An automatic differentiation library written in C++ and CUDA. No cuBLAS dependency.

The following libraries are required for building cugrad:

• cuda

The following script will build the library and all the included examples:

git clone https://github.com/dmaivel/cugrad.git
cd cugrad
mkdir build
cd build
cmake ..
cmake --build . --config Release

Kernels are mostly unoptimized; do not expect highest performance possible (yet).

For use in your own project:

• Link the shared library (libcugrad.so)
• Include the primary header (#include <cugrad.hpp>)

The library uses shared smart pointers for tensor storage, so it is recommended you use auto when creating tensors.

auto a = cugrad::create_tensor({ 1 }); // create a tensor with shape (1)
auto b = cugrad::create_tensor({ 2, 2 }); // create a tensor with shape (2, 2)
auto c = cugrad::create_tensor({ 3, 4, 6 }); // create a tensor with shape (3, 4, 6)

You may use either type in place of auto:

std::shared_ptr<cugrad::tensor> a;
cugrad::tensor_ptr b;

You can not use non-pointer tensors.

The library provides many operations, all in the namespace cugrad.

auto a = cugrad::create_tensor({ 1 });
auto b = cugrad::create_tensor({ 1 });

auto c = cugrad::add(a, b);
auto d = cugrad::mul(a, c);

Note that it is not required to declare each operation as its own seperate variable:

// equivalent to `f(a, b) = ((a * b) + a) * (b - a)`
auto f(std::shared_ptr<cugrad::tensor> a, std::shared_ptr<cugrad::tensor> b) -> std::shared_ptr<cugrad::tensor>
{
    return cugrad::mul(cugrad::add(cugrad::mul(a, b), a), cugrad::sub(b, a));
}

Before computing the results or gradients, you need to set the values found in the input variables. This can be achieved with the following:

// set every value in `a` to `3.f`
a->set(3.f);

Before proceeding to the compute sections, its important to take note of another class provided: cugrad::raw_tensor. This is simply a storage type for managing buffers returned to the CPU. It is not stored in a smart pointer.

auto result = /* ... */; // result is raw_tensor

float x = result[0]; // linear access
float y = result({ 1, 2 }) // positional access

To compute the result of an equation, simply call compute() on the final variable.

auto y = f(a, b);
y->compute();

Because the result is stored on the GPU, you must call cpu() to extract the result.

auto result = y->cpu();

You may combine the two calls for the following one-liner:

auto result = y->compute()->cpu();

To extract a specific value, simply index it:

auto real_result = result[0];
// or
auto real_result = y->compute()->cpu()[0];
// or
auto real_result = y->compute()->cpu()({ 0, 0 }); // assuming 2d shape

To compute the gradient of an equation, ensure you have first computed a result (using compute()). Then, call grad().

y->grad();

To obtain the partial derivative with respect to a, we call cpu(wrt).

auto result = y->cpu(a);

Once again, you may combine all these calls into a one-liner:

// compute() included only if compute() was not previously called
auto result = y->compute()->grad()->cpu(a);

As mentioned before, the return type of cpu(...) is a raw tensor, so you must index it to extract actual values.