xchani / mou

Mou: Library for C++ 17 containing funtional features and so on.

Mou

Library for C++ 17 containing funtional features and so on.

Examples

Tensor

using namespace mou::tensor;
using namespace mou::expr;

Tensor<int> x({1, 2, 3, 4});
std::cout << x << std::endl;

x.Reshape(2, 2);
std::cout << x << std::endl;

x = x * 3;
std::cout << x << std::endl;

x = F<axpy>(x, x, x);
std::cout << x << std::endl;

x = -1;
std::cout << x << std::endl;

Output:

[ 1, 2, 3, 4]
<NDArray 4 @cpu(0)>

[[ 1, 2],
 [ 3, 4]]
<NDArray 2x2 @cpu(0)>

[[  3,  6],
 [  9, 12]]
<NDArray 2x2 @cpu(0)>

[[  12,  42],
 [  90, 156]]
<NDArray 2x2 @cpu(0)>

[[ -1, -1],
 [ -1, -1]]
<NDArray 2x2 @cpu(0)>

Pattern Matching

using namespace mou::pm;

using var_t = std::variant<int, long, double, std::string, unsigned int>;
std::vector<var_t> vec = {10, 15l, 1.5, "hello", 3u};
for (auto& v: vec) {
    std::visit(overloaded {
        [](int arg) { std::cout << "int::" << arg << '\n'; },
        [](long arg) { std::cout << "long::" << arg << '\n'; },
        [](double arg) { std::cout << "double::" << arg << '\n'; },
        [](std::string arg) { std::cout << "string::" << arg << '\n'; },
        [](auto arg) { std::cout << "auto::" << arg << '\n'; },
    }, v);
}

Output:

int::10
long::15
double::1.5
string::hello
auto:3

Reflection

using namespace mou::reflection;

struct sc {
    int a;
    char b;
};

sc s{ 1, 'a' };
auto ts = tuple_binding(s);
static_assert(std::is_same_v<decltype(ts), std::tuple<int, char>>);
std::cout << std::get<0>(ts) << std::endl;
std::cout << std::get<1>(ts) << std::endl;

Output:

1
a

Features

• Lazy Evaluation
  • Scalar Expression
  • Unary Expression for Vector
  • Binary Expression for Vector
  • Ternary Expression for Vector
  • Deduce return type of expression
• Pattern Matching
  • Type-matching with overloaded operator()'s
• Reflection
  • Simple static reflection
• Logger
  • Checker
  • Logger
• Tensor
  • Basic Tensor
  • Slicing
  • Dot Product
  • GEMM
  • SIMD using instrinsics for Dot Product
  • SIMD using instrinsics for GEMM
  • CUDA kernel for Dot Product
  • CUDA kernel for GEMM