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Build and optimize the Bolt C++ ML IDE with neural network module architecture. Use for C++ IDE development with GGML integration, RWKV neural networks, AI code completion, GPU acceleration, and modular component composition following nn patterns.

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name: bolt-cppml description: Build and optimize the Bolt C++ ML IDE with neural network module architecture. Use for C++ IDE development with GGML integration, RWKV neural networks, AI code completion, GPU acceleration, and modular component composition following nn patterns. source_chain: function-creator( c++( nn( bolt-new ) ) )

Bolt C++ ML — AI-Powered C++ IDE

Bolt C++ ML is a modular C++ IDE with integrated machine learning capabilities, combining GGML-based inference, RWKV neural networks, and a full-featured editor with split views, multi-cursor editing, code folding, theming, keyboard shortcuts, plugin system, LSP integration, and collaborative editing.

Architecture Overview

The project follows a neural network module composition pattern where each IDE component is treated as a composable module (analogous to nn.Module in Torch7), connected through a sequential pipeline.

Core Module Hierarchy

Module Layer Component Source Path
AI / Inference GGML Wrapper, RWKV Wrapper, Direct GGUF Inference src/bolt/ai/
Editor Split View, Multi-Cursor, Code Folding, Keyboard Shortcuts src/bolt/editor/
Core Chat Store, Editor Store, File Store, Workbench Store src/bolt/core/
GUI ImGui Integration, Theme System, Tab Bar, Minimap src/bolt/gui/
Git Repository Management, Diff, Staging src/bolt/git/
Network Collaboration, Network Optimizations src/bolt/network/
System Logging, Debugger, Memory Leak Detector, Profiler src/bolt/system/
Plugin Plugin System, Extension API src/bolt/plugin/
LSP Language Server Protocol Client src/bolt/lsp/

Key Files

File Purpose
CMakeLists.txt Top-level build configuration
test/CMakeLists.txt Test framework with CTest integration
src/bolt/ai/ggml_wrapper.cpp GGML backend for tensor operations
src/bolt/ai/rwkv_wrapper.cpp RWKV time-mixing and channel-mixing layers
src/bolt/ai/direct_gguf_inference.cpp Direct GGUF model loading and inference
include/bolt/ai/*.hpp AI module headers
include/bolt/editor/*.hpp Editor component headers
include/bolt/core/*.hpp Core store headers

Quick Start

Prerequisites

  • C++20 compatible compiler (GCC 11+, Clang 14+)
  • CMake 3.15+
  • GGML library (bundled as submodule)

Build

git clone https://github.com/cogpy/bolt-cppml.git
cd bolt-cppml
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

Test

cd build
ctest --output-on-failure

All 97 tests should pass with 0 warnings. The CTest configuration automatically sets LD_LIBRARY_PATH for shared library resolution.

Neural Network Module Pattern

Each component follows the nn module pattern:

Forward Pass (Data Flow)

Input → [Tokenizer] → [RWKV TimeMixing] → [RWKV ChannelMixing] → [LayerNorm] → [Output Projection] → Completion

RWKV Time-Mixing (WKV Attention)

The RWKV wrapper implements the full WKV attention mechanism:

xk = last_x + (x - last_x) * time_mix_k
xv = last_x + (x - last_x) * time_mix_v
xr = last_x + (x - last_x) * time_mix_r

k = Wk @ xk,  v = Wv @ xv,  r = Wr @ xr

wkv = (last_num + exp(bonus + k) * v) / (last_den + exp(bonus + k))
output = Wout @ (sigmoid(r) * wkv)

RWKV Channel-Mixing (Feed-Forward with Memory)

xk = last_x + (x - last_x) * time_mix_k
xr = last_x + (x - last_x) * time_mix_r

k = Wk @ xk,  r = Wr @ xr
vk = Wv @ (relu(k))^2
output = sigmoid(r) * vk

Test Framework

The project uses a custom lightweight test framework with CTest integration:

Test Suite Tests Description
Core 7 Chat, Memory, Store, String, FileTree, Minimap
Editor 5 SplitView, MultiCursor, KeyboardShortcuts, Theme, CodeFolding
ErrorHandling 7 Error recovery, boundary conditions
AI 15 GGML wrapper, AI models, KoboldCpp provider (13 suites)
Comprehensive E2E 27 Cross-module, DrawKern VM, Styx, Git, Benchmark, Plugin
Extended E2E 11 DataProcessor, MathUtils, FileSystem, LineNumbers, VectorDB, OT edge cases
Integration 2 Full integration tests
System 4 Debugger, Logging, MemoryLeak, Sanitizer

Running Specific Test Labels

ctest -L Unit          # All unit tests
ctest -L Editor        # Editor component tests
ctest -L AI            # AI/ML tests
ctest -L Integration   # Integration tests

Extending the IDE

Adding New Modules

  1. Create header in include/bolt/<category>/
  2. Create implementation in src/bolt/<category>/
  3. Add source to CMakeLists.txt bolt_lib target
  4. Add test in test/
  5. Register test in test/CMakeLists.txt

Plugin System

Plugins follow the module pattern with lifecycle hooks:

class MyPlugin : public bolt::Plugin {
    void onActivate() override;
    void onDeactivate() override;
    std::string getName() const override;
};

Build Status

  • Errors: 0
  • Warnings: 0
  • Tests: 97/97 passing (100%)
  • CTest: Fully configured with LD_LIBRARY_PATH and labels
  • Labels: Unit, Core, Editor, ErrorHandling, AI, KoboldCpp, Extended, Integration