ext: Update pybind11 to v2.8.1

Change-Id: Ia1c7081377f53fd470addf35526f8b28a949a7b0
Signed-off-by: Jason Lowe-Power <jason@lowepower.com>
Reviewed-on: https://gem5-review.googlesource.com/c/public/gem5/+/52523
Maintainer: Bobby R. Bruce <bbruce@ucdavis.edu>
Tested-by: kokoro <noreply+kokoro@google.com>
Reviewed-by: Gabe Black <gabe.black@gmail.com>
This commit is contained in:
Jason Lowe-Power
2021-11-06 13:16:21 -07:00
committed by Jason Lowe-Power
parent ba5f68db3d
commit 1e8aeee698
161 changed files with 7820 additions and 3191 deletions

View File

@@ -171,6 +171,31 @@ template parameter, and it ensures that non-conforming arguments are converted
into an array satisfying the specified requirements instead of trying the next
function overload.
There are several methods on arrays; the methods listed below under references
work, as well as the following functions based on the NumPy API:
- ``.dtype()`` returns the type of the contained values.
- ``.strides()`` returns a pointer to the strides of the array (optionally pass
an integer axis to get a number).
- ``.flags()`` returns the flag settings. ``.writable()`` and ``.owndata()``
are directly available.
- ``.offset_at()`` returns the offset (optionally pass indices).
- ``.squeeze()`` returns a view with length-1 axes removed.
- ``.view(dtype)`` returns a view of the array with a different dtype.
- ``.reshape({i, j, ...})`` returns a view of the array with a different shape.
``.resize({...})`` is also available.
- ``.index_at(i, j, ...)`` gets the count from the beginning to a given index.
There are also several methods for getting references (described below).
Structured types
================
@@ -233,8 +258,8 @@ by the compiler. The result is returned as a NumPy array of type
.. code-block:: pycon
>>> x = np.array([[1, 3],[5, 7]])
>>> y = np.array([[2, 4],[6, 8]])
>>> x = np.array([[1, 3], [5, 7]])
>>> y = np.array([[2, 4], [6, 8]])
>>> z = 3
>>> result = vectorized_func(x, y, z)
@@ -345,21 +370,21 @@ The returned proxy object supports some of the same methods as ``py::array`` so
that it can be used as a drop-in replacement for some existing, index-checked
uses of ``py::array``:
- ``r.ndim()`` returns the number of dimensions
- ``.ndim()`` returns the number of dimensions
- ``r.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to
- ``.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to
the ``const T`` or ``T`` data, respectively, at the given indices. The
latter is only available to proxies obtained via ``a.mutable_unchecked()``.
- ``itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``.
- ``.itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``.
- ``ndim()`` returns the number of dimensions.
- ``.ndim()`` returns the number of dimensions.
- ``shape(n)`` returns the size of dimension ``n``
- ``.shape(n)`` returns the size of dimension ``n``
- ``size()`` returns the total number of elements (i.e. the product of the shapes).
- ``.size()`` returns the total number of elements (i.e. the product of the shapes).
- ``nbytes()`` returns the number of bytes used by the referenced elements
- ``.nbytes()`` returns the number of bytes used by the referenced elements
(i.e. ``itemsize()`` times ``size()``).
.. seealso::
@@ -378,7 +403,7 @@ In Python 2, the syntactic sugar ``...`` is not available, but the singleton
.. code-block:: python
a = # a NumPy array
a = ... # a NumPy array
b = a[0, ..., 0]
The function ``py::ellipsis()`` function can be used to perform the same
@@ -390,7 +415,7 @@ operation on the C++ side:
py::array b = a[py::make_tuple(0, py::ellipsis(), 0)];
.. versionchanged:: 2.6
``py::ellipsis()`` is now also avaliable in Python 2.
``py::ellipsis()`` is now also available in Python 2.
Memory view
===========

View File

@@ -20,6 +20,40 @@ Available types include :class:`handle`, :class:`object`, :class:`bool_`,
Be sure to review the :ref:`pytypes_gotchas` before using this heavily in
your C++ API.
.. _instantiating_compound_types:
Instantiating compound Python types from C++
============================================
Dictionaries can be initialized in the :class:`dict` constructor:
.. code-block:: cpp
using namespace pybind11::literals; // to bring in the `_a` literal
py::dict d("spam"_a=py::none(), "eggs"_a=42);
A tuple of python objects can be instantiated using :func:`py::make_tuple`:
.. code-block:: cpp
py::tuple tup = py::make_tuple(42, py::none(), "spam");
Each element is converted to a supported Python type.
A `simple namespace`_ can be instantiated using
.. code-block:: cpp
using namespace pybind11::literals; // to bring in the `_a` literal
py::object SimpleNamespace = py::module_::import("types").attr("SimpleNamespace");
py::object ns = SimpleNamespace("spam"_a=py::none(), "eggs"_a=42);
Attributes on a namespace can be modified with the :func:`py::delattr`,
:func:`py::getattr`, and :func:`py::setattr` functions. Simple namespaces can
be useful as lightweight stand-ins for class instances.
.. _simple namespace: https://docs.python.org/3/library/types.html#types.SimpleNamespace
.. _casting_back_and_forth:
Casting back and forth
@@ -30,7 +64,7 @@ types to Python, which can be done using :func:`py::cast`:
.. code-block:: cpp
MyClass *cls = ..;
MyClass *cls = ...;
py::object obj = py::cast(cls);
The reverse direction uses the following syntax:
@@ -132,6 +166,7 @@ Keyword arguments are also supported. In Python, there is the usual call syntax:
def f(number, say, to):
... # function code
f(1234, say="hello", to=some_instance) # keyword call in Python
In C++, the same call can be made using:

View File

@@ -28,7 +28,7 @@ Capturing standard output from ostream
Often, a library will use the streams ``std::cout`` and ``std::cerr`` to print,
but this does not play well with Python's standard ``sys.stdout`` and ``sys.stderr``
redirection. Replacing a library's printing with `py::print <print>` may not
redirection. Replacing a library's printing with ``py::print <print>`` may not
be feasible. This can be fixed using a guard around the library function that
redirects output to the corresponding Python streams:
@@ -47,15 +47,26 @@ redirects output to the corresponding Python streams:
call_noisy_func();
});
.. warning::
The implementation in ``pybind11/iostream.h`` is NOT thread safe. Multiple
threads writing to a redirected ostream concurrently cause data races
and potentially buffer overflows. Therefore it is currently a requirement
that all (possibly) concurrent redirected ostream writes are protected by
a mutex. #HelpAppreciated: Work on iostream.h thread safety. For more
background see the discussions under
`PR #2982 <https://github.com/pybind/pybind11/pull/2982>`_ and
`PR #2995 <https://github.com/pybind/pybind11/pull/2995>`_.
This method respects flushes on the output streams and will flush if needed
when the scoped guard is destroyed. This allows the output to be redirected in
real time, such as to a Jupyter notebook. The two arguments, the C++ stream and
the Python output, are optional, and default to standard output if not given. An
extra type, `py::scoped_estream_redirect <scoped_estream_redirect>`, is identical
extra type, ``py::scoped_estream_redirect <scoped_estream_redirect>``, is identical
except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful with
`py::call_guard`, which allows multiple items, but uses the default constructor:
``py::call_guard``, which allows multiple items, but uses the default constructor:
.. code-block:: py
.. code-block:: cpp
// Alternative: Call single function using call guard
m.def("noisy_func", &call_noisy_function,
@@ -63,7 +74,7 @@ except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful wi
py::scoped_estream_redirect>());
The redirection can also be done in Python with the addition of a context
manager, using the `py::add_ostream_redirect() <add_ostream_redirect>` function:
manager, using the ``py::add_ostream_redirect() <add_ostream_redirect>`` function:
.. code-block:: cpp
@@ -92,7 +103,7 @@ arguments to disable one of the streams if needed.
Evaluating Python expressions from strings and files
====================================================
pybind11 provides the `eval`, `exec` and `eval_file` functions to evaluate
pybind11 provides the ``eval``, ``exec`` and ``eval_file`` functions to evaluate
Python expressions and statements. The following example illustrates how they
can be used.