xtensor-python/include/xtensor-python/pytensor.hpp at master · BenJamesbabala/xtensor-python · GitHub

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/***************************************************************************

* *

* Distributed under the terms of the BSD 3-Clause License. *

* *

* The full license is in the file LICENSE, distributed with this software. *

****************************************************************************/

#ifndef PY_TENSOR_HPP

#define PY_TENSOR_HPP

#include <cstddef>

#include <array>

#include <algorithm>

#include "xtensor/xutils.hpp"

#include "xtensor/xsemantic.hpp"

#include "xtensor/xiterator.hpp"

#include "pycontainer.hpp"

#include "pybuffer_adaptor.hpp"

namespace xt

{

template <class T, std::size_t N>

class pytensor;

}

namespace pybind11

{

namespace detail

{

template <class T, std::size_t N>

struct handle_type_name<xt::pytensor<T, N>>

{

static PYBIND11_DESCR name()

{

return _("numpy.ndarray[") + make_caster<T>::name() + _("]");

}

};

template <class T, std::size_t N>

struct pyobject_caster<xt::pytensor<T, N>>

{

using type = xt::pytensor<T, N>;

bool load(handle src, bool)

{

value = type::ensure(src);

return static_cast<bool>(value);

}

static handle cast(const handle& src, return_value_policy, handle)

{

return src.inc_ref();

}

PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name());

};

}

namespace xt

{

template <class T, std::size_t N>

struct xiterable_inner_types<pytensor<T, N>>

: xcontainer_iterable_types<pytensor<T, N>>

{

};

template <class T, std::size_t N>

struct xcontainer_inner_types<pytensor<T, N>>

{

using container_type = pybuffer_adaptor<T>;

using shape_type = std::array<npy_intp, N>;

using strides_type = shape_type;

using backstrides_type = shape_type;

using inner_shape_type = shape_type;

using inner_strides_type = strides_type;

using inner_backstrides_type = backstrides_type;

using temporary_type = pytensor<T, N>;

};

template <class T, std::size_t N>

class pytensor : public pycontainer<pytensor<T, N>>,

public xcontainer_semantic<pytensor<T, N>>

{

public:

using self_type = pytensor<T, N>;

using semantic_base = xcontainer_semantic<self_type>;

using base_type = pycontainer<self_type>;

using container_type = typename base_type::container_type;

using value_type = typename base_type::value_type;

using pointer = typename base_type::pointer;

using size_type = typename base_type::size_type;

using shape_type = typename base_type::shape_type;

using strides_type = typename base_type::strides_type;

using backstrides_type = typename base_type::backstrides_type;

using inner_shape_type = typename base_type::inner_shape_type;

using inner_strides_type = typename base_type::inner_strides_type;

using inner_backstrides_type = typename base_type::inner_backstrides_type;

pytensor() = default;

pytensor(nested_initializer_list_t<T, N> t);

pytensor(pybind11::handle h, pybind11::object::borrowed_t);

pytensor(pybind11::handle h, pybind11::object::stolen_t);

pytensor(const pybind11::object &o);

explicit pytensor(const shape_type& shape, layout l = layout::row_major);

pytensor(const shape_type& shape, const strides_type& strides);

template <class E>

pytensor(const xexpression<E>& e);

template <class E>

self_type& operator=(const xexpression<E>& e);

using base_type::begin;

using base_type::end;

static self_type ensure(pybind11::handle h);

static bool check_(pybind11::handle h);

private:

inner_shape_type m_shape;

inner_strides_type m_strides;

inner_backstrides_type m_backstrides;

container_type m_data;

void init_tensor(const shape_type& shape, const strides_type& strides);

void init_from_python();

void compute_backstrides();

const inner_shape_type& shape_impl() const noexcept;

const inner_strides_type& strides_impl() const noexcept;

const inner_backstrides_type& backstrides_impl() const noexcept;

container_type& data_impl() noexcept;

const container_type& data_impl() const noexcept;

friend class xcontainer<pytensor<T, N>>;

};

/***************************

* pytensor implementation *

***************************/

template <class T, std::size_t N>

inline pytensor<T, N>::pytensor(nested_initializer_list_t<T, N> t)

{

base_type::reshape(xt::shape<shape_type>(t), layout::row_major);

nested_copy(m_data.begin(), t);

}

template <class T, std::size_t N>

inline pytensor<T, N>::pytensor(pybind11::handle h, pybind11::object::borrowed_t)

: base_type(h, pybind11::object::borrowed)

{

init_from_python();

}

template <class T, std::size_t N>

inline pytensor<T, N>::pytensor(pybind11::handle h, pybind11::object::stolen_t)

: base_type(h, pybind11::object::stolen)

{

init_from_python();

}

template <class T, std::size_t N>

inline pytensor<T, N>::pytensor(const pybind11::object& o)

: base_type(o)

{

init_from_python();

}

template <class T, std::size_t N>

inline pytensor<T, N>::pytensor(const shape_type& shape, layout l)

{

base_type::fill_default_strides(shape, l, m_strides);

init_tensor(shape, m_strides);

}

template <class T, std::size_t N>

inline pytensor<T, N>::pytensor(const shape_type& shape,

const strides_type& strides)

{

init_tensor(shape, strides);

}

template <class T, std::size_t N>

template <class E>

inline pytensor<T, N>::pytensor(const xexpression<E>& e)

{

semantic_base::assign(e);

}

template <class T, std::size_t N>

template <class E>

inline auto pytensor<T, N>::operator=(const xexpression<E>& e) -> self_type&

{

return semantic_base::operator=(e);

}

template <class T, std::size_t N>

inline auto pytensor<T, N>::ensure(pybind11::handle h) -> self_type

{

return base_type::ensure(h);

}

template <class T, std::size_t N>

inline bool pytensor<T, N>::check_(pybind11::handle h)

{

return base_type::check_(h);

}

template <class T, std::size_t N>

inline void pytensor<T, N>::init_tensor(const shape_type& shape, const strides_type& strides)

{

npy_intp python_strides[N];

std::transform(strides.begin(), strides.end(), python_strides,

[](auto v) { return sizeof(T) * v; });

int flags = NPY_ARRAY_ALIGNED;

if(!std::is_const<T>::value)

{

flags |= NPY_ARRAY_WRITEABLE;

}

int type_num = detail::numpy_traits<T>::type_num;

auto tmp = pybind11::reinterpret_steal<pybind11::object>(

PyArray_New(&PyArray_Type, N, const_cast<npy_intp*>(shape.data()),

type_num, python_strides, nullptr, sizeof(T), flags, nullptr)

);

if(!tmp)

throw std::runtime_error("NumPy: unable to create ndarray");

this->m_ptr = tmp.release().ptr();

m_shape = shape;

m_strides = strides;

compute_backstrides();

m_data = container_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),

static_cast<size_type>(PyArray_SIZE(this->python_array())));

}

template <class T, std::size_t N>

inline void pytensor<T, N>::init_from_python()

{

if(PyArray_NDIM(this->python_array()) != N)

throw std::runtime_error("NumPy: ndarray has incorrect number of dimensions");

std::copy(PyArray_DIMS(this->python_array()), PyArray_DIMS(this->python_array()) + N, m_shape.begin());

std::transform(PyArray_STRIDES(this->python_array()), PyArray_STRIDES(this->python_array()) + N, m_strides.begin(),

[](auto v) { return v / sizeof(T); });

compute_backstrides();

m_data = container_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),

static_cast<size_type>(PyArray_SIZE(this->python_array())));

}

template <class T, std::size_t N>

inline void pytensor<T, N>::compute_backstrides()

{

for(size_type i = 0; i < m_shape.size(); ++i)

{

m_backstrides[i] = m_strides[i] * (m_shape[i] - 1);

}

template <class T, std::size_t N>

inline auto pytensor<T, N>::shape_impl() const noexcept -> const inner_shape_type&

{

return m_shape;

}

template <class T, std::size_t N>

inline auto pytensor<T, N>::strides_impl() const noexcept -> const inner_strides_type&

{

return m_strides;

}

template <class T, std::size_t N>

inline auto pytensor<T, N>::backstrides_impl() const noexcept -> const inner_backstrides_type&

{

return m_backstrides;

}

template <class T, std::size_t N>

inline auto pytensor<T, N>::data_impl() noexcept -> container_type&

{

return m_data;

}

template <class T, std::size_t N>

inline auto pytensor<T, N>::data_impl() const noexcept -> const container_type&

{

return m_data;

}

#endif

xtensor-python/include/xtensor-python/pytensor.hpp at master · BenJamesbabala/xtensor-python · GitHub

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