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#include <torch/extension.h>
// test include_dirs in setuptools.setup with relative path
#include <tmp.h>
#include <ATen/OpMathType.h>
torch::Tensor sigmoid_add(torch::Tensor x, torch::Tensor y) {
return x.sigmoid() + y.sigmoid();
}
struct MatrixMultiplier {
MatrixMultiplier(int A, int B) {
tensor_ =
torch::ones({A, B}, torch::dtype(torch::kFloat64).requires_grad(true));
}
torch::Tensor forward(torch::Tensor weights) {
return tensor_.mm(weights);
}
torch::Tensor get() const {
return tensor_;
}
private:
torch::Tensor tensor_;
};
bool function_taking_optional(std::optional<torch::Tensor> tensor) {
return tensor.has_value();
}
torch::Tensor random_tensor() {
return torch::randn({1});
}
at::ScalarType get_math_type(at::ScalarType other) {
return at::toOpMathType(other);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("sigmoid_add", &sigmoid_add, "sigmoid(x) + sigmoid(y)");
m.def(
"function_taking_optional",
&function_taking_optional,
"function_taking_optional");
py::class_<MatrixMultiplier>(m, "MatrixMultiplier")
.def(py::init<int, int>())
.def("forward", &MatrixMultiplier::forward)
.def("get", &MatrixMultiplier::get);
m.def("get_complex", []() { return c10::complex<double>(1.0, 2.0); });
m.def("get_device", []() { return at::device_of(random_tensor()).value(); });
m.def("get_generator", []() { return at::detail::getDefaultCPUGenerator(); });
m.def("get_intarrayref", []() { return at::IntArrayRef({1, 2, 3}); });
m.def("get_memory_format", []() { return c10::get_contiguous_memory_format(); });
m.def("get_storage", []() { return random_tensor().storage(); });
m.def("get_symfloat", []() { return c10::SymFloat(1.0); });
m.def("get_symint", []() { return c10::SymInt(1); });
m.def("get_symintarrayref", []() { return at::SymIntArrayRef({1, 2, 3}); });
m.def("get_tensor", []() { return random_tensor(); });
m.def("get_math_type", &get_math_type);
}
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