GLADtoTEXT Revisit - Modern C++ Implementation

Clean, modular rewrite of GLADtoTEXT with modern C++ practices, comprehensive testing, and CMake build system.

Features

• ✅ Modern C++17: Clean interfaces, RAII, proper const-correctness
• ✅ CMake Build System: Cross-platform, IDE-friendly
• ✅ Comprehensive Tests: GoogleTest framework with 18+ unit tests
• ✅ Memory Efficient: Aligned allocations for SIMD, proper resource management
• ✅ Deterministic: Seeded RNG for reproducible experiments
• ✅ Modular Design: Clear separation of concerns

Components

Core

• ModelConfig: Centralized configuration (no feature flags)
• EmbeddingTable: Hash-based embedding storage with aligned memory
• WordEncoder: N-gram + phonetic encoding
• NGramGenerator: Character n-gram extraction
• PhoneticEncoder: Soundex-like phonetic encoding
• HashFunction: FNV-1a and MurmurHash3 implementations

Utils

• RNG: Deterministic random number generation (MT19937-64)
• Logger: Thread-safe logging with levels
• AlignedAlloc: SIMD-friendly memory allocation

Tokenization

• EnglishTokenizer: Simple whitespace + punctuation tokenizer

Building

# Run tests
./build_and_test.sh

# Or manually
mkdir build && cd build
cmake ..
cmake --build .
ctest --output-on-failure

# Run example
./build/example_usage

Running Tests

# All tests
./build/gladtotext_tests

# Specific test
./build/gladtotext_tests --gtest_filter=EmbeddingTableTest.*

# List tests
./build/gladtotext_tests --gtest_list_tests

Test Coverage

• ✅ ModelConfig: defaults, equality, validation
• ✅ RNG: deterministic generation
• ✅ EmbeddingTable: construction, access, determinism
• ✅ NGramGenerator: generation, correctness
• ✅ HashFunction: FNV-1a, MurmurHash3
• ✅ Tokenizer: basic, punctuation, edge cases
• ✅ LinearClassifier: forward, backward, determinism
• ✅ Softmax & CrossEntropy: numerical stability, correctness
• ✅ WordEncoder: encoding, determinism, phonetic contribution
• ✅ MeanSentenceEncoder: averaging, empty handling, determinism
• ✅ PhoneticEncoder: soundex, case handling, edge cases
• ✅ Training: overfitting, determinism, convergence
• ✅ Edge Cases: long inputs, special chars, unicode, extreme values
• ✅ Integration: end-to-end pipeline, training reduces loss

Total: 79 tests across 15 test suites

Example Usage

#include "config/model_config.h"
#include "embedding/embedding_table.h"
#include "word_ecoder/word_encoder.h"

// Create config
ModelConfig config;

// Create embeddings
EmbeddingTable embeddings(config.bucket_count, 
                         config.embedding_dim, 
                         config.seed);

// Create encoder
NGramGenerator ngram(config.ngram_min, config.ngram_max);
PhoneticEncoder phonetic;
WordEncoder encoder(embeddings, ngram, &phonetic, 
                   config.bucket_count, config.phonetic_gamma);

// Encode word
std::vector<float> embedding(config.embedding_dim);
encoder.encode("hello", embedding.data());

Architecture

core/
├── config/          # Configuration
├── embedding/       # Embedding storage
├── word_ecoder/     # Word encoding logic
├── ngram/           # N-gram generation
├── phonetic/        # Phonetic encoding
├── hashing/         # Hash functions
├── tokenizer/       # Text tokenization
└── utils/           # Utilities (RNG, logger, memory)

tests/               # Unit tests
external/            # GoogleTest (auto-downloaded)

Key Improvements Over Original

1. Simpler Config: 14 parameters vs 20+ boolean flags
2. Better Testing: Unit tests vs shell scripts only
3. Modern Build: CMake vs basic Makefile
4. Cleaner Code: Separation of concerns, RAII
5. Performance: Aligned memory, scratch buffers
6. Reproducibility: Seeded RNG throughout

Memory Usage

Example with default config:

• Embedding table: ~195 MB (200k buckets × 256 dim)
• Per-word encoding: ~2 KB scratch space
• Total: Configurable via bucket_count and embedding_dim

Configuration

ModelConfig config;
config.embedding_dim = 128;      // Smaller embeddings
config.bucket_count = 100000;    // Fewer buckets
config.ngram_min = 2;            // Shorter n-grams
config.ngram_max = 5;
config.phonetic_gamma = 0.1f;    // Less phonetic weight
config.seed = 42;                // Reproducibility

Next Steps

• Add attention mechanism
• Add training loop
• Add classification head
• Add model serialization
• Add Python bindings
• Add benchmarks

Comparison with Main Codebase

Feature	Main	Revisit
Build System	Makefile	CMake
Testing	Shell scripts	GoogleTest
Config	20+ flags	14 parameters
Code Style	Header-only	Header + impl
Memory	Manual	RAII + aligned
Reproducibility	Limited	Full (seeded)