keras_sig 1.0.1

Path Signature in Pure Keras

keras_sig: The most Efficient and Easy Path Signature computation

This package started as backend-agnostic Keras implementation of path signature computations, focusing on simplicity and ease of integration. Since we proposed a GPU-optimized computation methods that leverages fully parallel operations, it has become the fastest and most efficient path signature computation package available at date. This method is available either in full Keras for model training, but also as a standalone JAX function for direct computation.

Overview

keras_sig provides path signature computations as a Keras layer. It aims to offer:

• Native Keras implementation supporting all backends (JAX, PyTorch, TensorFlow)
• Simple integration within Keras models
• Pure Python implementation avoiding C++ dependencies
• Consistent API across different backends
• GPU-optimized computation for faster training

The package builds upon several key projects in the signature computation ecosystem:

Historical Context

1. iisignature (repo): The foundational C++ implementation providing highly optimized signature computations with a python wrapper
2. signatory (repo): A PyTorch-specific implementation using C++ level optimizations for GPU acceleration
3. iisignature-tensorflow-2 (repo): An attempt at wrapping iisignature for TensorFlow 2, which faced limitations with model compilation
4. signax (repo): A breakthrough pure JAX implementation showing that C++ optimization could be avoided
5. keras_sig (this package): Bringing the pure Python approach to all Keras backends and optimizing further the computation for the GPU.

Installation

pip install keras_sig

Or install from source:

git clone https://github.com/yourusername/keras_sig
cd keras_sig
pip install -e .

Quick Start

Basic usage with Keras:

import keras
from keras_sig import SigLayer

model = keras.Sequential([
    keras.layers.Input(shape=(timesteps, features)),
    SigLayer(depth=3, stream=False, gpu_optimized=True),  # Enable GPU optimization
    keras.layers.Dense(output_dim)
])

Direct JAX computation (fastest option):

from keras_sig import jax_gpu_signature

# Pre-compiled GPU-optimized computation
signatures = jax_gpu_signature(paths, depth=3, stream=False)

Performance & Implementation Options

Computation Methods

1. GPU-Optimized (Recommended when GPU available)

   • Uses parallel operations instead of loops
   • 5x faster than standard implementation
   • Higher memory usage
   • Enable with gpu_optimized=True or use jax_gpu_signature

2. Standard Implementation

   • Loop-based computation with scan operations
   • Lower memory footprint
   • Better for CPU-only systems
   • Default when GPU unavailable

Performance Benchmarks

All benchmarks run on AMD EPYC-7302P 16-cores with RTX-3090.

Forward Pass (128 batch, 100 sequence, 5 features, depth 4)

Backend	Version	GPU Time	CPU Time
JAX	Pure Jax-GPU function	163µs	46.5ms
JAX	keras Standard	713ms	378ms
JAX	keras GPU-optimized	-	80.5ms
JAX	signax	668µs	11.7ms
TensorFlow	keras GPU-optimized	55.2ms	180ms
TensorFlow	keras Standard	375ms	317ms
Torch	keras GPU-optimized	2.84ms	50.6ms
Torch	keras Standard	92.4ms	91.4ms
None	iisignature	36.4ms	36.4ms

Here the Keras version are not performing optimally as direct Jax function because the keras operation are not runned on GPU nor compiled with jit. This phase is only happening at training time. However we can easily compare the performance of the Pure Jax function with signax and iisignature and see that our proposed approach is the fastest when a GPU is available. When no GPU is available, the standard version is very similar to the signax implementation.

Training Performance

Test conditions: We created a model following the SigKAN paper the following way

model = keras.Sequential([
    Input(shape=X.shape[1:]),
    Dense(7),
    SigDense(10, depth, SigLayer),
    Flatten(),
    Dense(10, 'relu'),
    Dense(n_ahead),
])

and trained it with jit_compilation enable when possible for 10 epochs with Adam optimizer on randomly generated datas.

Long Sequences (length=500)

Backend	Version	Compile Time (GPU)	Compile Time (CPU)	Step Time (GPU)	Step Time (CPU)
JAX	GPU-opt	5s	25s	2ms	213ms
JAX	Standard	7s	14s	14ms	108ms
JAX	Signax	6s	12s	14ms	83ms
TensorFlow	GPU-opt	9s	26s	2ms	214ms
TensorFlow	Standard	Compile fail	Compile fail	-	-
TensorFlow	iisignature	No compile	No compile	340-345ms	340-345ms
Torch	GPU-opt	53s	26s	21ms	218ms
Torch	Standard	No compile	No compile	590ms	643ms

Short Sequences (length=20)

Backend	Version	Compile Time (GPU)	Compile Time (CPU)	Step Time (GPU)	Step Time (CPU)
JAX	GPU-opt	4s	6s	1ms	19ms
JAX	Standard	8s	8s	1ms	9ms
JAX	signax	5s	4s	2ms	6ms
TensorFlow	GPU-opt	4s	13s	1ms	102ms
TensorFlow	Standard	19s	14s	2ms	28ms
TensorFlow	iisignature	No compile	No compile	27ms	27ms
Torch	GPU-opt	9s	8s	21ms	17ms
Torch	Standard	No compile	No compile	38ms	31ms

Key Findings:

1. Pure JAX GPU-optimized version is fastest for forward pass (4x faster than signax)
2. GPU-optimized variants excel with GPU availability across all backends
3. For training:
   • JAX: Best balance of compilation/execution
   • TensorFlow: GPU-optimized version required for long sequences
   • PyTorch: Longer compilation but good runtime with GPU-optimization
4. Standard implementations struggle with:
   • PyTorch: Compilation issues
   • TensorFlow: Long sequence compilation
   • All backends: Slower execution without GPU optimization

Implementation Recommendations

1. JAX + GPU (Best Overall)

   • Use pure JAX implementation for forward pass
   • Use GPU-optimized SigLayer for training

2. PyTorch + GPU

   • Use GPU-optimized version only
   • Expect longer compilation times

3. TensorFlow + GPU

   • Use GPU-optimized version
   • Avoid standard version for long sequences

4. CPU-Only Systems

   • JAX standard implementation offers best balance
   • GPU-optimized versions still usable but with performance penalty

Features

Currently implements:

• Standard signature computations
• Support for both streaming and non-streaming modes
• Configurable signature depth
• Backend-agnostic implementation

Not yet implemented (available in other packages):

• Log signatures
• Lyndon words
• Other advanced signature computations

Citations

If using this package, please cite both this work and the foundational packages that inspired it:

@article{reizenstein2017iisignature,
  title={iisignature: A python package for computing iterated-integral signatures},
  author={Reizenstein, Jeremy},
  journal={Journal of Open Source Software},
  volume={2},
  number={10},
  pages={189},
  year={2017}
}

@article{kidger2021signatory,
  title={Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU},
  author={Kidger, Patrick and Lyons, Terry},
  journal={International Conference on Learning Representations},
  year={2021}
}

@software{signax2024github,
  author = {Anh Tong},
  title = {signax: Path Signatures in JAX},
  url = {https://github.com/anh-tong/signax},
  year = {2024},
}

@software{genet2024iisignaturetf2,
  author = {Remi Genet, Hugo Inzirillo},
  title = {iisignature-tensorflow-2: TensorFlow 2 Wrapper for iisignature},
  url = {https://github.com/remigenet/iisignature-tensorflow-2},
  year = {2024},
}

Contributing

Contributions are welcome! Feel free to submit issues and pull requests.

Would you like me to adjust any section or add more details? Shield:

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.