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ext: Upgrade PyBind11 to version 2.2.1

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This upgrade is necessary for pybind to build with GCC 7.2.

We still need to add the patch for stl.h. MSC_FULL_VER change is no longer
needed.
See https://gem5-review.googlesource.com/c/public/gem5/+/2230

Change-Id: I806729217d022070583994c2dfcaa74476aef30f
Signed-off-by: Jason Lowe-Power <jason@lowepower.com>
Reviewed-on: https://gem5-review.googlesource.com/5801
Reviewed-by: Andreas Sandberg <andreas.sandberg@arm.com>
Maintainer: Andreas Sandberg <andreas.sandberg@arm.com>
This commit is contained in:
Jason Lowe-Power
2017-11-17 17:02:05 -08:00
parent 3f64b374c4
commit f07d5069d8
160 changed files with 15668 additions and 7238 deletions
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79
ext/pybind11/docs/advanced/pycpp/numpy.rst
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@@ -57,11 +57,11 @@ specification.
struct buffer_info {
void *ptr;
size_t itemsize;
ssize_t itemsize;
std::string format;
int ndim;
std::vector<size_t> shape;
std::vector<size_t> strides;
ssize_t ndim;
std::vector<ssize_t> shape;
std::vector<ssize_t> strides;
};
To create a C++ function that can take a Python buffer object as an argument,
@@ -95,11 +95,11 @@ buffer objects (e.g. a NumPy matrix).
throw std::runtime_error("Incompatible buffer dimension!");
auto strides = Strides(
info.strides[rowMajor ? 0 : 1] / sizeof(Scalar),
info.strides[rowMajor ? 1 : 0] / sizeof(Scalar));
info.strides[rowMajor ? 0 : 1] / (py::ssize_t)sizeof(Scalar),
info.strides[rowMajor ? 1 : 0] / (py::ssize_t)sizeof(Scalar));
auto map = Eigen::Map<Matrix, 0, Strides>(
static_cat<Scalar *>(info.ptr), info.shape[0], info.shape[1], strides);
static_cast<Scalar *>(info.ptr), info.shape[0], info.shape[1], strides);
new (&m) Matrix(map);
});
@@ -111,18 +111,14 @@ as follows:
.def_buffer([](Matrix &m) -> py::buffer_info {
return py::buffer_info(
m.data(), /* Pointer to buffer */
sizeof(Scalar), /* Size of one scalar */
/* Python struct-style format descriptor */
py::format_descriptor<Scalar>::format(),
/* Number of dimensions */
2,
/* Buffer dimensions */
{ (size_t) m.rows(),
(size_t) m.cols() },
/* Strides (in bytes) for each index */
m.data(), /* Pointer to buffer */
sizeof(Scalar), /* Size of one scalar */
py::format_descriptor<Scalar>::format(), /* Python struct-style format descriptor */
2, /* Number of dimensions */
{ m.rows(), m.cols() }, /* Buffer dimensions */
{ sizeof(Scalar) * (rowMajor ? m.cols() : 1),
sizeof(Scalar) * (rowMajor ? 1 : m.rows()) }
/* Strides (in bytes) for each index */
);
})
@@ -194,7 +190,7 @@ expects the type followed by field names:
};
// ...
PYBIND11_PLUGIN(test) {
PYBIND11_MODULE(test, m) {
// ...
PYBIND11_NUMPY_DTYPE(A, x, y);
@@ -202,6 +198,13 @@ expects the type followed by field names:
/* now both A and B can be used as template arguments to py::array_t */
}
The structure should consist of fundamental arithmetic types, ``std::complex``,
previously registered substructures, and arrays of any of the above. Both C++
arrays and ``std::array`` are supported. While there is a static assertion to
prevent many types of unsupported structures, it is still the user's
responsibility to use only "plain" structures that can be safely manipulated as
raw memory without violating invariants.
Vectorizing functions
=====================
@@ -236,27 +239,13 @@ by the compiler. The result is returned as a NumPy array of type
The scalar argument ``z`` is transparently replicated 4 times. The input
arrays ``x`` and ``y`` are automatically converted into the right types (they
are of type ``numpy.dtype.int64`` but need to be ``numpy.dtype.int32`` and
``numpy.dtype.float32``, respectively)
``numpy.dtype.float32``, respectively).
Sometimes we might want to explicitly exclude an argument from the vectorization
because it makes little sense to wrap it in a NumPy array. For instance,
suppose the function signature was
.. note::
.. code-block:: cpp
double my_func(int x, float y, my_custom_type *z);
This can be done with a stateful Lambda closure:
.. code-block:: cpp
// Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
m.def("vectorized_func",
[](py::array_t<int> x, py::array_t<float> y, my_custom_type *z) {
auto stateful_closure = [z](int x, float y) { return my_func(x, y, z); };
return py::vectorize(stateful_closure)(x, y);
}
);
Only arithmetic, complex, and POD types passed by value or by ``const &``
reference are vectorized; all other arguments are passed through as-is.
Functions taking rvalue reference arguments cannot be vectorized.
In cases where the computation is too complicated to be reduced to
``vectorize``, it will be necessary to create and access the buffer contents
@@ -295,10 +284,8 @@ simply using ``vectorize``).
return result;
}
PYBIND11_PLUGIN(test) {
py::module m("test");
PYBIND11_MODULE(test, m) {
m.def("add_arrays", &add_arrays, "Add two NumPy arrays");
return m.ptr();
}
.. seealso::
@@ -322,17 +309,17 @@ where ``N`` gives the required dimensionality of the array:
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++)
for (ssize_t i = 0; i < r.shape(0); i++)
for (ssize_t j = 0; j < r.shape(1); j++)
for (ssize_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++)
for (ssize_t i = 0; i < r.shape(0); i++)
for (ssize_t j = 0; j < r.shape(1); j++)
for (ssize_t k = 0; k < r.shape(2); k++)
r(i, j, k) += 1.0;
}, py::arg().noconvert());

76
ext/pybind11/docs/advanced/pycpp/object.rst
View File

@@ -33,12 +33,50 @@ The reverse direction uses the following syntax:
When conversion fails, both directions throw the exception :class:`cast_error`.
.. _python_libs:
Accessing Python libraries from C++
===================================
It is also possible to import objects defined in the Python standard
library or available in the current Python environment (``sys.path``) and work
with these in C++.
This example obtains a reference to the Python ``Decimal`` class.
.. code-block:: cpp
// Equivalent to "from decimal import Decimal"
py::object Decimal = py::module::import("decimal").attr("Decimal");
.. code-block:: cpp
// Try to import scipy
py::object scipy = py::module::import("scipy");
return scipy.attr("__version__");
.. _calling_python_functions:
Calling Python functions
========================
It is also possible to call python functions via ``operator()``.
It is also possible to call Python classes, functions and methods
via ``operator()``.
.. code-block:: cpp
// Construct a Python object of class Decimal
py::object pi = Decimal("3.14159");
.. code-block:: cpp
// Use Python to make our directories
py::object os = py::module::import("os");
py::object makedirs = os.attr("makedirs");
makedirs("/tmp/path/to/somewhere");
One can convert the result obtained from Python to a pure C++ version
if a ``py::class_`` or type conversion is defined.
.. code-block:: cpp
@@ -46,6 +84,37 @@ It is also possible to call python functions via ``operator()``.
py::object result_py = f(1234, "hello", some_instance);
MyClass &result = result_py.cast<MyClass>();
.. _calling_python_methods:
Calling Python methods
========================
To call an object's method, one can again use ``.attr`` to obtain access to the
Python method.
.. code-block:: cpp
// Calculate e^π in decimal
py::object exp_pi = pi.attr("exp")();
py::print(py::str(exp_pi));
In the example above ``pi.attr("exp")`` is a *bound method*: it will always call
the method for that same instance of the class. Alternately one can create an
*unbound method* via the Python class (instead of instance) and pass the ``self``
object explicitly, followed by other arguments.
.. code-block:: cpp
py::object decimal_exp = Decimal.attr("exp");
// Compute the e^n for n=0..4
for (int n = 0; n < 5; n++) {
py::print(decimal_exp(Decimal(n));
}
Keyword arguments
=================
Keyword arguments are also supported. In Python, there is the usual call syntax:
.. code-block:: python
@@ -62,6 +131,9 @@ In C++, the same call can be made using:
using namespace pybind11::literals; // to bring in the `_a` literal
f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++
Unpacking arguments
===================
Unpacking of ``*args`` and ``**kwargs`` is also possible and can be mixed with
other arguments:
@@ -90,7 +162,7 @@ Generalized unpacking according to PEP448_ is also supported:
.. seealso::
The file :file:`tests/test_python_types.cpp` contains a complete
The file :file:`tests/test_pytypes.cpp` contains a complete
example that demonstrates passing native Python types in more detail. The
file :file:`tests/test_callbacks.cpp` presents a few examples of calling
Python functions from C++, including keywords arguments and unpacking.

103
ext/pybind11/docs/advanced/pycpp/utilities.rst
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@@ -21,19 +21,81 @@ expected in Python:
auto args = py::make_tuple("unpacked", true);
py::print("->", *args, "end"_a="<-"); // -> unpacked True <-
.. _ostream_redirect:
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
be feasible. This can be fixed using a guard around the library function that
redirects output to the corresponding Python streams:
.. code-block:: cpp
#include <pybind11/iostream.h>
...
// Add a scoped redirect for your noisy code
m.def("noisy_func", []() {
py::scoped_ostream_redirect stream(
std::cout, // std::ostream&
py::module::import("sys").attr("stdout") // Python output
);
call_noisy_func();
});
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
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:
.. code-block:: py
// Alternative: Call single function using call guard
m.def("noisy_func", &call_noisy_function,
py::call_guard<py::scoped_ostream_redirect,
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:
.. code-block:: cpp
py::add_ostream_redirect(m, "ostream_redirect");
The name in Python defaults to ``ostream_redirect`` if no name is passed. This
creates the following context manager in Python:
.. code-block:: python
with ostream_redirect(stdout=True, stderr=True):
noisy_function()
It defaults to redirecting both streams, though you can use the keyword
arguments to disable one of the streams if needed.
.. note::
The above methods will not redirect C-level output to file descriptors, such
as ``fprintf``. For those cases, you'll need to redirect the file
descriptors either directly in C or with Python's ``os.dup2`` function
in an operating-system dependent way.
.. _eval:
Evaluating Python expressions from strings and files
====================================================
pybind11 provides the :func:`eval` and :func:`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.
Both functions accept a template parameter that describes how the argument
should be interpreted. Possible choices include ``eval_expr`` (isolated
expression), ``eval_single_statement`` (a single statement, return value is
always ``none``), and ``eval_statements`` (sequence of statements, return value
is always ``none``).
.. code-block:: cpp
// At beginning of file
@@ -48,10 +110,35 @@ is always ``none``).
int result = py::eval("my_variable + 10", scope).cast<int>();
// Evaluate a sequence of statements
py::eval<py::eval_statements>(
py::exec(
"print('Hello')\n"
"print('world!');",
scope);
// Evaluate the statements in an separate Python file on disk
py::eval_file("script.py", scope);
C++11 raw string literals are also supported and quite handy for this purpose.
The only requirement is that the first statement must be on a new line following
the raw string delimiter ``R"(``, ensuring all lines have common leading indent:
.. code-block:: cpp
py::exec(R"(
x = get_answer()
if x == 42:
print('Hello World!')
else:
print('Bye!')
)", scope
);
.. note::
`eval` and `eval_file` accept a template parameter that describes how the
string/file should be interpreted. Possible choices include ``eval_expr``
(isolated expression), ``eval_single_statement`` (a single statement, return
value is always ``none``), and ``eval_statements`` (sequence of statements,
return value is always ``none``). `eval` defaults to ``eval_expr``,
`eval_file` defaults to ``eval_statements`` and `exec` is just a shortcut
for ``eval<eval_statements>``.