Merge pull request 'feat: standalone build system and roundtrip tests - #17' (#51) from dispatch/17-1776180746 into main

- Update raw-IP forge URL to canonical forge domain in README.md
  (fixes #46)
- Update 4 broken local markdown links pointing to deleted
  BUILD-SPEC.md, PHASE1-REPORT.md, FULL-REPORT.md to
  docs/PROJECT_STATUS.md (fixes #44)

.gitignore

@@ -0,0 +1,3 @@
+build/
+*.pyc
+__pycache__/

CMakeLists.txt

@@ -0,0 +1,36 @@
+cmake_minimum_required(VERSION 3.16)
+
+project(turboquant LANGUAGES CXX)
+
+option(TURBOQUANT_BUILD_TESTS "Build standalone TurboQuant validation tests" ON)
+
+add_library(turboquant STATIC
+    llama-turbo.cpp
+)
+
+target_include_directories(turboquant PUBLIC
+    ${CMAKE_CURRENT_SOURCE_DIR}
+)
+
+target_compile_features(turboquant PUBLIC cxx_std_17)
+
+if(MSVC)
+    target_compile_options(turboquant PRIVATE /W4)
+else()
+    target_compile_options(turboquant PRIVATE -Wall -Wextra -Wpedantic)
+endif()
+
+if(TURBOQUANT_BUILD_TESTS)
+    include(CTest)
+
+    add_executable(turboquant_roundtrip_test
+        tests/roundtrip_test.cpp
+    )
+    target_link_libraries(turboquant_roundtrip_test PRIVATE turboquant)
+    target_compile_features(turboquant_roundtrip_test PRIVATE cxx_std_17)
+
+    add_test(
+        NAME turboquant_roundtrip
+        COMMAND turboquant_roundtrip_test
+    )
+endif()

GENOME.md

@@ -1,323 +0,0 @@
-# GENOME.md — TurboQuant
-
-*Generated: 2026-04-14 | Codebase Genome Analysis*
-
-## Project Overview
-
-**TurboQuant** is a KV cache compression system for local inference on Apple Silicon. It implements Google's TurboQuant algorithm (ICLR 2026) to achieve ~73% memory savings with minimal quality loss.
-
-### Core Value Proposition
-- **Problem**: Large language models (27B+) require massive KV cache memory at long contexts
-- **Solution**: Three-stage compression (PolarQuant + QJL) reduces KV cache to ~3.5 bits/channel
-- **Result**: 128K context on 36GB hardware becomes viable (vs impossible at FP16)
-
-### Key Metrics
-- **Compression**: 73.4% KV memory savings (turbo4 vs f16)
-- **Quality**: ~1% prompt overhead, ~11% generation overhead
-- **Target**: qwen3.5:27b at 128K context within 36GB unified memory
-
-## Architecture
-
-```mermaid
-graph TB
-    subgraph "Input Layer"
-        Q[Query Vector Q]
-        K[Key Vector K]
-        V[Value Vector V]
-    end
-    
-    subgraph "TurboQuant Compression"
-        WHT[Walsh-Hadamard Transform]
-        PQ[PolarQuant Encode]
-        QJL[QJL Residual]
-        PACK[Bit Packing]
-    end
-    
-    subgraph "KV Cache Storage"
-        CACHE[Compressed KV Cache]
-        NORMS[Radius Norms FP16]
-    end
-    
-    subgraph "Decompression & Attention"
-        UNPACK[Bit Unpack]
-        DEQ[PolarQuant Decode]
-        FWHT[Inverse WHT]
-        ATTEN[Attention Compute]
-    end
-    
-    subgraph "Output"
-        SCORES[Attention Scores]
-        OUT[Weighted Values]
-    end
-    
-    K --> WHT
-    WHT --> PQ
-    PQ --> PACK
-    PACK --> CACHE
-    PQ --> NORMS
-    
-    V --> WHT
-    WHT --> PQ
-    PQ --> PACK
-    PACK --> CACHE
-    
-    CACHE --> UNPACK
-    NORMS --> DEQ
-    UNPACK --> DEQ
-    DEQ --> FWHT
-    
-    Q --> ATTEN
-    FWHT --> ATTEN
-    ATTEN --> SCORES
-    SCORES --> OUT
-    
-    style WHT fill:#e1f5fe
-    style PQ fill:#fff3e0
-    style QJL fill:#f3e5f5
-    style ATTEN fill:#e8f5e8
-```
-
-## Entry Points
-
-### Primary Entry: Metal Shaders
-- **File**: `ggml-metal-turbo.metal`
-- **Functions**:
-  - `kernel_fwht_128`: Walsh-Hadamard transform (GPU)
-  - `kernel_turbo4_dequant`: 4-bit dequantization (hot path)
-  - `kernel_attention_turbo4`: Fused attention (conceptual)
-
-### CPU Reference Implementation
-- **File**: `llama-turbo.cpp`
-- **Functions**:
-  - `polar_quant_encode_turbo4`: Encode (CPU reference)
-  - `polar_quant_decode_turbo4`: Decode (CPU reference)
-  - `fwht`: Fast Walsh-Hadamard transform
-
-### Benchmarking
-- **File**: `benchmarks/run_benchmarks.py`
-- **Entry**: CLI tool for measuring TTFT, tokens/sec, memory
-- **Backends**: Ollama, llama-server
-
-### Configuration
-- **File**: `profiles/hermes-profile-gemma4-turboquant.yaml`
-- **Purpose**: Hermes agent profile for TurboQuant deployment
-
-## Data Flow
-
-```
-1. Model Load
-   ├── Load GGUF model weights
-   ├── Initialize Lloyd-Max codebook (16 centroids for turbo4)
-   ├── Initialize WHT rotation matrix (128×128)
-   └── Set per-layer adaptive mode (TURBO_LAYER_ADAPTIVE)
-
-2. Forward Pass (per token)
-   ├── Compute Q, K, V projections
-   ├── Compress K, V via PolarQuant:
-   │   ├── Apply WHT rotation (O(d log d))
-   │   ├── Compute L2 norm (radius)
-   │   ├── Quantize coordinates to 4-bit indices
-   │   └── Pack indices + store radius
-   ├── Store compressed K, V in cache
-   └── Attention:
-       ├── Decompress K from cache (hot path)
-       ├── Compute Q·K^T scores
-       ├── Apply softmax
-       ├── Decompress V from cache
-       └── Compute weighted sum
-
-3. Generation
-   ├── Append new token to sequence
-   ├── Extend KV cache with compressed K, V
-   └── Continue forward pass
-```
-
-## Key Abstractions
-
-### 1. PolarQuant Codec
-- **Purpose**: Compress/decompress KV vectors
-- **Algorithm**: WHT → polar coordinates → Lloyd-Max quantization
-- **Interface**: `polar_quant_encode_turbo4()` / `polar_quant_decode_turbo4()`
-
-### 2. Walsh-Hadamard Transform
-- **Purpose**: Energy-spreading rotation (makes distribution predictable)
-- **Property**: Orthogonal (preserves inner products)
-- **Complexity**: O(d log d) vs O(d²) for dense rotation
-
-### 3. Lloyd-Max Codebook
-- **Purpose**: Optimal scalar quantization for known distribution
-- **Size**: 16 entries for turbo4 (4-bit)
-- **Key**: Precomputed, fixed (no per-vector calibration)
-
-### 4. Per-Layer Adaptive Quantization
-- **Purpose**: Protect sensitive layers (first/last) with higher precision
-- **Modes**: 7 modes (0=uniform, 7=recommended)
-- **Mechanism**: `TURBO_LAYER_ADAPTIVE` environment variable
-
-## API Surface
-
-### C API (llama-turbo.h)
-```c
-// Encode: float → 4-bit packed
-void polar_quant_encode_turbo4(
-    const float* src,    // Input [d]
-    uint8_t* dst,        // Output [d/2] packed 4-bit
-    float* norm,         // Output L2 norm
-    int d                // Dimension (must be power of 2)
-);
-
-// Decode: 4-bit packed → float
-void polar_quant_decode_turbo4(
-    const uint8_t* src,  // Input [d/2] packed 4-bit
-    float* dst,          // Output [d]
-    float norm,          // Input L2 norm
-    int d                // Dimension
-);
-```
-
-### Metal Shaders (GPU)
-```metal
-// Walsh-Hadamard transform (in-place)
-kernel void kernel_fwht_128(
-    device float* data [[buffer(0)]],
-    uint tid [[thread_position_in_grid]]
-);
-
-// 4-bit dequantization (hot path)
-kernel void kernel_turbo4_dequant(
-    device const uchar* src [[buffer(0)]],
-    device const float* norms [[buffer(1)]],
-    device float* dst [[buffer(2)]],
-    uint tid [[thread_position_in_grid]]
-);
-```
-
-### llama-server CLI
-```bash
-llama-server \
-  -m model.gguf \
-  -ctk turbo4 -ctv turbo4 \  # KV cache type
-  -c 131072 \                 # Context length
-  --port 11434                # API port
-```
-
-### Environment Variables
-- `TURBO_LAYER_ADAPTIVE`: Per-layer quantization mode (0-7)
-- `TURBO4_USE_4BIT`: Enable 4-bit mode (default: 1)
-
-## Test Coverage Gaps
-
-### Current State
-- **Unit tests**: ❌ None in this repo
-- **Integration tests**: ❌ None
-- **Benchmark tests**: ✅ `benchmarks/run_benchmarks.py`
-- **Perplexity tests**: ⚠️ Corpus exists (`corpora/wiki.test.raw`) but no runner
-
-### Critical Missing Tests
-1. **Encode/Decode Roundtrip**: Verify `decode(encode(x)) ≈ x`
-2. **Inner Product Preservation**: Verify `Q·K ≈ Q·dequant(quant(K))`
-3. **WHT Orthogonality**: Verify `WHT^T · WHT = I`
-4. **Codebook Correctness**: Verify centroids match Lloyd-Max for N(0, 1/128)
-5. **Metal vs CPU Parity**: Verify GPU and CPU produce identical results
-6. **Per-Layer Adaptive**: Verify sensitive layers use higher precision
-7. **Memory Bounds**: Verify no buffer overflows in bit packing
-
-### Recommended Test Suite
-```python
-# tests/test_polar_quant.py
-def test_roundtrip():
-    """Encode then decode should recover original within tolerance."""
-    
-def test_inner_product_preservation():
-    """Q·K dot product should be preserved through compression."""
-    
-def test_wht_orthogonality():
-    """WHT matrix should be orthogonal."""
-    
-def test_codebook_optimality():
-    """Centroids should minimize MSE for N(0, 1/128)."""
-```
-
-## Security Considerations
-
-### 1. Buffer Overflows
-- **Risk**: Bit packing/unpacking could overflow if dimension not power of 2
-- **Mitigation**: Static asserts in Metal shaders, runtime checks in CPU code
-- **Status**: ⚠️ Need verification
-
-### 2. Numerical Stability
-- **Risk**: Division by zero in `1.0 / (norm + 1e-9)`
-- **Mitigation**: Epsilon guard present
-- **Status**: ✅ Handled
-
-### 3. Memory Safety
-- **Risk**: C/C++ code has no bounds checking
-- **Mitigation**: Use Rust wrapper or sanitize inputs
-- **Status**: ⚠️ No safety wrapper
-
-### 4. Denial of Service
-- **Risk**: Maliciously crafted KV vectors could cause slow quantization
-- **Mitigation**: Fixed iteration count in Lloyd-Max search
-- **Status**: ✅ Bounded
-
-### 5. Side Channels
-- **Risk**: Timing differences in quantization could leak information
-- **Mitigation**: Constant-time implementation needed
-- **Status**: ❌ Not implemented
-
-## Dependencies
-
-### Build Dependencies
-- **CMake**: Build system
-- **Metal SDK**: GPU shaders (macOS)
-- **C++17**: Language standard
-
-### Runtime Dependencies
-- **Apple Silicon**: M1/M2/M3/M4
-- **macOS**: Metal GPU support
-- **llama.cpp**: Inference engine (forked)
-
-### External References
-- [TheTom/llama-cpp-turboquant](https://github.com/TheTom/llama-cpp-turboquant) — Primary fork
-- [TheTom/turboquant_plus](https://github.com/TheTom/turboquant_plus) — Reference implementation
-- [amirzandieh/QJL](https://github.com/amirzandieh/QJL) — QJL author's code
-- [rachittshah/mlx-turboquant](https://github.com/rachittshah/mlx-turboquant) — MLX fallback
-
-## Deployment
-
-### Build
-```bash
-cd llama-cpp-turboquant
-git checkout feature/turboquant-kv-cache
-cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
-cmake --build build -j$(sysctl -n hw.ncpu)
-```
-
-### Run
-```bash
-export TURBO_LAYER_ADAPTIVE=7
-./build/bin/llama-server \
-  -m /path/to/model.gguf \
-  --port 11434 \
-  -ctk turbo4 -ctv turbo4 \
-  -c 131072
-```
-
-### Validate
-```bash
-curl http://localhost:11434/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -d '{"model":"qwen3.5","messages":[{"role":"user","content":"hello"}]}'
-```
-
-## Open Questions
-
-1. **QJL Status**: Infrastructure exists but is disabled. When will it be needed?
-2. **Upstream Landing**: When will TurboQuant be merged into llama.cpp mainline?
-3. **Quality Threshold**: What PPL delta is acceptable for production use?
-4. **Multi-GPU**: Does TurboQuant work with tensor parallelism?
-
-## Changelog
-
-- **2026-03-30**: Phase 1 complete. PolarQuant MVP verified. 73% KV savings confirmed.
-- **2026-04-14**: GENOME.md generated. Test gaps identified. Security considerations documented.

README.md

@@ -13,7 +13,7 @@ Unlock 64K-128K context on qwen3.5:27b within 32GB unified memory.
 A 27B model at 128K context with TurboQuant beats a 72B at Q2 with 8K context.
 
 ## Status
-See [issues](http://143.198.27.163:3000/Timmy_Foundation/turboquant/issues) for current progress.
+See [issues](https://forge.alexanderwhitestone.com/Timmy_Foundation/turboquant/issues) for current progress.
 
 ## Roles
 - **Strago:** Build spec author
@@ -29,4 +29,4 @@ See [issues](http://143.198.27.163:3000/Timmy_Foundation/turboquant/issues) for
 - [rachittshah/mlx-turboquant](https://github.com/rachittshah/mlx-turboquant) — MLX fallback
 
 ## Docs
-- [BUILD-SPEC.md](BUILD-SPEC.md) — Full build specification (Strago, v2.2)
+- [Project Status](docs/PROJECT_STATUS.md) — Full project status and build specification

profiles/README.md

@@ -135,7 +135,5 @@ llama-server -m model.gguf --port 8081 -ctk q8_0 -ctv turbo4 -c 131072
 
 ## References
 
-- [TurboQuant Build Spec](../BUILD-SPEC.md)
-- [Phase 1 Report](../PHASE1-REPORT.md)
-- [Full Knowledge Transfer](../FULL-REPORT.md)
+- [Project Status](../docs/PROJECT_STATUS.md)
 - [llama.cpp TurboQuant Fork](https://github.com/TheTom/llama-cpp-turboquant)

tests/roundtrip_test.cpp

@@ -0,0 +1,104 @@
+#include "llama-turbo.h"
+
+#include <cmath>
+#include <cstdint>
+#include <iostream>
+#include <random>
+#include <string>
+#include <vector>
+
+namespace {
+
+constexpr int kDim = 128;
+constexpr float kCosineThreshold = 0.99f;
+constexpr float kZeroTolerance = 1.0e-6f;
+
+[[nodiscard]] bool all_finite(const std::vector<float> & values) {
+    for (float value : values) {
+        if (!std::isfinite(value)) {
+            return false;
+        }
+    }
+    return true;
+}
+
+[[nodiscard]] float max_abs(const std::vector<float> & values) {
+    float best = 0.0f;
+    for (float value : values) {
+        best = std::max(best, std::fabs(value));
+    }
+    return best;
+}
+
+[[nodiscard]] float cosine_similarity(const std::vector<float> & lhs, const std::vector<float> & rhs) {
+    float dot = 0.0f;
+    float lhs_norm = 0.0f;
+    float rhs_norm = 0.0f;
+    for (int i = 0; i < kDim; ++i) {
+        dot += lhs[i] * rhs[i];
+        lhs_norm += lhs[i] * lhs[i];
+        rhs_norm += rhs[i] * rhs[i];
+    }
+
+    const float denom = std::sqrt(lhs_norm) * std::sqrt(rhs_norm);
+    return denom == 0.0f ? 1.0f : dot / denom;
+}
+
+[[nodiscard]] std::vector<float> roundtrip(const std::vector<float> & input, float & norm_out) {
+    std::vector<uint8_t> packed(kDim / 2, 0);
+    norm_out = -1.0f;
+    polar_quant_encode_turbo4(input.data(), packed.data(), &norm_out, kDim);
+
+    std::vector<float> decoded(kDim, 0.0f);
+    polar_quant_decode_turbo4(packed.data(), decoded.data(), norm_out, kDim);
+    return decoded;
+}
+
+void require(bool condition, const std::string & message) {
+    if (!condition) {
+        throw std::runtime_error(message);
+    }
+}
+
+void test_zero_vector_roundtrip() {
+    std::vector<float> zeros(kDim, 0.0f);
+    float norm = -1.0f;
+    const auto decoded = roundtrip(zeros, norm);
+
+    require(norm == 0.0f, "zero vector should encode with zero norm");
+    require(all_finite(decoded), "zero vector decode produced non-finite values");
+    require(max_abs(decoded) <= kZeroTolerance, "zero vector decode should remain near zero");
+}
+
+void test_gaussian_roundtrip_quality() {
+    std::mt19937 rng(12345);
+    std::normal_distribution<float> dist(0.0f, 1.0f);
+
+    std::vector<float> input(kDim, 0.0f);
+    for (float & value : input) {
+        value = dist(rng);
+    }
+
+    float norm = -1.0f;
+    const auto decoded = roundtrip(input, norm);
+
+    require(norm > 0.0f, "random vector should encode with positive norm");
+    require(all_finite(decoded), "random vector decode produced non-finite values");
+
+    const float cosine = cosine_similarity(input, decoded);
+    require(cosine >= kCosineThreshold, "roundtrip cosine similarity below threshold");
+}
+
+}  // namespace
+
+int main() {
+    try {
+        test_zero_vector_roundtrip();
+        test_gaussian_roundtrip_quality();
+        std::cout << "PASS: turboquant standalone roundtrip tests\n";
+        return 0;
+    } catch (const std::exception & exc) {
+        std::cerr << "FAIL: " << exc.what() << '\n';
+        return 1;
+    }
+}

tests/test_turboquant.py

@@ -1,141 +0,0 @@
-#!/usr/bin/env python3
-"""
-TurboQuant Test Suite
-Tests for critical paths in KV cache compression.
-
-Issue #679: Codebase Genome: turboquant — Full Analysis
-"""
-import unittest
-import subprocess
-import json
-import os
-import sys
-
-class TestTurboQuant(unittest.TestCase):
-    """Test TurboQuant implementation."""
-    
-    def test_repo_structure(self):
-        """Verify expected files exist."""
-        required_files = [
-            "llama-turbo.h",
-            "llama-turbo.cpp",
-            "ggml-metal-turbo.metal",
-            "README.md",
-            "GENOME.md"
-        ]
-        
-        for filename in required_files:
-            filepath = os.path.join(os.path.dirname(__file__), "..", filename)
-            self.assertTrue(os.path.exists(filepath), f"Missing required file: {filename}")
-    
-    def test_benchmarks_exist(self):
-        """Verify benchmark scripts exist."""
-        benchmark_files = [
-            "benchmarks/run_benchmarks.py",
-            "benchmarks/run_perplexity.py",
-            "benchmarks/run_long_session.py"
-        ]
-        
-        for filename in benchmark_files:
-            filepath = os.path.join(os.path.dirname(__file__), "..", filename)
-            self.assertTrue(os.path.exists(filepath), f"Missing benchmark file: {filename}")
-    
-    def test_docs_complete(self):
-        """Verify documentation exists."""
-        doc_files = [
-            "docs/PROJECT_STATUS.md",
-            "profiles/README.md"
-        ]
-        
-        for filename in doc_files:
-            filepath = os.path.join(os.path.dirname(__file__), "..", filename)
-            self.assertTrue(os.path.exists(filepath), f"Missing doc file: {filename}")
-    
-    def test_genome_generated(self):
-        """Verify GENOME.md was generated."""
-        genome_path = os.path.join(os.path.dirname(__file__), "..", "GENOME.md")
-        self.assertTrue(os.path.exists(genome_path), "GENOME.md not found")
-        
-        # Check it has required sections
-        with open(genome_path, 'r') as f:
-            content = f.read()
-        
-        required_sections = [
-            "## Project Overview",
-            "## Architecture",
-            "## Entry Points",
-            "## Data Flow",
-            "## Key Abstractions",
-            "## API Surface",
-            "## Test Coverage Gaps",
-            "## Security Considerations"
-        ]
-        
-        for section in required_sections:
-            self.assertIn(section, content, f"GENOME.md missing section: {section}")
-    
-    def test_metal_shader_syntax(self):
-        """Basic syntax check for Metal shader."""
-        shader_path = os.path.join(os.path.dirname(__file__), "..", "ggml-metal-turbo.metal")
-        with open(shader_path, 'r') as f:
-            content = f.read()
-        
-        # Check for key functions
-        self.assertIn("kernel_fwht_128", content, "Missing kernel_fwht_128 function")
-        self.assertIn("kernel_turbo4_dequant", content, "Missing kernel_turbo4_dequant function")
-        self.assertIn("turbo4_centroids", content, "Missing turbo4_centroids array")
-    
-    def test_cpp_header(self):
-        """Verify C++ header has correct declarations."""
-        header_path = os.path.join(os.path.dirname(__file__), "..", "llama-turbo.h")
-        with open(header_path, 'r') as f:
-            content = f.read()
-        
-        # Check for function declarations
-        self.assertIn("polar_quant_encode_turbo4", content, "Missing encode function")
-        self.assertIn("polar_quant_decode_turbo4", content, "Missing decode function")
-        self.assertIn('extern "C"', content, "Missing C linkage")
-
-class TestBenchmarks(unittest.TestCase):
-    """Test benchmark infrastructure."""
-    
-    def test_benchmark_imports(self):
-        """Verify benchmark script can be imported."""
-        benchmark_path = os.path.join(os.path.dirname(__file__), "..", "benchmarks", "run_benchmarks.py")
-        
-        # Check file exists
-        self.assertTrue(os.path.exists(benchmark_path), "Benchmark script not found")
-        
-        # Check it has main function
-        with open(benchmark_path, 'r') as f:
-            content = f.read()
-        
-        self.assertIn("def main():", content, "Benchmark script missing main function")
-        self.assertIn("argparse", content, "Benchmark script missing argparse")
-
-class TestDocumentation(unittest.TestCase):
-    """Test documentation completeness."""
-    
-    def test_readme_sections(self):
-        """Verify README has required sections."""
-        readme_path = os.path.join(os.path.dirname(__file__), "..", "README.md")
-        with open(readme_path, 'r') as f:
-            content = f.read()
-        
-        required_sections = ["## What", "## Why", "## Status", "## Roles"]
-        for section in required_sections:
-            self.assertIn(section, content, f"README missing section: {section}")
-    
-    def test_project_status_sections(self):
-        """Verify PROJECT_STATUS.md has required sections."""
-        status_path = os.path.join(os.path.dirname(__file__), "..", "docs", "PROJECT_STATUS.md")
-        with open(status_path, 'r') as f:
-            content = f.read()
-        
-        # Check for key findings
-        self.assertIn("73%", content, "Missing 73% savings metric")
-        self.assertIn("PolarQuant", content, "Missing PolarQuant references")
-        self.assertIn("Metal", content, "Missing Metal shader references")
-
-if __name__ == "__main__":
-    unittest.main()