mirror of
https://github.com/ruvnet/RuVector.git
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04b26c8d69
## Summary - Add PowerInfer-style sparse inference engine with precision lanes - Add memory module with QuantizedWeights and NeuronCache - Fix compilation and test issues - Demonstrated 2.9-8.7x speedup at typical sparsity levels - Published to crates.io as ruvector-sparse-inference v0.1.30 ## Key Features - Low-rank predictor using P·Q matrix factorization for fast neuron selection - Sparse FFN kernels that only compute active neurons - SIMD optimization for AVX2, SSE4.1, NEON, and WASM SIMD - GGUF parser with full quantization support (Q4_0 through Q6_K) - Precision lanes (3/5/7-bit layered quantization) - π integration for low-precision systems 🤖 Generated with [Claude Code](https://claude.com/claude-code)
70 lines
2.5 KiB
Rust
70 lines
2.5 KiB
Rust
//! Basic usage example for the sparse inference engine
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use ruvector_sparse_inference::backend::get_backend;
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use ruvector_sparse_inference::sparse::ActivationType;
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use ndarray::Array2;
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fn main() {
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// Get the best available backend for this platform
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let backend = get_backend();
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println!("Using backend: {}", backend.name());
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println!("SIMD width: {} f32s per register", backend.simd_width());
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// Example 1: Dot product
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println!("\n=== Dot Product ===");
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let a = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0];
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let b = vec![2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0];
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let dot = backend.dot_product(&a, &b);
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println!("a · b = {}", dot);
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assert_eq!(dot, 72.0);
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// Example 2: ReLU activation
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println!("\n=== ReLU Activation ===");
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let mut data = vec![-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, -4.0, 5.0];
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println!("Before: {:?}", data);
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backend.activation(&mut data, ActivationType::Relu);
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println!("After: {:?}", data);
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assert_eq!(data, vec![0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 0.0, 5.0]);
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// Example 3: AXPY (a = a + b * scalar)
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println!("\n=== AXPY (a = a + b * 2.5) ===");
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let mut a = vec![1.0, 2.0, 3.0, 4.0];
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let b = vec![1.0, 1.0, 1.0, 1.0];
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println!("Before: a = {:?}", a);
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backend.axpy(&mut a, &b, 2.5);
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println!("After: a = {:?}", a);
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assert_eq!(a, vec![3.5, 4.5, 5.5, 6.5]);
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// Example 4: Sparse matrix-vector multiplication
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println!("\n=== Sparse MatMul ===");
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let matrix = Array2::from_shape_vec(
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(4, 4),
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vec![
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1.0, 0.0, 2.0, 0.0,
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0.0, 3.0, 0.0, 4.0,
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5.0, 0.0, 6.0, 0.0,
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0.0, 7.0, 0.0, 8.0,
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],
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).unwrap();
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let input = vec![1.0, 2.0, 3.0, 4.0];
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// Only compute rows 0 and 2 (sparse computation)
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let active_rows = vec![0, 2];
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let output = backend.sparse_matmul(&matrix, &input, &active_rows);
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println!("Matrix (4x4):");
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println!("{:?}", matrix);
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println!("Input: {:?}", input);
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println!("Active rows: {:?}", active_rows);
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println!("Output: {:?}", output);
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assert_eq!(output, vec![7.0, 23.0]); // row 0: 1*1 + 2*3 = 7, row 2: 5*1 + 6*3 = 23
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// Example 5: Different activation functions
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println!("\n=== Activation Functions ===");
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for activation in [ActivationType::Relu, ActivationType::Gelu, ActivationType::Silu] {
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let mut data = vec![-1.0, 0.0, 1.0, 2.0];
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backend.activation(&mut data, activation);
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println!("{:?}: {:?}", activation, data);
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}
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println!("\n✓ All examples completed successfully!");
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}
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