-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
creation.py
299 lines (248 loc) · 11.1 KB
/
creation.py
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
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed 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.
import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import core, dygraph_only
from paddle.fluid.framework import _current_expected_place, _get_paddle_place
from paddle.tensor import to_tensor, max
from paddle.fluid.data_feeder import (
check_variable_and_dtype,
check_type,
check_dtype,
convert_dtype,
)
from paddle import in_dynamic_mode
from paddle.fluid.layer_helper import LayerHelper
import numpy as np
__all__ = [
'sparse_coo_tensor',
'sparse_csr_tensor',
]
def _handle_dtype(data, dtype):
if dtype:
if convert_dtype(dtype) != convert_dtype(data.dtype):
return data.astype(convert_dtype(dtype))
return data
def _infer_dense_shape(indices, values):
assert len(indices.shape) == 2
lens = max(indices, axis=1)
lens = lens + 1
lens = lens.numpy()
if len(values.shape) > 1:
lens = np.append(lens, values.shape[1:])
return list(lens)
def _get_place(place):
place = _get_paddle_place(place)
if place is None:
place = _current_expected_place()
elif not isinstance(
place, (core.Place, core.CPUPlace, core.CUDAPinnedPlace, core.CUDAPlace)
):
raise ValueError(
"'place' must be any of paddle.Place, paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace"
)
return place
def _check_indices_dtype(dtype):
if dtype not in [paddle.int8, paddle.int16, paddle.int32, paddle.int64]:
raise TypeError(
"the dtype of indices must be 'int8' or 'int16' or 'int32' or 'int64'"
)
def sparse_coo_tensor(
indices, values, shape=None, dtype=None, place=None, stop_gradient=True
):
r"""
Constructs a sparse ``paddle.Tensor`` in coordinate format according to the indices
and values of the specified non-zero elements.
Args:
indices(list|tuple|ndarray|Tensor): the indices of non-zero elements.
Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. The indices must be 2-D.
values(list|tuple|ndarray|Tensor): Initial values for the tensor.
Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of
original dense tensor. If not provided the smallest shape will be inferred to
hold all elements.
dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' ,
'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
'complex64' , 'complex128'. Default: None, infers dtype from ``data``
except for python float number which gets dtype from ``get_default_type`` .
place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be
CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is
string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs.
stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.
Returns:
Tensor: A Tensor constructed from ``indices`` and ``values`` .
Examples:
.. code-block:: python
import paddle
indices = [[0, 1, 2], [1, 2, 0]]
values = [1.0, 2.0, 3.0]
dense_shape = [3, 3]
coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
# print(coo)
# Tensor(shape=[2, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# indices=[[0, 1, 2],
# [1, 2, 0]],
# values=[1., 2., 3.])
"""
if in_dynamic_mode():
place = _get_place(place)
if not isinstance(indices, core.eager.Tensor):
indices = to_tensor(
indices, dtype=None, place=place, stop_gradient=True
)
if not isinstance(values, core.eager.Tensor):
values = to_tensor(values, dtype, place, stop_gradient)
if len(indices.shape) != 2:
raise ValueError("'indices' must be 2-D.")
nnz = indices.shape[1]
sparse_dim = indices.shape[0]
_check_indices_dtype(indices.dtype)
if nnz != values.shape[0]:
raise ValueError(
"the indices and values must have same number of non-zero, but get {} and {}".format(
nnz, values.shape[0]
)
)
dense_dim = len(values.shape) - 1
if not indices.place._equals(place):
indices = indices._copy_to(place, False)
if not values.place._equals(place):
values = values._copy_to(place, False)
values = _handle_dtype(values, dtype)
values.stop_gradient = stop_gradient
min_shape = _infer_dense_shape(indices, values)
if shape is None:
shape = min_shape
else:
if shape < min_shape:
raise ValueError(
"the minimun shape required is {}, but get {}".format(
min_shape, shape
)
)
if len(shape) != sparse_dim + dense_dim:
raise ValueError(
"the number of dimensions(len(shape) must be sparse_dim({}) + dense_dim({}), but get {}".format(
sparse_dim, dense_dim, len(shape)
)
)
return _C_ops.sparse_sparse_coo_tensor(values, indices, shape)
else:
op_type = 'sparse_sparse_coo_tensor'
inputs = {'values': values, 'indices': indices}
if shape[0] is None:
shape[0] = -1
attrs = {'shape': shape}
helper = LayerHelper(op_type)
out = helper.create_sparse_variable_for_type_inference(dtype)
helper.append_op(
type=op_type, inputs=inputs, outputs={'out': out}, attrs=attrs
)
return out
# TODO: need to support shape is None
@dygraph_only
def sparse_csr_tensor(
crows, cols, values, shape, dtype=None, place=None, stop_gradient=True
):
r"""
Constructs a sparse ``paddle.Tensor`` in CSR(Compressed Sparse Row) format according to the
``crows``, ``cols`` and ``values``.
Currently, the crows and cols of each batch must be incrementd.
Args:
crows(list|tuple|ndarray|Tensor): 1-D array, each element in the rows represents the
starting position of the first non-zero element of each row in values.
Can be a list, tuple, numpy\.ndarray, paddle\.Tensor.
cols(list|tuple|ndarray|Tensor): 1-D array, the column of non-zero elements.
Can be a list, tuple, numpy\.ndarray, paddle\.Tensor.
values(list|tuple|ndarray|Tensor): 1-D array, the non-zero elements.
Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of
original dense tensor.
hold all elements.
dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' ,
'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
'complex64' , 'complex128'. Default: None, infers dtype from ``data``
except for python float number which gets dtype from ``get_default_type`` .
place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be
CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is
string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs.
stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.
Returns:
Tensor: A Tensor constructed from ``crows``, ``cols`` and ``values`` .
Examples:
.. code-block:: python
import paddle
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
# print(csr)
# Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5],
# cols=[1, 3, 2, 0, 1],
# values=[1, 2, 3, 4, 5])
"""
place = _get_place(place)
if not isinstance(crows, core.eager.Tensor):
crows = to_tensor(crows, dtype=None, place=place, stop_gradient=True)
if not isinstance(cols, core.eager.Tensor):
cols = to_tensor(cols, dtype=None, place=place, stop_gradient=True)
if not isinstance(values, core.eager.Tensor):
values = to_tensor(values, dtype, place, stop_gradient)
_check_indices_dtype(crows.dtype)
_check_indices_dtype(cols.dtype)
if len(shape) != 2 and len(shape) != 3:
raise ValueError(
"SparseCsrTensor only support 2-D or 3-D matrix. but get shape {}".format(
shape
)
)
rows = shape[len(shape) - 2]
if not crows.place._equals(place):
crows = crows._copy_to(place, False)
if not cols.place._equals(place):
cols = cols._copy_to(place, False)
if not values.place._equals(place):
values = values._copy_to(place, False)
values = _handle_dtype(values, dtype)
values.stop_gradient = stop_gradient
if len(crows.shape) != 1 or len(cols.shape) != 1 or len(values.shape) != 1:
raise ValueError("The 'crows', 'cols' and 'values' must be 1-D.")
if len(cols) != len(values):
raise ValueError("the length of cols must be same as length of values")
if len(shape) == 2:
if crows.shape[0] != rows + 1:
raise ValueError(
"The length({}) of crows must be equal to the rows({})+1 of matrix.".format(
crows.shape[0], rows
)
)
if crows[0] != 0:
raise ValueError("the 0th value of crows must be 0")
if crows[-1] != values.shape[0]:
raise ValueError(
"the last value of crows must be equal the number of non-zero"
)
else:
if crows.shape[0] % (rows + 1) != 0:
raise ValueError(
"The length({}) of crows must be divisible the rows({})+1 of matrix.".format(
crows.shape[0], rows
)
)
# TODO(zkh2016): check whether the value in crows and cols is legal
return core.eager.sparse_csr_tensor(
crows, cols, values, shape, stop_gradient
)