# Wrap your attention layer
attention = EchoAttention(
    hidden_dim=768,
    num_heads=12,
    cache_capacity=1000
)

# Use like normal attention
output = attention(
    query=q,
    key=k,
    value=v,
    prefix_length=prefix_len
)

# Check stats
print(attention.cache.stats)
# {'hits': 150, 'misses': 20, 'hit_rate': 0.88}

📊 How It Works

1. Semantic Hashing

When a request arrives, we compute a semantic hash of the prefix:

semantic_key = normalize(mean_pool(prefix_embeddings))

2. Cache Lookup

Search for similar cached patterns using cosine similarity:

for cached_key, entry in cache:
    similarity = cosine_sim(query_key, cached_key)
    if similarity > threshold:
        return entry  # Cache hit!

3. Echo Transform

Adjust the cached pattern for the new query:

# First-order Taylor adjustment
delta_q = new_query - cached_query
pattern_adjusted = cached_pattern + alpha * delta_q @ jacobian
pattern_final = softmax(pattern_adjusted)

📈 Performance

Scenario	Cache Hit Rate	Speedup
Chatbots (same system prompt)	90-95%	8-10x
RAG (same context)	70-85%	3-5x
Code assistants	60-80%	2-3x

🔧 Configuration

from attention_echo import EchoConfig

config = EchoConfig(
    # Cache settings
    capacity=1000,              # Max cached patterns
    similarity_threshold=0.85,  # Min similarity for hit
    
    # Pattern adjustment
    adjustment_strength=0.1,    # How much to adjust patterns
    enable_jacobian=True,       # Use first-order adjustment
    
    # Semantic hashing
    hash_dim=128,               # Dimension of semantic keys
)

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    AttentionEcho Pipeline                        │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  Input → Embeddings → Semantic Hash → Cache Lookup               │
│                                           │                      │
│              ┌────────────────────────────┴─────────────┐        │
│              │                                          │        │
│           [HIT]                                     [MISS]       │
│              │                                          │        │
│    Echo Transform                              Full Attention    │
│    (adjust cached pattern)                     (Q @ K.T)         │
│              │                                          │        │
│              │                                    Store in cache │
│              │                                          │        │
│              └────────────────────────────┬─────────────┘        │
│                                           │                      │
│                                    pattern @ V                   │
│                                           │                      │
│                                       Output                     │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

🧪 Running Tests

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# With coverage
pytest tests/ --cov=attention_echo --cov-report=html

📚 Examples

See the examples/ directory:

• basic_usage.py - Simple NumPy example
• torch_integration.py - PyTorch model integration
• benchmark.py - Performance benchmarking
• multi_user_serving.py - Simulated serving scenario

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines first.

📄 License

MIT License - see LICENSE for details.

🔗 Related Work

• Prefix Caching - Caches KV tensors (we cache patterns)
• EchoAtt - Shares attention across layers (we share across requests)
• AttMEMO - Memoization within sequences (we do cross-request)

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