Add DestructuringBindingsForFieldClass to use pystencils kernels in a more C++-ish way
DestructuringBindingsForFieldClass defines all field-related variables
in its subordinated block.
However, it leaves a TypedSymbol of type Field for each field
undefined.
By that trick we can generate kernels that accept structs as
kernelparameters.
Either to include a pystencils specific Field struct of the following
definition:
template<DTYPE_T, DIMENSION>
struct Field
{
DTYPE_T* data;
std::array<int64_t, DIMENSION> shape;
std::array<int64_t, DIMENSION> stride;
}or to be able to destructure user defined types like pybind11::array,
at::Tensor, tensorflow::Tensor.
The test generates a kernel like that:
FUNC_PREFIX void kernel(Field<double, 2>& x, Field<double, 2>& y, Field<double, 2>& z)
{
_stride_z_1 = z.stride[1];
_size_x_0 = x.shape[0];
_stride_x_1 = x.stride[1];
_stride_z_0 = z.stride[0];
_size_x_1 = x.shape[1];
_stride_y_1 = y.stride[1];
_data_x = x.data;
_stride_x_0 = x.stride[0];
_data_z = z.data;
_stride_y_0 = y.stride[0];
_data_y = y.data;
{
for (int ctr_0 = 0; ctr_0 < _size_x_0; ctr_0 += 1)
{
double * RESTRICT _data_z_00 = _data_z + _stride_z_0*ctr_0;
double * RESTRICT const _data_y_00 = _data_y + _stride_y_0*ctr_0;
double * RESTRICT const _data_x_00 = _data_x + _stride_x_0*ctr_0;
for (int ctr_1 = 0; ctr_1 < _size_x_1; ctr_1 += 1)
{
_data_z_00[_stride_z_1*ctr_1] = log(_data_x_00[_stride_x_1*ctr_1]*_data_y_00[_stride_y_1*ctr_1])*_data_y_00[_stride_y_1*ctr_1];
}
}
}
}