This repository was archived by the owner on Nov 17, 2023. It is now read-only.
Repository navigation
Expand file tree
/
Copy pathdlpack.py
More file actions
213 lines (178 loc) · 7.48 KB
/
Copy pathdlpack.py
File metadata and controls
213 lines (178 loc) · 7.48 KB
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
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# coding: utf-8
# pylint: disable=protected-access
# pylint: disable=import-error, no-name-in-module, undefined-variable
"""DLPack API of MXNet."""
import ctypes
import enum
from mxnet.device import current_device
from .base import _LIB, c_str, check_call, NDArrayHandle, mx_int
DLPackHandle = ctypes.c_void_p
PyCapsuleDestructor = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
_c_str_dltensor = c_str('dltensor')
_c_str_used_dltensor = c_str('used_dltensor')
def _dlpack_deleter(pycapsule):
pycapsule = ctypes.c_void_p(pycapsule)
if ctypes.pythonapi.PyCapsule_IsValid(pycapsule, _c_str_dltensor):
ptr = ctypes.c_void_p(
ctypes.pythonapi.PyCapsule_GetPointer(pycapsule, _c_str_dltensor))
check_call(_LIB.MXNDArrayCallDLPackDeleter(ptr))
_c_dlpack_deleter = PyCapsuleDestructor(_dlpack_deleter)
class DLDeviceType(enum.IntEnum):
DLCPU = 1,
DLGPU = 2,
DLCPUPINNED = 3,
DLOPENCL = 4,
DLVULKAN = 7,
DLMETAL = 8,
DLVPI = 9,
DLROCM = 10,
DLEXTDEV = 12,
class DLContext(ctypes.Structure):
_fields_ = [("device_type", ctypes.c_int),
("device_id", ctypes.c_int)]
class DLDataType(ctypes.Structure):
_fields_ = [("type_code", ctypes.c_uint8),
("bits", ctypes.c_uint8),
("lanes", ctypes.c_uint16)]
TYPE_MAP = {
"int32": (0, 32, 1),
"int64": (0, 64, 1),
"bool": (1, 1, 1),
"uint8": (1, 8, 1),
"uint32": (1, 32, 1),
"uint64": (1, 64, 1),
'float16': (2, 16, 1),
"float32": (2, 32, 1),
"float64": (2, 64, 1),
}
class DLTensor(ctypes.Structure):
_fields_ = [("data", ctypes.c_void_p),
("ctx", DLContext),
("ndim", ctypes.c_int),
("dtype", DLDataType),
("shape", ctypes.POINTER(ctypes.c_int64)),
("strides", ctypes.POINTER(ctypes.c_int64)),
("byte_offset", ctypes.c_uint64)]
class DLManagedTensor(ctypes.Structure):
pass
DeleterFunc = ctypes.CFUNCTYPE(None, ctypes.POINTER(DLManagedTensor))
DLManagedTensor._fields_ = [("dl_tensor", DLTensor), # pylint: disable=protected-access
("manager_ctx", ctypes.c_void_p),
("deleter", DeleterFunc)]
@DeleterFunc
def dl_managed_tensor_deleter(dl_managed_tensor_handle):
void_p = dl_managed_tensor_handle.contents.manager_ctx
pyobj = ctypes.cast(void_p, ctypes.py_object)
ctypes.pythonapi.Py_DecRef(pyobj)
def ndarray_from_dlpack(array_cls):
"""Returns a function that returns specified array_cls from dlpack.
Returns
-------
fn : dlpack -> array_cls
"""
def from_dlpack(dlpack):
tp = type(dlpack)
if tp.__module__ == "builtins" and tp.__name__ == "PyCapsule":
dlpack = ctypes.py_object(dlpack)
elif hasattr(dlpack, "__dlpack__"):
device, device_id = dlpack.__dlpack_device__()
if device != DLDeviceType.DLGPU:
dlpack = ctypes.py_object(dlpack.__dlpack__())
else:
s = mx_int()
check_call(_LIB.MXGetCurrentStream(
ctypes.c_int(device_id), ctypes.byref(s)))
dlpack = ctypes.py_object(dlpack.__dlpack__(stream=s.value))
else:
raise AttributeError("Required PyCapsule or object with __dlpack__")
handle = NDArrayHandle()
assert ctypes.pythonapi.PyCapsule_IsValid(dlpack, _c_str_dltensor), ValueError(
'Invalid DLPack Tensor. DLTensor capsules can be consumed only once.')
dlpack_handle = ctypes.c_void_p(ctypes.pythonapi.PyCapsule_GetPointer(dlpack, _c_str_dltensor))
check_call(_LIB.MXNDArrayFromDLPack(dlpack_handle, False, ctypes.byref(handle)))
# Rename PyCapsule (DLPack)
ctypes.pythonapi.PyCapsule_SetName(dlpack, _c_str_used_dltensor)
# delete the deleter of the old dlpack
ctypes.pythonapi.PyCapsule_SetDestructor(dlpack, None)
return array_cls(handle=handle)
return from_dlpack
def ndarray_to_dlpack_for_read():
"""Returns a function that returns dlpack for reading from mxnet array.
Returns
-------
fn : tensor -> dlpack
"""
def to_dlpack_for_read(data):
data.wait_to_read()
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter)
return to_dlpack_for_read
def ndarray_to_dlpack_for_write():
"""Returns a function that returns dlpack for writing from mxnet array.
Returns
-------
fn : tensor -> dlpack
"""
def to_dlpack_for_write(data):
check_call(_LIB.MXNDArrayWaitToWrite(data.handle))
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter)
return to_dlpack_for_write
def ndarray_from_numpy(array_cls, array_create_fn):
"""Returns a function that creates array_cls from numpy array.
Returns
-------
fn : tensor -> dlpack
"""
def from_numpy(ndarray, zero_copy=True):
def _make_manager_ctx(obj):
pyobj = ctypes.py_object(obj)
void_p = ctypes.c_void_p.from_buffer(pyobj)
ctypes.pythonapi.Py_IncRef(pyobj)
return void_p
def _make_dl_tensor(array):
if str(array.dtype) not in DLDataType.TYPE_MAP:
raise ValueError(str(array.dtype) + " is not supported.")
dl_tensor = DLTensor()
dl_tensor.data = array.ctypes.data_as(ctypes.c_void_p)
dl_tensor.ctx = DLContext(1, 0)
dl_tensor.ndim = array.ndim
dl_tensor.dtype = DLDataType.TYPE_MAP[str(array.dtype)]
dl_tensor.shape = array.ctypes.shape_as(ctypes.c_int64)
dl_tensor.strides = None
dl_tensor.byte_offset = 0
return dl_tensor
def _make_dl_managed_tensor(array):
c_obj = DLManagedTensor()
c_obj.dl_tensor = _make_dl_tensor(array)
c_obj.manager_ctx = _make_manager_ctx(array)
c_obj.deleter = dl_managed_tensor_deleter
return c_obj
if not zero_copy:
return array_create_fn(ndarray, dtype=ndarray.dtype)
if not ndarray.flags['C_CONTIGUOUS']:
raise ValueError("Only c-contiguous arrays are supported for zero-copy")
ndarray.flags['WRITEABLE'] = False
c_obj = _make_dl_managed_tensor(ndarray)
handle = NDArrayHandle()
check_call(_LIB.MXNDArrayFromDLPack(ctypes.byref(c_obj), True, ctypes.byref(handle)))
return array_cls(handle=handle)
return from_numpy