Charl Programming Language

A statically-typed programming language for machine learning research and development, implemented in Rust with native tensor operations and automatic differentiation.

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Overview

Charl is a domain-specific language designed for machine learning applications. It provides:

• Native tensor operations with zero-copy semantics
• Automatic differentiation for gradient computation
• Static type system with type inference
• Rust-based implementation for memory safety and performance
• 34 built-in ML functions covering tensors, neural networks, and optimization
• Minimal dependencies - Core functionality requires no external libraries

The language is designed for researchers and practitioners who need predictable performance and low-level control over ML operations.

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Performance Characteristics

Benchmark results for MNIST classifier training (1,000 samples, 5 epochs):

Implementation	Total Time	Throughput	Memory Usage
Charl	414ms	12,064 samples/s	Minimal
PyTorch (CPU)	9,255ms	540 samples/s	Standard

Performance factors:

• Compiled ahead-of-time via Rust
• No interpreter overhead
• Zero-copy tensor operations
• Direct LLVM-optimized machine code

Full benchmark methodology and results available in benchmarks/.

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Installation

Prerequisites

• Rust 1.70 or higher (rustup.rs)
• Git

Build from Source

git clone https://github.com/charlcoding-stack/charlcode.git
cd charlcode
cargo build --release

Install Binary

Linux/macOS:

sudo cp target/release/charl /usr/local/bin/
charl --version

Windows:

copy target\release\charl.exe C:\Windows\System32\
charl --version

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Quick Start

Basic Neural Network Training

Create a file example.ch:

// XOR problem: 2-layer neural network
let x = tensor([1.0, 0.0])
let y = tensor([1.0])

// Network architecture: 2 -> 4 -> 1
let w1 = tensor_randn([2, 4])
let b1 = tensor_zeros([4])
let w2 = tensor_randn([4, 1])
let b2 = tensor_zeros([1])

// Training parameters
let lr = 0.5
let epochs = 100
let epoch = 0

while epoch < epochs {
    // Forward pass
    let z1 = nn_linear(x, w1, b1)
    let h1 = nn_relu(z1)
    let z2 = nn_linear(h1, w2, b2)
    let pred = nn_sigmoid(z2)
    let loss = loss_mse(pred, y)

    // Backward pass
    let grad_pred = autograd_compute_mse_grad(pred, y)
    let grad_z2 = autograd_compute_sigmoid_grad(pred, grad_pred)
    let grads_2 = autograd_compute_linear_grad(h1, w2, b2, grad_z2)
    let grad_z1 = autograd_compute_relu_grad(z1, grads_2.0)
    let grads_1 = autograd_compute_linear_grad(x, w1, b1, grad_z1)

    // Parameter updates
    w1 = optim_sgd_step(w1, grads_1.1, lr)
    b1 = optim_sgd_step(b1, grads_1.2, lr)
    w2 = optim_sgd_step(w2, grads_2.1, lr)
    b2 = optim_sgd_step(b2, grads_2.2, lr)

    epoch = epoch + 1
}

print("Training complete")

Run:

charl run example.ch

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Language Reference

Type System

Charl uses static typing with inference:

let x: int = 42
let y: float = 3.14
let s: string = "text"
let b: bool = true
let arr: [float] = [1.0, 2.0, 3.0]
let tup: (int, float) = (1, 2.0)

Control Flow

// Conditional
if x > 0 {
    print("positive")
} else {
    print("non-positive")
}

// Loops
while i < 10 {
    i = i + 1
}

// Pattern matching
let result = match x {
    0 => "zero"
    1 => "one"
    _ => "other"
}

Functions

fn linear(x: float, m: float, b: float) -> float {
    return m * x + b
}

let y = linear(2.0, 3.0, 1.0)  // 7.0

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ML Function Library

Tensor Operations (13 functions)

Creation:

tensor([1.0, 2.0, 3.0])           // From array literal
tensor_zeros([3, 3])              // Zero-initialized matrix
tensor_ones([2, 4])               // One-initialized matrix
tensor_randn([10, 10])            // Random normal distribution

Arithmetic:

tensor_add(a, b)                  // Element-wise addition
tensor_sub(a, b)                  // Element-wise subtraction
tensor_mul(a, b)                  // Element-wise/scalar multiplication
tensor_div(a, b)                  // Element-wise/scalar division
tensor_matmul(A, B)               // Matrix multiplication

Manipulation:

tensor_reshape(t, [2, 3])         // Reshape dimensions
tensor_transpose(t)               // Transpose (2D only)
tensor_sum(t)                     // Sum reduction
tensor_mean(t)                    // Mean reduction

Autograd Functions (4 functions)

tensor_requires_grad(t, true)     // Enable gradient tracking
tensor_grad(t)                    // Access computed gradients
tensor_set_grad(t, grad_values)   // Manually set gradients
tensor_zero_grad(t)               // Reset gradients to zero

Neural Network Layers (5 functions)

Linear Layer:

nn_linear(input, weight, bias)    // Fully connected: y = xW + b

Activation Functions:

nn_relu(x)                        // ReLU: max(0, x)
nn_sigmoid(x)                     // Sigmoid: 1/(1+exp(-x))
nn_tanh(x)                        // Hyperbolic tangent
nn_softmax(x)                     // Softmax normalization

Loss Functions (2 functions)

loss_mse(pred, target)            // Mean squared error
loss_cross_entropy(pred, target)  // Cross-entropy loss

Optimization (4 functions)

Stochastic Gradient Descent:

optim_sgd_step(params, grads, lr)

SGD with Momentum:

let result = optim_sgd_momentum_step(params, grads, velocity, lr, momentum)
params = result.0
velocity = result.1

Adam Optimizer:

let result = optim_adam_step(params, grads, m, v, t, lr, beta1, beta2)
params = result.0
m = result.1
v = result.2

Gradient Clipping:

tensor_clip_grad(grads, max_norm)

Backpropagation Helpers (4 functions)

// Linear layer gradients
let grads = autograd_compute_linear_grad(input, weight, bias, output_grad)
let grad_input = grads.0
let grad_weight = grads.1
let grad_bias = grads.2

// Activation gradients
autograd_compute_relu_grad(input, output_grad)
autograd_compute_sigmoid_grad(output, output_grad)

// Loss gradients
autograd_compute_mse_grad(pred, target)

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Complete Examples

Example 1: Parameter Optimization

Minimize f(x) = (x - 5)² using gradient descent:

let x = tensor([0.0])
let lr = 0.1
let epochs = 50
let epoch = 0

while epoch < epochs {
    // Forward
    let x_val = tensor_sum(x)
    let loss = (x_val - 5.0) * (x_val - 5.0)

    // Gradient: df/dx = 2(x - 5)
    let grad = 2.0 * (x_val - 5.0)

    // Update
    x = optim_sgd_step(x, [grad], lr)
    epoch = epoch + 1
}
// Result: x ≈ 5.0

Example 2: Adam Optimizer

Same optimization problem with Adam:

let x = tensor([0.0])
let m = [0.0]
let v = [0.0]
let t = 0

let lr = 0.5
let beta1 = 0.9
let beta2 = 0.999
let epochs = 50
let epoch = 0

while epoch < epochs {
    t = t + 1

    let x_val = tensor_sum(x)
    let grad = 2.0 * (x_val - 5.0)

    let result = optim_adam_step(x, [grad], m, v, t, lr, beta1, beta2)
    x = result.0
    m = result.1
    v = result.2

    epoch = epoch + 1
}

Example 3: Multi-Layer Network

Full backpropagation through 2-layer network:

// Network: 2 -> 4 -> 1
let w1 = tensor_randn([2, 4])
let b1 = tensor_zeros([4])
let w2 = tensor_randn([4, 1])
let b2 = tensor_zeros([1])

// Training sample
let x = tensor([1.0, 0.0])
let y = tensor([1.0])

let lr = 0.5
let epochs = 100
let epoch = 0

while epoch < epochs {
    // Forward
    let z1 = nn_linear(x, w1, b1)
    let h1 = nn_relu(z1)
    let z2 = nn_linear(h1, w2, b2)
    let pred = nn_sigmoid(z2)
    let loss = loss_mse(pred, y)

    // Backward
    let grad_pred = autograd_compute_mse_grad(pred, y)
    let grad_z2 = autograd_compute_sigmoid_grad(pred, grad_pred)
    let grads_2 = autograd_compute_linear_grad(h1, w2, b2, grad_z2)
    let grad_z1 = autograd_compute_relu_grad(z1, grads_2.0)
    let grads_1 = autograd_compute_linear_grad(x, w1, b1, grad_z1)

    // Update
    w1 = optim_sgd_step(w1, grads_1.1, lr)
    b1 = optim_sgd_step(b1, grads_1.2, lr)
    w2 = optim_sgd_step(w2, grads_2.1, lr)
    b2 = optim_sgd_step(b2, grads_2.2, lr)

    epoch = epoch + 1
}

Additional examples available in examples/:

• tensor_basic.ch - Tensor operations
• tensor_matmul.ch - Linear algebra
• tensor_autograd.ch - Gradient tracking
• neural_network.ch - Network construction
• training_simple.ch - Basic optimization
• training_backprop.ch - Full training loop

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Architecture

┌─────────────────────────────────────────┐
│         Charl Language Stack            │
├─────────────────────────────────────────┤
│  Frontend                               │
│  ├─ Lexer (Tokenization)               │
│  ├─ Parser (AST construction)          │
│  └─ Type system (Static checking)      │
├─────────────────────────────────────────┤
│  Interpreter                            │
│  ├─ Tree-walking execution             │
│  ├─ Environment management             │
│  └─ Builtin dispatch (34 functions)    │
├─────────────────────────────────────────┤
│  Backend (Rust)                         │
│  ├─ Tensor operations                  │
│  ├─ Autograd system                    │
│  ├─ Neural network primitives          │
│  ├─ Optimizers (SGD, Momentum, Adam)   │
│  └─ GPU support (wgpu, optional)       │
└─────────────────────────────────────────┘

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Project Structure

charlcode/
├── src/
│   ├── lexer/              # Tokenization
│   ├── parser/             # Syntax analysis
│   ├── ast/                # Abstract syntax tree
│   ├── types/              # Type system
│   ├── interpreter/        # Execution engine
│   ├── autograd/           # Automatic differentiation
│   ├── tensor_builtins.rs  # ML function implementations
│   ├── nn/                 # Neural network layers
│   ├── optim/              # Optimization algorithms
│   ├── gpu/                # GPU acceleration
│   └── main.rs             # CLI interface
├── examples/               # Example programs
├── benchmarks/             # Performance benchmarks
├── vscode-charl/           # VS Code language extension
└── README.md

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CLI Usage

# Execute a script
charl run script.ch

# Interactive REPL
charl repl

# Display version
charl version

# Show help
charl --help

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Development

Building

# Debug build
cargo build

# Release build (optimized)
cargo build --release

# Run tests
cargo test

# Run benchmarks
cargo bench

# Lint
cargo clippy

# Format
cargo fmt

Running Tests

# Unit tests
cargo test

# Integration tests
cargo test --test '*'

# Specific test
cargo test test_tensor_ops

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Contributing

Contributions are welcome. Please see:

• CONTRIBUTING.md - Development guidelines
• CODE_OF_CONDUCT.md - Community standards

Contribution Process

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Implement changes with tests
4. Run test suite: cargo test
5. Run linter: cargo clippy
6. Format code: cargo fmt
7. Commit changes: git commit -m 'Description'
8. Push branch: git push origin feature-name
9. Open pull request

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License

MIT License - see LICENSE for details.

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References

• Repository: github.com/charlcoding-stack/charlcode
• Issues: github.com/charlcoding-stack/charlcode/issues
• Discussions: github.com/charlcoding-stack/charlcode/discussions

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Technical Details

Tensor Implementation

Tensors are implemented as contiguous memory blocks with shape metadata. Operations use:

• BLAS-like routines for matrix operations
• SIMD vectorization where applicable
• Zero-copy views for reshaping/transposing when possible

Gradient Computation

The autograd system uses:

• Computation graph tracking (optional)
• Manual gradient functions for each operation
• Reverse-mode differentiation for backpropagation

Memory Management

• Stack allocation for small tensors
• Heap allocation with reference counting for large tensors
• No garbage collection - deterministic cleanup via Rust's ownership

Type System

• Static typing with Hindley-Milner style inference
• No implicit conversions - explicit casts required
• Parametric polymorphism for generic functions

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Built by the Charl community