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/***************************************************************************
* Copyright (c) Wolf Vollprecht, Johan Mabille and Sylvain Corlay *
* Copyright (c) QuantStack *
* *
* 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 <algorithm>
#include <array>
#include <cstddef>
#include "xtensor/containers/xbuffer_adaptor.hpp"
#include "xtensor/core/xiterator.hpp"
#include "xtensor/core/xsemantic.hpp"
#include "xtensor/utils/xutils.hpp"
#include "pycontainer.hpp"
#include "pystrides_adaptor.hpp"
#include "pynative_casters.hpp"
#include "xtensor_type_caster_base.hpp"
#include "xtensor_python_config.hpp"
namespace xt
{
template <class T, std::size_t N, layout_type L = layout_type::dynamic>
class pytensor;
}
namespace pybind11
{
namespace detail
{
#ifdef PYBIND11_DESCR // The macro is removed from pybind11 since 2.3
template <class T, std::size_t N, xt::layout_type L>
struct handle_type_name<xt::pytensor<T, N, L>>
{
static PYBIND11_DESCR name()
{
return _("numpy.ndarray[") + npy_format_descriptor<T>::name() + _("]");
}
};
#endif
template <class T, std::size_t N, xt::layout_type L>
struct pyobject_caster<xt::pytensor<T, N, L>>
{
using type = xt::pytensor<T, N, L>;
bool load(handle src, bool convert)
{
if (!convert)
{
if (!xt::detail::check_array<T>(src))
{
return false;
}
}
try
{
value = type::ensure(src);
}
catch (const std::runtime_error&)
{
return false;
}
return static_cast<bool>(value);
}
static handle cast(const handle& src, return_value_policy, handle)
{
return src.inc_ref();
}
#ifdef PYBIND11_DESCR // The macro is removed from pybind11 since 2.3
PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name());
#else
PYBIND11_TYPE_CASTER(type, _("numpy.ndarray[") + npy_format_descriptor<T>::name + _("]"));
#endif
};
// Type caster for casting ndarray to xexpression<pytensor>
template <class T, std::size_t N, xt::layout_type L>
struct type_caster<xt::xexpression<xt::pytensor<T, N, L>>> : pyobject_caster<xt::pytensor<T, N, L>>
{
using Type = xt::xexpression<xt::pytensor<T, N, L>>;
operator Type&()
{
return this->value;
}
operator const Type&()
{
return this->value;
}
};
} // namespace detail
}
namespace xt
{
namespace detail {
template <std::size_t N>
struct numpy_strides
{
npy_intp value[N];
};
template <>
struct numpy_strides<0>
{
npy_intp* value = nullptr;
};
} // namespace detail
template <class T, std::size_t N, layout_type L>
struct xiterable_inner_types<pytensor<T, N, L>>
: xcontainer_iterable_types<pytensor<T, N, L>>
{
};
template <class T, std::size_t N, layout_type L>
struct xcontainer_inner_types<pytensor<T, N, L>>
{
using storage_type = xbuffer_adaptor<T*>;
using reference = typename storage_type::reference;
using const_reference = typename storage_type::const_reference;
using size_type = typename storage_type::size_type;
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, L>;
static constexpr layout_type layout = L;
};
/**
* @class pytensor
* @brief Multidimensional container providing the xtensor container semantics wrapping a numpy array.
*
* pytensor is similar to the xtensor container in that it has a static dimensionality.
*
* Unlike the pyarray container, pytensor cannot be reshaped with a different number of dimensions
* and reshapes are not reflected on the Python side. However, pytensor has benefits compared to pyarray
* in terms of performances. pytensor shapes are stack-allocated which makes iteration upon pytensor
* faster than with pyarray.
*
* @tparam T The type of the element stored in the pyarray.
* @sa pyarray
*/
template <class T, std::size_t N, layout_type L>
class pytensor : public pycontainer<pytensor<T, N, L>>,
public xcontainer_semantic<pytensor<T, N, L>>
{
public:
using self_type = pytensor<T, N, L>;
using semantic_base = xcontainer_semantic<self_type>;
using base_type = pycontainer<self_type>;
using storage_type = typename base_type::storage_type;
using value_type = typename base_type::value_type;
using reference = typename base_type::reference;
using const_reference = typename base_type::const_reference;
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;
constexpr static std::size_t rank = N;
pytensor();
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_type l = layout_type::row_major);
explicit pytensor(const shape_type& shape, const_reference value, layout_type l = layout_type::row_major);
explicit pytensor(const shape_type& shape, const strides_type& strides, const_reference value);
explicit pytensor(const shape_type& shape, const strides_type& strides);
template <class S = shape_type>
static pytensor from_shape(S&& shape);
pytensor(const self_type& rhs);
self_type& operator=(const self_type& rhs);
pytensor(self_type&&) = default;
self_type& operator=(self_type&& e) = default;
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);
#if (PYBIND11_VERSION_MAJOR == 2 && PYBIND11_VERSION_MINOR >= 3) || (PYBIND11_VERSION_MAJOR >= 3)
// Prevent ambiguous overload resolution for operators defined for
// both xt::xcontainer_semantic and pybind11::object.
using semantic_base::operator+=;
using semantic_base::operator-=;
using semantic_base::operator*=;
using semantic_base::operator/=;
using semantic_base::operator|=;
using semantic_base::operator&=;
using semantic_base::operator^=;
// using semantic_base::operator<<=;
// using semantic_base::operator>>=;
#endif
private:
inner_shape_type m_shape;
inner_strides_type m_strides;
inner_backstrides_type m_backstrides;
storage_type m_storage;
void init_tensor(const shape_type& shape, const strides_type& strides);
void init_from_python();
inner_shape_type& shape_impl() noexcept;
const inner_shape_type& shape_impl() const noexcept;
inner_strides_type& strides_impl() noexcept;
const inner_strides_type& strides_impl() const noexcept;
inner_backstrides_type& backstrides_impl() noexcept;
const inner_backstrides_type& backstrides_impl() const noexcept;
storage_type& storage_impl() noexcept;
const storage_type& storage_impl() const noexcept;
friend class xcontainer<pytensor<T, N, L>>;
friend class pycontainer<pytensor<T, N, L>>;
};
/***************************
* pytensor implementation *
***************************/
/**
* @name Constructors
*/
//@{
/**
* Allocates an uninitialized pytensor that holds 1 element.
*/
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor()
: base_type()
{
m_shape = xtl::make_sequence<shape_type>(N, size_type(1));
m_strides = xtl::make_sequence<strides_type>(N, size_type(0));
init_tensor(m_shape, m_strides);
detail::default_initialize(m_storage);
}
/**
* Allocates a pytensor with a nested initializer list.
*/
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(nested_initializer_list_t<T, N> t)
: base_type()
{
base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
nested_copy(m_storage.begin(), t);
}
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(pybind11::handle h, pybind11::object::borrowed_t b)
: base_type(h, b)
{
init_from_python();
}
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(pybind11::handle h, pybind11::object::stolen_t s)
: base_type(h, s)
{
init_from_python();
}
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(const pybind11::object& o)
: base_type(o)
{
init_from_python();
}
/**
* Allocates an uninitialized pytensor with the specified shape and
* layout.
* @param shape the shape of the pytensor
* @param l the layout_type of the pytensor
*/
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(const shape_type& shape, layout_type l)
{
compute_strides(shape, l, m_strides);
init_tensor(shape, m_strides);
}
/**
* Allocates a pytensor with the specified shape and layout. Elements
* are initialized to the specified value.
* @param shape the shape of the pytensor
* @param value the value of the elements
* @param l the layout_type of the pytensor
*/
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(const shape_type& shape,
const_reference value,
layout_type l)
{
compute_strides(shape, l, m_strides);
init_tensor(shape, m_strides);
std::fill(m_storage.begin(), m_storage.end(), value);
}
/**
* Allocates an uninitialized pytensor with the specified shape and strides.
* Elements are initialized to the specified value.
* @param shape the shape of the pytensor
* @param strides the strides of the pytensor
* @param value the value of the elements
*/
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(const shape_type& shape,
const strides_type& strides,
const_reference value)
{
init_tensor(shape, strides);
std::fill(m_storage.begin(), m_storage.end(), value);
}
/**
* Allocates an uninitialized pytensor with the specified shape and strides.
* @param shape the shape of the pytensor
* @param strides the strides of the pytensor
*/
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(const shape_type& shape,
const strides_type& strides)
{
init_tensor(shape, strides);
}
/**
* Allocates and returns an pytensor with the specified shape.
* @param shape the shape of the pytensor
*/
template <class T, std::size_t N, layout_type L>
template <class S>
inline pytensor<T, N, L> pytensor<T, N, L>::from_shape(S&& shape)
{
detail::check_dims<shape_type>::run(shape.size());
auto shp = xtl::forward_sequence<shape_type, S>(shape);
return self_type(shp);
}
//@}
/**
* @name Copy semantic
*/
//@{
/**
* The copy constructor.
*/
template <class T, std::size_t N, layout_type L>
inline pytensor<T, N, L>::pytensor(const self_type& rhs)
: base_type(), semantic_base(rhs)
{
init_tensor(rhs.shape(), rhs.strides());
std::copy(rhs.storage().cbegin(), rhs.storage().cend(), this->storage().begin());
}
/**
* The assignment operator.
*/
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::operator=(const self_type& rhs) -> self_type&
{
self_type tmp(rhs);
*this = std::move(tmp);
return *this;
}
//@}
/**
* @name Extended copy semantic
*/
//@{
/**
* The extended copy constructor.
*/
template <class T, std::size_t N, layout_type L>
template <class E>
inline pytensor<T, N, L>::pytensor(const xexpression<E>& e)
: base_type()
{
shape_type shape = xtl::forward_sequence<shape_type, decltype(e.derived_cast().shape())>(e.derived_cast().shape());
strides_type strides = xtl::make_sequence<strides_type>(N, size_type(0));
compute_strides(shape, layout_type::row_major, strides);
init_tensor(shape, strides);
semantic_base::assign(e);
}
/**
* The extended assignment operator.
*/
template <class T, std::size_t N, layout_type L>
template <class E>
inline auto pytensor<T, N, L>::operator=(const xexpression<E>& e) -> self_type&
{
return semantic_base::operator=(e);
}
//@}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::ensure(pybind11::handle h) -> self_type
{
return base_type::ensure(h);
}
template <class T, std::size_t N, layout_type L>
inline bool pytensor<T, N, L>::check_(pybind11::handle h)
{
return base_type::check_(h);
}
template <class T, std::size_t N, layout_type L>
inline void pytensor<T, N, L>::init_tensor(const shape_type& shape, const strides_type& strides)
{
detail::numpy_strides<N> python_strides;
std::transform(strides.begin(), strides.end(), python_strides.value,
[](auto v) { return sizeof(T) * v; });
int flags = NPY_ARRAY_ALIGNED;
if (!std::is_const<T>::value)
{
flags |= NPY_ARRAY_WRITEABLE;
}
auto dtype = pybind11::detail::npy_format_descriptor<T>::dtype();
auto tmp = pybind11::reinterpret_steal<pybind11::object>(
PyArray_NewFromDescr(&PyArray_Type, (PyArray_Descr*) dtype.release().ptr(), static_cast<int>(shape.size()),
const_cast<npy_intp*>(shape.data()), python_strides.value,
nullptr, 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;
adapt_strides(m_shape, m_strides, m_backstrides);
m_storage = storage_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, layout_type L>
inline void pytensor<T, N, L>::init_from_python()
{
if (!static_cast<bool>(*this))
{
return;
}
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); });
adapt_strides(m_shape, m_strides, m_backstrides);
if (L != layout_type::dynamic && !do_strides_match(m_shape, m_strides, L, 1))
{
throw std::runtime_error("NumPy: passing container with bad strides for layout (is it a view?).");
}
m_storage = storage_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),
this->get_buffer_size());
}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::shape_impl() noexcept -> inner_shape_type&
{
return m_shape;
}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::shape_impl() const noexcept -> const inner_shape_type&
{
return m_shape;
}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::strides_impl() noexcept -> inner_strides_type&
{
return m_strides;
}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::strides_impl() const noexcept -> const inner_strides_type&
{
return m_strides;
}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::backstrides_impl() noexcept -> inner_backstrides_type&
{
return m_backstrides;
}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::backstrides_impl() const noexcept -> const inner_backstrides_type&
{
return m_backstrides;
}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::storage_impl() noexcept -> storage_type&
{
return m_storage;
}
template <class T, std::size_t N, layout_type L>
inline auto pytensor<T, N, L>::storage_impl() const noexcept -> const storage_type&
{
return m_storage;
}
}
#endif