forked from NVIDIA/cutlass
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgenerator.py
6806 lines (5566 loc) · 271 KB
/
generator.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
#################################################################################################
#
# Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#################################################################################################
"""
Utilities for enumerating CUTLASS library kernels
"""
import argparse
import enum
from itertools import product
import logging
import os.path
import shutil
import sys
import copy
from typing import Any, Optional, Sequence, Tuple
_LOGGER = logging.getLogger(__name__)
def logging_prefix(indent_level: int = 0) -> str:
"""String prefix for start of each debug log entry"""
prefix = '*** '
indent = ' '
return f"{prefix}{indent_level * indent}"
def log_debug_line(line: str, indent_level: int = 0) -> None:
"""Log one line of debug output"""
prefix = logging_prefix(indent_level)
_LOGGER.debug(prefix + line)
# Certain usecases of cutlass_library nearly always prefer to run as scripts with
# relative imports, rather than via an installed Python package. An example of this
# is using CUTLASS's CMake system to generate a library of kernels to be profiled.
# To make it easy to use these use cases when an existing installation of cutlass_library
# exists, this global flag can be set to true (via command-line arguments) to ensure
# that package-based installations are not used.
# Create a temporary argument parser to check only for the availability of the
# --disable-cutlass-package-imports argument, which controls whether package-based
# imports are disabled.
def _add_package_disablement_flag(argparser):
argparser.add_argument("--disable-cutlass-package-imports", action='store_true', required=False,
help="Disable use of cutlass_library from Python package")
_parser = argparse.ArgumentParser()
_add_package_disablement_flag(_parser)
_args, _ = _parser.parse_known_args()
# Add `CUTLASS_IGNORE_PACKAGE` to `builtins` so that it is visible for gating future
# imports without requiring importing another module. Ideally, we would just place this
# as a global variable in a module to that could be imported and checked (e.g.,
# utils.CUTLASS_IGNORE_PACKAGE). However, this raises the issue of determining
# where this module should be sourced (from the cutlass_library package or from
# a relative import), which is the problem this variable is being used to solve in the
# first place.
import builtins
builtins.CUTLASS_IGNORE_PACKAGE = _args.disable_cutlass_package_imports
try:
if CUTLASS_IGNORE_PACKAGE:
raise ImportError("Disabling attempt to import cutlass_library")
from cutlass_library.library import *
from cutlass_library.manifest import *
except ImportError:
from library import *
from manifest import *
###################################################################################################
#
def CudaToolkitVersionSatisfies(semantic_ver_string, major, minor, patch = 0):
# by default, use the latest CUDA Toolkit version
cuda_version = [11, 0, 132]
# Update cuda_version based on parsed string
if semantic_ver_string != '':
for i, x in enumerate([int(x) for x in semantic_ver_string.split('.')]):
if i < len(cuda_version):
cuda_version[i] = x
else:
cuda_version.append(x)
return cuda_version >= [major, minor, patch]
###################################################################################################
###################################################################################################
#
def EpilogueAlignment(max_alignment, tile, epilogue_steps = 8):
''' Helper to compute the maximum alignment of the epilogue '''
def product(X, identity = 1):
result = identity
for item in X:
result *= item
return result
elements_per_thread = product(tile.threadblock_shape[:-1]) // product(tile.warp_count) // 32 // epilogue_steps
return min(max_alignment, elements_per_thread)
def DefaultSwizzlingFunctor():
return SwizzlingFunctor.Identity8
# To use StreamK decomposition for basic GEMMs, set `swizzling_functor = SwizzlingFunctor.StreamK`
#
def CreateGemmOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = DefaultSwizzlingFunctor()):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
# If alignment is a tuple or a list, then we have different alignments for A and B
alignment_a = alignment if isinstance(alignment, int) else alignment[0]
alignment_b = alignment if isinstance(alignment, int) else alignment[1]
alignment_c = min(8, alignment_a) if isinstance(alignment, int) else alignment[2]
A = TensorDescription(element_a, layout[0], alignment_a, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment_b, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GemmOperation(GemmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Generates 3.0 API based GemmUniversal API kernels. Alignment constraints are folded in with layouts
def CreateGemmUniversal3xOperator(
manifest, layouts, tile_descriptions, data_types,
schedules = [[KernelScheduleType.ScheduleAuto, EpilogueScheduleType.ScheduleAuto]],
complex_transforms=None,
epilogue_functor=EpilogueFunctor.LinearCombination,
swizzling_functor=SwizzlingFunctor.Identity1,
tile_schedulers=[TileSchedulerType.Persistent]):
if type(data_types) is dict:
data_types = [data_types]
for s in schedules:
assert(len(s) == 2)
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none), ]
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0]]
combinations = product(layouts, tile_descriptions, data_types, complex_transforms, schedules, tile_schedulers)
for layout, tile_description, data_type, complex_transform, schedules, tile_scheduler in combinations:
kernel_schedule, epilogue_schedule = schedules
A = TensorDescription(
data_type["a_type"], layout[0][0], layout[0][1], complex_transform[0])
B = TensorDescription(
data_type["b_type"], layout[1][0], layout[1][1], complex_transform[1])
C = TensorDescription(data_type["c_type"], layout[2][0], layout[2][1])
D = TensorDescription(data_type["d_type"], layout[2][0], layout[2][1])
gemm_op_extra_args = {}
gemm_kind = GemmKind.Universal3x
element_compute = data_type.get("epi_type", data_type["acc_type"])
operation = GemmOperation(
gemm_kind, tile_description.minimum_compute_capability,
tile_description, A, B, C, element_compute, epilogue_functor, swizzling_functor, D,
kernel_schedule, epilogue_schedule, tile_scheduler, **gemm_op_extra_args)
manifest.append(operation)
operations.append(operation)
return operations
#
def CreateSparseGemmOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
gemm_kinds = [GemmKind.Sparse]
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GemmOperation(GemmKind.Sparse, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateGemmPlanarComplexOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
gemm_kinds = [GemmKind.PlanarComplex, GemmKind.PlanarComplexArray]
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for gemm_kind in gemm_kinds:
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
manifest.append(GemmOperation(gemm_kind, \
tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue))
return
#
def CreateGemmGroupedOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GroupedGemmOperation(GemmKind.Grouped, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateRankKOperator(manifest, layouts, fill_modes, tile_descriptions, data_type, \
alignment_constraints, blas_mode, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
element_a, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for fill_mode in fill_modes:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
# SERK supported layouts (RowMajor, ColumnMajor) with no conjugation
complex_transform = ComplexTransform.none
# HERK supported layouts (RowMajor + conj, ColumnMajor)
if blas_mode == BlasMode.hermitian and layout[0] == LayoutType.RowMajor:
complex_transform = ComplexTransform.conj
alignment_c = 1 # Alignment only applies to A in SYRK
A = TensorDescription(element_a, layout[0], alignment, complex_transform)
C = SymmetricTensorDescription(element_c, layout[1], fill_mode, alignment_c)
# Rank-K update
new_operation = RankKOperation(RankKKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
# Rank-2K update
new_operation = Rank2KOperation(RankKKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateTrmmOperator(manifest, layouts, side_modes, fill_modes, diag_types, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for side_mode in side_modes:
for fill_mode in fill_modes:
for diag_type in diag_types:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TriangularTensorDescription(element_a, layout[0], side_mode, fill_mode, diag_type,
alignment, complex_transform)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = TrmmOperation(TrmmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateSymmOperator(manifest, layouts, side_modes, fill_modes, tile_descriptions, data_type, \
alignment_constraints, blas_mode, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for side_mode in side_modes:
for fill_mode in fill_modes:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
# SYMM supported layouts (RowMajor, ColumnMajor) with no conjugation
complex_transform = ComplexTransform.none
alignment_a = 1 # No vectorized access for the triangular matrix
alignment_c = min(8, alignment)
A = SymmetricTensorDescription(element_a, layout[0], fill_mode, alignment_a, complex_transform, side_mode)
# tensor A and B have same data type and layout
B = TensorDescription(element_b, layout[0], alignment)
C = TensorDescription(element_c, layout[1], alignment_c)
# SYMM/HEMM update
new_operation = SymmOperation(SymmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
# SYMM/HEMM update
new_operation = SymmOperation(SymmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
return operations
###########################################################################################################
# ConvolutionOperator support variations
# ____________________________________________________________________
# ConvolutionalOperator | Analytic | Optimized
# ____________________________________________________________________
# | Fprop | (strided) | (strided)
# | Dgrad | (strided, unity*) | (strided, unity)
# | Wgrad | (strided) | (strided)
# ____________________________________________________________________
#
# Note : Operator marked (*) are supported but not generated to keep the instantiated kernel count low
###########################################################################################################
# Convolution for 2D operations
def CreateConv2dOperator(manifest, layout, tile_descriptions, data_type, alignment_constraints, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.Analytic, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
iterator_algorithms = [IteratorAlgorithm.Optimized]
operations = []
for tile in tile_descriptions:
for alignment in alignment_constraints:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operations = [
# None grouped kernel
Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_),
]
# Instance group conv kernel
if tile.math_instruction.opcode_class == OpcodeClass.TensorOp and A.layout == LayoutType.TensorNHWC and \
tile.minimum_compute_capability >= 80:
# SingleGroup kernel
new_operations.append(Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_, group_mode=GroupMode.SingleGroup))
# Analytic iterator supports MultipleGroup mode
if iterator_algorithm == IteratorAlgorithm.Analytic:
new_operations.append(Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_, group_mode=GroupMode.MultipleGroup))
for new_operation in new_operations:
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv2d Dgrad
#
if ConvKind.Dgrad in conv_kinds:
# Unity stride for Analytic and Optimized Dgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Analytic Dgrad
# strided dgrad uses a special threadblock swizzle
# note that SwizzlingFunctor.StridedDgradHorizontal might be
# better for problem sizes with large activation channel count
swizzling_functor_strided_dgrad_ = SwizzlingFunctor.StridedDgradIdentity1
if IteratorAlgorithm.Analytic in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Optimized Dgrad
if IteratorAlgorithm.Optimized in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Optimized, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv2d Wgrad
#
if ConvKind.Wgrad in conv_kinds:
# Strided support for Analytic and Optimized Wgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Wgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 2D operations specialized for few channels
def CreateConv2dFixedChannelsOperator(manifest, layout, tile_descriptions, data_type, channel_counts, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.FixedChannels,]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
channel_counts = [channel_counts[0],]
operations = []
for tile in tile_descriptions:
for channel_count in channel_counts:
alignment_c = EpilogueAlignment(channel_count, tile)
A = TensorDescription(element_a, layout[0], channel_count)
B = TensorDescription(element_b, layout[1], channel_count)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 2D operations specialized for few channels
def CreateConv2dFewChannelsOperator(manifest, layout, tile_descriptions, data_type, channel_counts, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.FewChannels,]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
channel_counts = [channel_counts[0],]
operations = []
for tile in tile_descriptions:
for channel_count in channel_counts:
alignment_c = EpilogueAlignment(channel_count, tile)
A = TensorDescription(element_a, layout[0], channel_count)
B = TensorDescription(element_b, layout[1], channel_count)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 3D operations
def CreateConv3dOperator(manifest, layout, tile_descriptions, data_type, alignment, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], epilogue_functor = EpilogueFunctor.LinearCombination):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
alignment_c = min(8, alignment)
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.Analytic, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size and optimized iterators
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
iterator_algorithms = [IteratorAlgorithm.Optimized]
operations = []
# All tile sizes for Conv3dFprop and Conv3dWgrad
for tile in tile_descriptions:
A = TensorDescription(element_a, layout, alignment)
B = TensorDescription(element_b, layout, alignment)
C = TensorDescription(element_c, layout, alignment_c)
#
# Conv3d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv3dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided)
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv3d Wgrad
#
if ConvKind.Wgrad in conv_kinds:
# Strided support for Analytic and Optimized Wgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv3dOperation(ConvKind.Wgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
# All tile sizes for Conv3dDgrad
for tile in tile_descriptions:
A = TensorDescription(element_a, layout, alignment)
B = TensorDescription(element_b, layout, alignment)
C = TensorDescription(element_c, layout, alignment_c)
#
# Conv3d Dgrad
#
if ConvKind.Dgrad in conv_kinds:
# Unity stride for Optimized Dgrad
new_operation = Conv3dOperation(ConvKind.Dgrad, IteratorAlgorithm.Optimized, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Analytic Dgrad
# Conv3dDgrad has a naive strided support which does not cut down redundant MMAs
new_operation = Conv3dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for Depthwise 2d conv
def CreateDepthwiseConv2dOperator(manifest, layout, tile_descriptions, data_type, alignment_constraints, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# iterator algorithm (FixedStrideDilation, Optimized)
iterator_algorithms = [IteratorAlgorithm.FixedStrideDilation, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
operations = []
for tile in tile_descriptions:
for alignment in alignment_constraints:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
if ConvKind.Fprop in conv_kinds:
# Strided support for Optimized and FixedStridedDilation Depthwise Conv
for iterator_algorithm in iterator_algorithms:
stride_support = StrideSupport.Strided
if iterator_algorithm == IteratorAlgorithm.FixedStrideDilation:
if tile.stride == [-1, -1] or tile.dilation == [-1,-1]:
continue
stride_support = StrideSupport.Fixed
if iterator_algorithm == IteratorAlgorithm.Optimized:
if tile.stride != [-1, -1] or tile.dilation != [-1,-1]:
continue
new_operation = Conv2dOperation(ConvKind.Fprop,
iterator_algorithm,
tile.minimum_compute_capability,
tile,
A, B, C,
element_epilogue,
stride_support,
epilogue_functor,
swizzling_functor_,
group_mode=GroupMode.Depthwise)
manifest.append(new_operation)
operations.append(new_operation)
return operations
class ConvOperation3x:
"""All parameters of a CUTLASS 3 convolution operation.
Unlike CUTLASS 2 convolutions, CUTLASS 3 convolutions do not
distinguish between 2-D and 3-D convolutions by kernel class name.
Instead, for CUTLASS 3 convolutions, the tensor layouts encode
whether the convolution is 2-D or 3-D. Thus, this class deduces
the OperationKind (either Conv2d or Conv3d) from the layouts,
rather than taking it as a constructor parameter.
"""
def __init__(self,
conv_kind: ConvKind,
tile_description: TileDescription,
A: TensorDescription,
B: TensorDescription,
C: TensorDescription,
element_compute: Optional[DataType] = None,
D: Optional[TensorDescription] = None,
kernel_schedule: KernelScheduleType = KernelScheduleType.ScheduleAuto,
epilogue_schedule: EpilogueScheduleType = EpilogueScheduleType.ScheduleAuto,
tile_scheduler: TileSchedulerType = TileSchedulerType.Default,
log_indent_level: int = 1):
log_debug_line(f'ConvOperation3x::init: conv_kind: {conv_kind}', log_indent_level)
log_indent_level = log_indent_level + 1
self.conv_kind = conv_kind
self.tile_description = tile_description
self.A = A
self.B = B
self.C = C
self.element_compute = C.element if element_compute is None else element_compute
self.kernel_schedule = kernel_schedule
self.epilogue_schedule = epilogue_schedule
self.arch = tile_description.minimum_compute_capability
self.tile_scheduler = tile_scheduler
if D == None:
self.D = C
else:
self.D = D
self.is_3x = True
self.group_mode = GroupMode.NoneGroup # CUTLASS 3 convolutions currently aren't grouped
operation_kind = None
for layout in (A.layout, B.layout, C.layout):
assert(isinstance(layout, LayoutType))
new_operation_kind = convolution_tensor_layout_type_to_operation_kind(layout)
if operation_kind is None:
operation_kind = new_operation_kind
else: # CUTLASS 3 convolutions don't permit mixing 2-D and 3-D layouts.
assert(operation_kind == new_operation_kind)
assert(operation_kind is not None)
self.operation_kind = operation_kind
def __str__(self):
return f"ConvOperation3x: operation_kind={self.operation_kind}, conv_kind={self.conv_kind}, tile_description={self.tile_description}"
def is_complex(self):
complex_operators = [
MathOperation.multiply_add_complex,
MathOperation.multiply_add_complex_gaussian,
MathOperation.multiply_add_complex_fast_f32
]
return self.tile_description.math_instruction.math_operation in complex_operators
def is_mixed_input(self):
return self.A.element != self.B.element
def accumulator_type(self):
accum = self.tile_description.math_instruction.element_accumulator
if self.is_complex():
return get_complex_from_real(accum)
return accum
def short_math_name(self):
prefix = ''
if self.tile_description.math_instruction.math_operation == MathOperation.multiply_add_complex_gaussian:
prefix = 'g'
return prefix + ShortDataTypeNames[self.accumulator_type()]
def is_tensor_op(self):
tensor_ops = [
OpcodeClass.TensorOp,
OpcodeClass.WmmaTensorOp
]
return self.tile_description.math_instruction.opcode_class in tensor_ops
def instruction_shape_string(self):
math_operations_map = {
MathOperation.xor_popc: 'xor',
MathOperation.and_popc: 'and'
}
if self.is_tensor_op():
is0, is1, is2 = self.tile_description.math_instruction.instruction_shape
math_op = self.tile_description.math_instruction.math_operation
math_op_string = math_operations_map[math_op] if math_op in math_operations_map.keys() else ''
return f"{is0}x{is1}x{is2}{math_op_string}"
else:
return ''
def intermediate_type_string(self):
'''
Name of the distinct intermediate type used by the tensor operation,
or the empty string if none.
Tensor ops (opcode_clas *TensorOp) may use an intermediate data type
that differs from the element type of A or the accumulator type.
'''
if not self.is_tensor_op():
return ''
elif self.tile_description.math_instruction.element_a == self.A.element:
return ''
elif self.tile_description.math_instruction.element_a == self.tile_description.math_instruction.element_accumulator:
return ''
else:
return DataTypeNames[self.tile_description.math_instruction.element_a]
def core_name(self):
inst_shape = self.instruction_shape_string()
intermediate_type = self.intermediate_type_string()
conv_kind_name = ConvKindNames[self.conv_kind]
return f"{self.short_math_name()}{inst_shape}{intermediate_type}{conv_kind_name}"
def extended_name(self):
core_name = self.core_name()
element_a = DataTypeNames[self.A.element]
element_b = DataTypeNames[self.B.element]
element_acc = DataTypeNames[self.tile_description.math_instruction.element_accumulator]
element_c = DataTypeNames[self.C.element]
element_d = DataTypeNames[self.D.element]
return f"{core_name}_{element_a}_{element_b}_{element_acc}_{element_c}_{element_d}"
def is_complex(self):
complex_operators = [
MathOperation.multiply_add_complex,
MathOperation.multiply_add_complex_gaussian,
MathOperation.multiply_add_complex_fast_f32
]
return self.tile_description.math_instruction.math_operation in complex_operators
def layout_names(self):
'''Layout strings for A and B, respectively'''
if self.is_complex():
return (ShortComplexLayoutNames[(self.A.layout, self.A.complex_transform)],
ShortComplexLayoutNames[(self.B.layout, self.B.complex_transform)])
else:
return (ShortLayoutTypeNames[self.A.layout],
ShortLayoutTypeNames[self.B.layout])
def extended_name(self):
core_name = self.core_name()
element_a = DataTypeNames[self.A.element]
element_b = DataTypeNames[self.B.element]
element_acc = DataTypeNames[self.tile_description.math_instruction.element_accumulator]
element_c = DataTypeNames[self.C.element]
element_d = DataTypeNames[self.D.element]
layout_a, layout_b = self.layout_names()
return f"{core_name}_{element_a}{layout_a}_{element_b}{layout_b}_{element_acc}_{element_c}_{element_d}"
def configuration_name(self):
prefix = 'cutlass3x'
arch = self.arch
opcode_class_name = OpcodeClassNames[self.tile_description.math_instruction.opcode_class]
tbm = self.tile_description.tile_shape[0]
tbn = self.tile_description.tile_shape[1]
tbk = self.tile_description.tile_shape[2]
cm = self.tile_description.cluster_shape[0]
cn = self.tile_description.cluster_shape[1]
ck = self.tile_description.cluster_shape[2]
alignment = max(self.A.alignment, self.B.alignment)
tile_scheduler = TileSchedulerSuffixes[self.tile_scheduler]
kernel_schedule = KernelScheduleSuffixes[self.kernel_schedule]
epilogue_schedule = EpilogueScheduleSuffixes[self.epilogue_schedule]
return f"{prefix}_sm{arch}_{opcode_class_name}_{self.extended_name()}_{tbm}x{tbn}x{tbk}_{cm}x{cn}x{ck}_{self.tile_description.stages}_align{alignment}{tile_scheduler}{kernel_schedule}{epilogue_schedule}"
def procedural_name(self):
return self.configuration_name()
def convolution_tensor_layout_type_to_operation_kind(layout: LayoutType) -> OperationKind:
if layout == LayoutType.TensorNHWC or layout == LayoutType.TensorKCSR:
return OperationKind.Conv2d
elif layout == LayoutType.TensorNDHWC or layout == LayoutType.TensorKCSRT:
return OperationKind.Conv3d
else:
raise RuntimeError(f'LayoutType {layout} does not have a corresponding OperationKind')
def CreateConvOperator3x(manifest: Manifest,
dims_and_alignments: Sequence[Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]]],
tile_descriptions: Sequence[Sequence[TileDescription]],
data_types,
schedule_pairs: Sequence[Tuple[KernelScheduleType, KernelScheduleType]] = \