-
Notifications
You must be signed in to change notification settings - Fork 2.5k
/
basic.py
4298 lines (3309 loc) · 119 KB
/
basic.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
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
"""
Classes for handling sparse matrices.
To read about different sparse formats, see
http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps
"""
from __future__ import absolute_import, print_function, division
# TODO
# Automatic methods for determining best sparse format?
import sys
import numpy as np
from numpy.lib.stride_tricks import as_strided
from six import integer_types
from six.moves import xrange
import scipy.sparse
import theano
from theano import gof, tensor, scalar, config
from theano.gradient import DisconnectedType
from theano.sparse.utils import hash_from_sparse
from theano.gradient import grad_not_implemented, grad_undefined
from theano.sparse.type import SparseType, _is_sparse
sparse_formats = ['csc', 'csr']
"""
Types of sparse matrices to use for testing.
"""
_mtypes = [scipy.sparse.csc_matrix, scipy.sparse.csr_matrix]
# _mtypes = [sparse.csc_matrix, sparse.csr_matrix, sparse.dok_matrix,
# sparse.lil_matrix, sparse.coo_matrix]
# * new class ``dia_matrix`` : the sparse DIAgonal format
# * new class ``bsr_matrix`` : the Block CSR format
_mtype_to_str = {scipy.sparse.csc_matrix: "csc",
scipy.sparse.csr_matrix: "csr"}
def _is_sparse_variable(x):
"""
Returns
-------
boolean
True iff x is a L{SparseVariable} (and not a L{tensor.TensorType},
for instance).
"""
if not isinstance(x, gof.Variable):
raise NotImplementedError("this function should only be called on "
"*variables* (of type sparse.SparseType "
"or tensor.TensorType, for instance), not ",
x)
return isinstance(x.type, SparseType)
def _is_dense_variable(x):
"""
Returns
-------
boolean
True if x is a L{tensor.TensorType} (and not a L{SparseVariable},
for instance).
"""
if not isinstance(x, gof.Variable):
raise NotImplementedError("this function should only be called on "
"*variables* (of type sparse.SparseType or "
"tensor.TensorType, for instance), not ", x)
return isinstance(x.type, tensor.TensorType)
def _is_dense(x):
"""
Returns
-------
boolean
True unless x is a L{scipy.sparse.spmatrix} (and not a
L{numpy.ndarray}).
"""
if not isinstance(x, (scipy.sparse.spmatrix, np.ndarray)):
raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x)
return isinstance(x, np.ndarray)
# Wrapper type
def as_sparse_variable(x, name=None):
"""
Wrapper around SparseVariable constructor to construct
a Variable with a sparse matrix with the same dtype and
format.
Parameters
----------
x
A sparse matrix.
Returns
-------
object
SparseVariable version of `x`.
"""
# TODO
# Verify that sp is sufficiently sparse, and raise a
# warning if it is not
if isinstance(x, gof.Apply):
if len(x.outputs) != 1:
raise ValueError("It is ambiguous which output of a "
"multi-output Op has to be fetched.", x)
else:
x = x.outputs[0]
if isinstance(x, gof.Variable):
if not isinstance(x.type, SparseType):
raise TypeError("Variable type field must be a SparseType.", x,
x.type)
return x
try:
return constant(x, name=name)
except TypeError:
raise TypeError("Cannot convert %s to SparseType" % x, type(x))
as_sparse = as_sparse_variable
def as_sparse_or_tensor_variable(x, name=None):
"""
Same as `as_sparse_variable` but if we can't make a
sparse variable, we try to make a tensor variable.
Parameters
----------
x
A sparse matrix.
Returns
-------
SparseVariable or TensorVariable version of `x`
"""
try:
return as_sparse_variable(x, name)
except (ValueError, TypeError):
return theano.tensor.as_tensor_variable(x, name)
def constant(x, name=None):
if not isinstance(x, scipy.sparse.spmatrix):
raise TypeError("sparse.constant must be called on a "
"scipy.sparse.spmatrix")
try:
return SparseConstant(SparseType(format=x.format,
dtype=x.dtype), x.copy(), name=name)
except TypeError:
raise TypeError("Could not convert %s to SparseType" % x, type(x))
def sp_ones_like(x):
"""
Construct a sparse matrix of ones with the same sparsity pattern.
Parameters
----------
x
Sparse matrix to take the sparsity pattern.
Returns
-------
A sparse matrix
The same as `x` with data changed for ones.
"""
# TODO: don't restrict to CSM formats
data, indices, indptr, shape = csm_properties(x)
return CSM(format=x.format)(tensor.ones_like(data), indices, indptr, shape)
def sp_zeros_like(x):
"""
Construct a sparse matrix of zeros.
Parameters
----------
x
Sparse matrix to take the shape.
Returns
-------
A sparse matrix
The same as `x` with zero entries for all element.
"""
# TODO: don't restrict to CSM formats
_, _, indptr, shape = csm_properties(x)
return CSM(format=x.format)(data=np.array([], dtype=x.type.dtype),
indices=np.array([], dtype='int32'),
indptr=tensor.zeros_like(indptr),
shape=shape)
class _sparse_py_operators:
T = property(lambda self: transpose(self),
doc="Return aliased transpose of self (read-only)")
def astype(self, dtype):
return cast(self, dtype)
def __neg__(self):
return neg(self)
def __add__(left, right):
return add(left, right)
def __radd__(right, left):
return add(left, right)
def __sub__(left, right):
return sub(left, right)
def __rsub__(right, left):
return sub(left, right)
def __mul__(left, right):
return mul(left, right)
def __rmul__(left, right):
return mul(left, right)
# comparison operators
def __lt__(self, other):
return lt(self, other)
def __le__(self, other):
return le(self, other)
def __gt__(self, other):
return gt(self, other)
def __ge__(self, other):
return ge(self, other)
# extra pseudo-operator symbols
def __dot__(left, right):
return structured_dot(left, right)
def __rdot__(right, left):
return structured_dot(left, right)
# N.B. THIS IS COMMENTED OUT ON PURPOSE!!!
# Discussion with Fred & James (at least, and maybe others before)
# we decided that casting from a sparse to dense should be explicit
# because it's usually something you just want to be pretty careful
# about, and not to do by accident.
# def _as_TensorVariable(self):
# return dense_from_sparse(self)
def toarray(self):
return dense_from_sparse(self)
shape = property(lambda self: tensor.shape(dense_from_sparse(self)))
# don't worry!
# the plan is that the ShapeFeature in tensor.opt will do shape propagation
# and remove the dense_from_sparse from the graph. This will *NOT*
# actually expand your sparse matrix just to get the shape.
ndim = property(lambda self: self.type.ndim)
dtype = property(lambda self: self.type.dtype)
# Note that the `size` attribute of sparse matrices behaves differently
# from dense matrices: it is the number of elements stored in the matrix
# rather than the total number of elements that may be stored. Note also
# that stored zeros *do* count in the size.
size = property(lambda self: csm_data(self).size)
def zeros_like(model):
return sp_zeros_like(model)
def __getitem__(self, args):
if not isinstance(args, tuple):
args = args,
if len(args) == 2:
scalar_arg_1 = (np.isscalar(args[0]) or
getattr(args[0], 'type', None) == tensor.iscalar)
scalar_arg_2 = (np.isscalar(args[1]) or
getattr(args[1], 'type', None) == tensor.iscalar)
if scalar_arg_1 and scalar_arg_2:
ret = get_item_scalar(self, args)
elif isinstance(args[0], list):
ret = get_item_2lists(self, args[0], args[1])
else:
ret = get_item_2d(self, args)
elif isinstance(args[0], list):
ret = get_item_list(self, args[0])
else:
ret = get_item_2d(self, args)
return ret
class SparseVariable(_sparse_py_operators, gof.Variable):
dtype = property(lambda self: self.type.dtype)
format = property(lambda self: self.type.format)
def __str__(self):
return '%s{%s,%s}' % (
self.__class__.__name__,
self.format,
self.dtype)
def __repr__(self):
return str(self)
class SparseConstantSignature(tuple):
def __eq__(self, other):
(a, b), (x, y) = self, other
return (a == x and
(b.dtype == y.dtype) and
(type(b) == type(y)) and
(b.shape == y.shape) and
(abs(b - y).sum() < 1e-6 * b.nnz))
def __ne__(self, other):
return not self == other
def __hash__(self):
(a, b) = self
return hash(type(self)) ^ hash(a) ^ hash(type(b))
def theano_hash(self):
(_, d) = self
return hash_from_sparse(d)
class SparseConstant(gof.Constant, _sparse_py_operators):
dtype = property(lambda self: self.type.dtype)
format = property(lambda self: self.type.format)
def signature(self):
assert self.data is not None
return SparseConstantSignature((self.type, self.data))
def __str__(self):
return '%s{%s,%s,shape=%s,nnz=%s}' % (
self.__class__.__name__,
self.format,
self.dtype,
self.data.shape,
self.data.nnz)
def __repr__(self):
return str(self)
SparseType.Variable = SparseVariable
SparseType.Constant = SparseConstant
# for more dtypes, call SparseType(format, dtype)
def matrix(format, name=None, dtype=None):
if dtype is None:
dtype = config.floatX
type = SparseType(format=format, dtype=dtype)
return type(name)
def csc_matrix(name=None, dtype=None):
return matrix('csc', name, dtype)
def csr_matrix(name=None, dtype=None):
return matrix('csr', name, dtype)
def bsr_matrix(name=None, dtype=None):
return matrix('bsr', name, dtype)
# for more dtypes, call SparseType(format, dtype)
csc_dmatrix = SparseType(format='csc', dtype='float64')
csr_dmatrix = SparseType(format='csr', dtype='float64')
bsr_dmatrix = SparseType(format='bsr', dtype='float64')
csc_fmatrix = SparseType(format='csc', dtype='float32')
csr_fmatrix = SparseType(format='csr', dtype='float32')
bsr_fmatrix = SparseType(format='bsr', dtype='float32')
all_dtypes = SparseType.dtype_set
complex_dtypes = [t for t in all_dtypes if t[:7] == 'complex']
float_dtypes = [t for t in all_dtypes if t[:5] == 'float']
int_dtypes = [t for t in all_dtypes if t[:3] == 'int']
uint_dtypes = [t for t in all_dtypes if t[:4] == 'uint']
integer_dtypes = int_dtypes + uint_dtypes
continuous_dtypes = complex_dtypes + float_dtypes
discrete_dtypes = int_dtypes + uint_dtypes
# CONSTRUCTION
class CSMProperties(gof.Op):
# See doc in instance of this Op or function after this class definition.
# NOTE
# We won't implement infer_shape for this op now. This will
# ask that we implement an GetNNZ op, and this op will keep
# the dependence on the input of this op. So this won't help
# to remove computations in the graph. To remove computation,
# we will need to make an infer_sparse_pattern feature to
# remove computations. Doing this is trickier then the
# infer_shape feature. For example, how do we handle the case
# when some op create some 0 values? So there is dependence
# on the values themselves. We could write an infer_shape for
# the last output that is the shape, but I dough this will
# get used.
# we don't return a view of the shape, we create a new ndarray from the
# shape tuple.
__props__ = ()
view_map = {0: [0], 1: [0], 2: [0]}
"""
Indexing to speficied what part of the data parameter
should be use to construct the sparse matrix.
"""
def __init__(self, kmap=None):
if kmap is not None:
raise Exception("Do not use kmap, it is removed")
def make_node(self, csm):
csm = as_sparse_variable(csm)
assert csm.format in ["csr", "csc"]
data = tensor.TensorType(dtype=csm.type.dtype,
broadcastable=(False,))()
return gof.Apply(self, [csm],
[data, tensor.ivector(),
tensor.ivector(), tensor.ivector()])
def perform(self, node, inputs, out):
(csm,) = inputs
out[0][0] = csm.data
if str(csm.data.dtype) == 'int32':
out[0][0] = theano._asarray(out[0][0], dtype='int32')
# backport
out[1][0] = theano._asarray(csm.indices, dtype='int32')
out[2][0] = theano._asarray(csm.indptr, dtype='int32')
out[3][0] = theano._asarray(csm.shape, dtype='int32')
def grad(self, inputs, g):
# g[1:] is all integers, so their Jacobian in this op
# is 0. We thus don't need to worry about what their values
# are.
# if g[0] is disconnected, then this op doesn't contribute
# any gradient anywhere. but we know that at least one of
# g[1:] is connected, or this grad method wouldn't have been
# called, so we should report zeros
(csm,) = inputs
if isinstance(g[0].type, DisconnectedType):
return [csm.zeros_like()]
data, indices, indptr, shape = csm_properties(csm)
return [CSM(csm.format)(g[0], indices, indptr, shape)]
# don't make this a function or it breaks some optimizations below
csm_properties = CSMProperties()
"""
Extract all of .data, .indices, .indptr and .shape field.
For specific field, `csm_data`, `csm_indices`, `csm_indptr`
and `csm_shape` are provided.
Parameters
----------
csm
Sparse matrix in CSR or CSC format.
Returns
(data, indices, indptr, shape), the properties of `csm`.
Notes
-----
The grad implemented is regular, i.e. not structured.
`infer_shape` method is not available for this op.
"""
def csm_data(csm):
"""
Return the data field of the sparse variable.
"""
return csm_properties(csm)[0]
def csm_indices(csm):
"""
Return the indices field of the sparse variable.
"""
return csm_properties(csm)[1]
def csm_indptr(csm):
"""
Return the indptr field of the sparse variable.
"""
return csm_properties(csm)[2]
def csm_shape(csm):
"""
Return the shape field of the sparse variable.
"""
return csm_properties(csm)[3]
class CSM(gof.Op):
# See doc in instance of this Op or function after this class definition.
"""
Indexing to speficied what part of the data parameter
should be used to construct the sparse matrix.
"""
__props__ = ('format',)
"""
Pre-computed hash value, defined by __init__.
"""
def __init__(self, format, kmap=None):
if format not in ('csr', 'csc'):
raise ValueError("format must be one of: 'csr', 'csc'", format)
self.format = format
if kmap is not None:
raise Exception("Do not use kmap, it is removed")
# should view the other inputs too, but viewing multiple
# inputs is not currently supported by the destroyhandler
self.view_map = {0: [0]}
def make_node(self, data, indices, indptr, shape):
data = tensor.as_tensor_variable(data)
if not isinstance(indices, gof.Variable):
indices_ = np.asarray(indices)
indices_32 = theano._asarray(indices, dtype='int32')
assert (indices_ == indices_32).all()
indices = indices_32
if not isinstance(indptr, gof.Variable):
indptr_ = np.asarray(indptr)
indptr_32 = theano._asarray(indptr, dtype='int32')
assert (indptr_ == indptr_32).all()
indptr = indptr_32
if not isinstance(shape, gof.Variable):
shape_ = np.asarray(shape)
shape_32 = theano._asarray(shape, dtype='int32')
assert (shape_ == shape_32).all()
shape = shape_32
indices = tensor.as_tensor_variable(indices)
indptr = tensor.as_tensor_variable(indptr)
shape = tensor.as_tensor_variable(shape)
if data.type.ndim != 1:
raise TypeError('data argument must be a vector', data.type,
data.type.ndim)
if indices.type.ndim != 1 or indices.type.dtype not in discrete_dtypes:
raise TypeError('indices must be vector of integers', indices,
indices.type)
if indptr.type.ndim != 1 or indptr.type.dtype not in discrete_dtypes:
raise TypeError('indices must be vector of integers', indptr,
indptr.type)
if shape.type.ndim != 1 or shape.type.dtype not in discrete_dtypes:
raise TypeError('n_rows must be integer type', shape, shape.type)
return gof.Apply(self,
[data, indices, indptr, shape],
[SparseType(dtype=data.type.dtype,
format=self.format)()])
def perform(self, node, inputs, outputs):
# for efficiency, if remap does nothing, then do not apply it
(data, indices, indptr, shape) = inputs
(out,) = outputs
if len(shape) != 2:
raise ValueError('Shape should be an array of length 2')
if data.shape != indices.shape:
errmsg = ('Data (shape ' + repr(data.shape) +
' must have the same number of elements ' +
'as indices (shape' + repr(indices.shape) +
')')
raise ValueError(errmsg)
if self.format == 'csc':
out[0] = scipy.sparse.csc_matrix((data, indices.copy(),
indptr.copy()),
np.asarray(shape), copy=False)
else:
assert self.format == 'csr'
out[0] = scipy.sparse.csr_matrix((data, indices.copy(),
indptr.copy()), shape.copy(),
copy=False)
def connection_pattern(self, node):
return [[True], [False], [False], [False]]
def grad(self, inputs, gout):
(x_data, x_indices, x_indptr, x_shape) = inputs
(g_out,) = gout
g_data, g_indices, g_indptr, g_shape = csm_properties(g_out)
# unpack the data vector and wrap it as a 1d TensorType
g_data = csm_grad()(x_data, x_indices, x_indptr, x_shape,
g_data, g_indices, g_indptr, g_shape)
return [g_data, DisconnectedType()(), DisconnectedType()(), DisconnectedType()()]
def infer_shape(self, node, shapes):
# node.inputs[3] is of length as we only support sparse matrix.
return [(node.inputs[3][0], node.inputs[3][1])]
CSC = CSM('csc')
"""
Construct a CSC matrix from the internal representation.
Parameters
----------
data
One dimensional tensor representing the data of the sparse matrix to
construct.
indices
One dimensional tensor of integers representing the indices of the sparse
matrix to construct.
indptr
One dimensional tensor of integers representing the indice pointer for
the sparse matrix to construct.
shape
One dimensional tensor of integers representing the shape of the sparse
matrix to construct.
Returns
-------
sparse matrix
A sparse matrix having the properties specified by the inputs.
Notes
-----
The grad method returns a dense vector, so it provides a regular grad.
"""
CSR = CSM('csr')
"""
Construct a CSR matrix from the internal representation.
Parameters
----------
data
One dimensional tensor representing the data of the sparse matrix to
construct.
indices
One dimensional tensor of integers representing the indices of the sparse
matrix to construct.
indptr
One dimensional tensor of integers representing the indice pointer for
the sparse matrix to construct.
shape
One dimensional tensor of integers representing the shape of the sparse
matrix to construct.
Returns
-------
sparse matrix
A sparse matrix having the properties specified by the inputs.
Notes
-----
The grad method returns a dense vector, so it provides a regular grad.
"""
class CSMGrad(gof.op.Op):
# Note
# This Op computes the gradient of the CSM Op. CSM creates a matrix from
# data, indices, and indptr vectors; it's gradient is the gradient of
# the data vector only. There are two complexities to calculate this
# gradient:
# 1. The gradient may be sparser than the input matrix defined by (data,
# indices, indptr). In this case, the data vector of the gradient will have
# less elements than the data vector of the input because sparse formats
# remove 0s. Since we are only returning the gradient of the data vector,
# the relevant 0s need to be added back.
# 2. The elements in the sparse dimension are not guaranteed to be sorted.
# Therefore, the input data vector may have a different order than the
# gradient data vector.
__props__ = ()
def __init__(self, kmap=None):
if kmap is not None:
raise Exception("Do not use kmap, it is removed")
# This class always allocate a new output.
# I keep this here to help GD understand what this kmap think is.
# if self.kmap is None:
# self.view_map = {0: [1]}
def make_node(self, x_data, x_indices, x_indptr, x_shape,
g_data, g_indices, g_indptr, g_shape):
gout_data = g_data.type()
return gof.Apply(self, [x_data, x_indices, x_indptr, x_shape,
g_data, g_indices, g_indptr, g_shape], [gout_data])
def perform(self, node, inputs, outputs):
(x_data, x_indices, x_indptr, x_shape,
g_data, g_indices, g_indptr, g_shape) = inputs
(g_out,) = outputs
if len(x_indptr) - 1 == x_shape[0]:
sp_dim = x_shape[1]
else:
sp_dim = x_shape[0]
g_row = np.zeros(sp_dim, dtype=g_data.dtype)
gout_data = np.zeros(x_data.shape, dtype=node.outputs[0].dtype)
for i in range(len(x_indptr) - 1):
for j_ptr in range(g_indptr[i], g_indptr[i + 1]):
g_row[g_indices[j_ptr]] += g_data[j_ptr]
for j_ptr in range(x_indptr[i], x_indptr[i + 1]):
gout_data[j_ptr] = g_row[x_indices[j_ptr]]
for j_ptr in range(g_indptr[i], g_indptr[i + 1]):
g_row[g_indices[j_ptr]] = 0
g_out[0] = gout_data
def infer_shape(self, node, shapes):
return [shapes[1]]
csm_grad = CSMGrad
class Cast(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ("out_type",)
def __init__(self, out_type):
self.out_type = out_type
def make_node(self, x):
x = as_sparse_variable(x)
assert x.format in ["csr", "csc"]
return gof.Apply(
self, [x],
[SparseType(dtype=self.out_type, format=x.format)()])
def perform(self, node, inputs, outputs):
(x,) = inputs
(out,) = outputs
assert _is_sparse(x)
out[0] = x.astype(self.out_type)
def grad(self, inputs, outputs_gradients):
gz = outputs_gradients[0]
if gz.dtype in complex_dtypes:
raise NotImplementedError("grad not implemented for complex types")
if inputs[0].dtype in complex_dtypes:
raise NotImplementedError("grad not implemented for complex types")
if gz.dtype in discrete_dtypes:
if inputs[0].dtype in discrete_dtypes:
return [inputs[0].zeros_like(dtype=theano.config.floatX)]
else:
return [inputs[0].zeros_like()]
else:
if inputs[0].dtype in discrete_dtypes:
return [gz]
else:
return [Cast(inputs[0].dtype)(gz)]
def infer_shape(self, node, ins_shapes):
return ins_shapes
def __str__(self):
return "%s(%s)" % (self.__class__.__name__, self.out_type)
bcast = Cast('int8')
wcast = Cast('int16')
icast = Cast('int32')
lcast = Cast('int64')
fcast = Cast('float32')
dcast = Cast('float64')
ccast = Cast('complex64')
zcast = Cast('complex128')
def cast(variable, dtype):
"""
Cast sparse variable to the desired dtype.
Parameters
----------
variable
Sparse matrix.
dtype
The dtype wanted.
Returns
-------
Same as `x` but having `dtype` as dtype.
Notes
-----
The grad implemented is regular, i.e. not structured.
"""
return Cast(dtype)(variable)
#
# Conversion
#
class DenseFromSparse(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = () # We don't put sparse_grad in the props.
def __init__(self, structured=True):
self.sparse_grad = structured
def __str__(self):
return "%s{structured_grad=%s}" % (
self.__class__.__name__,
self.sparse_grad)
def make_node(self, x):
x = as_sparse_variable(x)
return gof.Apply(self,
[x],
[tensor.TensorType(dtype=x.type.dtype,
broadcastable=(False, False))()])
def perform(self, node, inputs, outputs):
(x,) = inputs
(out,) = outputs
if _is_dense(x):
print((
"WARNING: You just called DenseFromSparse on a dense matrix."
), file=sys.stderr)
out[0] = x
else:
out[0] = x.toarray()
assert _is_dense(out[0])
def grad(self, inputs, gout):
(x,) = inputs
(gz,) = gout
if self.sparse_grad:
left = sp_ones_like(x)
right = gz
# Do upcasting if necessary to avoid an unimplemented case
# of mul
if right.dtype == 'float64' and left.dtype == 'float32':
left = left.astype('float64')
if right.dtype == 'float32' and left.dtype == 'float64':
right = right.astype('float64')
return [left * right]
else:
return [SparseFromDense(x.type.format)(gz)]
def infer_shape(self, node, shapes):
return [shapes[0]]
dense_from_sparse = DenseFromSparse()
"""
Convert a sparse matrix to a dense one.
Parameters
----------
x
A sparse matrix.
Returns
-------
theano.tensor.matrix
A dense matrix, the same as `x`.
Notes
-----
The grad implementation can be controlled through the constructor via the
`structured` parameter. `True` will provide a structured grad while `False`
will provide a regular grad. By default, the grad is structured.
"""
class SparseFromDense(gof.op.Op):
__props__ = ()
def __init__(self, format):
self.format = format
def __str__(self):
return "%s{%s}" % (
self.__class__.__name__,
self.format)
def make_node(self, x):
x = tensor.as_tensor_variable(x)
if x.ndim > 2:
raise TypeError(
"Theano does not have sparse tensor types with more "
"than 2 dimensions, but %s.ndim = %i" % (x, x.ndim))
elif x.ndim == 1:
x = x.dimshuffle('x', 0)
elif x.ndim == 0:
x = x.dimshuffle('x', 'x')
else:
assert x.ndim == 2
return gof.Apply(self,
[x],
[SparseType(dtype=x.type.dtype,
format=self.format)()])
def perform(self, node, inputs, outputs):
(x,) = inputs
(out,) = outputs
out[0] = SparseType.format_cls[self.format](x)
def grad(self, inputs, gout):
(x,) = inputs
(gz,) = gout
gx = dense_from_sparse(gz)
gx = tensor.patternbroadcast(gx, x.broadcastable)
return gx,
def infer_shape(self, node, shapes):
return [shapes[0]]
csr_from_dense = SparseFromDense('csr')
"""
Convert a dense matrix to a sparse csr matrix.
Parameters
----------
x
A dense matrix.
Returns
-------
sparse matrix
The same as `x` in a sparse csr matrix format.
"""
csc_from_dense = SparseFromDense('csc')
"""
Convert a dense matrix to a sparse csc matrix.
Parameters
----------
x
A dense matrix.
Returns
-------
sparse matrix
The same as `x` in a sparse csc matrix format.
"""
# Indexing
class GetItemList(gof.op.Op):
__props__ = ()
def infer_shape(self, node, shapes):
return [(shapes[1][0], shapes[0][1])]