VENLACPU

CPU-first deep learning framework modern C++17.

VENLACPU is an actively developed deep learning framework focused on a clean, portable CPU implementation. The native engine is written in C++17 and is designed to work across desktop and mobile environments without requiring CUDA.

Status: Alpha / active development. APIs may change before the first stable release.

Author

VENLACPU is created and developed by Frandika Imam Arifin.

Current capabilities

The current C++ core includes:

Tensor storage, shape, stride, dtype, and device abstractions
CPU tensor operations and manipulation
Autograd and gradient propagation
Elementwise mathematics and reductions
Linear layers and sequential models
Activation functions
MSE and cross-entropy loss
Embedding
Positional encoding
Layer normalization
Multi-head attention
Feed-forward networks
Transformer encoder
Transformer decoder
KV cache and cached attention
Causal language model
Causal language-model dataset batching
Training loop with gradient accumulation
Adam optimizer
Evaluation and autoregressive generation
Tokenizer and vocabulary components

The project currently contains a comprehensive C++ test suite covering the implemented components.

Architecture

Physical CPU engine
           |
          T
ensor / Storage / Shape / Stride / Dtype / Device
           |
          Autograd
          |
          Math Ops
           |
          Neural network
          |
      Transformer / Language Model
          |
          Training System

The native implementation is the primary implementation at this stage.

Build from source

Requirements:

CMake 3.16 or newer
C++17 compiler
Git

cmake -S . -B build
cmake --build build -j2

Run the complete test suite:

ctest --test-dir build --output-on-failure

Python / PyPI

VENLACPU has a modular Python package prepared for PyPI. The current Python package exposes the package version, while the native C++ engine remains the primary implementation.

Platform-specific native wheels will be added when the Python binding layer is ready. GitHub Actions is used as the build and packaging engine.

Causal language-model training

The current training API supports:

Causal token shifting
Dataset batching
Padding with ignore index
Cross-entropy loss
Autograd and gradient propagation
Gradient accumulation
Adam optimization
Evaluation
Autoregressive generation

CPU-first design

VENLACPU is intentionally CPU-first. The current implementation does not require CUDA or a GPU runtime.

This makes the project suitable for desktop CPUs and ARM/mobile development environments.

Repository layout

venlacpu/
└ ── include/venla/
    └ ── autograd/
    │      core/
    └        math/
    └        nn/
    └ ── optim/
    │        tensor/
    └ ── tokenizer/
    └        training/
  │   src/
    │      core/
    └        math/
    └        nn/
    └ ── optim/
    │      tensor/
    │      tokenizer/
    │      training/
  │   tests/
  │  examples/
   │  benchmarks/
   │   docs/
   └ ── python/
   │  CMakeLists.tx
  │  pyproject.toml
   └ ── README.md

Development philosophy

VENLACPU begins with the low-level foundations and builds upward. The long-term goal is a portable CPU-first framework with a native C++ core and convenient Python access.

License

The project is currently in alpha and the final public-release license is being finalized.

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