forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathscript_init.cpp
1868 lines (1754 loc) · 67.5 KB
/
script_init.cpp
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
#include <torch/csrc/jit/python/script_init.h>
#include <torch/csrc/Device.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/frontend/sugared_value.h>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/module.h>
#include <torch/csrc/jit/python/module_python.h>
#include <torch/csrc/jit/python/python_ivalue.h>
#include <torch/csrc/jit/python/python_sugared_value.h>
#include <torch/csrc/jit/serialization/import.h>
#include <torch/csrc/jit/testing/file_check.h>
#include <torch/csrc/jit/frontend/parser.h>
#include <torch/csrc/jit/frontend/tracer.h>
#include <torch/csrc/jit/ir/constants.h>
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/passes/inliner.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <torch/csrc/jit/python/python_tracer.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <torch/csrc/jit/runtime/logging.h>
#include <torch/csrc/jit/serialization/export.h>
#include <torch/csrc/jit/serialization/import_source.h>
#include <torch/csrc/jit/serialization/python_print.h>
#include <torch/csrc/jit/testing/hooks_for_testing.h>
#include <torch/csrc/api/include/torch/ordered_dict.h>
#include <ATen/ATen.h>
#include <ATen/core/function_schema.h>
#include <ATen/core/qualified_name.h>
#include <pybind11/functional.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <pybind11/stl_bind.h>
#include <chrono>
#include <cstddef>
#include <memory>
#include <sstream>
#include <string>
#include <tuple>
#include <utility>
#include <vector>
namespace torch {
namespace jit {
using ::c10::Argument;
using ::c10::FunctionSchema;
using ResolutionCallback = std::function<py::object(std::string)>;
using FunctionDefaults = std::unordered_map<std::string, py::object>;
using ClassMethodDefaults = std::unordered_map<std::string, FunctionDefaults>;
namespace {
// A resolver that will inspect the outer Python scope to find `name`.
struct PythonResolver : public Resolver {
explicit PythonResolver(ResolutionCallback rcb) : rcb_(std::move(rcb)) {}
/**
* While compiling classes, the class type we're compiling will not be
* available in Python, since we haven't fowner_ defining the class yet. So
* in order to make the class type available to its own methods, we need to
* explicitly resolve it.
*
* @param rcb Python function to resolve a name to its Python object in the
* enclosing scope
* @param classname The unqualified classname of the class currently being
* compiled.
* @param classType The class's type.
*/
explicit PythonResolver(
ResolutionCallback rcb,
std::string classname,
ClassTypePtr classType)
: rcb_(std::move(rcb)),
classname_(std::move(classname)),
classType_(std::move(classType)) {}
std::shared_ptr<SugaredValue> resolveValue(
const std::string& name,
Function& m,
const SourceRange& loc) override {
pybind11::gil_scoped_acquire ag;
py::object obj = rcb_(name);
if (obj.is(py::none())) {
return nullptr;
}
return toSugaredValue(obj, m, loc);
}
static bool isNamedTupleClass(py::object obj) {
auto tuple_type = reinterpret_cast<PyObject*>(&PyTuple_Type);
return PyObject_IsSubclass(obj.ptr(), tuple_type) &&
py::hasattr(obj, "_fields");
}
TypePtr resolveTypeFromObject(const py::object& obj, const SourceRange& loc) {
if (py::isinstance<ScriptClass>(obj)) {
auto script_class = py::cast<ScriptClass>(obj);
return script_class.class_type_.type_;
}
py::bool_ isClass = py::module::import("inspect").attr("isclass")(obj);
if (!py::cast<bool>(isClass)) {
return nullptr;
}
if (isNamedTupleClass(obj)) {
return registerNamedTuple(obj, loc);
}
auto qualifiedName = c10::QualifiedName(
py::cast<std::string>(py::module::import("torch._jit_internal")
.attr("_qualified_name")(obj)));
return get_python_cu()->get_type(qualifiedName);
}
TypePtr resolveType(const std::string& name, const SourceRange& loc)
override {
if (classType_ && name == classname_) {
return classType_;
}
pybind11::gil_scoped_acquire ag;
py::object obj = rcb_(name);
if (obj.is(py::none())) {
return nullptr;
}
auto annotation_type = py::module::import("torch.jit.annotations")
.attr("try_ann_to_type")(obj, loc);
if (!annotation_type.is_none()) {
return py::cast<TypePtr>(annotation_type);
}
return resolveTypeFromObject(obj, loc);
}
private:
ResolutionCallback rcb_;
std::string classname_;
ClassTypePtr classType_;
};
std::shared_ptr<PythonResolver> pythonResolver(const ResolutionCallback& rcb) {
return std::make_shared<PythonResolver>(rcb);
}
std::shared_ptr<PythonResolver> pythonResolver(
const ResolutionCallback& rcb,
std::string classname,
ClassTypePtr classType) {
return std::make_shared<PythonResolver>(
rcb, std::move(classname), std::move(classType));
}
void checkOverloadDecl(const Decl& new_decl, const Decl& old_decl) {
const auto& new_params = new_decl.params();
const auto& old_params = old_decl.params();
// TODO. same number of parameters not strictly necessary.
TORCH_INTERNAL_ASSERT(
new_params.size() == old_params.size(),
"Overload must have same number of parameters\n",
new_decl.range(),
old_decl.range());
for (size_t i = 0; i < new_decl.params().size(); ++i) {
TORCH_INTERNAL_ASSERT(
new_params[i].ident().name() == old_params[i].ident().name(),
"Overload parameters must have the same names\n",
new_params[i].ident(),
old_params[i].ident());
}
}
c10::optional<IValue> tryCalculateDefaultParam(
const Argument& arg,
const py::object& def_value) {
auto n = arg.N();
auto list_type = arg.type()->cast<ListType>();
try {
if (n && *n > 0 && list_type) {
// BroadcastingList, allow default values T for arg types List[T]
return toIValue(def_value, list_type->getElementType());
} else {
return toIValue(def_value, arg.type());
}
} catch (...) {
return c10::nullopt;
}
}
// An overloaded function may have a default that does not subtype all overloads
// @overload
// def foo(x: str)
// def foo(x=1)
FunctionDefaults calcOverloadedFunctionDefaults(
const FunctionSchema& schema,
const FunctionDefaults& defaults) {
FunctionDefaults updated_defaults;
for (const auto& arg : schema.arguments()) {
const std::string& arg_name = arg.name();
auto value = defaults.find(arg_name);
if (value == defaults.end()) {
continue;
}
auto maybe_ivalue = tryCalculateDefaultParam(arg, value->second);
if (maybe_ivalue) {
updated_defaults[arg_name] = value->second;
}
}
return updated_defaults;
}
} // namespace
bool checkMutableFunctionDefault(const py::object& def_arg) {
if (py::isinstance<py::list>(def_arg) || py::isinstance<py::dict>(def_arg)) {
return true;
}
if (py::isinstance<py::tuple>(def_arg)) {
auto pytuple = def_arg.cast<py::tuple>();
for (py::handle t : pytuple) {
py::object obj = py::reinterpret_borrow<py::object>(t);
if (checkMutableFunctionDefault(obj)) {
return true;
}
}
}
return false;
}
void checkMutableFunctionDefault(
const SourceRange& range,
const Argument& arg,
const py::object& def_arg) {
if (checkMutableFunctionDefault(def_arg) || arg.type()->cast<ClassType>()) {
throw ErrorReport(range)
<< "Mutable default parameters are not supported because Python binds them to the function"
<< " and they persist across function calls.\n As a workaround, make the default None and instantiate"
<< " the default parameter within the body of the function. Found "
<< def_arg.get_type() << " on parameter " << arg.name();
}
}
FunctionSchema getSchemaWithNameAndDefaults(
const SourceRange& range,
const FunctionSchema& schema,
const at::optional<std::string>& new_name,
const FunctionDefaults& default_args) {
std::vector<Argument> new_args;
for (auto& arg : schema.arguments()) {
auto it = default_args.find(arg.name());
if (it != default_args.end()) {
checkMutableFunctionDefault(range, arg, it->second);
c10::optional<IValue> value = tryCalculateDefaultParam(arg, it->second);
if (!value) {
ErrorReport error(range);
error << "Expected a default value of type " << arg.type()->repr_str()
<< " on parameter \"" << arg.name() << "\".";
if (arg.is_inferred_type()) {
error << "Because \"" << arg.name()
<< "\" was not annotated with an explicit type "
<< "it is assumed to be type 'Tensor'.";
}
throw error;
}
new_args.emplace_back(
arg.name(), arg.type(), arg.N(), *value, arg.kwarg_only());
} else {
new_args.push_back(arg);
}
}
return FunctionSchema(
new_name.value_or(schema.name()),
schema.overload_name(),
new_args,
schema.returns(),
schema.is_vararg(),
schema.is_varret());
}
static Decl mergeDefaultsAndExtraParametersToOverloadDecl(
const Decl& overload_decl,
const Decl& impl_decl,
const FunctionDefaults& defaults) {
std::vector<Param> adjusted_params;
const auto& overload_params = overload_decl.params();
const auto& impl_params = impl_decl.params();
// following PEP specification that the following should work:
// @overload
// def mouse_event(x1: int, y1: int) -> ClickEvent: ...
// ...
// def mouse_event(x1: int, y1: int, x2: Optional[int] = None, y2:
// Optional[int] = None)
TORCH_CHECK(
overload_params.size() <= impl_params.size(),
"Overload should not have more parameters than implementation function",
overload_decl.range(),
impl_decl.range());
for (size_t i = 0; i < overload_params.size(); ++i) {
auto overload_name = overload_params[i].ident().name();
auto impl_name = impl_params[i].ident().name();
if (overload_name != impl_name) {
throw ErrorReport(overload_decl.range())
<< "Overload parameters must have the same names. "
<< "Found " << overload_name << " and " << impl_name
<< " on argument " << i;
}
adjusted_params.push_back(overload_params[i]);
}
for (size_t i = overload_params.size(); i < impl_params.size(); ++i) {
if (!defaults.count(impl_params[i].ident().name())) {
throw ErrorReport(impl_decl.range())
<< "Expected to find default parameter on argument"
<< impl_params[i].ident().name()
<< " because it is not defined on the overloaded declaration";
}
if (!impl_params[i].type().present()) {
throw ErrorReport(impl_decl.range())
<< "Parameters not specified on the overloaded declaration must have a type annotation in the implementation function."
<< " Did not find type for param " << impl_params[i].ident().name();
}
adjusted_params.push_back(impl_params[i]);
}
return Decl::create(
overload_decl.range(),
List<Param>::create(overload_decl.range(), adjusted_params),
overload_decl.return_type());
}
static StrongFunctionPtr script_compile_overloaded_function(
const c10::QualifiedName& name,
const Decl& overload_decl,
const Def& implementation_def,
const ResolutionCallback& rcb,
const FunctionDefaults& implementation_defaults,
const py::object& signature) {
if (signature.is(py::none())) {
throw ErrorReport(overload_decl.range())
<< "Must explicitly add type annotations to overloaded functions";
}
auto adjusted_decl = mergeDefaultsAndExtraParametersToOverloadDecl(
overload_decl, implementation_def.decl(), implementation_defaults);
auto new_def = implementation_def.withDecl(adjusted_decl);
auto cu = get_python_cu();
auto defined_functions = cu->define(
QualifiedName(name.prefix()),
/*properties=*/{},
/*propResolvers=*/{},
{new_def},
{pythonResolver(rcb)},
nullptr,
true);
TORCH_INTERNAL_ASSERT(defined_functions.size() == 1);
auto& defined = defined_functions[0];
FunctionDefaults updated_defaults = calcOverloadedFunctionDefaults(
defined->getSchema(), implementation_defaults);
defined->setSchema(getSchemaWithNameAndDefaults(
new_def.range(),
defined->getSchema(),
new_def.name().name(),
updated_defaults));
StrongFunctionPtr ret(std::move(cu), defined);
didFinishEmitFunction(ret);
return ret;
}
static StrongFunctionPtr script_compile_function(
const c10::QualifiedName& name,
const Def& def,
const FunctionDefaults& defaults,
const ResolutionCallback& rcb) {
auto cu = get_python_cu();
auto defined_functions = cu->define(
QualifiedName(name.prefix()),
/*properties=*/{},
/*propResolvers=*/{},
{def},
{pythonResolver(rcb)},
nullptr,
true);
TORCH_INTERNAL_ASSERT(defined_functions.size() == 1);
auto& defined = defined_functions[0];
defined->setSchema(getSchemaWithNameAndDefaults(
def.range(), defined->getSchema(), def.name().name(), defaults));
StrongFunctionPtr ret(std::move(cu), defined);
didFinishEmitFunction(ret);
return ret;
}
struct VISIBILITY_HIDDEN ModuleSelf : public Self {
ModuleSelf(std::shared_ptr<ConcreteModuleType> concreteType)
: Self(), concreteType_(std::move(concreteType)) {}
std::shared_ptr<SugaredValue> makeSugared(Value* v) const override {
v->setType(getClassType());
return std::make_shared<ModuleValue>(v, concreteType_);
}
ClassTypePtr getClassType() const override {
return concreteType_->getJitType()->expect<ClassType>();
}
private:
std::shared_ptr<ConcreteModuleType> concreteType_;
};
static TypePtr getTensorType(const at::Tensor& t, bool complete) {
auto r = TensorType::create(t);
if (!complete) {
r = r->dimensionedOnly();
}
return r;
}
static TupleTypePtr getTupleTensorType(
const Stack::const_iterator& s_iter,
const Stack::const_iterator& s_iter_end,
const TypePtr& tupleType,
bool complete) {
AT_ASSERT(tupleType->kind() == TupleType::Kind);
AT_ASSERT(s_iter != s_iter_end);
std::vector<TypePtr> types;
for (const auto& subType : tupleType->containedTypes()) {
if (subType->kind() == TupleType::Kind) {
types.push_back(
getTupleTensorType(s_iter + 1, s_iter_end, subType, complete));
} else {
types.push_back(getTensorType(s_iter->toTensor(), complete));
}
}
return TupleType::create(types);
}
static void setInputTensorTypes(Graph& g, const Stack& stack, bool complete) {
at::ArrayRef<Value*> input_values = g.inputs();
auto s_iter = stack.begin();
for (auto v : input_values) {
AT_ASSERT(s_iter != stack.end());
// Leave packed param types alone. This is needed for downstream passes
// (like alias analysis) to work properly. This will be unpacked later
// in unpackQuantizedWeights.
if (auto named_type = v->type()->cast<c10::NamedType>()) {
if (auto qualname = named_type->name()) {
if (getCustomClass(qualname->qualifiedName())) {
s_iter++;
continue;
}
}
}
if (v->type()->kind() == TupleType::Kind) {
AT_ASSERT(v->node()->kind() == prim::Param);
v->setType(getTupleTensorType(s_iter, stack.end(), v->type(), complete));
} else {
v->setType(getTensorType(s_iter->toTensor(), complete));
s_iter++;
}
}
}
static std::shared_ptr<Graph> _propagate_shapes(
Graph& graph,
std::vector<at::Tensor> inputs,
bool with_grad = false) {
Stack stack(inputs.begin(), inputs.end());
auto retval = graph.copy();
setInputTensorTypes(*retval, stack, /*complete=*/false);
PropagateInputShapes(retval);
return retval;
}
static std::shared_ptr<Graph> _propagate_and_assign_input_shapes(
Graph& graph,
const std::vector<at::Tensor>& inputs,
bool with_grad = false,
bool propagate = true) {
auto retval = graph.copy();
setInputTensorTypes(*retval, fmap<IValue>(inputs), /*complete=*/true);
if (propagate) {
PropagateInputShapes(retval);
}
return retval;
}
void addFunctionToModule(Module& module, const StrongFunctionPtr& func) {
// Make a graph with a fake self argument
auto graph = func.function_->graph()->copy();
auto v = graph->insertInput(0, "self");
v->setType(module._ivalue()->type());
const auto name = QualifiedName(*module.type()->name(), "forward");
auto method =
module._ivalue()->compilation_unit()->create_function(name, graph);
module.type()->addMethod(method);
}
// this is used in our test suite to check that we correctly preserved type tags
bool ivalue_tags_match(const Module& lhs, const Module& rhs) {
struct Work {
IValue a;
IValue b;
};
std::unordered_set<const void*> visited;
std::vector<Work> work = {{lhs._ivalue(), rhs._ivalue()}};
while (!work.empty()) {
Work item = work.back();
work.pop_back();
if (item.a.isPtrType()) {
// uncomment to debug type matching errors
// std::cout << "MATCHING " << /*item.a <<*/ "(" << *item.a.type() << ") "
// << item.a.internalToPointer() << " " << /*item.b <<*/ " ("
// << *item.b.type() << ") " << item.b.internalToPointer() <<
// "\n";
if (visited.count(item.a.internalToPointer())) {
continue;
}
visited.emplace(item.a.internalToPointer());
}
if (!unshapedType(item.b.type())
->isSubtypeOf(unshapedType(item.b.type()))) {
// Since named types are saved and loaded in the test suite, we cannot
// expect them to be equal. We should still check their slots however.
if (!item.a.type()->cast<c10::NamedType>()) {
return false;
}
}
// check tags for objects that contain subobjects
if (item.a.isObject()) {
auto ao = item.a.toObject();
auto bo = item.b.toObject();
for (size_t i = 0; i < ao->slots().size(); ++i) {
work.emplace_back(Work{ao->slots().at(i), bo->slots().at(i)});
}
} else if (item.a.isTuple()) {
auto at = item.a.toTuple();
auto bt = item.b.toTuple();
for (size_t i = 0; i < at->elements().size(); ++i) {
work.emplace_back(Work{at->elements().at(i), bt->elements().at(i)});
}
} else if (item.a.isList()) {
auto al = item.a.toList();
auto bl = item.b.toList();
for (size_t i = 0; i < al.size(); ++i) {
work.emplace_back(Work{al.get(i), bl.get(i)});
}
} else if (item.a.isGenericDict()) {
auto ad = item.a.toGenericDict();
auto bd = item.b.toGenericDict();
for (auto& item : ad) {
// Dictionaory keys cannot contain List/Dicts that require tags
// so we do not have to check them.
// Furthermore without ordered dicts it is expensive to find the
// equivalent key
work.emplace_back(Work{item.value(), bd.at(item.key())});
}
} else if (item.a.isFuture()) {
auto af = item.a.toFuture();
auto bf = item.b.toFuture();
af->wait();
bf->wait();
work.emplace_back(Work{af->value(), bf->value()});
}
}
return true;
}
// helper used to implement ._parameters, ._buffers, ._modules dicts
// inside of script nn.Module
template <typename Policy>
struct slot_dict_impl {
slot_dict_impl(ModulePtr module) : module_(std::move(module)) {}
bool contains(const std::string& name) const {
if (auto slot = module_->type()->findAttributeSlot(name)) {
if (Policy::valid(module_->type(), *slot, module_->getSlot(*slot))) {
return true;
}
}
return false;
}
std::vector<std::pair<std::string, py::object>> items() const {
std::vector<std::pair<std::string, py::object>> result;
for (size_t i = 0, N = module_->type()->numAttributes(); i < N; ++i) {
if (Policy::valid(module_->type(), i, module_->getSlot(i))) {
result.emplace_back(
module_->type()->getAttributeName(i),
toPyObject(module_->getSlot(i)));
}
}
return result;
}
void setattr(const std::string& name, py::object value) {
const TypePtr& type = module_->type()->getAttribute(name);
Module(module_).setattr(name, toIValue(std::move(value), type));
}
py::object getattr(const std::string& name) {
return toPyObject(Module(module_).attr(name));
}
static void bind(const py::module& m, const char* name) {
py::class_<slot_dict_impl<Policy>>(m, name)
.def(py::init(
[](Module& m) { return slot_dict_impl<Policy>(m._ivalue()); }))
.def("contains", &slot_dict_impl<Policy>::contains)
.def("items", &slot_dict_impl<Policy>::items)
.def("setattr", &slot_dict_impl<Policy>::setattr)
.def("getattr", &slot_dict_impl<Policy>::getattr);
}
private:
ModulePtr module_;
};
template <typename T>
py::list debugMakeList(const T& list) {
py::list result;
for (const auto& elem : list) {
result.append(py::cast(elem));
}
return result;
}
template <typename T>
py::list debugMakeNamedList(const T& list) {
py::list result;
for (auto elem : list) {
result.append(py::cast(std::make_pair(elem.name, elem.value)));
}
return result;
}
template <typename T>
py::set debugMakeSet(const T& list) {
py::set result;
for (const auto& elem : list) {
result.add(py::cast(elem));
}
return result;
}
static py::dict _jit_debug_module_iterators(Module& module) {
py::dict result;
result["children"] = debugMakeList(module.children());
result["named_children"] = debugMakeNamedList(module.named_children());
result["modules"] = debugMakeList(module.modules());
result["named_modules"] = debugMakeNamedList(module.named_modules());
result["parameters"] = debugMakeList(module.parameters(false));
result["named_parameters"] =
debugMakeNamedList(module.named_parameters(false));
result["parameters_r"] = debugMakeList(module.parameters(true));
result["named_parameters_r"] =
debugMakeNamedList(module.named_parameters(true));
result["buffers"] = debugMakeList(module.buffers(false));
result["named_buffers"] = debugMakeNamedList(module.named_buffers(false));
result["buffers_r"] = debugMakeList(module.buffers(true));
result["named_buffers_r"] = debugMakeNamedList(module.named_buffers(true));
result["named_attributes"] =
debugMakeNamedList(module.named_attributes(false));
result["named_attributes_r"] =
debugMakeNamedList(module.named_attributes(true));
return result;
}
static constexpr std::array<const char*, 47> magic_method_names = {
"__lt__", "__le__", "__eq__", "__ne__",
"__ge__", "__gt__", "__not__", "__abs__",
"__add__", "__and__", "__floordiv__", "__index__",
"__inv__", "__invert__", "__lshift__", "__mod__",
"__mul__", "__matmul__", "__neg__", "__or__",
"__pos__", "__pow__", "__rshift__", "__sub__",
"__truediv__", "__xor__", "__concat__", "__contains__",
"__delitem__", "__getitem__", "__setitem__", "__iadd__",
"__iand__", "__iconcat__", "__ifloordiv__", "__ilshift__",
"__imod__", "__imul__", "__imatmul__", "__ior__",
"__ipow__", "__irshift__", "__isub__", "__itruediv__",
"__ixor__", "__str__", "__len__",
};
struct DeepCopyMemoTable {
std::shared_ptr<IValue::HashAliasedIValueMap> map;
};
IValue pyIValueDeepcopy(const IValue& ivalue, const py::dict& memo) {
if (!memo.contains(py::str("__torch_script_memo_table"))) {
memo["__torch_script_memo_table"] =
DeepCopyMemoTable{std::make_shared<IValue::HashAliasedIValueMap>()};
}
auto& ivalue_memo =
*py::cast<DeepCopyMemoTable>(memo["__torch_script_memo_table"]).map;
return ivalue.deepcopy(ivalue_memo);
}
ExtraFilesMap extra_files_from_python(const py::dict& pydict) {
ExtraFilesMap r;
for (const auto& it : pydict) {
r[py::cast<std::string>(it.first)] = "";
}
return r;
}
void extra_files_to_python(const ExtraFilesMap& m, const py::dict& pydict) {
// py::dict is pointer-like type so it gets modified despite const&
for (const auto& it : m) {
pydict[py::str(it.first)] = py::bytes(it.second);
}
}
void pyCompilationUnitDefine(
CompilationUnit& cu,
const std::string& src,
const ResolutionCallback* rcb,
const uint32_t _frames_up) {
if (rcb && *rcb) {
cu.define(c10::nullopt, src, pythonResolver(*rcb), nullptr);
} else {
py::object py_default_rcb =
py::module::import("torch._jit_internal")
.attr("createResolutionCallbackFromFrame")(_frames_up);
auto default_rcb = py_default_rcb.cast<ResolutionCallback>();
cu.define(c10::nullopt, src, pythonResolver(default_rcb), nullptr);
}
}
void initJitScriptBindings(PyObject* module) {
auto m = py::handle(module).cast<py::module>();
// NOLINTNEXTLINE(bugprone-unused-raii)
py::class_<c10::Capsule>(m, "Capsule");
auto object_class =
py::class_<Object>(m, "ScriptObject")
.def("_type", [](Module& m) { return m.type(); })
.def(
"_get_method",
[](Object& self, const std::string& name) -> Method {
return self.get_method(name);
},
py::keep_alive<0, 1>())
.def(
"setattr",
[](Object& self, const std::string& name, py::object value) {
if (self.type()->hasConstant(name)) {
TORCH_CHECK(
false,
"Can't set constant '",
name,
"' which has value:",
self.type()->getConstant(name));
}
TypePtr type = self.type()->getAttribute(name);
auto ivalue = toIValue(std::move(value), type);
self.setattr(name, ivalue);
})
.def(
"getattr",
[](Object& self, const std::string& name) {
try {
return toPyObject(self.attr(name));
} catch (const ObjectAttributeError& err) {
throw AttributeError("%s", err.what());
}
})
.def(
"__getattr__",
[](Object& self, const std::string& name) -> py::object {
try {
if (name == "__qualname__") {
return py::cast(self.type()->name()->name());
}
if (auto method = self.find_method(name)) {
return py::cast(*method);
}
return toPyObject(self.attr(name));
} catch (const ObjectAttributeError& err) {
throw AttributeError("%s", err.what());
}
})
.def(
"hasattr",
[](Object& self, const std::string& name) {
return self.hasattr(name);
})
.def(
"_has_method",
[](Object& self, const std::string& name) {
return bool(self.find_method(name));
})
.def(
"_method_names",
[](Object& self) {
return fmap(self.get_methods(), [](const Method& method) {
return method.name();
});
})
.def("__copy__", &Object::copy)
.def(
"__hash__",
[](const Object& self) {
// Similar to Tensor's `__hash__`, which is `id()`.
return std::hash<c10::ivalue::Object*>{}(self._ivalue().get());
})
.def(py::pickle(
[](const Object& self)
-> std::tuple<py::object, std::string> { // __getstate__
if (auto getstate_method = self.find_method("__getstate__")) {
auto object_state = toPyObject((*getstate_method)(Stack{}));
TORCH_INTERNAL_ASSERT(self.type()->name());
return std::make_tuple(
object_state, self.type()->name()->qualifiedName());
}
std::stringstream err;
err << "Tried to serialize object ";
if (auto qualname = self.type()->name()) {
err << qualname->qualifiedName() << " ";
}
err << "which does not have a __getstate__ method defined!";
throw std::runtime_error(err.str());
},
[](const std::tuple<py::object, std::string>& state_tup)
-> Object {
py::object state;
std::string qualname;
std::tie(state, qualname) = state_tup;
auto class_type = getCustomClass(qualname);
TORCH_CHECK(
class_type,
"Tried to deserialize class ",
qualname,
" which is not known to the runtime. "
"If this is a custom C++ class, make "
"sure the appropriate code is linked.");
auto self = Object(c10::ivalue::Object::create(
c10::StrongTypePtr(
std::shared_ptr<torch::jit::CompilationUnit>(),
class_type),
1));
if (auto setstate_method = self.find_method("__setstate__")) {
auto setstate_schema =
setstate_method->function().getSchema();
TORCH_INTERNAL_ASSERT(
setstate_schema.arguments().size() == 2,
"__setstate__ method for class ",
class_type->repr_str(),
" must have exactly 2 arguments!");
auto state_type = setstate_schema.arguments().at(1).type();
(*setstate_method)(Stack{toIValue(state, state_type)});
return self;
}
std::stringstream err;
err << "Tried to deserialize object ";
if (auto qualname = class_type->name()) {
err << qualname->qualifiedName() << " ";
}
err << "which does not have a __setstate__ method defined!";
throw std::runtime_error(err.str());
}));
// Special case __str__ to make sure we can print Objects/Modules regardless
// of if the user defined a __str__
using MagicMethodImplType = std::function<py::object(
const Object& self, py::args args, py::kwargs kwargs)>;
std::unordered_map<std::string, MagicMethodImplType> special_magic_methods{
{"__str__",
[](const Object& self, py::args args, py::kwargs kwargs) -> py::object {
auto method = self.find_method("__str__");
if (!method) {
return py::str("ScriptObject");
}
return invokeScriptMethodFromPython(
*method, std::move(args), std::move(kwargs));
}}};
for (const char* mm_name : magic_method_names) {
if (special_magic_methods.count(mm_name)) {
object_class.def(mm_name, special_magic_methods[mm_name]);
} else {
object_class.def(
mm_name,
[mm_name](const Object& self, py::args args, py::kwargs kwargs) {
auto method = self.find_method(mm_name);
if (!method) {
throw NotImplementedError();
}
return invokeScriptMethodFromPython(
*method, std::move(args), std::move(kwargs));
});
}
}
// NOLINTNEXTLINE(bugprone-unused-raii)
py::class_<DeepCopyMemoTable>(m, "DeepCopyMemoTable");
object_class.def(
"__deepcopy__", [](const Object& self, const py::dict& memo) {
return Object(
pyIValueDeepcopy(IValue(self._ivalue()), memo).toObject());
});
// torch.jit.ScriptModule is a subclass of this C++ object.
// Methods here are prefixed with _ since they should not be
// public.
py::class_<Module, Object>(m, "ScriptModule")
.def(py::init<std::string, std::shared_ptr<CompilationUnit>, bool>())
.def(
"save",
[](Module& m,
const std::string& filename,
const ExtraFilesMap& _extra_files = ExtraFilesMap()) {
m.save(filename, _extra_files);
},
py::arg("filename"),
py::arg("_extra_files") = ExtraFilesMap())
.def(
"save_to_buffer",
[](Module& m, const ExtraFilesMap& _extra_files = ExtraFilesMap()) {
std::ostringstream buf;
m.save(buf, _extra_files);
return py::bytes(buf.str());
},
py::arg("_extra_files") = ExtraFilesMap())
.def(
"_save_for_mobile",
[](Module& m,
const std::string& filename,
const ExtraFilesMap& _extra_files = ExtraFilesMap(),
bool _save_mobile_debug_info = false) {
m._save_for_mobile(filename, _extra_files, _save_mobile_debug_info);
},
py::arg("filename"),
py::arg("_extra_files") = ExtraFilesMap(),
py::arg("_save_mobile_debug_info") = false)
.def(
"_save_to_buffer_for_mobile",
[](Module& m,
const ExtraFilesMap& _extra_files = ExtraFilesMap(),
bool _save_mobile_debug_info = false) {
std::ostringstream buf;
m._save_for_mobile(buf, _extra_files, _save_mobile_debug_info);
return py::bytes(buf.str());
},
py::arg("_extra_files") = ExtraFilesMap(),
py::arg("_save_mobile_debug_info") = false)
.def("_set_optimized", &Module::set_optimized)
.def(
"dump",
&Module::dump,
py::arg("code") = true,
py::arg("attrs") = true,
py::arg("params") = true)
.def(
"dump_to_str",
&Module::dump_to_str,
py::arg("code") = true,
py::arg("attrs") = true,
py::arg("params") = true,
py::arg("indent") = 0)
.def(
"_replicate_for_data_parallel",
[](Module& module) {
const ModulePtr& obj = module._ivalue();
auto copy = c10::ivalue::Object::create(
c10::StrongTypePtr(obj->compilation_unit(), obj->type()),
obj->slots().size());
for (size_t i = 0; i < obj->slots().size(); ++i) {
copy->setSlot(i, obj->getSlot(i));
}
return Module(std::move(copy));
})
.def(
"get_debug_state",
[](Module& self) {
if (auto m = self.find_method("forward")) {
return m->get_executor().getDebugState();
}
throw std::runtime_error(
"Attempted to call get_debug_state on a Module without a compiled forward()");
})
.def(
"_define",
[](Module& m,
std::shared_ptr<ConcreteModuleType> concreteType,
const std::string& script,
const ResolutionCallback& rcb) {
const auto self = ModuleSelf(std::move(concreteType));
m._ivalue()->compilation_unit()->define(
*m.type()->name(), script, pythonResolver(rcb), &self);