This commit addresses issue #17 by providing a comprehensive review
of the TurboQuant initiative and implementing key improvements.
## Changes
### 1. Initiative Review (docs/INITIATIVE_REVIEW.md)
- Comprehensive assessment of current state
- Code quality findings and recommendations
- Contributor feedback for @manus, @Timmy, @Rockachopa
- Implementation plan with clear milestones
### 2. Code Improvements
#### llama-turbo.cpp
- Added input validation with assertions
- Optimized Lloyd-Max search with binary search (O(log n) vs O(n))
- Added stack allocation for d=128 (avoids heap allocation in hot path)
- Added error handling for edge cases
- Added decision boundaries for efficient quantization
#### ggml-metal-turbo.metal
- Added bounds checking to all kernels
- Added NaN/Inf handling for numerical stability
- Completed fused attention kernel (was stub)
- Added fused attention with softmax kernel
- Added Metal encoding kernel for completeness
- Added binary search for quantization
### 3. Testing (tests/test_turbo.cpp)
- Unit tests for encode/decode round-trip
- Tests for known values (zeros, ones)
- Tests for edge cases (large/small values)
- Error handling tests
### 4. Build System (CMakeLists.txt)
- Added CMake configuration for building library
- Added test executable
- Added install targets
### 5. Documentation (README.md)
- Added build instructions
- Added API documentation
- Added contributing guidelines
- Added code style guide
## Key Improvements
1. **Performance**: Binary search instead of linear search for Lloyd-Max quantization
2. **Memory**: Stack allocation for common case (d=128)
3. **Reliability**: Input validation and error handling
4. **Metal Integration**: Complete fused attention implementation
5. **Testing**: Unit tests for correctness verification
6. **Documentation**: Contributor guidelines and API docs
## Next Steps
1. Run benchmarks to verify performance improvements
2. Test with actual models (qwen3.5:27b)
3. Integrate with llama.cpp fork
4. Deploy to production
Closes#17
