radix-mlp 0.0.1rc0

RadixMLP: Prefix-based computation sharing for transformer models

RadixMLP Python Bindings

Python bindings for the RadixMLP algorithm, enabling prefix-based computation sharing for transformer models.

Installation

Development Installation

# Using uv (recommended)
uv pip install -e .

# Using pip
pip install -e .

Release Installation

pip install radix-mlp

Usage

import numpy as np
from radix_mlp import compute_fold_and_scatter

# Example: Two sequences with shared prefix
input_ids = np.array([1, 2, 3, 1, 2, 4], dtype=np.uint32)
position_ids = np.array([0, 1, 2, 0, 1, 2], dtype=np.uint32)
cu_seq_lengths = np.array([0, 3, 6], dtype=np.uint32)

compact_ids, compact_pos, scatter, fold = compute_fold_and_scatter(
    input_ids, position_ids, cu_seq_lengths
)

print(f"Original tokens: {len(input_ids)}")
print(f"Compact tokens: {len(compact_ids)}")
print(f"Compression ratio: {len(compact_ids) / len(input_ids):.2%}")

PyTorch Interface

For PyTorch users, a convenient interface is available:

import torch
from radix_mlp import compute_fold_and_scatter_torch # only available if you installed torch in your environment

# Example: Two sequences with shared prefix
input_ids = torch.tensor([1, 2, 3, 1, 2, 4], dtype=torch.int32)
position_ids = torch.tensor([0, 1, 2, 0, 1, 2], dtype=torch.int32)
cu_seq_lengths = torch.tensor([0, 3, 6], dtype=torch.int32)

compact_ids, compact_pos, scatter, fold = compute_fold_and_scatter_torch(
    input_ids, position_ids, cu_seq_lengths
)

print(f"Original tokens: {len(input_ids)}")
print(f"Compact tokens: {len(compact_ids)}")
print(f"Compression ratio: {len(compact_ids) / len(input_ids):.2%}")

# Works with GPU tensors too (via CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
input_ids = input_ids.to(device)

API Reference

compute_fold_and_scatter

Computes indices for RadixMLP-style folding and scattering.

Parameters:

• input_ids (np.ndarray[np.uint32]): Flattened token IDs
• position_ids (np.ndarray[np.uint32]): Flattened position IDs
• cu_seq_lengths (np.ndarray[np.uint32]): Cumulative sequence lengths
• pad_multiple_of (bool): Pad output for performance (default: False)

Returns:

• compact_input_ids: Unique token IDs
• compact_position_ids: Corresponding position IDs
• scatter_indices: Unfold indices (compact -> original)
• fold_gather: Gather indices (original -> compact)

Development

Building

# Development build
maturin develop

# Release build
maturin build --release

License

MIT License - Copyright (c) 2025 michaelfeil