torch-lambda-happy 0.1.3

Python package exposing the LambdaHappy class

Lambda Happy

A high-performance CPU/GPU solver for estimating the lambda_happy factor in sparse linear models (≈99% sparsity) using PyTorch, compatible with Windows, Linux, and macOS.

Installation

# Core functionality
pip install torch-lambda-happy

# Benchmark GUI (PyQt5)
pip install torch-lambda-happy[benchmark]

# Validation tools (PyQt5 + pandas)
pip install torch-lambda-happy[validation]

# All extras (Benchmark + Validation tools)
pip install torch-lambda-happy[all]

Then install torch (see: https://pytorch.org/get-started/locally/)

Here is the current command in August 2025 for Linux :

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Quickstart

Recommended use case

import torch
from torch_lambda_happy import LambdaHappy

# Prepare data
X = torch.randn(1000, 5000, device="cuda")

# Initialize solver (auto‐select fastest backend)
solver = LambdaHappy(X, force_fastest=True)

# Single estimate
lambda_value = solver.compute(m=10000)
print(f"lambda_value ≈ {lambda_value:.4f}")

# Multiple runs
lambda_values = solver.compute_many(m=10000, nb_run=50)
print(f"lambda_values ≈ {lambda_values}")

# Aggregated (mean)
lambda_mean = solver.compute_agg(m=10000, nb_run=500, func=torch.median)
print(f"lambda_mean ≈ {lambda_mean:.4f}")

Example with all parameters (single estimation)

import torch
from torch_lambda_happy import LambdaHappy

matX = torch.randn(1_000, 1_000)
model = LambdaHappy(X=matX, force_fastest=False, use_multigpu=False)
lambda_ = model.compute(m=10_000, dtype=torch.float16, device_type="cuda")
print(f"Estimated λ: {lambda_:.4f}")

Example with all parameters (many estimations)

import torch
from torch_lambda_happy import LambdaHappy

matX = torch.randn(1_000, 1_000)
model = LambdaHappy(X=matX, force_fastest=True, use_multigpu=False)
lambda_ = model.compute_many(m=10_000, dtype=torch.float32, device_type="cuda", nb_run=100)
print(f"Estimated λs: {lambda_}")

Example with all parameters (aggregated estimation)

import torch
from torch_lambda_happy import LambdaHappy

matX = torch.randn(1_000, 1_000)
model = LambdaHappy(X=matX, force_fastest=True, use_multigpu=True)
lambda_ = model.compute_agg(
    m=10_000, dtype=torch.float32, device_type="cpu", nb_run=10, func=torch.median
)
print(f"Estimated λ: {lambda_:.4f}")

⚠️ The examples above illustrate different ways of using the library, but they are not necessarily the fastest methods.
For the most efficient versions, please refer to the Recommended use case section.

ℹ️ Use float16 (or force_fastest=True) on GPU only if the input matrix X is normalized. Setting use_multigpu=True will utilize all available GPUs if more than one is present.

What is lambda happy ?

In a sparse model, lambda_happy balances data fidelity against model sparsity.

Given:

• X ∈ R -> The feature matrix
• Z ∈ R -> A random Gaussian projection matrix
• Z_centrer -> centered Z matrix

Lambda_happy is estimated as the 95th percentile of (norm of the transpose of X times the centered Z matrix, measured in Chebyshev norm) divided by (norm of the centered Z matrix measured in 2-norm), that is:

• lambda_happy = Quantile_0.95 ( || X^T * Z_centrer ||_infinity / || Z_centrer ||_2 )

which requires:

• p: The number of features in the matrix X (affects only the matmul)
• n: The number of rows in the matrix X
• m: The number of projections (larger m ⇒ higher precision, but each step’s cost scales with m)

Performance Trade-Offs

Projection Dimension (m)

• ↑ m → improves lambda_happy precision.
• ↑ m → linearly increases compute time (all kernels scale with m).
• Recommended: m = 10,000 provides good accuracy in most cases.

ℹ️ Use float16 on GPU only if the input matrix X is normalized. Otherwise, lambda_happy estimation may be unstable or inconsistent.

Sample Dimension (n)

• ↑ n → increases cost in all kernels (since Z ∈ R^(n × m)), except for the quantile post-processing step.

Feature Dimension (p)

• ↑ p → only affects the X^T·Z matrix multiplication.

---

Recommended Settings

Context	Data Type	Notes
CPU	float32	Stable, widely supported, and generally the fastest on CPU.
CUDA GPU	float16	High performance if X is normalized; otherwise use float32.
Backend	"AUTOMATIC"	Selects the best available implementation based on hardware and dtype.

Extras

Benchmark

The torch-lambda-happy-benchmark script measures and compares the performance of LambdaHappy on CPU and GPU. It offers various benchmarking options and displays live throughput plots. Example usage:

torch-lambda-happy-benchmark --benchmark_2D --benchmark_3D --benchmark_float --device cuda --dtype float32 -n 1000 -p 1000 -m 10000

This runs a 2D benchmark using CUDA with specified matrix dimensions and then run a 3D benchmark.

ℹ️ Note: Not all hyperparameters are used for every plot, but if provided, they will be applied when relevant.

Validation

The torch-lambda-happy-validation script runs tests to validate lambda_happy estimation accuracy. It generates detailed reports and distribution plots using pandas and PyQt5.

Example usage:

torch-lambda-happy-validation --distribution_small --distribution_large --device cuda --dtype float32 -n 1000 -p 1000

This plots small-scale lambda_happy distributions on cuda for the given parameters.

Results

Here are the results for a CUDA calculation :

Rang	Mode	Version	Précision	FPS	Speed-up
1	Mono-GPU	SMART_TENSOR	Float32	449	1.00x
2	Mono-GPU	GPU_DEDICATED	Float32	501	1.12x
3	Multi-GPU	SMART_TENSOR	Float32	511	1.14x
4	Multi-GPU	SMART_TENSOR	Float16	664	1.48x
5	Multi-GPU	GPU_DEDICATED	Float32	911	2.03x
6	Mono-GPU	SMART_TENSOR	Float16	1'215	2.71x
7	Mono-GPU	GPU_DEDICATED	Float16	1'618	3.60x
8	Multi-GPU	GPU_DEDICATED	Float16	2'104	4.69x

ℹ️ FPS : number of times the lambda_happy factor is estimated per second.

The test server is equipped with an Intel Xeon E5-2699 v3 processor (2014) and three NVIDIA GeForce RTX 2080 Ti graphics cards (2018).

It uses the default parameters for the evaluation with X of size 1000x1000 and m=10000.

ℹ️ Note: Use device="cuda" when you create X.

About This Project

This package, including performance optimizations, was developed as part of a Bachelor’s thesis at HE-Arc by Sevan Yerly ([e-mail]), under the supervision of Cédric Bilat ([e-mail]). The mathematical foundations were developed by Sylvain Sardy ([e-mail]).

For questions or contact: [e-mail] or [e-mail]