torch-continuum 0.2.0

Accelerate any PyTorch workload in one line.

torch-continuum

Accelerate any PyTorch workload in one line.

import torch_continuum
torch_continuum.optimize()

That's it. Your training and inference run faster — automatically tuned for your hardware.

Installation

pip install torch-continuum

# For maximum LLM training speedups:
pip install torch-continuum[liger]

Three Optimization Levels

import torch_continuum

torch_continuum.optimize("safe")    # No precision change — pure speed
torch_continuum.optimize("fast")    # ~2x matmul throughput
torch_continuum.optimize("max")     # Maximum speed — fused kernels + compilation

Level	Precision Impact	Best For
"safe"	None	Any workload — risk-free speedup
"fast"	Minor (invisible to most models)	Training & inference with heavy linear layers
"max"	Mixed precision	LLM training, large transformers

Benchmarks

Measured on NVIDIA H100 80GB. Real training loop (forward + loss + backward + optimizer step), 5 independent trials, 200 iterations each.

GPT-style Decoder (6 layers, d=768, vocab=32K)

Config	Time (200 iters)	Speedup
PyTorch baseline	9.622s	—
torch-continuum "fast"	3.912s	+59.3%

Large Linear Stack (67M params, batch 256)

Config	Time (200 iters)	Speedup
PyTorch baseline	0.900s	—
torch-continuum "fast"	0.554s	+38.4%

CNN / ConvNet (5 layers, 224x224, batch 64)

Config	Time (200 iters)	Speedup
PyTorch baseline	3.173s	—
torch-continuum "fast"	1.539s	+51.5%

Standard deviations across 5 trials: 0.001–0.004s (highly reproducible).

Smart Compilation

import torch_continuum

model = torch_continuum.smart_compile(model)

Automatically selects the best compilation strategy based on your model size and use case.

Built-in Benchmarking

Test the speedup on your own model:

import torch_continuum

torch_continuum.benchmark(model, example_input, level="fast")

Outputs a side-by-side comparison of baseline PyTorch vs torch-continuum on your exact workload.

Hardware Support

• NVIDIA GPUs (Ampere, Hopper, Ada): Full acceleration
• Apple Silicon (M1/M2/M3): Supported
• CPU: Supported

torch-continuum auto-detects your hardware and applies the right optimizations. No configuration needed.

info = torch_continuum.detect_device()
print(info.summary())

API

Function	Description
optimize(level)	Apply hardware-tuned optimizations
smart_compile(model)	Compile with auto-tuned settings
benchmark(model, input)	Measure speedup on your model
detect_device()	Get hardware capability profile
apply_liger_kernels()	Enable fused kernels for LLM training

License

MIT