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array: add unchecked access via proxy object

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This adds bounds-unchecked access to arrays through a `a.unchecked<Type,
Dimensions>()` method.  (For `array_t<T>`, the `Type` template parameter
is omitted).  The mutable version (which requires the array have the
`writeable` flag) is available as `a.mutable_unchecked<...>()`.

Specifying the Dimensions as a template parameter allows storage of an
std::array; having the strides and sizes stored that way (as opposed to
storing a copy of the array's strides/shape pointers) allows the
compiler to make significant optimizations of the shape() method that it
can't make with a pointer; testing with nested loops of the form:

    for (size_t i0 = 0; i0 < r.shape(0); i0++)
        for (size_t i1 = 0; i1 < r.shape(1); i1++)
            ...
                r(i0, i1, ...) += 1;

over a 10 million element array gives around a 25% speedup (versus using
a pointer) for the 1D case, 33% for 2D, and runs more than twice as fast
with a 5D array.
This commit is contained in:
Jason Rhinelander 2017-03-19 01:14:23 -03:00
parent 0d765f4a7c
commit 423a49b8be
4 changed files with 217 additions and 7 deletions
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45
docs/advanced/pycpp/numpy.rst
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@ -305,3 +305,48 @@ simply using ``vectorize``).
The file :file:`tests/test_numpy_vectorize.cpp` contains a complete
example that demonstrates using :func:`vectorize` in more detail.
Direct access
=============
For performance reasons, particularly when dealing with very large arrays, it
is often desirable to directly access array elements without internal checking
of dimensions and bounds on every access when indices are known to be already
valid. To avoid such checks, the ``array`` class and ``array_t<T>`` template
class offer an unchecked proxy object that can be used for this unchecked
access through the ``unchecked<N>`` and ``mutable_unchecked<N>`` methods,
where ``N`` gives the required dimensionality of the array:
.. code-block:: cpp
m.def("sum_3d", [](py::array_t<double> x) {
auto r = x.unchecked<3>(); // x must have ndim = 3; can be non-writeable
double sum = 0;
for (size_t i = 0; i < r.shape(0); i++)
for (size_t j = 0; j < r.shape(1); j++)
for (size_t k = 0; k < r.shape(2); k++)
sum += r(i, j, k);
return sum;
});
m.def("increment_3d", [](py::array_t<double> x) {
auto r = x.mutable_unchecked<3>(); // Will throw if ndim != 3 or flags.writeable is false
for (size_t i = 0; i < r.shape(0); i++)
for (size_t j = 0; j < r.shape(1); j++)
for (size_t k = 0; k < r.shape(2); k++)
r(i, j, k) += 1.0;
}, py::arg().noconvert());
To obtain the proxy from an ``array`` object, you must specify both the data
type and number of dimensions as template arguments, such as ``auto r =
myarray.mutable_unchecked<float, 2>()``.
Note that the returned proxy object directly references the array's data, and
only reads its shape, strides, and writeable flag when constructed. You must
take care to ensure that the referenced array is not destroyed or reshaped for
the duration of the returned object, typically by limiting the scope of the
returned instance.
.. seealso::
The file :file:`tests/test_numpy_array.cpp` contains additional examples
demonstrating the use of this feature.

127
include/pybind11/numpy.h
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@ -35,6 +35,9 @@
static_assert(sizeof(size_t) == sizeof(Py_intptr_t), "size_t != Py_intptr_t");
NAMESPACE_BEGIN(pybind11)
class array; // Forward declaration
NAMESPACE_BEGIN(detail)
template <typename type, typename SFINAE = void> struct npy_format_descriptor;
@ -232,6 +235,78 @@ template <typename T> using is_pod_struct = all_of<
satisfies_none_of<T, std::is_reference, std::is_array, is_std_array, std::is_arithmetic, is_complex, std::is_enum>
>;
template <size_t Dim = 0, typename Strides> size_t byte_offset_unsafe(const Strides &) { return 0; }
template <size_t Dim = 0, typename Strides, typename... Ix>
size_t byte_offset_unsafe(const Strides &strides, size_t i, Ix... index) {
return i * strides[Dim] + byte_offset_unsafe<Dim + 1>(strides, index...);
}
/** Proxy class providing unsafe, unchecked const access to array data. This is constructed through
* the `unchecked<T, N>()` method of `array` or the `unchecked<N>()` method of `array_t<T>`.
*/
template <typename T, size_t Dims>
class unchecked_reference {
protected:
const unsigned char *data_;
// Storing the shape & strides in local variables (i.e. these arrays) allows the compiler to
// make large performance gains on big, nested loops.
std::array<size_t, Dims> shape_, strides_;
friend class pybind11::array;
unchecked_reference(const void *data, const size_t *shape, const size_t *strides)
: data_{reinterpret_cast<const unsigned char *>(data)} {
for (size_t i = 0; i < Dims; i++) {
shape_[i] = shape[i];
strides_[i] = strides[i];
}
}
public:
/** Unchecked const reference access to data at the given indices. Omiting trailing indices
* is equivalent to specifying them as 0.
*/
template <typename... Ix> const T& operator()(Ix... index) const {
static_assert(sizeof...(Ix) <= Dims, "Invalid number of indices for unchecked array reference");
return *reinterpret_cast<const T *>(data_ + byte_offset_unsafe(strides_, size_t{index}...));
}
/** Unchecked const reference access to data; this operator only participates if the reference
* is to a 1-dimensional array. When present, this is exactly equivalent to `obj(index)`.
*/
template <size_t D = Dims, typename = enable_if_t<D == 1>>
const T &operator[](size_t index) const { return operator()(index); }
/// Returns the shape (i.e. size) of dimension `dim`
size_t shape(size_t dim) const { return shape_[dim]; }
/// Returns the number of dimensions of the array
constexpr static size_t ndim() { return Dims; }
};
template <typename T, size_t Dims>
class unchecked_mutable_reference : public unchecked_reference<T, Dims> {
friend class pybind11::array;
using ConstBase = unchecked_reference<T, Dims>;
using ConstBase::ConstBase;
public:
/// Mutable, unchecked access to data at the given indices.
template <typename... Ix> T& operator()(Ix... index) {
static_assert(sizeof...(Ix) == Dims, "Invalid number of indices for unchecked array reference");
return const_cast<T &>(ConstBase::operator()(index...));
}
/** Mutable, unchecked access data at the given index; this operator only participates if the
* reference is to a 1-dimensional array. When present, this is exactly equivalent to `obj(index)`.
*/
template <size_t D = Dims, typename = enable_if_t<D == 1>>
T &operator[](size_t index) { return operator()(index); }
};
template <typename T, size_t Dim>
struct type_caster<unchecked_reference<T, Dim>> {
static_assert(Dim == (size_t) -1 /* always fail */, "unchecked array proxy object is not castable");
};
template <typename T, size_t Dim>
struct type_caster<unchecked_mutable_reference<T, Dim>> : type_caster<unchecked_reference<T, Dim>> {};
NAMESPACE_END(detail)
class dtype : public object {
@ -500,6 +575,31 @@ public:
return offset_at(index...) / itemsize();
}
/** Returns a proxy object that provides access to the array's data without bounds or
* dimensionality checking. Will throw if the array is missing the `writeable` flag. Use with
* care: the array must not be destroyed or reshaped for the duration of the returned object,
* and the caller must take care not to access invalid dimensions or dimension indices.
*/
template <typename T, size_t Dims> detail::unchecked_mutable_reference<T, Dims> mutable_unchecked() {
if (ndim() != Dims)
throw std::domain_error("array has incorrect number of dimensions: " + std::to_string(ndim()) +
"; expected " + std::to_string(Dims));
return detail::unchecked_mutable_reference<T, Dims>(mutable_data(), shape(), strides());
}
/** Returns a proxy object that provides const access to the array's data without bounds or
* dimensionality checking. Unlike `mutable_unchecked()`, this does not require that the
* underlying array have the `writable` flag. Use with care: the array must not be destroyed or
* reshaped for the duration of the returned object, and the caller must take care not to access
* invalid dimensions or dimension indices.
*/
template <typename T, size_t Dims> detail::unchecked_reference<T, Dims> unchecked() const {
if (ndim() != Dims)
throw std::domain_error("array has incorrect number of dimensions: " + std::to_string(ndim()) +
"; expected " + std::to_string(Dims));
return detail::unchecked_reference<T, Dims>(data(), shape(), strides());
}
/// Return a new view with all of the dimensions of length 1 removed
array squeeze() {
auto& api = detail::npy_api::get();
@ -525,15 +625,9 @@ protected:
template<typename... Ix> size_t byte_offset(Ix... index) const {
check_dimensions(index...);
return byte_offset_unsafe(index...);
return detail::byte_offset_unsafe(strides(), size_t{index}...);
}
template<size_t dim = 0, typename... Ix> size_t byte_offset_unsafe(size_t i, Ix... index) const {
return i * strides()[dim] + byte_offset_unsafe<dim + 1>(index...);
}
template<size_t dim = 0> size_t byte_offset_unsafe() const { return 0; }
void check_writeable() const {
if (!writeable())
throw std::domain_error("array is not writeable");
@ -637,6 +731,25 @@ public:
return *(static_cast<T*>(array::mutable_data()) + byte_offset(size_t(index)...) / itemsize());
}
/** Returns a proxy object that provides access to the array's data without bounds or
* dimensionality checking. Will throw if the array is missing the `writeable` flag. Use with
* care: the array must not be destroyed or reshaped for the duration of the returned object,
* and the caller must take care not to access invalid dimensions or dimension indices.
*/
template <size_t Dims> detail::unchecked_mutable_reference<T, Dims> mutable_unchecked() {
return array::mutable_unchecked<T, Dims>();
}
/** Returns a proxy object that provides const access to the array's data without bounds or
* dimensionality checking. Unlike `unchecked()`, this does not require that the underlying
* array have the `writable` flag. Use with care: the array must not be destroyed or reshaped
* for the duration of the returned object, and the caller must take care not to access invalid
* dimensions or dimension indices.
*/
template <size_t Dims> detail::unchecked_reference<T, Dims> unchecked() const {
return array::unchecked<T, Dims>();
}
/// Ensure that the argument is a NumPy array of the correct dtype (and if not, try to convert
/// it). In case of an error, nullptr is returned and the Python error is cleared.
static array_t ensure(handle h) {

32
tests/test_numpy_array.cpp
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@ -184,4 +184,36 @@ test_initializer numpy_array([](py::module &m) {
sm.def("issue685", [](std::string) { return "string"; });
sm.def("issue685", [](py::array) { return "array"; });
sm.def("issue685", [](py::object) { return "other"; });
sm.def("proxy_add2", [](py::array_t<double> a, double v) {
auto r = a.mutable_unchecked<2>();
for (size_t i = 0; i < r.shape(0); i++)
for (size_t j = 0; j < r.shape(1); j++)
r(i, j) += v;
}, py::arg().noconvert(), py::arg());
sm.def("proxy_init3", [](double start) {
py::array_t<double, py::array::c_style> a({ 3, 3, 3 });
auto r = a.mutable_unchecked<3>();
for (size_t i = 0; i < r.shape(0); i++)
for (size_t j = 0; j < r.shape(1); j++)
for (size_t k = 0; k < r.shape(2); k++)
r(i, j, k) = start++;
return a;
});
sm.def("proxy_init3F", [](double start) {
py::array_t<double, py::array::f_style> a({ 3, 3, 3 });
auto r = a.mutable_unchecked<3>();
for (size_t k = 0; k < r.shape(2); k++)
for (size_t j = 0; j < r.shape(1); j++)
for (size_t i = 0; i < r.shape(0); i++)
r(i, j, k) = start++;
return a;
});
sm.def("proxy_squared_L2_norm", [](py::array_t<double> a) {
auto r = a.unchecked<1>();
double sumsq = 0;
for (size_t i = 0; i < r.shape(0); i++)
sumsq += r[i] * r(i); // Either notation works for a 1D array
return sumsq;
});
});

20
tests/test_numpy_array.py
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@ -339,3 +339,23 @@ def test_greedy_string_overload(): # issue 685
assert issue685("abc") == "string"
assert issue685(np.array([97, 98, 99], dtype='b')) == "array"
assert issue685(123) == "other"
def test_array_unchecked(msg):
from pybind11_tests.array import proxy_add2, proxy_init3F, proxy_init3, proxy_squared_L2_norm
z1 = np.array([[1, 2], [3, 4]], dtype='float64')
proxy_add2(z1, 10)
assert np.all(z1 == [[11, 12], [13, 14]])
with pytest.raises(ValueError) as excinfo:
proxy_add2(np.array([1., 2, 3]), 5.0)
assert msg(excinfo.value) == "array has incorrect number of dimensions: 1; expected 2"
expect_c = np.ndarray(shape=(3, 3, 3), buffer=np.array(range(3, 30)), dtype='int')
assert np.all(proxy_init3(3.0) == expect_c)
expect_f = np.transpose(expect_c)
assert np.all(proxy_init3F(3.0) == expect_f)
assert proxy_squared_L2_norm(np.array(range(6))) == 55
assert proxy_squared_L2_norm(np.array(range(6), dtype="float64")) == 55