3 releases
new 0.0.5 | Oct 30, 2024 |
---|---|
0.0.4 | Oct 28, 2024 |
0.0.3 | Oct 27, 2024 |
#139 in Science
358 downloads per month
18KB
MaidenX
Rust-based CUDA library designed for learning purposes and building my AI engines named Maiden Engine
TODOS
[!NOTE]
This project is structured to resemble the PyTorch framework where possible, to aid in familiarization and learning.
[!WARNING] 🚧 This project is for personal learning and testing purposes, so it may not function properly. 🚧
Getting Started
Prerequisites
- CUDA Toolkit
- CMake
How to use
Using PyTorch | Using MaidenX | |
---|---|---|
Creation | torch.Tensor([[1, 2], [3, 4]]) |
Tensor::new(vec![vec![1.0, 2.0], vec![3.0, 4.0]]) |
Creation | torch.Tensor([[1, 2], [3, 4]]) |
Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0], &[2, 2]) |
Creation | nn.Sequential(A, B, C) |
nn::ModuleBuilder::new().add_layer(A).add_layer(B).add_layer(C) |
Example
[dependencies]
maidenx = { version = "0.0.5", features = ["full"] }
# only cpu
# maidenx = { version = "0.0.5" }
# only cuda, but cpu is default
# maidenx = {version = "0.0.5", features = ["cuda"]}
How to use Tensor:
use maidenx::prelude::*;
fn main() -> Result<(), Box<dyn std::error::Error>> {
let tensor1 = Tensor::new(vec![
vec![1.0, 2.0, 3.0],
vec![4.0, 5.0, 6.0],
])?;
let tensor2 = Tensor::new(vec![
vec![7.0, 8.0, 9.0],
vec![10.0, 11.0, 12.0],
])?;
let result = tensor1.add(&tensor2)?;
println!("Shape: {:?}", result.shape());
println!("Result:\n{}", result);
Ok(())
}
How to use linear module:
use maidenx::prelude::*;
fn main() -> Result<(), Box<dyn std::error::Error>> {
let input = Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0], &[2, 2])?;
let linear = nn::Linear::new_with_bias(2, 3, true)?;
let linear_output = linear.forward(&input)?;
let relu = nn::ReLU::new();
let relu_output = relu.forward(&linear_output)?;
let sigmoid = nn::Sigmoid::new();
let sigmoid_output = sigmoid.forward(&relu_output)?;
let tanh = nn::Tanh::new();
let tanh_output = tanh.forward(&sigmoid_output)?;
println!("Input:\n{}", input);
println!("Linear output:\n{}", linear_output);
println!("ReLU output:\n{}", relu_output);
println!("Sigmoid output:\n{}", sigmoid_output);
println!("Tanh output:\n{}", tanh_output);
Ok(())
}
How to select device: default: cpu
fn main() -> Result<(), Box<dyn std::error::Error>> {
// Set CUDA if present on startup
if let Ok(cuda_device) = Device::cuda(0) {
set_current_device(cuda_device)?;
}
// all subsequent operations will use the set device
...
Ok(())
}
How to use DeviceGuard
:
use maidenx::prelude::*;
fn main() -> Result<(), Box<dyn std::error::Error>> {
// CPU
{
let _guard = DeviceGuard::new(Device::cpu())?;
let a = Tensor::new(vec![1.0, 2.0, 3.0])?;
let b = Tensor::new(vec![4.0, 5.0, 6.0])?;
let c = a.add(&b)?;
println!("CPU Result: {}", c);
} // CPU guard drops here
// CUDA
if let Ok(cuda_device) = Device::cuda(0) {
let _guard = DeviceGuard::new(cuda_device)?;
let x = Tensor::new(vec![1.0, 2.0, 3.0])?;
let y = Tensor::new(vec![4.0, 5.0, 6.0])?;
let z = x.add(&y)?;
println!("CUDA Result: {}", z);
} // CUDA guard drops here
Ok(())
}
For more examples, see examples
.
Development Setup
Prerequisites
- Rust
- CUDA Toolkit
- CMake
- clangd
LSP Setup
For IDE support with CUDA files, create .clangd
file in crates/maidenx_cuda_kernels/
:
CompileFlags:
Remove:
- "-forward-unknown-to-host-compiler"
- "-rdc=*"
- "-Xcompiler*"
- "--options-file"
- "--generate-code*"
Add:
- "-xcuda"
- "-std=c++14"
- "-I/YOUR/CUDA/PATH/include" # Update this path
- "-I../../cuda-headers"
- "--cuda-gpu-arch=sm_75"
Compiler: clang
Index:
Background: Build
Diagnostics:
UnusedIncludes: None
Find your CUDA include path:
# Linux
which nvcc | sed 's/\/bin\/nvcc//'
# Windows (PowerShell)
(Get-Command nvcc).Path -replace '\\bin\\nvcc.exe',''
Dependencies
~4MB
~79K SLoC