New level of optimization beyond quantization means LESS memory, HIGHER speed, WITHOUT the need to change the model architecture.
🟢 What is Sparse Inference?
Sparsity means that in model's weights and activations, most values are zeroed out (for example, 80–90%).
Now PyTorch can:
- Use N:M sparsity (e.g., 2:4 sparsity)
- Accelerate inference on GPU and CPU
- Support this in torch.compile() & torch.export
How it work?
1. model is zeroed out using Pruning / Structured Sparsity
2. Converted via torch.sparse.to_sparse() or torch.export
3. Run through TorchInductor + XNNPACK or CUTLASS
What is supported?
- CPU (x86, M1/M2) via XNNPACK backend
- GPU (Ampere+) via CUTLASS
- Integration with torch.compile() (TorchInductor)
IMPORTANCE
- Less memory → lower latency on edge devices
- Higher performance, No compromises
- Easily integrates in current PyTorch pipeline
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