forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
setup.py
1258 lines (1113 loc) · 44.5 KB
/
setup.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
# Welcome to the PyTorch setup.py.
#
# Environment variables you are probably interested in:
#
# DEBUG
# build with -O0 and -g (debug symbols)
#
# MAX_JOBS
# maximum number of compile jobs we should use to compile your code
#
# NO_CUDA
# disables CUDA build
#
# CFLAGS
# flags to apply to both C and C++ files to be compiled (a quirk of setup.py
# which we have faithfully adhered to in our build system is that CFLAGS
# also applies to C++ files, in contrast to the default behavior of autogoo
# and cmake build systems.)
#
# CC
# the C/C++ compiler to use (NB: the CXX flag has no effect for distutils
# compiles, because distutils always uses CC to compile, even for C++
# files.
#
# Environment variables for feature toggles:
#
# NO_CUDNN
# disables the cuDNN build
#
# NO_TEST
# disables the test build
#
# NO_MIOPEN
# disables the MIOpen build
#
# NO_MKLDNN
# disables the MKLDNN build
#
# NO_NNPACK
# disables NNPACK build
#
# NO_DISTRIBUTED
# disables distributed (c10d, gloo, mpi, etc.) build
#
# NO_SYSTEM_NCCL
# disables use of system-wide nccl (we will use our submoduled
# copy in third_party/nccl)
#
# NO_CAFFE2_OPS
# disable Caffe2 operators build
#
# USE_GLOO_IBVERBS
# toggle features related to distributed support
#
# USE_OPENCV
# enables use of OpenCV for additional operators
#
# USE_FFMPEG
# enables use of ffmpeg for additional operators
#
# USE_LEVELDB
# enables use of LevelDB for storage
#
# USE_LMDB
# enables use of LMDB for storage
#
# BUILD_BINARY
# enables the additional binaries/ build
#
# PYTORCH_BUILD_VERSION
# PYTORCH_BUILD_NUMBER
# specify the version of PyTorch, rather than the hard-coded version
# in this file; used when we're building binaries for distribution
#
# TORCH_CUDA_ARCH_LIST
# specify which CUDA architectures to build for.
# ie `TORCH_CUDA_ARCH_LIST="6.0;7.0"`
# These are not CUDA versions, instead, they specify what
# classes of NVIDIA hardware we should generate PTX for.
#
# ONNX_NAMESPACE
# specify a namespace for ONNX built here rather than the hard-coded
# one in this file; needed to build with other frameworks that share ONNX.
#
# Environment variables we respect (these environment variables are
# conventional and are often understood/set by other software.)
#
# CUDA_HOME (Linux/OS X)
# CUDA_PATH (Windows)
# specify where CUDA is installed; usually /usr/local/cuda or
# /usr/local/cuda-x.y
# CUDAHOSTCXX
# specify a different compiler than the system one to use as the CUDA
# host compiler for nvcc.
#
# CUDNN_LIB_DIR
# CUDNN_INCLUDE_DIR
# CUDNN_LIBRARY
# specify where cuDNN is installed
#
# MIOPEN_LIB_DIR
# MIOPEN_INCLUDE_DIR
# MIOPEN_LIBRARY
# specify where MIOpen is installed
#
# NCCL_ROOT_DIR
# NCCL_LIB_DIR
# NCCL_INCLUDE_DIR
# specify where nccl is installed
#
# MKLDNN_LIB_DIR
# MKLDNN_LIBRARY
# MKLDNN_INCLUDE_DIR
# specify where MKLDNN is installed
#
# NVTOOLSEXT_PATH (Windows only)
# specify where nvtoolsext is installed
#
# LIBRARY_PATH
# LD_LIBRARY_PATH
# we will search for libraries in these paths
from setuptools import setup, Extension, distutils, Command, find_packages
import setuptools.command.build_ext
import setuptools.command.install
import setuptools.command.develop
import setuptools.command.build_py
import distutils.unixccompiler
import distutils.command.build
import distutils.command.clean
import distutils.sysconfig
import filecmp
import platform
import subprocess
import shutil
import multiprocessing
import sys
import os
import json
import glob
import importlib
from tools.setup_helpers.env import check_env_flag, check_negative_env_flag
def hotpatch_var(var, prefix='USE_'):
if check_env_flag('NO_' + var):
os.environ[prefix + var] = '0'
elif check_negative_env_flag('NO_' + var):
os.environ[prefix + var] = '1'
elif check_env_flag('WITH_' + var):
os.environ[prefix + var] = '1'
elif check_negative_env_flag('WITH_' + var):
os.environ[prefix + var] = '0'
# Before we run the setup_helpers, let's look for NO_* and WITH_*
# variables and hotpatch environment with the USE_* equivalent
use_env_vars = ['CUDA', 'CUDNN', 'MIOPEN', 'MKLDNN', 'NNPACK', 'DISTRIBUTED',
'OPENCV', 'FFMPEG', 'SYSTEM_NCCL', 'GLOO_IBVERBS']
list(map(hotpatch_var, use_env_vars))
# Also hotpatch a few with BUILD_* equivalent
build_env_vars = ['BINARY', 'TEST', 'CAFFE2_OPS']
[hotpatch_var(v, 'BUILD_') for v in build_env_vars]
from tools.setup_helpers.cuda import USE_CUDA, CUDA_HOME, CUDA_VERSION
from tools.setup_helpers.build import (BUILD_BINARY, BUILD_TEST,
BUILD_CAFFE2_OPS, USE_LEVELDB,
USE_LMDB, USE_OPENCV, USE_FFMPEG)
from tools.setup_helpers.rocm import USE_ROCM, ROCM_HOME, ROCM_VERSION
from tools.setup_helpers.cudnn import (USE_CUDNN, CUDNN_LIBRARY,
CUDNN_LIB_DIR, CUDNN_INCLUDE_DIR)
from tools.setup_helpers.miopen import (USE_MIOPEN, MIOPEN_LIBRARY,
MIOPEN_LIB_DIR, MIOPEN_INCLUDE_DIR)
from tools.setup_helpers.nccl import USE_NCCL, USE_SYSTEM_NCCL, NCCL_LIB_DIR, \
NCCL_INCLUDE_DIR, NCCL_ROOT_DIR, NCCL_SYSTEM_LIB
from tools.setup_helpers.mkldnn import (USE_MKLDNN, MKLDNN_LIBRARY,
MKLDNN_LIB_DIR, MKLDNN_INCLUDE_DIR)
from tools.setup_helpers.nnpack import USE_NNPACK
from tools.setup_helpers.nvtoolext import NVTOOLEXT_HOME
from tools.setup_helpers.generate_code import generate_code
from tools.setup_helpers.ninja_builder import NinjaBuilder, ninja_build_ext
from tools.setup_helpers.dist_check import USE_DISTRIBUTED, \
USE_GLOO_IBVERBS
################################################################################
# Parameters parsed from environment
################################################################################
DEBUG = check_env_flag('DEBUG')
IS_WINDOWS = (platform.system() == 'Windows')
IS_DARWIN = (platform.system() == 'Darwin')
IS_LINUX = (platform.system() == 'Linux')
BUILD_PYTORCH = check_env_flag('BUILD_PYTORCH')
USE_CUDA_STATIC_LINK = check_env_flag('USE_CUDA_STATIC_LINK')
RERUN_CMAKE = True
NUM_JOBS = multiprocessing.cpu_count()
max_jobs = os.getenv("MAX_JOBS")
if max_jobs is not None:
NUM_JOBS = min(NUM_JOBS, int(max_jobs))
ONNX_NAMESPACE = os.getenv("ONNX_NAMESPACE")
if not ONNX_NAMESPACE:
ONNX_NAMESPACE = "onnx_torch"
# Ninja
try:
import ninja
USE_NINJA = True
ninja_global = NinjaBuilder('global')
except ImportError:
USE_NINJA = False
ninja_global = None
# Constant known variables used throughout this file
cwd = os.path.dirname(os.path.abspath(__file__))
lib_path = os.path.join(cwd, "torch", "lib")
third_party_path = os.path.join(cwd, "third_party")
tmp_install_path = lib_path + "/tmp_install"
caffe2_build_dir = os.path.join(cwd, "build")
# lib/pythonx.x/site-packages
rel_site_packages = distutils.sysconfig.get_python_lib(prefix='')
# full absolute path to the dir above
full_site_packages = distutils.sysconfig.get_python_lib()
# CMAKE: full path to python library
cmake_python_library = "{}/{}".format(
distutils.sysconfig.get_config_var("LIBDIR"),
distutils.sysconfig.get_config_var("INSTSONAME"))
cmake_python_include_dir = distutils.sysconfig.get_python_inc()
class PytorchCommand(setuptools.Command):
"""
Base Pytorch command to avoid implementing initialize/finalize_options in
every subclass
"""
user_options = []
def initialize_options(self):
pass
def finalize_options(self):
pass
################################################################################
# Patches and workarounds
################################################################################
# Monkey-patch setuptools to compile in parallel
if not USE_NINJA:
def parallelCCompile(self, sources, output_dir=None, macros=None,
include_dirs=None, debug=0, extra_preargs=None,
extra_postargs=None, depends=None):
# those lines are copied from distutils.ccompiler.CCompiler directly
macros, objects, extra_postargs, pp_opts, build = self._setup_compile(
output_dir, macros, include_dirs, sources, depends, extra_postargs)
cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
# compile using a thread pool
import multiprocessing.pool
def _single_compile(obj):
src, ext = build[obj]
self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
multiprocessing.pool.ThreadPool(NUM_JOBS).map(_single_compile, objects)
return objects
distutils.ccompiler.CCompiler.compile = parallelCCompile
# Patch for linking with ccache
original_link = distutils.unixccompiler.UnixCCompiler.link
def patched_link(self, *args, **kwargs):
_cxx = self.compiler_cxx
self.compiler_cxx = None
result = original_link(self, *args, **kwargs)
self.compiler_cxx = _cxx
return result
distutils.unixccompiler.UnixCCompiler.link = patched_link
# Workaround setuptools -Wstrict-prototypes warnings
# I lifted this code from https://stackoverflow.com/a/29634231/23845
cfg_vars = distutils.sysconfig.get_config_vars()
for key, value in cfg_vars.items():
if type(value) == str:
cfg_vars[key] = value.replace("-Wstrict-prototypes", "")
################################################################################
# Version, create_version_file, and package_name
################################################################################
package_name = os.getenv('TORCH_PACKAGE_NAME', 'torch')
version = '1.0.0a0'
if os.getenv('PYTORCH_BUILD_VERSION'):
assert os.getenv('PYTORCH_BUILD_NUMBER') is not None
build_number = int(os.getenv('PYTORCH_BUILD_NUMBER'))
version = os.getenv('PYTORCH_BUILD_VERSION')
if build_number > 1:
version += '.post' + str(build_number)
else:
try:
sha = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=cwd).decode('ascii').strip()
version += '+' + sha[:7]
except Exception:
pass
print("Building wheel {}-{}".format(package_name, version))
class create_version_file(PytorchCommand):
def run(self):
global version, cwd
print('-- Building version ' + version)
version_path = os.path.join(cwd, 'torch', 'version.py')
with open(version_path, 'w') as f:
f.write("__version__ = '{}'\n".format(version))
# NB: This is not 100% accurate, because you could have built the
# library code with DEBUG, but csrc without DEBUG (in which case
# this would claim to be a release build when it's not.)
f.write("debug = {}\n".format(repr(DEBUG)))
f.write("cuda = {}\n".format(repr(CUDA_VERSION)))
################################################################################
# Building dependent libraries
################################################################################
# All libraries that torch could depend on
dep_libs = [
'nccl', 'caffe2',
'libshm', 'libshm_windows', 'gloo', 'THD', 'c10d',
]
missing_pydep = '''
Missing build dependency: Unable to `import {importname}`.
Please install it via `conda install {module}` or `pip install {module}`
'''.strip()
def check_pydep(importname, module):
try:
importlib.import_module(importname)
except ImportError:
raise RuntimeError(missing_pydep.format(importname=importname, module=module))
# Calls build_pytorch_libs.sh/bat with the correct env variables
def build_libs(libs):
for lib in libs:
assert lib in dep_libs, 'invalid lib: {}'.format(lib)
if IS_WINDOWS:
build_libs_cmd = ['tools\\build_pytorch_libs.bat']
else:
build_libs_cmd = ['bash', os.path.join('..', 'tools', 'build_pytorch_libs.sh')]
my_env = os.environ.copy()
my_env["PYTORCH_PYTHON"] = sys.executable
my_env["PYTORCH_PYTHON_LIBRARY"] = cmake_python_library
my_env["PYTORCH_PYTHON_INCLUDE_DIR"] = cmake_python_include_dir
my_env["PYTORCH_BUILD_VERSION"] = version
my_env["CMAKE_PREFIX_PATH"] = full_site_packages
my_env["NUM_JOBS"] = str(NUM_JOBS)
my_env["ONNX_NAMESPACE"] = ONNX_NAMESPACE
if not IS_WINDOWS:
if USE_NINJA:
my_env["CMAKE_GENERATOR"] = '-GNinja'
my_env["CMAKE_INSTALL"] = 'ninja install'
else:
my_env['CMAKE_GENERATOR'] = ''
my_env['CMAKE_INSTALL'] = 'make install'
if USE_SYSTEM_NCCL:
my_env["NCCL_ROOT_DIR"] = NCCL_ROOT_DIR
if USE_CUDA:
my_env["CUDA_BIN_PATH"] = CUDA_HOME
build_libs_cmd += ['--use-cuda']
if IS_WINDOWS:
my_env["NVTOOLEXT_HOME"] = NVTOOLEXT_HOME
if USE_CUDA_STATIC_LINK:
build_libs_cmd += ['--cuda-static-link']
if USE_ROCM:
build_libs_cmd += ['--use-rocm']
if USE_NNPACK:
build_libs_cmd += ['--use-nnpack']
if USE_CUDNN:
my_env["CUDNN_LIB_DIR"] = CUDNN_LIB_DIR
my_env["CUDNN_LIBRARY"] = CUDNN_LIBRARY
my_env["CUDNN_INCLUDE_DIR"] = CUDNN_INCLUDE_DIR
if USE_MIOPEN:
my_env["MIOPEN_LIB_DIR"] = MIOPEN_LIB_DIR
my_env["MIOPEN_LIBRARY"] = MIOPEN_LIBRARY
my_env["MIOPEN_INCLUDE_DIR"] = MIOPEN_INCLUDE_DIR
if USE_MKLDNN:
my_env["MKLDNN_LIB_DIR"] = MKLDNN_LIB_DIR
my_env["MKLDNN_LIBRARY"] = MKLDNN_LIBRARY
my_env["MKLDNN_INCLUDE_DIR"] = MKLDNN_INCLUDE_DIR
build_libs_cmd += ['--use-mkldnn']
if USE_GLOO_IBVERBS:
build_libs_cmd += ['--use-gloo-ibverbs']
if not RERUN_CMAKE:
build_libs_cmd += ['--dont-rerun-cmake']
my_env["BUILD_TORCH"] = "ON"
my_env["BUILD_PYTHON"] = "ON"
my_env["BUILD_BINARY"] = "ON" if BUILD_BINARY else "OFF"
my_env["BUILD_TEST"] = "ON" if BUILD_TEST else "OFF"
my_env["BUILD_CAFFE2_OPS"] = "ON" if BUILD_CAFFE2_OPS else "OFF"
my_env["INSTALL_TEST"] = "ON" if BUILD_TEST else "OFF"
my_env["USE_LEVELDB"] = "ON" if USE_LEVELDB else "OFF"
my_env["USE_LMDB"] = "ON" if USE_LMDB else "OFF"
my_env["USE_OPENCV"] = "ON" if USE_OPENCV else "OFF"
my_env["USE_FFMPEG"] = "ON" if USE_FFMPEG else "OFF"
try:
os.mkdir('build')
except OSError:
pass
kwargs = {'cwd': 'build'} if not IS_WINDOWS else {}
if subprocess.call(build_libs_cmd + libs, env=my_env, **kwargs) != 0:
print("Failed to run '{}'".format(' '.join(build_libs_cmd + libs)))
sys.exit(1)
# Copy Caffe2's Python proto files (generated during the build with the
# protobuf python compiler) from the build folder to the root folder
# cp root/build/caffe2/proto/proto.py root/caffe2/proto/proto.py
def copy_protos():
print('setup.py::copy_protos()')
for src in glob.glob(
os.path.join(caffe2_build_dir, 'caffe2', 'proto', '*.py')):
dst = os.path.join(
cwd, os.path.relpath(src, caffe2_build_dir))
shutil.copyfile(src, dst)
# Build all dependent libraries
class build_deps(PytorchCommand):
def run(self):
print('setup.py::build_deps::run()')
# Check if you remembered to check out submodules
def check_file(f):
if not os.path.exists(f):
print("Could not find {}".format(f))
print("Did you run 'git submodule update --init'?")
sys.exit(1)
check_file(os.path.join(third_party_path, "gloo", "CMakeLists.txt"))
check_file(os.path.join(third_party_path, "pybind11", "CMakeLists.txt"))
check_file(os.path.join(third_party_path, 'cpuinfo', 'CMakeLists.txt'))
check_file(os.path.join(third_party_path, 'catch', 'CMakeLists.txt'))
check_file(os.path.join(third_party_path, 'onnx', 'CMakeLists.txt'))
check_pydep('yaml', 'pyyaml')
check_pydep('typing', 'typing')
libs = []
if USE_NCCL and not USE_SYSTEM_NCCL:
libs += ['nccl']
libs += ['caffe2']
if IS_WINDOWS:
libs += ['libshm_windows']
else:
libs += ['libshm']
if USE_DISTRIBUTED:
if IS_LINUX:
libs += ['gloo']
libs += ['c10d']
libs += ['THD']
build_libs(libs)
# Use copies instead of symbolic files.
# Windows has very poor support for them.
sym_files = ['tools/shared/cwrap_common.py', 'tools/shared/_utils_internal.py']
orig_files = ['aten/src/ATen/common_with_cwrap.py', 'torch/_utils_internal.py']
for sym_file, orig_file in zip(sym_files, orig_files):
same = False
if os.path.exists(sym_file):
if filecmp.cmp(sym_file, orig_file):
same = True
else:
os.remove(sym_file)
if not same:
shutil.copyfile(orig_file, sym_file)
self.copy_tree('torch/lib/tmp_install/share', 'torch/share')
self.copy_tree('third_party/pybind11/include/pybind11/',
'torch/lib/include/pybind11')
build_dep_cmds = {}
rebuild_dep_cmds = {}
for lib in dep_libs:
# wrap in function to capture lib
class build_dep(build_deps):
description = 'Build {} external library'.format(lib)
def run(self):
build_libs([self.lib])
build_dep.lib = lib
build_dep_cmds['build_' + lib.lower()] = build_dep
class rebuild_dep(build_deps):
description = 'Rebuild {} external library'.format(lib)
def run(self):
global RERUN_CMAKE
RERUN_CMAKE = False
build_libs([self.lib])
rebuild_dep.lib = lib
rebuild_dep_cmds['rebuild_' + lib.lower()] = rebuild_dep
class build_module(PytorchCommand):
def run(self):
print('setup.py::build_module::run()')
self.run_command('build_py')
self.run_command('build_ext')
class build_py(setuptools.command.build_py.build_py):
def run(self):
print('setup.py::build_py::run()')
self.run_command('create_version_file')
setuptools.command.build_py.build_py.run(self)
class develop(setuptools.command.develop.develop):
def run(self):
print('setup.py::develop::run()')
self.run_command('create_version_file')
setuptools.command.develop.develop.run(self)
self.create_compile_commands()
copy_protos()
def create_compile_commands(self):
def load(filename):
with open(filename) as f:
return json.load(f)
ninja_files = glob.glob('build/*compile_commands.json')
cmake_files = glob.glob('torch/lib/build/*/compile_commands.json')
all_commands = [entry
for f in ninja_files + cmake_files
for entry in load(f)]
# cquery does not like c++ compiles that start with gcc.
# It forgets to include the c++ header directories.
# We can work around this by replacing the gcc calls that python
# setup.py generates with g++ calls instead
for command in all_commands:
if command['command'].startswith("gcc "):
command['command'] = "g++ " + command['command'][4:]
new_contents = json.dumps(all_commands, indent=2)
contents = ''
if os.path.exists('compile_commands.json'):
with open('compile_commands.json', 'r') as f:
contents = f.read()
if contents != new_contents:
with open('compile_commands.json', 'w') as f:
f.write(new_contents)
if not USE_NINJA:
print("WARNING: 'develop' is not building C++ code incrementally")
print("because ninja is not installed. Run this to enable it:")
print(" > pip install ninja")
def monkey_patch_THD_link_flags():
'''
THD's dynamic link deps are not determined until after build_deps is run
So, we need to monkey-patch them in later
'''
# read tmp_install_path/THD_deps.txt for THD's dynamic linkage deps
with open(tmp_install_path + '/THD_deps.txt', 'r') as f:
thd_deps_ = f.read()
thd_deps = []
# remove empty lines
for l in thd_deps_.split(';'):
if l != '':
thd_deps.append(l)
C.extra_link_args += thd_deps
def monkey_patch_C10D_inc_flags():
'''
C10D's include deps are not determined until after build c10d is run, so
we need to monkey-patch it.
'''
mpi_include_path_file = tmp_install_path + "/include/c10d/mpi_include_path"
if os.path.exists(mpi_include_path_file):
with open(mpi_include_path_file, 'r') as f:
mpi_include_paths = f.readlines()
mpi_include_paths = [p.strip() for p in mpi_include_paths]
C.include_dirs += mpi_include_paths
print("-- For c10d, will include MPI paths: {}".format(mpi_include_paths))
build_ext_parent = ninja_build_ext if USE_NINJA \
else setuptools.command.build_ext.build_ext
class build_ext(build_ext_parent):
def run(self):
# Print build options
if USE_NUMPY:
print('-- Building with NumPy bindings')
else:
print('-- NumPy not found')
if USE_CUDNN:
print('-- Detected cuDNN at ' + CUDNN_LIBRARY + ', ' + CUDNN_INCLUDE_DIR)
else:
print('-- Not using cuDNN')
if USE_MIOPEN:
print('-- Detected MIOpen at ' + MIOPEN_LIBRARY + ', ' + MIOPEN_INCLUDE_DIR)
else:
print('-- Not using MIOpen')
if USE_CUDA:
print('-- Detected CUDA at ' + CUDA_HOME)
else:
print('-- Not using CUDA')
if USE_MKLDNN:
print('-- Detected MKLDNN at ' + MKLDNN_LIBRARY + ', ' + MKLDNN_INCLUDE_DIR)
else:
print('-- Not using MKLDNN')
if USE_NCCL and USE_SYSTEM_NCCL:
print('-- Using system provided NCCL library at ' +
NCCL_SYSTEM_LIB + ', ' + NCCL_INCLUDE_DIR)
elif USE_NCCL:
print('-- Building NCCL library')
else:
print('-- Not using NCCL')
if USE_DISTRIBUTED:
print('-- Building with THD distributed package ')
monkey_patch_THD_link_flags()
if IS_LINUX:
print('-- Building with c10d distributed package ')
monkey_patch_C10D_inc_flags()
else:
print('-- Building without c10d distributed package')
else:
print('-- Building without distributed package')
generate_code(ninja_global)
if USE_NINJA:
# before we start the normal build make sure all generated code
# gets built
ninja_global.run()
# It's an old-style class in Python 2.7...
setuptools.command.build_ext.build_ext.run(self)
# Copy the essential export library to compile C++ extensions.
if IS_WINDOWS:
build_temp = self.build_temp
ext_filename = self.get_ext_filename('_C')
lib_filename = '.'.join(ext_filename.split('.')[:-1]) + '.lib'
export_lib = os.path.join(
build_temp, 'torch', 'csrc', lib_filename).replace('\\', '/')
build_lib = self.build_lib
target_lib = os.path.join(
build_lib, 'torch', 'lib', '_C.lib').replace('\\', '/')
self.copy_file(export_lib, target_lib)
def build_extensions(self):
# The caffe2 extensions are created in
# tmp_install/lib/pythonM.m/site-packages/caffe2/python/
# and need to be copied to build/lib.linux.... , which will be a
# platform dependent build folder created by the "build" command of
# setuptools. Only the contents of this folder are installed in the
# "install" command by default.
# We only make this copy for Caffe2's pybind extensions
caffe2_pybind_exts = [
'caffe2.python.caffe2_pybind11_state',
'caffe2.python.caffe2_pybind11_state_gpu',
'caffe2.python.caffe2_pybind11_state_hip',
]
i = 0
while i < len(self.extensions):
ext = self.extensions[i]
if ext.name not in caffe2_pybind_exts:
i += 1
continue
fullname = self.get_ext_fullname(ext.name)
filename = self.get_ext_filename(fullname)
print("\nCopying extension {}".format(ext.name))
src = os.path.join(tmp_install_path, rel_site_packages, filename)
if not os.path.exists(src):
print("{} does not exist".format(src))
del self.extensions[i]
else:
dst = os.path.join(os.path.realpath(self.build_lib), filename)
print("Copying {} from {} to {}".format(ext.name, src, dst))
dst_dir = os.path.dirname(dst)
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
self.copy_file(src, dst)
i += 1
distutils.command.build_ext.build_ext.build_extensions(self)
def get_outputs(self):
outputs = distutils.command.build_ext.build_ext.get_outputs(self)
outputs.append(os.path.join(self.build_lib, "caffe2"))
print("setup.py::get_outputs returning {}".format(outputs))
return outputs
class build(distutils.command.build.build):
sub_commands = [
('build_deps', lambda self: True),
] + distutils.command.build.build.sub_commands
class rebuild(distutils.command.build.build):
sub_commands = [
('build_deps', lambda self: True),
] + distutils.command.build.build.sub_commands
def run(self):
global RERUN_CMAKE
RERUN_CMAKE = False
distutils.command.build.build.run(self)
class install(setuptools.command.install.install):
def run(self):
print('setup.py::run()')
if not self.skip_build:
self.run_command('build_deps')
setuptools.command.install.install.run(self)
class rebuild_libtorch(distutils.command.build.build):
def run(self):
if subprocess.call(['ninja', 'install'], cwd='build') != 0:
print("Failed to run `ninja install` for the `rebuild_libtorch` command")
sys.exit(1)
class clean(distutils.command.clean.clean):
def run(self):
import glob
import re
with open('.gitignore', 'r') as f:
ignores = f.read()
pat = re.compile(r'^#( BEGIN NOT-CLEAN-FILES )?')
for wildcard in filter(None, ignores.split('\n')):
match = pat.match(wildcard)
if match:
if match.group(1):
# Marker is found and stop reading .gitignore.
break
# Ignore lines which begin with '#'.
else:
for filename in glob.glob(wildcard):
try:
os.remove(filename)
except OSError:
shutil.rmtree(filename, ignore_errors=True)
# It's an old-style class in Python 2.7...
distutils.command.clean.clean.run(self)
################################################################################
# Configure compile flags
################################################################################
include_dirs = []
library_dirs = []
if IS_WINDOWS:
# /NODEFAULTLIB makes sure we only link to DLL runtime
# and matches the flags set for protobuf and ONNX
extra_link_args = ['/NODEFAULTLIB:LIBCMT.LIB']
# /MD links against DLL runtime
# and matches the flags set for protobuf and ONNX
# /Z7 turns on symbolic debugging information in .obj files
# /EHa is about native C++ catch support for asynchronous
# structured exception handling (SEH)
# /DNOMINMAX removes builtin min/max functions
# /wdXXXX disables warning no. XXXX
extra_compile_args = ['/MD', '/Z7',
'/EHa', '/DNOMINMAX',
'/wd4267', '/wd4251', '/wd4522', '/wd4522', '/wd4838',
'/wd4305', '/wd4244', '/wd4190', '/wd4101', '/wd4996',
'/wd4275']
if sys.version_info[0] == 2:
if not check_env_flag('FORCE_PY27_BUILD'):
print('The support for PyTorch with Python 2.7 on Windows is very experimental.')
print('Please set the flag `FORCE_PY27_BUILD` to 1 to continue build.')
sys.exit(1)
# /bigobj increases number of sections in .obj file, which is needed to link
# against libaries in Python 2.7 under Windows
extra_compile_args.append('/bigobj')
else:
extra_link_args = []
extra_compile_args = [
'-std=c++11',
'-Wall',
'-Wextra',
'-Wno-strict-overflow',
'-Wno-unused-parameter',
'-Wno-missing-field-initializers',
'-Wno-write-strings',
'-Wno-zero-length-array',
'-Wno-unknown-pragmas',
# This is required for Python 2 declarations that are deprecated in 3.
'-Wno-deprecated-declarations',
# Python 2.6 requires -fno-strict-aliasing, see
# http://legacy.python.org/dev/peps/pep-3123/
# We also depend on it in our code (even Python 3).
'-fno-strict-aliasing',
# Clang has an unfixed bug leading to spurious missing
# braces warnings, see
# https://bugs.llvm.org/show_bug.cgi?id=21629
'-Wno-missing-braces',
# gcc7 seems to report spurious warnings with this enabled
"-Wno-stringop-overflow",
]
if check_env_flag('WERROR'):
extra_compile_args.append('-Werror')
include_dirs += [
cwd,
tmp_install_path + "/include",
tmp_install_path + "/include/TH",
tmp_install_path + "/include/THNN",
tmp_install_path + "/include/ATen",
third_party_path + "/pybind11/include",
os.path.join(cwd, "torch", "csrc"),
"build/third_party",
]
library_dirs.append(lib_path)
# we specify exact lib names to avoid conflict with lua-torch installs
CAFFE2_LIBS = [os.path.join(lib_path, 'libcaffe2.so')]
if USE_CUDA:
CAFFE2_LIBS.extend(['-Wl,--no-as-needed', os.path.join(lib_path, 'libcaffe2_gpu.so'), '-Wl,--as-needed'])
if USE_ROCM:
CAFFE2_LIBS.extend(['-Wl,--no-as-needed', os.path.join(lib_path, 'libcaffe2_hip.so'), '-Wl,--as-needed'])
THD_LIB = os.path.join(lib_path, 'libTHD.a')
NCCL_LIB = os.path.join(lib_path, 'libnccl.so.2')
C10D_LIB = os.path.join(lib_path, 'libc10d.a')
# static library only
if DEBUG:
PROTOBUF_STATIC_LIB = os.path.join(lib_path, 'libprotobufd.a')
else:
PROTOBUF_STATIC_LIB = os.path.join(lib_path, 'libprotobuf.a')
if IS_DARWIN:
CAFFE2_LIBS = [os.path.join(lib_path, 'libcaffe2.dylib')]
if USE_CUDA:
CAFFE2_LIBS.append(os.path.join(lib_path, 'libcaffe2_gpu.dylib'))
if USE_ROCM:
CAFFE2_LIBS.append(os.path.join(lib_path, 'libcaffe2_hip.dylib'))
NCCL_LIB = os.path.join(lib_path, 'libnccl.2.dylib')
if IS_WINDOWS:
CAFFE2_LIBS = [os.path.join(lib_path, 'caffe2.lib')]
if USE_CUDA:
CAFFE2_LIBS.append(os.path.join(lib_path, 'caffe2_gpu.lib'))
if USE_ROCM:
CAFFE2_LIBS.append(os.path.join(lib_path, 'caffe2_hip.lib'))
if DEBUG:
PROTOBUF_STATIC_LIB = os.path.join(lib_path, 'libprotobufd.lib')
else:
PROTOBUF_STATIC_LIB = os.path.join(lib_path, 'libprotobuf.lib')
main_compile_args = ['-D_THP_CORE', '-DONNX_NAMESPACE=' + ONNX_NAMESPACE]
main_libraries = ['shm']
main_link_args = CAFFE2_LIBS + [PROTOBUF_STATIC_LIB]
if IS_WINDOWS:
main_link_args.append(os.path.join(lib_path, 'torch.lib'))
elif IS_DARWIN:
main_link_args.append(os.path.join(lib_path, 'libtorch.dylib'))
else:
main_link_args.append(os.path.join(lib_path, 'libtorch.so'))
main_sources = [
"torch/csrc/DataLoader.cpp",
"torch/csrc/Device.cpp",
"torch/csrc/Dtype.cpp",
"torch/csrc/DynamicTypes.cpp",
"torch/csrc/Exceptions.cpp",
"torch/csrc/Generator.cpp",
"torch/csrc/Layout.cpp",
"torch/csrc/Module.cpp",
"torch/csrc/PtrWrapper.cpp",
"torch/csrc/Size.cpp",
"torch/csrc/Storage.cpp",
"torch/csrc/autograd/functions/init.cpp",
"torch/csrc/autograd/generated/python_functions.cpp",
"torch/csrc/autograd/generated/python_nn_functions.cpp",
"torch/csrc/autograd/generated/python_torch_functions.cpp",
"torch/csrc/autograd/generated/python_variable_methods.cpp",
"torch/csrc/autograd/init.cpp",
"torch/csrc/autograd/python_anomaly_mode.cpp",
"torch/csrc/autograd/python_cpp_function.cpp",
"torch/csrc/autograd/python_engine.cpp",
"torch/csrc/autograd/python_function.cpp",
"torch/csrc/autograd/python_hook.cpp",
"torch/csrc/autograd/python_legacy_variable.cpp",
"torch/csrc/autograd/python_variable.cpp",
"torch/csrc/autograd/python_variable_indexing.cpp",
"torch/csrc/byte_order.cpp",
"torch/csrc/jit/batched/BatchTensor.cpp",
"torch/csrc/jit/init.cpp",
"torch/csrc/jit/passes/onnx.cpp",
"torch/csrc/jit/passes/onnx/fixup_onnx_loop.cpp",
"torch/csrc/jit/passes/onnx/prepare_division_for_onnx.cpp",
"torch/csrc/jit/passes/onnx/peephole.cpp",
"torch/csrc/jit/passes/to_batch.cpp",
"torch/csrc/jit/python_arg_flatten.cpp",
"torch/csrc/jit/python_interpreter.cpp",
"torch/csrc/jit/python_ir.cpp",
"torch/csrc/jit/python_tracer.cpp",
"torch/csrc/jit/script/init.cpp",
"torch/csrc/jit/script/lexer.cpp",
"torch/csrc/jit/script/module.cpp",
"torch/csrc/jit/script/python_tree_views.cpp",
"torch/csrc/nn/THNN.cpp",
"torch/csrc/onnx/init.cpp",
"torch/csrc/serialization.cpp",
"torch/csrc/tensor/python_tensor.cpp",
"torch/csrc/utils.cpp",
"torch/csrc/utils/cuda_lazy_init.cpp",
"torch/csrc/utils/invalid_arguments.cpp",
"torch/csrc/utils/object_ptr.cpp",
"torch/csrc/utils/python_arg_parser.cpp",
"torch/csrc/utils/tensor_apply.cpp",
"torch/csrc/utils/tensor_conversion_dispatch.cpp",
"torch/csrc/utils/tensor_dtypes.cpp",
"torch/csrc/utils/tensor_flatten.cpp",
"torch/csrc/utils/tensor_layouts.cpp",
"torch/csrc/utils/tensor_list.cpp",
"torch/csrc/utils/tensor_new.cpp",
"torch/csrc/utils/tensor_numpy.cpp",
"torch/csrc/utils/tensor_types.cpp",
"torch/csrc/utils/tuple_parser.cpp",
]
try:
import numpy as np
include_dirs.append(np.get_include())
extra_compile_args.append('-DUSE_NUMPY')
USE_NUMPY = True
except ImportError:
USE_NUMPY = False
if USE_DISTRIBUTED:
extra_compile_args += ['-DUSE_DISTRIBUTED']
main_sources += [
"torch/csrc/distributed/Module.cpp",
]
include_dirs += [tmp_install_path + "/include/THD"]
main_link_args += [THD_LIB]
if IS_LINUX:
extra_compile_args.append('-DUSE_C10D')
main_sources.append('torch/csrc/distributed/c10d/init.cpp')
if USE_CUDA:
main_sources.append('torch/csrc/distributed/c10d/ddp.cpp')
main_link_args.append(C10D_LIB)
if USE_CUDA:
nvtoolext_lib_name = None
if IS_WINDOWS:
cuda_lib_path = CUDA_HOME + '/lib/x64/'
nvtoolext_lib_path = NVTOOLEXT_HOME + '/lib/x64/'
nvtoolext_include_path = os.path.join(NVTOOLEXT_HOME, 'include')
library_dirs.append(nvtoolext_lib_path)
include_dirs.append(nvtoolext_include_path)
nvtoolext_lib_name = 'nvToolsExt64_1'
# MSVC doesn't support runtime symbol resolving, `nvrtc` and `cuda` should be linked
main_libraries += ['nvrtc', 'cuda']
else:
cuda_lib_dirs = ['lib64', 'lib']