universal-set-transformers 0.1.0

Universal Set Transformers for scalable set/multiset modeling

Universal Set Transformers

Universal Set Transformers (UST) is a PyTorch library for processing sets and multisets with transformer-based architectures. This library provides efficient implementations of set attention mechanisms that are permutation invariant and can handle sets of varying sizes.

Features

• Set and Multiset Processing: Process sets and multisets with explicit multiplicity handling
• Minibatch Consistency: Process large sets in minibatches with guaranteed consistency
• Efficient Attention Mechanisms: Multiple attention implementations (softmax, sigmoid, flash attention)
• State Management: Explicit state handling for incremental processing
• Numerical Stability: Robust handling of large values and edge cases

Installation

pip install universal-set-transformers

Or install from source:

git clone https://github.com/yourusername/universal-set-transformers.git
cd universal-set-transformers
pip install -e .

Quick Start

Basic Usage

import torch
from ust.modules import SelfSetAttentionBlock, CrossSetAttentionBlock

# Create a self-attention block
self_attn = SelfSetAttentionBlock(
    d_model=32,
    nhead=4,
    dim_feedforward=64
)

# Process a set
x = torch.randn(2, 10, 32)  # batch_size=2, set_size=10, d_model=32
output = self_attn(x)

# Process a multiset with multiplicities
multiplicities = torch.randint(1, 5, (2, 10)).float()
output_with_mult = self_attn(x, multiplicities)

Cross-Set Attention

import torch
from ust.modules import CrossSetAttentionBlock

# Create a cross-attention block
cross_attn = CrossSetAttentionBlock(
    d_model=32,
    nhead=4,
    dim_feedforward=64
)

# Process two sets
x = torch.randn(2, 5, 32)   # batch_size=2, set_size=5, d_model=32
y = torch.randn(2, 10, 32)  # batch_size=2, set_size=10, d_model=32
output = cross_attn(x, y)

# Process with multiplicities
y_multiplicities = torch.randint(1, 5, (2, 10)).float()
output_with_mult = cross_attn(x, y, y_multiplicities)

Minibatch Processing

import torch
from ust.modules import CrossSetAttentionBlock

# Create a cross-attention block
cross_attn = CrossSetAttentionBlock(
    d_model=32,
    nhead=4,
    dim_feedforward=64
)

# Query set
x = torch.randn(2, 5, 32)  # batch_size=2, set_size=5, d_model=32

# Initialize state
state = cross_attn.attention.scaled_dot_product_attention.initial_state()

# Process first minibatch
y1 = torch.randn(2, 10, 32)
m1 = torch.randint(1, 5, (2, 10)).float()
output1, state = cross_attn(x, y1, m1, state=state)

# Process second minibatch
y2 = torch.randn(2, 8, 32)
m2 = torch.randint(1, 5, (2, 8)).float()
output2, state = cross_attn(x, y2, m2, state=state)

# Final output is output2

Architecture

The library is organized into several modules:

• ust.modules: Core modules for set processing
  • scaled_dot_product_attention.py: Various attention mechanisms
  • set_transformer.py: Set Transformer implementation
• ust.api: Abstract interfaces and type definitions
• ust.utils: Utility functions

Key Components

Attention Mechanisms

• ScaledDotProductSoftmaxSetAttention: Standard softmax attention for sets
• ScaledDotProductSoftmaxFlashSetAttention: Optimized flash attention for sets
• ScaledDotProductSigmoidSetAttention: Sigmoid attention for sets

Attention Blocks

• MultiheadSetAttention: Multi-head attention for sets
• SelfSetAttentionBlock: Self-attention block for sets
• CrossSetAttentionBlock: Cross-attention block for sets

Set Transformer Components

• InducedSetAttentionBlock: Induced set attention block
• PoolingByMultiheadSetAttention: Pooling mechanism for sets

Advanced Features

Explicit State Handling

import torch
from ust.modules.scaled_dot_product_attention import ScaledDotProductSoftmaxSetAttention

# Create attention mechanism
attention = ScaledDotProductSoftmaxSetAttention()

# Initialize state
state = attention.initial_state()

# Process query and key-value pairs
query = torch.randn(2, 5, 32)
key = torch.randn(2, 10, 32)
value = torch.randn(2, 10, 32)

# Update state with first batch
state = attention.compute_aggregated_attention(query, key, value, None, state)

# Update state with second batch
key2 = torch.randn(2, 8, 32)
value2 = torch.randn(2, 8, 32)
state = attention.compute_aggregated_attention(query, key2, value2, None, state)

# Get final result
output = attention.get(state)

Multiset Processing

import torch
from ust.modules import SelfSetAttentionBlock

# Create a self-attention block
self_attn = SelfSetAttentionBlock(
    d_model=32,
    nhead=4,
    dim_feedforward=64
)

# Process a set with duplicates
x = torch.randn(2, 10, 32)
multiplicities = torch.tensor([
    [1, 2, 1, 3, 1, 1, 2, 1, 1, 1],
    [2, 1, 1, 1, 3, 2, 1, 1, 1, 2]
]).float()

# Process with multiplicities
output = self_attn(x, multiplicities)

License

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