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pycontainer.hpp
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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_CONTAINER_HPP
#define PY_CONTAINER_HPP
#include <cmath>
#include <functional>
#include <numeric>
#include <sstream>
#include "pybind11/complex.h"
#include "pybind11/pybind11.h"
#include "pybind11/numpy.h"
#ifndef FORCE_IMPORT_ARRAY
#define NO_IMPORT_ARRAY
#endif
#ifndef PY_ARRAY_UNIQUE_SYMBOL
#define PY_ARRAY_UNIQUE_SYMBOL xtensor_python_ARRAY_API
#endif
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include "numpy/arrayobject.h"
// Required because pyconfig.hpp defines copysign to _copysign
#undef copysign
#include <cmath>
#include "xtensor/xcontainer.hpp"
#include "xtl/xsequence.hpp"
namespace xt
{
inline void import_numpy();
/**
* @class pycontainer
* @brief Base class for xtensor containers wrapping numpy arryays.
*
* The pycontainer class should not be instantiated directly. Instead, used should
* use pytensor and pyarray instancs.
*
* @tparam D The derived type, i.e. the inheriting class for which pycontainer
* provides the interface.
*/
template <class D>
class pycontainer : public pybind11::object,
public xcontainer<D>
{
public:
using derived_type = D;
using base_type = xcontainer<D>;
using inner_types = xcontainer_inner_types<D>;
using storage_type = typename inner_types::storage_type;
using value_type = typename storage_type::value_type;
using reference = typename storage_type::reference;
using const_reference = typename storage_type::const_reference;
using pointer = typename storage_type::pointer;
using const_pointer = typename storage_type::const_pointer;
using size_type = typename storage_type::size_type;
using difference_type = typename storage_type::difference_type;
using shape_type = typename inner_types::shape_type;
using strides_type = typename inner_types::strides_type;
using backstrides_type = typename inner_types::backstrides_type;
using inner_shape_type = typename inner_types::inner_shape_type;
using inner_strides_type = typename inner_types::inner_strides_type;
using iterable_base = xcontainer<D>;
using iterator = typename iterable_base::iterator;
using const_iterator = typename iterable_base::const_iterator;
using stepper = typename iterable_base::stepper;
using const_stepper = typename iterable_base::const_stepper;
template <class S = shape_type>
void resize(const S& shape);
template <class S = shape_type>
void resize(const S& shape, layout_type l);
template <class S = shape_type>
void resize(const S& shape, const strides_type& strides);
template <class S = shape_type>
auto& reshape(S&& shape, layout_type layout = base_type::static_layout) &;
layout_type layout() const;
bool is_contiguous() const noexcept;
using base_type::operator();
using base_type::operator[];
using base_type::begin;
using base_type::end;
protected:
pycontainer();
~pycontainer() = default;
pycontainer(pybind11::handle h, borrowed_t);
pycontainer(pybind11::handle h, stolen_t);
pycontainer(const pybind11::object& o);
pycontainer(const pycontainer&) = default;
pycontainer& operator=(const pycontainer&) = default;
pycontainer(pycontainer&&) = default;
pycontainer& operator=(pycontainer&&) = default;
static derived_type ensure(pybind11::handle h);
static bool check_(pybind11::handle h);
static PyObject* raw_array_t(PyObject* ptr);
derived_type& derived_cast();
const derived_type& derived_cast() const;
PyArrayObject* python_array() const;
size_type get_buffer_size() const;
private:
#if PYBIND11_VERSION_MAJOR == 2 && PYBIND11_VERSION_MINOR >= 3
// Prevent ambiguous overload resolution for operators defined for
// both xt::xcontainer and pybind11::object.
using pybind11::object::operator~;
using pybind11::object::operator+;
using pybind11::object::operator-;
using pybind11::object::operator*;
using pybind11::object::operator/;
using pybind11::object::operator|;
using pybind11::object::operator&;
using pybind11::object::operator^;
using pybind11::object::operator<<;
using pybind11::object::operator>>;
#endif
};
namespace detail
{
template <class T, class E = void>
struct numpy_traits;
template <class T>
struct numpy_traits<T, std::enable_if_t<pybind11::detail::satisfies_any_of<T, std::is_arithmetic, xtl::is_complex>::value>>
{
private:
// On Windows 64 bits, NPY_INT != NPY_INT32 and NPY_UINT != NPY_UINT32
// We use the NPY_INT32 and NPY_UINT32 which are consistent with the values
// of NPY_LONG and NPY_ULONG
// On Linux x64, NPY_INT64 != NPY_LONGLONG and NPY_UINT64 != NPY_ULONGLONG,
// we use the values of NPY_INT64 and NPY_UINT64 which are consistent with the
// values of NPY_LONG and NPY_ULONG.
constexpr static const int value_list[15] = {
NPY_BOOL,
NPY_BYTE, NPY_UBYTE, NPY_SHORT, NPY_USHORT,
NPY_INT32, NPY_UINT32, NPY_INT64, NPY_UINT64,
NPY_FLOAT, NPY_DOUBLE, NPY_LONGDOUBLE,
NPY_CFLOAT, NPY_CDOUBLE, NPY_CLONGDOUBLE};
public:
using value_type = std::remove_const_t<T>;
static constexpr int type_num = value_list[pybind11::detail::is_fmt_numeric<value_type>::index];
};
// On Linux x64, NPY_INT64 != NPY_LONGLONG and NPY_UINT64 != NPY_ULONGLONG
// NPY_LONGLONG and NPY_ULONGLONG must be adjusted so the right type is
// selected
template <bool>
struct numpy_enum_adjuster
{
static inline int pyarray_type(PyArrayObject* obj)
{
return PyArray_TYPE(obj);
}
};
template <>
struct numpy_enum_adjuster<true>
{
static inline int pyarray_type(PyArrayObject* obj)
{
int res = PyArray_TYPE(obj);
if(res == NPY_LONGLONG || res == NPY_ULONGLONG)
{
res -= 2;
}
return res;
}
};
inline int pyarray_type(PyArrayObject* obj)
{
return numpy_enum_adjuster<NPY_LONGLONG != NPY_INT64>::pyarray_type(obj);
}
template <class T>
void default_initialize_impl(T& /*storage*/, std::false_type)
{
}
template <class T>
void default_initialize_impl(T& storage, std::true_type)
{
using value_type = typename T::value_type;
storage[0] = value_type{};
}
template <class T>
void default_initialize(T& storage)
{
using value_type = typename T::value_type;
default_initialize_impl(storage, std::is_copy_assignable<value_type>());
}
template <class T>
bool check_array_type(const pybind11::handle& src, std::true_type)
{
int type_num = xt::detail::numpy_traits<T>::type_num;
return xt::detail::pyarray_type(reinterpret_cast<PyArrayObject*>(src.ptr())) == type_num;
}
template <class T>
bool check_array_type(const pybind11::handle& src, std::false_type)
{
return PyArray_EquivTypes((PyArray_Descr*) pybind11::detail::array_proxy(src.ptr())->descr,
(PyArray_Descr*) pybind11::dtype::of<T>().ptr());
}
template <class T>
bool check_array(const pybind11::handle& src)
{
using is_arithmetic_type = std::integral_constant<bool, bool(pybind11::detail::satisfies_any_of<T, std::is_arithmetic, xtl::is_complex>::value)>;
return PyArray_Check(src.ptr()) && check_array_type<T>(src, is_arithmetic_type{});
}
}
/******************************
* pycontainer implementation *
******************************/
template <class D>
inline pycontainer<D>::pycontainer()
: pybind11::object()
{
}
template <class D>
inline pycontainer<D>::pycontainer(pybind11::handle h, borrowed_t b)
: pybind11::object(h, b)
{
}
template <class D>
inline pycontainer<D>::pycontainer(pybind11::handle h, stolen_t s)
: pybind11::object(h, s)
{
}
template <class D>
inline pycontainer<D>::pycontainer(const pybind11::object& o)
: pybind11::object(raw_array_t(o.ptr()), pybind11::object::stolen_t{})
{
if (!this->m_ptr)
{
throw pybind11::error_already_set();
}
}
template <class D>
inline auto pycontainer<D>::ensure(pybind11::handle h) -> derived_type
{
auto result = pybind11::reinterpret_steal<derived_type>(raw_array_t(h.ptr()));
if (result.ptr() == nullptr)
{
PyErr_Clear();
}
return result;
}
template <class D>
inline bool pycontainer<D>::check_(pybind11::handle h)
{
return detail::check_array<typename D::value_type>(h);
}
template <class D>
inline PyObject* pycontainer<D>::raw_array_t(PyObject* ptr)
{
if (ptr == nullptr)
{
return nullptr;
}
auto dtype = pybind11::detail::npy_format_descriptor<value_type>::dtype();
auto res = PyArray_FromAny(ptr, (PyArray_Descr *) dtype.release().ptr(), 0, 0,
NPY_ARRAY_ENSUREARRAY | NPY_ARRAY_FORCECAST, nullptr);
return res;
}
template <class D>
inline PyArrayObject* pycontainer<D>::python_array() const
{
return reinterpret_cast<PyArrayObject*>(this->m_ptr);
}
template <class D>
inline auto pycontainer<D>::get_buffer_size() const -> size_type
{
const size_type& (*min)(const size_type&, const size_type&) = std::min<size_type>;
size_type min_stride = this->strides().empty() ? size_type(1) :
std::max(size_type(1), std::accumulate(this->strides().cbegin(),
this->strides().cend(),
std::numeric_limits<size_type>::max(),
min));
return min_stride * static_cast<size_type>(PyArray_SIZE(this->python_array()));
}
template <class D>
inline auto pycontainer<D>::derived_cast() -> derived_type&
{
return *static_cast<derived_type*>(this);
}
template <class D>
inline auto pycontainer<D>::derived_cast() const -> const derived_type&
{
return *static_cast<const derived_type*>(this);
}
namespace detail
{
template <class S>
struct check_dims
{
static bool run(std::size_t)
{
return true;
}
};
template <class T, std::size_t N>
struct check_dims<std::array<T, N>>
{
static bool run(std::size_t new_dim)
{
if(new_dim != N)
{
std::ostringstream err_msg;
err_msg << "Invalid conversion to pycontainer, expecting a container of dimension "
<< N << ", got a container of dimension " << new_dim << ".";
throw std::runtime_error(err_msg.str());
}
return new_dim == N;
}
};
}
/**
* resizes the container.
* @param shape the new shape
*/
template <class D>
template <class S>
inline void pycontainer<D>::resize(const S& shape)
{
if (shape.size() != this->dimension() || !std::equal(std::begin(shape), std::end(shape), std::begin(this->shape())))
{
resize(shape, layout_type::row_major);
}
}
/**
* resizes the container.
* @param shape the new shape
* @param l the new layout
*/
template <class D>
template <class S>
inline void pycontainer<D>::resize(const S& shape, layout_type l)
{
strides_type strides = xtl::make_sequence<strides_type>(shape.size(), size_type(1));
compute_strides(shape, l, strides);
resize(shape, strides);
}
/**
* resizes the container.
* @param shape the new shape
* @param strides the new strides
*/
template <class D>
template <class S>
inline void pycontainer<D>::resize(const S& shape, const strides_type& strides)
{
detail::check_dims<shape_type>::run(shape.size());
derived_type tmp(xtl::forward_sequence<shape_type, decltype(shape)>(shape), strides);
*static_cast<derived_type*>(this) = std::move(tmp);
}
template <class D>
template <class S>
inline auto& pycontainer<D>::reshape(S&& shape, layout_type layout) &
{
if (compute_size(shape) != this->size())
{
throw std::runtime_error("Cannot reshape with incorrect number of elements (" + std::to_string(this->size()) + " vs " + std::to_string(compute_size(shape)) + ")");
}
detail::check_dims<shape_type>::run(shape.size());
layout = default_assignable_layout(layout);
NPY_ORDER npy_layout;
if (layout == layout_type::row_major)
{
npy_layout = NPY_CORDER;
}
else if (layout == layout_type::column_major)
{
npy_layout = NPY_FORTRANORDER;
}
else
{
throw std::runtime_error("Cannot reshape with unknown layout_type.");
}
using shape_ptr = typename std::decay_t<S>::pointer;
PyArray_Dims dims = {reinterpret_cast<npy_intp*>(const_cast<shape_ptr>(shape.data())), static_cast<int>(shape.size())};
auto new_ptr = PyArray_Newshape((PyArrayObject*) this->ptr(), &dims, npy_layout);
auto old_ptr = this->ptr();
this->ptr() = new_ptr;
Py_XDECREF(old_ptr);
this->derived_cast().init_from_python();
return *this;
}
/**
* Return the layout_type of the container
* @return layout_type of the container
*/
template <class D>
inline layout_type pycontainer<D>::layout() const
{
if (PyArray_CHKFLAGS(python_array(), NPY_ARRAY_C_CONTIGUOUS))
{
return layout_type::row_major;
}
else if (PyArray_CHKFLAGS(python_array(), NPY_ARRAY_F_CONTIGUOUS))
{
return layout_type::column_major;
}
else
{
return layout_type::dynamic;
}
}
/**
* Return whether or not the container uses contiguous buffer
* @return Boolean for contiguous buffer
*/
template <class D>
inline bool pycontainer<D>::is_contiguous() const noexcept
{
if (this->strides().size() == 0)
{
return true;
}
else if (PyArray_CHKFLAGS(python_array(), NPY_ARRAY_C_CONTIGUOUS))
{
return 1 == this->strides().back();
}
else if (PyArray_CHKFLAGS(python_array(), NPY_ARRAY_F_CONTIGUOUS))
{
return 1 == this->strides().front();
}
else
{
return false;
}
}
/**
* Import the numpy Python module.
*/
inline void import_numpy()
{
#ifdef FORCE_IMPORT_ARRAY
if (_import_array() < 0)
{
PyErr_Print();
PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import");
}
#endif
}
#if defined(__GNUC__) && !defined(__clang__)
namespace workaround
{
// Fixes "undefined symbol" issues
inline void long_long_allocator()
{
std::allocator<long long> a;
std::allocator<unsigned long long> b;
std::allocator<double> c;
std::allocator<std::complex<double>> d;
}
}
#endif
}
#endif
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