1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
|
.. _python-spec:
Python Specification for DLPack
===============================
The Python specification for DLPack is a part of the
`Python array API standard <https://data-apis.org/array-api/latest/index.html>`_.
More details about the spec can be found under the :ref:`data-interchange` page.
Syntax for data interchange with DLPack
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The array API will offer the following syntax for data interchange:
1. A :func:`~array_api.from_dlpack` function, which accepts any (array) object with
the two DLPack methods implemented (see below) and uses them to construct
a new array containing the data from the input array.
2. :meth:`~array_api.array.__dlpack__` and :meth:`~array_api.array.__dlpack_device__` methods on the
array object, which will be called from within :func:`~array_api.from_dlpack`, to query
what device the array is on (may be needed to pass in the correct
stream, e.g. in the case of multiple GPUs) and to access the data.
Semantics
~~~~~~~~~
DLPack describes the memory layout of dense, strided, n-dimensional arrays.
When a user calls ``y = from_dlpack(x)``, the library implementing ``x`` (the
"producer") will provide access to the data from ``x`` to the library
containing ``from_dlpack`` (the "consumer"). If possible, this must be
zero-copy (i.e. ``y`` will be a *view* on ``x``). If not possible, that library
may flag this and make a copy of the data. In both cases:
- The producer keeps owning the memory of ``x`` (and ``y`` if a copy is made)
- ``y`` may or may not be a view, therefore the user must keep the recommendation to
avoid mutating ``y`` in mind - see :ref:`copyview-mutability`.
- Both ``x`` and ``y`` may continue to be used just like arrays created in other ways.
If an array that is accessed via the interchange protocol lives on a device that
the requesting (consumer) library does not support, it is recommended to raise a
:obj:`BufferError`, unless an explicit copy is requested (see below) and the producer
can support the request.
Stream handling through the ``stream`` keyword applies to CUDA and ROCm (perhaps
to other devices that have a stream concept as well, however those haven't been
considered in detail). The consumer must pass the stream it will use to the
producer; the producer must synchronize or wait on the stream when necessary.
In the common case of the default stream being used, synchronization will be
unnecessary so asynchronous execution is enabled.
Starting Python array API standard v2023, a copy can be explicitly requested (or
disabled) through the new ``copy`` argument of ``from_dlpack()``. When a copy is
made, the producer must set the :c:macro:`DLPACK_FLAG_BITMASK_IS_COPIED` bit flag.
It is also possible to request cross-device copies through the new ``device``
argument, though the v2023 standard only mandates the support of :c:enumerator:`kDLCPU`.
Implementation
~~~~~~~~~~~~~~
*Note that while this API standard largely tries to avoid discussing
implementation details, some discussion and requirements are needed
here because data interchange requires coordination between
implementers on, e.g., memory management.*
.. image:: /_static/images/DLPack_diagram.png
:alt: Diagram of DLPack structs
*DLPack diagram. Dark blue are the structs it defines, light blue
struct members, gray text enum values of supported devices and data
types.*
Starting Python array API standard v2023, a new ``max_version`` argument
is added to ``__dlpack__`` for the consumer to signal the producer the
maximal supported DLPack version. Starting DLPack 1.0, the :c:struct:`DLManagedTensorVersioned`
struct should be used and the existing :c:struct:`DLManagedTensor` struct is considered
deprecated, though a library should try to support both during the transition
period if possible.
.. note::
In the rest of this document, ``DLManagedTensorVersioned`` and ``DLManagedTensor``
are treated as synonyms, assuming a proper handling of ``max_version`` has been
done to choose the right struct. As far as the capsule name is concerned,
when ``DLManagedTensorVersioned`` is in use the capsule names ``dltensor``
and ``used_dltensor`` will need a ``_versioned`` suffix.
The ``__dlpack__`` method will produce a :c:type:`PyCapsule` containing a
``DLManagedTensor``, which will be consumed immediately within
``from_dlpack`` - therefore it is consumed exactly once, and it will not be
visible to users of the Python API.
The producer must set the ``PyCapsule`` name to ``"dltensor"`` so that
it can be inspected by name, and set :c:type:`PyCapsule_Destructor` that calls
the ``deleter`` of the ``DLManagedTensor`` when the ``"dltensor"``-named
capsule is no longer needed.
The consumer must transer ownership of the ``DLManagedTensor`` from the
capsule to its own object. It does so by renaming the capsule to
``"used_dltensor"`` to ensure that ``PyCapsule_Destructor`` will not get
called (ensured if ``PyCapsule_Destructor`` calls ``deleter`` only for
capsules whose name is ``"dltensor"``), but the ``deleter`` of the
``DLManagedTensor`` will be called by the destructor of the consumer
library object created to own the ``DLManagedTensor`` obtained from the
capsule. Below is an example of the capsule deleter written in the Python
C API which is called either when the refcount on the capsule named
``"dltensor"`` reaches zero or the consumer decides to deallocate its array:
.. code-block:: C
static void dlpack_capsule_deleter(PyObject *self){
if (PyCapsule_IsValid(self, "used_dltensor")) {
return; /* Do nothing if the capsule has been consumed. */
}
DLManagedTensor *managed = (DLManagedTensor *)PyCapsule_GetPointer(self, "dltensor");
if (managed == NULL) {
PyErr_WriteUnraisable(self);
return;
}
/* the spec says the deleter can be NULL if there is no way for the caller to provide a reasonable destructor. */
if (managed->deleter) {
managed->deleter(managed);
}
}
Note: the capsule names ``"dltensor"`` and ``"used_dltensor"`` must be
statically allocated.
The ``DLManagedTensor`` deleter must ensure that sharing beyond Python
boundaries is possible, this means that the GIL must be acquired explicitly
if it uses Python objects or API.
In Python, the deleter usually needs to :c:func:`Py_DECREF` the original owner
and free the ``DLManagedTensor`` allocation.
For example, NumPy uses the following code to ensure sharing with arbitrary
non-Python code is safe:
.. code-block:: C
static void array_dlpack_deleter(DLManagedTensor *self)
{
/*
* Leak the Python object if the Python runtime is not available.
* This can happen if the DLPack consumer destroys the tensor late
* after Python runtime finalization (for example in case the tensor
* was indirectly kept alive by a C++ static variable).
*/
if (!Py_IsInitialized()) {
return;
}
PyGILState_STATE state = PyGILState_Ensure();
PyObject *array = (PyObject *)self->manager_ctx;
// This will also free the shape and strides as it's one allocation.
PyMem_Free(self);
Py_XDECREF(array);
PyGILState_Release(state);
}
When the :c:member:`~DLTensor.strides` field in the :c:struct:`DLTensor` struct is ``NULL``, it indicates a
row-major compact array. If the array is of size zero, the data pointer in
``DLTensor`` should be set to either ``NULL`` or ``0``.
For further details on DLPack design and how to implement support for it,
refer to `github.com/dmlc/dlpack <https://github.com/dmlc/dlpack>`_.
.. warning::
DLPack contains a :c:member:`~DLDevice.device_id`, which will be the device
ID (an integer, ``0, 1, ...``) which the producer library uses. In
practice this will likely be the same numbering as that of the
consumer, however that is not guaranteed. Depending on the hardware
type, it may be possible for the consumer library implementation to
look up the actual device from the pointer to the data - this is
possible for example for CUDA device pointers.
It is recommended that implementers of this array API consider and document
whether the :attr:`~array_api.array.device` attribute of the array returned from ``from_dlpack`` is
guaranteed to be in a certain order or not.
Reference Implementations
~~~~~~~~~~~~~~~~~~~~~~~~~
Several Python libraries have adopted this standard using Python C API, C++, Cython,
ctypes, cffi, etc:
* NumPy: `Python C API <https://github.com/numpy/numpy/blob/main/numpy/core/src/multiarray/dlpack.c>`__
* CuPy: `Cython <https://github.com/cupy/cupy/blob/master/cupy/_core/dlpack.pyx>`__
* Tensorflow: `C++ <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/eager/dlpack.cc>`__,
`Python wrapper using Python C API <https://github.com/tensorflow/tensorflow/blob/a97b01a4ff009ed84a571c138837130a311e74a7/tensorflow/python/tfe_wrapper.cc#L1562>`__,
`XLA <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/python/dlpack.cc>`__
* PyTorch: `C++ <https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/DLConvertor.cpp>`__,
`Python wrapper using Python C API <https://github.com/pytorch/pytorch/blob/c22b8a42e6038ed2f6a161114cf3d8faac3f6e9a/torch/csrc/Module.cpp#L355>`__
* MXNet: `ctypes <https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/dlpack.py>`__
* TVM: `ctypes <https://github.com/apache/tvm/blob/main/python/tvm/_ffi/_ctypes/ndarray.py>`__,
`Cython <https://github.com/apache/tvm/blob/main/python/tvm/_ffi/_cython/ndarray.pxi>`__
* mpi4py: `Cython <https://github.com/mpi4py/mpi4py/blob/master/src/mpi4py/MPI/asdlpack.pxi>`_
|
