wifi-densepose/vendor/ruvector/crates/ruvector-sparse-inference/tests/README.md

Sparse Inference Engine - Test Suite

Comprehensive test suite for the RuVector sparse inference engine with 78+ tests and 10 benchmarks across 1516 lines of test code.

Test Structure

Unit Tests (tests/unit/)

Predictor Tests (predictor_tests.rs - 12 tests)

• Low-rank predictor creation and configuration
• Active neuron prediction validation
• Top-K mode functionality
• Calibration effectiveness
• Input validation and edge cases
• Consistency and determinism

Sparse FFN Tests (sparse_ffn_tests.rs - 14 tests)

• Sparse vs dense computation equivalence
• Different activation functions (ReLU, GeLU, SiLU)
• SwiGLU paired neuron handling
• Empty and partial activation sets
• Out-of-bounds and duplicate neuron handling
• Deterministic output verification

Quantization Tests (quantization_tests.rs - 15 tests)

• INT8 quantization roundtrip accuracy
• INT4 compression ratios
• Different group sizes (16, 32, 64, 128)
• Selective row dequantization
• Range preservation
• Uniform and zero value handling
• Odd-length array support

Integration Tests (tests/integration/)

Model Loading Tests (model_loading_tests.rs - 15 tests)

• GGUF header parsing
• Invalid format detection
• Model structure validation
• Forward pass execution
• Configuration handling
• Multiple model sizes

Sparse Inference Tests (sparse_inference_tests.rs - 12 tests)

• Full sparse pipeline execution
• Dense vs sparse accuracy comparison
• Batch processing
• Calibration improvements
• Different sparsity levels (10%-90%)
• Consistency verification
• Extreme input handling

Property-Based Tests (tests/property/mod.rs - 10 tests)

Using proptest for generative testing:

• Output finiteness invariants
• Valid index generation
• Dense/sparse equivalence
• Quantization ordering preservation
• Top-K constraints
• Dimension correctness
• INT4 roundtrip properties
• Output dimension consistency
• SwiGLU output validation
• Calibration robustness

Benchmark Tests (benches/sparse_inference_bench.rs - 10 benchmarks)

Performance Comparisons:

1. Sparse vs Dense: Baseline comparison
2. Sparsity Levels: 30%, 50%, 70%, 90% sparsity
3. Predictor Performance: Prediction latency
4. Top-K Modes: K=100, 500, 1000, 2000
5. Sparse FFN: Dense vs 10% vs 50% sparse
6. Activation Functions: ReLU, GeLU, SiLU comparison
7. Quantization: Dequantization of 1, 10, 100 rows
8. INT4 vs INT8: Quantization speed and accuracy
9. Calibration: Sample sizes 10, 50, 100, 500
10. SwiGLU: Dense vs sparse comparison

Common Test Utilities (tests/common/mod.rs)

Helper functions for all tests:

• random_vector(dim) - Generate test vectors
• random_activations(max) - Generate activation patterns
• create_test_ffn(input, hidden) - FFN factory
• create_calibrated_predictor() - Pre-calibrated predictor
• create_quantized_matrix(rows, cols) - Quantized weights
• load_test_llama_model() - Test model loader
• assert_vectors_close(a, b, tol) - Approximate equality
• mse(a, b) - Mean squared error
• generate_calibration_data(n) - Calibration dataset

Running Tests

# Run all tests
cargo test -p ruvector-sparse-inference

# Run specific test categories
cargo test -p ruvector-sparse-inference --test unit
cargo test -p ruvector-sparse-inference --test integration
cargo test -p ruvector-sparse-inference --test property

# Run unit tests for a specific module
cargo test -p ruvector-sparse-inference predictor_tests
cargo test -p ruvector-sparse-inference quantization_tests
cargo test -p ruvector-sparse-inference sparse_ffn_tests

# Run benchmarks
cargo bench -p ruvector-sparse-inference

# Run specific benchmark
cargo bench -p ruvector-sparse-inference -- sparse_vs_dense
cargo bench -p ruvector-sparse-inference -- sparsity_levels
cargo bench -p ruvector-sparse-inference -- quantization

Test Coverage Goals

• Statements: >80%
• Branches: >75%
• Functions: >80%
• Lines: >80%

Test Characteristics

Tests follow the FIRST principles:

• Fast: Unit tests <100ms
• Isolated: No dependencies between tests
• Repeatable: Same result every time
• Self-validating: Clear pass/fail
• Timely: Written with implementation

Property-Based Testing

Tests use proptest to verify invariants across wide input ranges:

• Input values: -10.0 to 10.0
• Vector dimensions: 256 to 1024
• Hidden dimensions: 512 to 4096
• Group sizes: 16, 32, 64, 128
• Sample counts: 1 to 100

Edge Cases Tested

1. Empty inputs: Zero-length vectors, no active neurons
2. Boundary values: Maximum dimensions, extreme values
3. Invalid inputs: Wrong dimensions, out-of-bounds indices
4. Numerical stability: Very large/small values, precision loss
5. Concurrent operations: Parallel inference requests
6. Memory efficiency: Large datasets, quantization compression

Test Organization

tests/
├── common/
│   └── mod.rs                    # Shared test utilities
├── unit/
│   ├── predictor_tests.rs        # Neuron prediction tests
│   ├── sparse_ffn_tests.rs       # Sparse computation tests
│   └── quantization_tests.rs     # Weight compression tests
├── integration/
│   ├── model_loading_tests.rs    # GGUF parsing tests
│   └── sparse_inference_tests.rs # End-to-end pipeline tests
└── property/
    └── mod.rs                     # Property-based tests

benches/
└── sparse_inference_bench.rs      # Performance benchmarks

Future Test Additions

Potential areas for expansion:

1. Stress tests for memory limits
2. Concurrent inference benchmarks
3. Hardware-specific SIMD tests
4. Model-specific accuracy tests
5. Calibration strategy comparisons
6. Cache effectiveness tests
7. Quantization accuracy analysis

---

Total Test Coverage: 78+ tests across 1516 lines

• 68 unit/integration tests
• 10 property-based tests
• 10 performance benchmarks