sparse-layers 0.2.4

Structured sparse layers for building memory-efficient neural networks

sparse-layers

Structured sparse layers for building memory-efficient neural networks on PyTorch. Drop-in replacements for standard layers using butterfly factorization, SSE attention, and other sparse primitives.

Install

pip install sparse-layers

Usage

import torch
from sparse_layers import ButterflyLinear, ButterflyMLP

# Drop-in replacement for nn.Linear with O(n log n) parameters
layer = ButterflyLinear(in_features=256, out_features=256)
x = torch.randn(32, 256)
y = layer(x)

# MLP with butterfly-factorized linear layers
mlp = ButterflyMLP(in_features=256, hidden_features=512, out_features=256)
y = mlp(x)

Attention

from sparse_layers import ButterflyMultiHeadAttention

# Multi-head attention with butterfly-factorized Q/K/V projections
bf_attn = ButterflyMultiHeadAttention(d_model=256, num_heads=8)

seq = torch.randn(32, 128, 256)  # (batch, seq_len, d_model)
out = bf_attn(seq, seq, seq)

SSE Attention

State Space Exploration modules for efficient sequence modeling with sparse attention patterns.

from sparse_layers.modules import SSEAttention, SSEAttentionConfig

config = SSEAttentionConfig(d_model=256, num_partitions=4)
sse = SSEAttention(config)

x = torch.randn(32, 128, 256)
out = sse(x)

Architecture

Organized following the Flash-Attention pattern:

Ops (sparse_layers.ops)

Primitive operations and utility functions.

Module	Description
butterfly	Butterfly factor multiply, power-of-2 utilities
SSEMaskingOps	Masking utilities for variable-length SSE
SSEVarlenOps	Variable-length sequence operations

Modules (sparse_layers.modules)

Composable building blocks — single units of computation.

Module	Description
ButterflyLinear	Linear layer using butterfly matrix factorization — O(n log n) parameters instead of O(n²)
PaddedButterflyLinear	ButterflyLinear with automatic padding for non-power-of-2 dimensions
SSEAttention	Sparse attention with state-space-inspired partitioning
SSEAttentionAdaptive	SSE with adaptive implementation selection (naive/batched)
SSEMultiPartitionState	Manages partition states across sequence chunks
SSEPartitionSelector	Selects active partitions per query position
SSESparseSoftmax	Sparse softmax over selected partitions
LinearAttention	Linear attention baseline (O(n) complexity)

Models (sparse_layers.models)

Composed architectures built from modules.

Module	Description
ButterflyMLP	Two-layer MLP with butterfly-factorized linear layers
ButterflyMultiHeadAttention	Multi-head attention with butterfly-factorized Q/K/V projections
SSEMultiHeadAttention	Multi-head variant of SSE attention

The package also includes baseline implementations (SimpleMLP, CustomMLP, CustomLinear, MultiHeadAttention) used internally for validation and testing.

License

MIT