/
test_pytorch_onnx_no_runtime.py
1135 lines (939 loc) · 39.2 KB
/
test_pytorch_onnx_no_runtime.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
# Owner(s): ["module: onnx"]
"""Tests for onnx export that don't run the exported model."""
import contextlib
import io
import itertools
import unittest
import unittest.mock
import warnings
from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
import onnx
import onnx.numpy_helper
import pytorch_test_common
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.onnx import OperatorExportTypes, symbolic_helper, utils
from torch.onnx._globals import GLOBALS
from torch.onnx._internal import registration
from torch.testing._internal import common_quantization, common_utils, jit_utils
def export_to_onnx(
model: Union[torch.nn.Module, torch.jit.ScriptFunction],
input: Union[torch.Tensor, Tuple[torch.Tensor]],
custom_ops: Optional[
Iterable[
Union[contextlib.AbstractContextManager, contextlib.ContextDecorator],
]
] = None,
mocks: Optional[Iterable] = None,
operator_export_type: torch.onnx.OperatorExportTypes = torch.onnx.OperatorExportTypes.ONNX,
opset_version: int = GLOBALS.export_onnx_opset_version,
**torch_onnx_export_kwargs,
) -> onnx.ModelProto:
"""Exports `model(input)` to ONNX and returns it.
Custom operators and/or unittest patches can be used help reproducing specific behaviors.
Args:
model: model to export
input: model input with same format as `torch.onnx.export(..,args,...)`
custom_ops: list of custom operators to use during export
mocks: list of mocks to use during export
operator_export_type: export type as described by `torch.onnx.export(...operator_export_type,...)`
opset_version: ONNX opset version as described by `torch.onnx.export(...opset_version,...)`
torch_onnx_export_kwargs: extra torch.onnx.export kwargs arguments
Returns:
A valid ONNX model (`onnx.ModelProto`)
"""
custom_ops = custom_ops or []
mocks = mocks or []
with contextlib.ExitStack() as stack:
for ctx in itertools.chain(custom_ops, mocks):
stack.enter_context(ctx)
f = io.BytesIO()
torch.onnx.export(
model,
input,
f,
operator_export_type=operator_export_type,
opset_version=opset_version,
**torch_onnx_export_kwargs,
)
# Validate ONNX graph before returning it
onnx_model = onnx.load_from_string(f.getvalue())
onnx.checker.check_model(onnx_model)
return onnx_model
class TestONNXExport(pytorch_test_common.ExportTestCase):
def test_fuse_addmm(self):
class AddmmModel(torch.nn.Module):
def forward(self, x):
return torch.mm(x, x) + x
x = torch.ones(3, 3)
f = io.BytesIO()
torch.onnx.export(AddmmModel(), x, f, verbose=False)
def test_onnx_transpose_incomplete_tensor_type(self):
# Smoke test to get us into the state where we are attempting to export
# a transpose op, where the input is a TensorType without size information.
# This would previously not work, since we would
# take the size of the input and use the length of its sizes as the
# number of dimensions in the permutation.
class Foo(torch.jit.ScriptModule):
@torch.jit.script_method
def forward(self, x):
return x.contiguous().transpose(0, 1).sum()
class TraceMe(torch.nn.Module):
def __init__(self):
super(TraceMe, self).__init__()
self.foo = Foo()
def forward(self, x):
return self.foo(x)
tm = TraceMe()
tm = torch.jit.trace(tm, torch.rand(3, 4))
f = io.BytesIO()
torch.onnx.export(tm, (torch.rand(3, 4),), f)
def test_export_tensoroption_to(self):
def foo(x):
return x[0].clone().detach().cpu() + x
traced = torch.jit.trace(foo, (torch.rand([2])))
torch.onnx.export_to_pretty_string(traced, (torch.rand([2]),))
def test_onnx_export_script_module(self):
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
@torch.jit.script_method
def forward(self, x):
y = x - x
return x + x
mte = ModuleToExport()
torch.onnx.export_to_pretty_string(mte, (torch.zeros(1, 2, 3),), verbose=False)
@common_utils.suppress_warnings
def test_onnx_export_func_with_warnings(self):
@torch.jit.script
def func_with_warning(inp):
return torch.nn.functional.sigmoid(inp) # triggers a deprecation warning
class WarningTest(torch.nn.Module):
def __init__(self):
super(WarningTest, self).__init__()
def forward(self, x):
return func_with_warning(x)
# no exception
torch.onnx.export_to_pretty_string(
WarningTest(), torch.randn(42), verbose=False
)
def test_onnx_export_script_python_fail(self):
class PythonModule(torch.jit.ScriptModule):
def __init__(self):
super(PythonModule, self).__init__()
@torch.jit.ignore
def forward(self, x):
return torch.neg(x)
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
self.mod = PythonModule()
@torch.jit.script_method
def forward(self, x):
y = self.mod(x)
return y + y
mte = ModuleToExport()
f = io.BytesIO()
with self.assertRaisesRegex(RuntimeError, "Couldn't export Python"):
torch.onnx.export(mte, (torch.zeros(1, 2, 3),), f, verbose=False)
def test_onnx_export_script_inline_trace(self):
class ModuleToInline(torch.nn.Module):
def __init__(self):
super(ModuleToInline, self).__init__()
def forward(self, x):
return torch.neg(x)
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
self.mod = torch.jit.trace(ModuleToInline(), torch.zeros(1, 2, 3))
@torch.jit.script_method
def forward(self, x):
y = self.mod(x)
return y + y
mte = ModuleToExport()
torch.onnx.export_to_pretty_string(mte, (torch.zeros(1, 2, 3),), verbose=False)
def test_onnx_export_script_inline_script(self):
class ModuleToInline(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToInline, self).__init__()
@torch.jit.script_method
def forward(self, x):
return torch.neg(x)
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
self.mod = ModuleToInline()
@torch.jit.script_method
def forward(self, x):
y = self.mod(x)
return y + y
mte = ModuleToExport()
torch.onnx.export_to_pretty_string(mte, (torch.zeros(1, 2, 3),), verbose=False)
def test_onnx_export_script_module_loop(self):
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
@torch.jit.script_method
def forward(self, x):
# test if we support end to end onnx export on loop and
# nested loops with and without loop index
for _ in range(5):
for i in range(3):
x = x + i
return x
mte = ModuleToExport()
torch.onnx.export_to_pretty_string(mte, (torch.zeros(1, 2, 3),), verbose=False)
@common_utils.suppress_warnings
def test_onnx_export_script_truediv(self):
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
@torch.jit.script_method
def forward(self, x):
z = x.size(0) / 2
return x + z
mte = ModuleToExport()
torch.onnx.export_to_pretty_string(
mte, (torch.zeros(1, 2, 3, dtype=torch.float),), verbose=False
)
def test_onnx_export_script_non_alpha_add_sub(self):
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
@torch.jit.script_method
def forward(self, x):
bs = x.size(0) + 1
return bs - 1
mte = ModuleToExport()
torch.onnx.export_to_pretty_string(mte, (torch.rand(3, 4),), verbose=False)
def test_onnx_export_script_module_if(self):
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
@torch.jit.script_method
def forward(self, x):
if bool(torch.sum(x) > 0):
x = torch.neg(x)
return x
mte = ModuleToExport()
torch.onnx.export_to_pretty_string(mte, (torch.zeros(1, 2, 3),), verbose=False)
def test_onnx_export_script_inline_params(self):
class ModuleToInline(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToInline, self).__init__()
self.m = torch.nn.Parameter(torch.ones(3, 3))
self.unused = torch.nn.Parameter(torch.ones(1, 2, 3))
@torch.jit.script_method
def forward(self, x):
return torch.mm(x, self.m)
class ModuleToExport(torch.jit.ScriptModule):
def __init__(self):
super(ModuleToExport, self).__init__()
self.mod = ModuleToInline()
self.param = torch.nn.Parameter(torch.ones(3, 4))
@torch.jit.script_method
def forward(self, x):
y = self.mod(x)
return torch.mm(y, self.param)
mte = ModuleToExport()
result = mte(torch.zeros(2, 3))
reference = torch.mm(
torch.mm(torch.zeros(2, 3), torch.ones(3, 3)), torch.ones(3, 4)
)
self.assertEqual(result, reference)
torch.onnx.export_to_pretty_string(mte, (torch.ones(2, 3),), verbose=False)
def test_onnx_export_speculate(self):
class Foo(torch.jit.ScriptModule):
def __init__(self, m):
super(Foo, self).__init__()
self.m = m
@torch.jit.script_method
def forward(self, x):
x += x
# because we are testing if we emit `if` statement correctly
# we cannot use `True` as the condition. Constant prop
# would remove the `if` statements.
c = torch.sum(x) > 4
if bool(c):
if bool(c):
y = self.m(x)
else:
y = self.m(x)
else:
y = self.m(x)
return y
linear = torch.jit.trace(
torch.nn.Linear(10, 20).float(), torch.zeros(1, 10, dtype=torch.float)
)
@torch.jit.script
def transpose(x):
return x.t()
f1 = Foo(transpose)
f2 = Foo(linear)
torch.onnx.export_to_pretty_string(f1, (torch.ones(1, 10, dtype=torch.float),))
torch.onnx.export_to_pretty_string(f2, (torch.ones(1, 10, dtype=torch.float),))
def test_onnx_export_shape_reshape(self):
class Foo(torch.nn.Module):
def forward(self, x):
import torch.onnx.operators
x = x.repeat(5, 1, 1)
shape = torch.onnx.operators.shape_as_tensor(x)
reshaped = torch.onnx.operators.reshape_from_tensor_shape(x, shape)
return reshaped
foo = torch.jit.trace(Foo(), torch.zeros(1, 2, 3))
torch.onnx.export_to_pretty_string(foo, (torch.zeros(1, 2, 3)))
def test_listconstruct_erasure(self):
class FooMod(torch.nn.Module):
def forward(self, x):
mask = x < 0.0
return x[mask]
torch.onnx.export_to_pretty_string(
FooMod(),
(torch.rand(3, 4),),
add_node_names=False,
do_constant_folding=False,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
)
def test_export_dynamic_slice(self):
class DynamicSliceExportMod(torch.jit.ScriptModule):
@torch.jit.script_method
def forward(self, x):
retval = x[0]
for i in range(x.size(1)):
retval += torch.sum(x[0:i], dim=0)
return retval
mod = DynamicSliceExportMod()
input = torch.rand(3, 4, 5)
torch.onnx.export_to_pretty_string(
DynamicSliceExportMod(), (input,), opset_version=10
)
def test_export_dict(self):
class DictModule(torch.nn.Module):
def forward(self, x_in: torch.Tensor) -> Dict[str, torch.Tensor]:
return {"test_key_out": x_in}
x_in = torch.tensor(1)
mod = DictModule()
mod.train(False)
torch.onnx.export_to_pretty_string(mod, (x_in,))
with self.assertRaisesRegex(RuntimeError, r"DictConstruct.+is not supported."):
torch.onnx.export_to_pretty_string(torch.jit.script(mod), (x_in,))
def test_source_range_propagation(self):
class ExpandingModule(torch.nn.Module):
def __init__(self):
super().__init__()
# Will be expanded during ONNX export
self.ln = torch.nn.LayerNorm([1])
def forward(self, input):
return self.ln(input)
mod = ExpandingModule()
graph, _, _ = utils._model_to_graph(
mod,
(torch.zeros(1),),
operator_export_type=torch.onnx.OperatorExportTypes.ONNX,
)
# Ensure that every node in the graph has a valid source range
for node in graph.nodes():
self.assertTrue(node.sourceRange())
@common_utils.skipIfCaffe2
def test_clip_aten_fallback_due_exception(self):
def bad_clamp(g, self, min, max):
return symbolic_helper._onnx_unsupported("Bad boy!")
class MyClip(torch.nn.Module):
def forward(self, x):
return torch.clamp(x, min=-0.5, max=0.5)
onnx_model = export_to_onnx(
MyClip(),
torch.randn(3, 4, requires_grad=True),
custom_ops=[
common_utils.custom_op(
"aten::clamp", bad_clamp, GLOBALS.export_onnx_opset_version
)
],
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
)
self.assertAtenOp(onnx_model, "clamp", "Tensor")
@common_utils.skipIfCaffe2
def test_clip_aten_fallback_explicit_request(self):
class MyClip(torch.nn.Module):
def forward(self, x):
return torch.clamp(x, min=-0.5, max=0.5)
def break_is_registered_op_api(name):
fake_missing_symbolics = {"aten::clamp"}
if name in fake_missing_symbolics:
return None
return registration.registry.get_function_group(name)
# Force missing symbolic for well-known op using a mock
onnx_model = export_to_onnx(
MyClip(),
torch.randn(3, 4, requires_grad=True),
mocks=[
unittest.mock.patch(
"torch.onnx._internal.registration.registry.get_function_group",
side_effect=break_is_registered_op_api,
)
],
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
)
self.assertAtenOp(onnx_model, "clamp", "Tensor")
def _helper_test_to_(self, cast_fn: Callable[[torch.Tensor], torch.Tensor]):
"""Helper to test aten::to(device) variants.
`cast_fn` is converted into a `torch.jit.script`. It wraps `aten::to`
during export to preventing the devices to be hard-coded.
Needed by detectron2 after https://github.com/facebookresearch/detectron2/pull/4132/
"""
cast_fn = torch.jit.script(cast_fn)
onnx_model = export_to_onnx(cast_fn, torch.zeros([1, 3, 32, 32]))
for n in onnx_model.graph.node:
self.assertNotEqual(n.op_type, "To")
self.assertNotEqual(n.op_type, "Cast")
def test_to__cpu_string(self):
def cast_cpu_string(src: torch.Tensor) -> torch.Tensor:
return src.to("cpu")
self._helper_test_to_(cast_cpu_string)
def test_to__device_cpu_string(self):
def cast_device_cpu_string(src: torch.Tensor) -> torch.Tensor:
return src.to(device="cpu")
self._helper_test_to_(cast_device_cpu_string)
def test_script_custom_class_error(self):
class BoxCoder:
def __init__(self, bbox_xform_clip: float) -> None:
self.bbox_xform_clip = bbox_xform_clip
def decode(self, rel_codes: Tensor, boxes: List[Tensor]) -> Tensor:
boxes = torch.cat(boxes, dim=0)
pred_ctr_x = (
torch.clamp(rel_codes[:, 0::4], max=self.bbox_xform_clip)
* boxes[:, 2]
)
return pred_ctr_x
class MyModule(torch.nn.Module):
__annotations__ = {
"box_coder": BoxCoder,
}
def __init__(self):
super().__init__()
self.box_coder = BoxCoder(1.4)
def forward(self, box_regression: Tensor, proposals: List[Tensor]):
return self.box_coder.decode(box_regression, proposals)
model = torch.jit.script(MyModule())
box_regression = torch.randn([4, 4])
proposal = [torch.randn(2, 4), torch.randn(2, 4)]
with self.assertRaises(RuntimeError) as cm:
onnx_model = io.BytesIO()
torch.onnx.export(
model,
(box_regression, proposal),
onnx_model,
)
def test_initializer_sequence(self):
class MyModule(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super().__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.relu = torch.nn.ReLU()
self.fc2 = torch.nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
test_model = MyModule(3, 4, 10)
state_dict_list = [k for (k, v) in test_model.state_dict().items()]
named_params_list = [k for (k, v) in test_model.named_parameters()]
x = torch.randn(32, 3)
f = io.BytesIO()
torch.onnx.export(test_model, (x,), f, do_constant_folding=False)
loaded_model = onnx.load_from_string(f.getvalue())
actual_list = [p.name for p in loaded_model.graph.initializer]
assert actual_list == state_dict_list, (
"Initializers' sequence is not as same as state_dict(). Expected: ("
+ ", ".join(state_dict_list)
+ "). Actual:("
+ ", ".join(actual_list)
+ ")."
)
assert actual_list == named_params_list, (
"Initializers' sequence is not as same as named_parameters(). Expected: ("
+ ", ".join(named_params_list)
+ "). Actual:("
+ ", ".join(actual_list)
+ ")."
)
def test_initializer_sequence_script_model(self):
def list_is_expected(short_list, long_list) -> bool:
if len(short_list) > len(long_list):
return False
for i in range(len(short_list)):
if short_list[i] not in long_list[i]:
return False
return True
def loop(x, y):
for i in range(int(y)):
x = x + i
return x
class MyModule(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super().__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.relu = torch.nn.ReLU()
self.fc2 = torch.nn.Linear(hidden_size, num_classes)
def forward(self, x, y):
x = loop(x, y)
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
test_model = torch.jit.script(MyModule(3, 4, 10))
state_dict_list = [k for (k, v) in test_model.state_dict().items()]
named_params_list = [k for (k, v) in test_model.named_parameters()]
x = torch.ones(2, 3, dtype=torch.float)
y = torch.tensor(5, dtype=torch.long)
f = io.BytesIO()
torch.onnx.export(test_model, (x, y), f, do_constant_folding=False)
loaded_model = onnx.load_from_string(f.getvalue())
actual_list = [p.name for p in loaded_model.graph.initializer]
assert list_is_expected(state_dict_list, actual_list), (
"ScriptModel - Initializers' sequence is not as same as state_dict(). Expected: ("
+ ", ".join(state_dict_list)
+ "). Actual:("
+ ", ".join(actual_list)
+ ")."
)
assert list_is_expected(named_params_list, actual_list), (
"ScriptModel - Initializers' sequence is not as same as named_parameters(). Expected: ("
+ ", ".join(named_params_list)
+ "). Actual:("
+ ", ".join(actual_list)
+ ")."
)
def test_onnx_checker_invalid_graph(self):
class CustomAddModule(torch.nn.Module):
def forward(self, x, y):
return torch.add(x, y)
def symbolic_custom_invalid_add(g, input, other, alpha=None):
return g.op("Add", input, other, invalid_attr_i=1)
torch.onnx.register_custom_op_symbolic(
"::add", symbolic_custom_invalid_add, opset_version=9
)
x = torch.randn(2, 3, 4)
y = torch.randn(2, 3, 4)
test_model = CustomAddModule()
f = io.BytesIO()
try:
with self.assertRaises(torch.onnx.errors.CheckerError):
torch.onnx.export(test_model, (x, y), f, opset_version=9)
finally:
torch.onnx.unregister_custom_op_symbolic("::add", 9)
self.assertTrue(f.getvalue(), "ONNX graph was not exported.")
loaded_model = onnx.load_from_string(f.getvalue())
def test_shape_value_map(self):
class RSoftMax(torch.nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
return x
radix = 2
cardinality = 1
x = torch.randn(10, 1, 128, 1)
f = io.BytesIO()
torch.onnx.export(
RSoftMax(radix, cardinality),
(x,),
f,
input_names=["x"],
dynamic_axes={"x": [0]},
)
loaded_model = onnx.load_from_string(f.getvalue())
self.assertEqual(
loaded_model.graph.output[0].type.tensor_type.shape.dim[1].dim_value, 128
)
def test_onnx_proto_checker(self):
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 2 * x
x = torch.randn(1, 2, 3, requires_grad=True)
f = io.BytesIO()
torch.onnx.export(Model(), x, f)
model = onnx.load(f)
model.ir_version = 0
def check_proto():
torch._C._check_onnx_proto(model.SerializeToString())
self.assertRaises(RuntimeError, check_proto)
def test_maintain_dynamic_shapes_of_unreliable_nodes(self):
def symbolic_pythonop(ctx: torch.onnx.SymbolicContext, g, *args, **kwargs):
return g.op("com.microsoft::PythonOp")
torch.onnx.register_custom_op_symbolic("prim::PythonOp", symbolic_pythonop, 1)
self.addCleanup(torch.onnx.unregister_custom_op_symbolic, "prim::PythonOp", 1)
# necessay parameters for transformer embeddings
hidden_size = 48
max_position_embeddings = 32
batch_size = 2
# issue found that autograd.function making downstream
# node unreliable but with static shape. The issue was first
# discovered with using Apex FusedLayerNorm in Transformers
class CustomLayerNorm(torch.autograd.Function):
@staticmethod
def forward(ctx, embedding):
layer_norm = torch.nn.LayerNorm(hidden_size, eps=1e-12)
return layer_norm(embedding)
class EmbeddingModule(torch.nn.Module):
def forward(
self,
embeddings=None,
):
embedding_output = CustomLayerNorm.apply(embeddings)
query = embedding_output.transpose(0, 1)
target_len, batch_size, embedding_dim = query.size()
# Reshape is used for consuming batch_size, and if it is static,
# this will be a Constant node in the graph
query = query.reshape(target_len, batch_size, embedding_dim)
return query
embeddings = torch.randn(batch_size, max_position_embeddings, hidden_size)
f = io.BytesIO()
torch.onnx.export(
EmbeddingModule().eval(),
(embeddings,),
f,
input_names=["embeddings"],
dynamic_axes={
"embeddings": {
0: "batch_size",
1: "max_position_embeddings",
2: "hidden_size",
}
},
custom_opsets={"com.microsoft": 1},
)
model = onnx.load(io.BytesIO(f.getvalue()))
# If there is a constant node with dim=3 and max_position_embeddings,
# batch_size, hidden_size as shape, it means the shape becomes static.
# Normally, with dynamic batch size, this constant node should not exist.
const_node = [n for n in model.graph.node if n.op_type == "Constant"]
self.assertNotEqual(len(const_node), 0)
for node in const_node:
for a in node.attribute:
if a.name == "value":
shape = onnx.numpy_helper.to_array(a.t)
self.assertNotEqual(
shape.tolist(),
[max_position_embeddings, batch_size, hidden_size],
)
def test_is_fp_for_C_TypeList(self):
class M(torch.nn.Module):
def forward(self, x):
x = x.squeeze(1)
w = x.shape[2]
pos = x.view(2, -1).argmax(1)
x_int = pos % w
y_int = (pos - x_int) // w
return y_int, x_int
model = torch.jit.script(M())
inputs = torch.randn(2, 4, 6)
f = io.BytesIO()
torch.onnx.export(
model, inputs, f, dynamic_axes={"x": [0, 1]}, input_names=["x"]
)
def test_dropout_script(self):
eg = torch.zeros(1, 2, 3, requires_grad=True)
@jit_utils._trace(eg)
def foo(x):
x = torch.neg(x)
return F.dropout(x)
class MyDrop(torch.nn.Module):
def forward(self, x):
return foo(x)
f = io.BytesIO()
with warnings.catch_warnings(record=True):
torch.onnx.export(MyDrop(), (eg,), f, verbose=False)
def test_pack_padded_pad_packed_trace(self):
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
T, B, C = 3, 5, 7
class PadPackedWrapper(torch.nn.Module):
def __init__(self):
super(PadPackedWrapper, self).__init__()
def forward(self, x, seq_lens):
x = pack_padded_sequence(x, seq_lens)
x, _ = pad_packed_sequence(x)
return x
x = np.ones((T, B, C))
seq_lens = np.array([3, 3, 2, 2, 1], dtype=np.int32)
# set padding value so we can test equivalence
for b in range(B):
if seq_lens[b] < T:
x[seq_lens[b] :, b, :] = 0
seq_lens = torch.from_numpy(seq_lens)
x = torch.autograd.Variable(torch.from_numpy(x), requires_grad=True)
m = PadPackedWrapper()
m_traced = torch.jit.trace(
m,
(
x,
seq_lens,
),
)
y = m(x, seq_lens)
loss = torch.sum(y)
loss.backward()
grad = x.grad.clone()
x.grad.zero_()
y_traced = m_traced(x, seq_lens)
loss_traced = torch.sum(y_traced)
loss_traced.backward()
grad_traced = x.grad.clone()
self.assertEqual(y_traced, x)
self.assertEqual(y_traced, y)
self.assertEqual(grad, grad_traced)
f = io.BytesIO()
torch.onnx.export(m, (x, seq_lens), f, verbose=False)
# Suppression: ONNX warns when exporting RNNs because of potential batch size mismatch.
@common_utils.suppress_warnings
def test_rnn_trace_override(self):
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
num_layers = 3
T, B, C = 11, 5, 7
class RNNTraceWrapper(torch.nn.Module):
def __init__(self, cell_type):
super(RNNTraceWrapper, self).__init__()
if cell_type == "RNN":
self.rnn = torch.nn.RNN(
input_size=C, hidden_size=C, num_layers=num_layers
)
elif cell_type == "LSTM":
self.rnn = torch.nn.LSTM(
input_size=C, hidden_size=C, num_layers=num_layers
)
elif cell_type == "GRU":
self.rnn = torch.nn.GRU(
input_size=C, hidden_size=C, num_layers=num_layers
)
def forward(self, x, seq_lens):
x = pack_padded_sequence(x, seq_lens)
x, _ = self.rnn(x)
x, _ = pad_packed_sequence(x)
return x
for cell_type in ["RNN", "LSTM", "GRU"]:
x = torch.ones(T, B, C, requires_grad=True)
seq_lens = torch.from_numpy(np.array([11, 3, 2, 2, 1], dtype=np.int32))
m = RNNTraceWrapper(cell_type)
m_traced = torch.jit.trace(
m,
(
x,
seq_lens,
),
)
y = m(x, seq_lens)
loss = torch.sum(y)
loss.backward()
grad = x.grad.clone()
x.grad.zero_()
y_traced = m_traced(x, seq_lens)
loss_traced = torch.sum(y_traced)
loss_traced.backward()
grad_traced = x.grad.clone()
self.assertEqual(y_traced, y)
self.assertEqual(grad, grad_traced)
f = io.BytesIO()
torch.onnx.export(m, (x, seq_lens), f, verbose=False)
def test_trace_fork_wait_inline_onnx(self):
def fork_body(x):
return torch.neg(x), torch.neg(x)
class MyMod(torch.nn.Module):
def forward(self, x):
fut = torch.jit._fork(fork_body, x)
val = torch.jit._wait(fut)
return val[1]
# smoke test for ONNX export
f = io.BytesIO()
torch.onnx.export(MyMod(), (torch.rand(3, 4),), f)
def test_trace_detach_onnx_erase(self):
class Mod(torch.nn.Module):
def forward(self, x, w):
return torch.matmul(x, w).detach()
torch.onnx.export_to_pretty_string(Mod(), (torch.rand(3, 4), torch.rand(4, 5)))
@common_utils.skipIfNoCaffe2
def test_caffe2_aten_fallback_must_fallback(self):
class ModelWithAtenNotONNXOp(torch.nn.Module):
def forward(self, x, y):
abcd = x + y
defg = torch.linalg.qr(abcd)
return defg
# TODO: Refactor common_utils._decide_skip_caffe2 to support parametrize
for operator_export_type in (
OperatorExportTypes.ONNX_ATEN,
OperatorExportTypes.ONNX_ATEN_FALLBACK,
):
x = torch.rand(3, 4)
y = torch.rand(3, 4)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenNotONNXOp(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=operator_export_type,
# support for linalg.qr was added in later op set versions.
opset_version=9,
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
self.assertAtenOp(onnx_model, "linalg_qr")
@common_utils.skipIfNoCaffe2
def test_caffe2_onnx_aten_must_not_fallback(self):
class ModelWithAtenFmod(torch.nn.Module):
def forward(self, x, y):
return torch.fmod(x, y)
# TODO: Refactor common_utils._decide_skip_caffe2 to support parametrize
for operator_export_type in (
OperatorExportTypes.ONNX_ATEN_FALLBACK,
OperatorExportTypes.ONNX_ATEN,
):
x = torch.randn(3, 4, dtype=torch.float32)
y = torch.randn(3, 4, dtype=torch.float32)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenFmod(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=operator_export_type,
opset_version=10, # or higher
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
assert onnx_model.graph.node[0].op_type == "Mod"
@common_utils.skipIfCaffe2
def test_aten_fallback_must_fallback(self):
class ModelWithAtenNotONNXOp(torch.nn.Module):
def forward(self, x, y):
abcd = x + y
defg = torch.linalg.qr(abcd)
return defg
x = torch.rand(3, 4)
y = torch.rand(3, 4)