-
Notifications
You must be signed in to change notification settings - Fork 21.4k
/
model.py
1008 lines (899 loc) · 40 KB
/
model.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
import re
from dataclasses import dataclass
from typing import List, Dict, Optional, Iterator, Tuple, Set, NoReturn
from enum import Enum
import itertools
# A little trick from https://github.com/python/mypy/issues/6366
# for getting mypy to do exhaustiveness checking
# TODO: put this somewhere else, maybe
def assert_never(x: NoReturn) -> NoReturn:
raise AssertionError("Unhandled type: {}".format(type(x).__name__))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# DATA MODEL
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# Some general principles for our data model.
#
# - Stop using C++ data types as the internal data representation
# format. Instead, the internal data structures are centered
# around JIT schema representation. This avoid a big problem
# with the old codegen where we read in all the types from
# native_functions.yaml and then immediately had to retranslate
# them into C++ types.
#
# - More semantic data representation. Instead of representing
# everything as dicts and strings, we define dataclasses for
# every interesting entity the code generation has to deal with.
# These dataclasses have strong semantic invariants: for example,
# we generally require them to roundtrip losslessly into the
# form they were parsed from. These structures are immutable
# and you're expected to populate information once during
# construction.
# Represent a source location; used for better error reporting
@dataclass(frozen=True)
class Location:
file: str
line: int
def __str__(self) -> str:
return "{}:{}".format(self.file, self.line)
# Valid values of the 'variants' field in native_functions.yaml
Variant = Enum('Variant', ('function', 'method'))
class UseC10Dispatcher(Enum):
full = 0
with_codegenerated_unboxing_wrapper = 1
hacky_wrapper_for_legacy_signatures = 2
def dispatcher_uses_new_style(self) -> bool:
return self in [UseC10Dispatcher.full, UseC10Dispatcher.hacky_wrapper_for_legacy_signatures]
# The basic input to the code generation is native_functions.yaml.
# The name "native", BTW, comes from the distinction between native
# functions and legacy TH functions. The legacy TH functions are gone,
# but the "native" descriptor has stuck.
#
# NativeFunction models a single entry in native_functions.yaml. Its
# fields roughly correspond to what you would see in the YAML itself,
# but after canonicalization and parsing has occurred.
#
# You can see some of the overall design patterns for how we setup
# dataclasses in this class, but we will defer a complete discussion
# of this at FunctionSchema.
@dataclass(frozen=True)
class NativeFunction:
# The function schema of the operator in question. This schema
# has been parsed; see FunctionSchema for more about its structure.
# (This type is quoted as we are forward referencing a type
# defined later in the file. I opted for this ordering of the
# classes for expository clarity.)
func: 'FunctionSchema'
# Corresponds to the 'use_c10_dispatcher' field. The default
# is 'with_codegenerated_unboxing_wrapper'
use_c10_dispatcher: UseC10Dispatcher
# Whether or not to omit automatic generation of a DeviceGuard
device_guard: bool
# What python module to put the function in
python_module: Optional[str]
# TODO: figure out what this does
category_override: Optional[str]
# If no variants are specified in native_functions.yaml, this is
# assumed to be {'function'}.
variants: Set[Variant]
# Whether or not we should skip generating registrations for
# this kernel. This is a bit of a double-edged sword, as manual
# registrations don't participate in codegen-based selective build!
manual_kernel_registration: bool
# A mapping of dispatch keys to names of functions implementing
# them. In native_functions.yaml, the dispatch entry is optional; in that
# case, that is equivalent to having written:
#
# dispatch:
# Math: $operator_name
#
# TODO: str key could be replaced with more explicit enum
dispatch: Dict[str, str]
# The location in the YAML file were this native function entry was
# defined. This is for conveniently reporting error messages!
loc: 'Location'
# Whether or not this out functions is a "structured kernel". Structured
# kernels are defined a little differently from normal kernels; in
# particular, their shape checking logic is defined separately from
# the kernel. Only out functions can be structured; other functions
# delegate to the out function using the structured_delegate keyword.
# Every structured kernel must have at least an out and a functional
# variant.
structured: bool
# Whether or not this non-out function is a structured kernel, defined
# in terms of the out kernel referenced by the string here.
structured_delegate: Optional['OperatorName']
# Note [Abstract ATen methods]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# An abstract ATen method is one whose dispatch differs between
# types. These are implemented in derived types (with a
# standard (throwing) definition in Type). A concrete ATen
# method is one which has the same dispatch for all types;
# we just implement it in the base Type. This is exposed
# in Declarations.yaml via a field named 'abstract'.
@property
def is_abstract(self) -> bool:
if self.structured_delegate:
# Structured functions MUST have a dispatch table
return True
else:
return self.dispatch.keys() != {'Math'}
# NB: The benefit of defining a dataclass is that we automatically get
# a constructor defined for all the fields we specify. No need
# to explicitly write it out.
@staticmethod
def from_yaml(ei: Dict[str, object], loc: 'Location') -> 'NativeFunction':
"""
Parse a NativeFunction from a dictionary as directly parsed
from native_functions.yaml
"""
e = ei.copy()
funcs = e.pop('func')
assert isinstance(funcs, str), f'not a str: {funcs}'
func = FunctionSchema.parse(funcs)
use_c10_dispatcher_s = e.pop('use_c10_dispatcher', None)
if use_c10_dispatcher_s is None:
use_c10_dispatcher = UseC10Dispatcher.with_codegenerated_unboxing_wrapper
elif use_c10_dispatcher_s == 'full':
use_c10_dispatcher = UseC10Dispatcher.full
elif use_c10_dispatcher_s == 'hacky_wrapper_for_legacy_signatures':
use_c10_dispatcher = UseC10Dispatcher.hacky_wrapper_for_legacy_signatures
else:
raise AssertionError(
f'use_c10_dispatcher must be unset or set to full, got {use_c10_dispatcher}')
variants_s = e.pop('variants', 'function')
assert isinstance(variants_s, str)
variants: Set[Variant] = set()
for v in variants_s.split(', '):
if v == 'function':
variants.add(Variant.function)
elif v == 'method':
variants.add(Variant.method)
else:
raise AssertionError(f'illegal variant {v}')
manual_kernel_registration = e.pop('manual_kernel_registration', False)
assert isinstance(manual_kernel_registration, bool), f'not a bool: {manual_kernel_registration}'
device_guard = e.pop('device_guard', True)
assert isinstance(device_guard, bool), f'not a bool: {device_guard}'
structured = e.pop('structured', False)
assert isinstance(structured, bool), f'not a bool: {structured}'
structured_delegate_s = e.pop('structured_delegate', None)
assert structured_delegate_s is None or isinstance(structured_delegate_s, str), f'not a str: {structured_delegate}'
structured_delegate: Optional[OperatorName] = None
if structured_delegate_s is not None:
structured_delegate = OperatorName.parse(structured_delegate_s)
python_module = e.pop('python_module', None)
assert python_module is None or isinstance(python_module, str), f'not a str: {python_module}'
category_override = e.pop('category_override', None)
assert category_override is None or isinstance(category_override, str), f'not a str: {category_override}'
raw_dispatch = e.pop('dispatch', None)
assert raw_dispatch is None or isinstance(raw_dispatch, dict), e
dispatch: Dict[str, str] = {}
if raw_dispatch is not None:
for ks, v in raw_dispatch.items():
if ks == '__line__':
continue # not worth tracking line numbers for dispatch entries
assert isinstance(ks, str), e
assert isinstance(v, str), e
for k in ks.split(","):
dispatch[k.strip()] = v
else:
from tools.codegen.api import cpp
dispatch['Math'] = cpp.name(func)
assert not ('DefaultBackend' in dispatch and 'Math' in dispatch), \
"cannot specify both DefaultBackend and Math on a single kernel; each " \
"strictly subsumes the other. If you wanted to provide an explicit autograd " \
"implementation, specify DefaultBackend; otherwise specify Math only"
e.pop('__line__')
assert not e, f"leftover entries: {e}"
return NativeFunction(
func=func,
use_c10_dispatcher=use_c10_dispatcher,
variants=variants,
structured=structured,
structured_delegate=structured_delegate,
manual_kernel_registration=manual_kernel_registration,
python_module=python_module,
category_override=category_override,
dispatch=dispatch,
device_guard=device_guard,
loc=loc,
)
def validate_unstructured(self) -> None:
# TODO: probably better to accumulate these errors and report them all
# at once
assert not self.structured, "This function is structured, but there was " \
"no valid functional variant of it."
assert self.structured_delegate, "This function delegates to another structured out function, " \
"but no valid function was found (the delegate may not exist, or it has the wrong type)"
# __post_init__ functions in dataclasses can be used to do extra
# validation after construction.
#
# Notice that we don't do any type validation here. In fact, we
# rely exclusively on mypy to check if you've done types correctly!
# Validation is for nontrivial invariants that cannot be (conveniently)
# encoded in the type system.
def __post_init__(self) -> None:
if self.func.out_arguments:
assert self.variants == {Variant.function}, "Native functions with out arguments MUST " \
"be declared with only function variant; e.g., variants: function; " \
"otherwise you will tickle a Python argument binding bug " \
"(which usually manifests itself as the result variable being undefined.)"
if self.structured:
assert self.func.kind() == SchemaKind.out, "Put structured field on the out= " \
"variant of a function; did you mean structured_delegate?"
if self.structured_delegate:
assert self.func.kind() != SchemaKind.out, "structured_delegate field not allowed " \
"on out= functions; did you mean structured?"
# Technically, with the asserts above, this assert is impossible to
# happen
assert not (self.structured and self.structured_delegate), \
"Cannot have both structured and structured_delegate on function"
SchemaKind = Enum('SchemaKind', ('functional', 'inplace', 'out'))
# A structured kernel is guaranteed to have a functional and out variant, and
# optionally an inplace variant.
@dataclass(frozen=True)
class StructuredNativeFunctions:
functional: NativeFunction
inplace: Optional[NativeFunction]
out: NativeFunction
def __post_init__(self) -> None:
test_sig: FunctionSchema = self.functional.func.signature()
for f in self.functions():
if test_sig != f.func.signature():
raise AssertionError(
"StructuredNativeFunctions constructed from two NativeFunctions "
f"that don't have matching signatures: {test_sig} != {f.func.signature()}"
)
assert self.functional.func.kind() == SchemaKind.functional
assert self.functional.structured_delegate == self.out.func.name, \
f"{self.functional.func.name} delegates to {self.functional.structured_delegate} " \
f"but its actual delegate is {self.out.func.name}"
assert self.out.func.kind() == SchemaKind.out
assert self.out.structured
# For now, structured composite kernels are not supported (need some
# design work to figure out how to make the composite case work)
assert self.out.dispatch.keys() != {'Math'}
if self.inplace is not None:
assert self.inplace.func.kind() == SchemaKind.inplace
assert self.inplace.structured_delegate == self.out.func.name
def signature(self) -> 'FunctionSchema':
return self.out.func.signature()
def functions(self) -> Iterator[NativeFunction]:
yield self.out
yield self.functional
if self.inplace is not None:
yield self.inplace
@staticmethod
def from_dict(d: Dict[SchemaKind, NativeFunction]) -> Optional['StructuredNativeFunctions']:
functional = d.get(SchemaKind.functional)
inplace = d.get(SchemaKind.inplace)
out = d.get(SchemaKind.out)
if functional is None or out is None or not out.structured:
return None
return StructuredNativeFunctions(
functional=functional,
inplace=inplace,
out=out,
)
# The function schema is undoubtedly the most important data structure
# in all of the codegen, as it defines the type signature for operators,
# and most of the code generation we do is type directed (e.g., look at
# the types, decide what to do. Think about how we code generate
# C++ function stubs!)
#
# We will also see in this class the general structure for how we model
# data in this code generation. A few notable properties to point out
# ahead of time:
#
# - These dataclasses are a *lossless* representation of the strings
# they are parsed from. In fact, we assert that given the
# information stored in the dataclass, we can exactly reconstruct
# the string we parsed from (and assert this inside the parse
# definition). There are a few reasons for this:
#
# - If you find that it is difficult to reconstruct the string
# given a dataclass, that is a clue that you are data
# representation is wrong.
#
# - It helps ensure that all relevant information is present
# in the dataclass, so that downstream users aren't tempted
# to reparse the original string to get some information
# that was omitted.
#
# - It forces you to represent the data in-memory in the same way
# it is recorded textually, which makes the dataclasses easier
# to understand for someone who is familiar with the
# textual format. (As a tradeoff, it means you have to model
# the syntax, even when it is inconvenient. But maybe that means
# the syntax is bad!) If you don't understand the internal
# representation, go look at the printing code to see how
# it maps onto the surface syntax!
#
# - It makes it easy to test the parsing code, as parsing code
# that is inconsistent with the string code will fail early
# and loudly. (As a tradeoff, it makes the parsing code a bit
# brittle (in particular, with trivial whitespace changes you
# are likely to trigger an assert error).
#
# In general, try to make the __str__ code as simple as possible
# (even at the cost of more complex parsing logic.) Additionally,
# try to minimize redundancy in data representation. (Precomputed
# fields are OK though: they are defined as a simple function on
# the canonical representation in question.)
#
# - These dataclasses are all frozen; once constructed their
# values never change. This makes it easy to tell where any
# given data came from: just look to the constructor. As a
# tradeoff, you can't easily "decorate" a schema with extra
# information from a post-facto analysis. We impose this
# restriction to make these structures more understandable.
#
@dataclass(frozen=True)
class FunctionSchema:
# The name of the operator this function schema describes.
name: 'OperatorName'
arguments: Tuple['Argument', ...]
kwarg_only_arguments: Tuple['Argument', ...] # but not including out args
# Unlike in the previous codegen, we have factored out 'out' arguments
# in the canonical representation, removing them from kwarg
# arguments. This choice is justified by numerous downstream
# transformations which treat out arguments specially; additionally,
# you can see that canonicity is not violated!
out_arguments: Tuple['Argument', ...] # these are also kwarg-only
# TODO: Need to handle collisions with argument names at some point
returns: Tuple['Return', ...]
def schema_order_arguments(self) -> Iterator['Argument']:
return itertools.chain(self.arguments, self.kwarg_only_arguments, self.out_arguments)
@staticmethod
def parse(func: str) -> 'FunctionSchema':
# We should probably get a proper parser here
assert ' -> ' in func, "function schema missing return type (spaces are mandatory)"
func_decl, return_decl = [x.strip() for x in func.split(' -> ')]
ops, args = func_decl.split('(', 1)
assert args[-1] == ")", "Expecting closing )"
args = args[:-1]
name = OperatorName.parse(ops)
arguments, kwarg_only_arguments, out_arguments = parse_arguments(args)
returns = parse_returns(return_decl)
r = FunctionSchema(
name=name,
arguments=arguments,
kwarg_only_arguments=kwarg_only_arguments,
out_arguments=out_arguments,
returns=returns
)
assert str(r) == func, f'{str(r)} != {func}'
return r
def __post_init__(self) -> None:
for arg, ret in zip(self.out_arguments, self.returns):
assert arg.annotation == ret.annotation, \
"Out arguments must have matching return Tensor; furthermore, " \
"the ith-argument needs to correspond to the ith return"
# Invariant: we expect out arguments to appear as keyword arguments in the schema.
# This means that all mutable returns should be aliased to a keyword argument
# (except for "self", which we explicitly don't treat as an out argument because of its use in methods)
# See Note [is_out_fn]
out_and_self = list(self.out_arguments) + [arg for arg in self.arguments if arg.name == "self"]
mutable_returns = [ret for ret in self.returns if ret.annotation is not None and ret.annotation.is_write]
for ret in mutable_returns:
assert any([ret.annotation == arg.annotation for arg in out_and_self]), \
"All mutable returns must be aliased either to a keyword argument, or to \"self\". " \
"Did you forget to mark an out argument as keyword-only?"
if self.out_arguments:
assert len(self.out_arguments) == len(self.returns), \
"Must return as many arguments as there are out arguments"
if self.name.name.inplace:
# TODO: fixme
if str(self.name) not in [
'_amp_foreach_non_finite_check_and_unscale_',
'_foreach_add_.ScalarList',
'_foreach_sub_.ScalarList',
'_foreach_mul_.ScalarList',
'_foreach_div_.ScalarList',
'_foreach_add_.Scalar',
'_foreach_sub_.Scalar',
'_foreach_mul_.Scalar',
'_foreach_div_.Scalar',
'_foreach_add_.List',
'_foreach_sub_.List',
'_foreach_mul_.List',
'_foreach_div_.List',
'_foreach_exp_',
'_foreach_sqrt_',
'_foreach_abs_',
'_foreach_acos_',
'_foreach_asin_',
'_foreach_atan_',
'_foreach_ceil_',
'_foreach_cos_',
'_foreach_cosh_',
'_foreach_erf_',
'_foreach_erfc_',
'_foreach_expm1_',
'_foreach_floor_',
'_foreach_log_',
'_foreach_log10_',
'_foreach_log1p_',
'_foreach_log2_',
'_foreach_neg_',
'_foreach_tan_',
'_foreach_tanh_',
'_foreach_sin_',
'_foreach_sinh_',
'_foreach_round_',
'_foreach_lgamma_',
'_foreach_frac_',
'_foreach_addcmul_.Scalar',
'_foreach_addcdiv_.Scalar',
'_foreach_addcmul_.ScalarList',
'_foreach_addcdiv_.ScalarList']:
assert len(self.returns) == 1
def is_out_fn(self) -> bool:
# Note [is_out_fn]
#
# out functions are the variants which take an explicit out= argument
# to populate into. We need to know if a schema corresponds to an
# out function for several reasons:
#
# - They codegen differently in C++ API
# - codegen to at::add_out rather than at::add
# - out argument is moved to front of C++ argument list
#
# out functions are DEFINED to be any function with a keyword-only
# argument that is mutable. In principle, this could lead to a
# false positive if you define a function that mutates a
# kwarg only argument, but this isn't the "true" output of this
# function. A more robust definition that would work in this
# case would also look at:
#
# - The output types. Out functions take in the arguments
# they mutate and then return them again; this is sort
# of "definitionally" what makes something an out function.
# Historically, we DO check this for consistency.
# - Correspondence with pure variant. An out function
# should have a signature equivalent to its pure variant,
# but just with extra kwargs for the output elements. This
# is difficult to actually check for and historically
# we only do this check in tools/
return bool(self.out_arguments)
def kind(self) -> SchemaKind:
"""
What kind of schema is this? A functional schema is one
that returns a newly allocated output; an inplace schema
modifies the self argument inplace; an out schema writes
the result into an explicitly provided out argument.
"""
is_inplace = self.name.name.inplace
is_out = bool(self.out_arguments)
assert not (is_inplace and is_out)
if is_inplace:
return SchemaKind.inplace
elif is_out:
return SchemaKind.out
else:
return SchemaKind.functional
# WARNING: This method is not currently tested in any meaningful way
def signature(self) -> 'FunctionSchema':
"""
Certain schemas are 'related', in that they are simply
inplace/out/functional versions of the same function. This method
factors these schemas into the "core" functional signature which
is equal across all versions.
Here is what normalization happens to the schema to convert
it to a signature:
- The overload name is stripped (name is retained, since
it expresses semantic content about what the function does)
- Inplace is set False
- Out arguments are stripped
- Mutability annotations are stripped (this is sound
because you cannot overload on mutability annotation)
This function is based off of get_signature in
tools.autograd.load_derivatives
"""
# dataclasses.replace could be used here, but it is less
# type safe so for now I've opted to type everything out
def strip_arg_annotation(a: Argument) -> Argument:
return Argument(
name=a.name,
type=a.type,
default=a.default, # hmmm
annotation=None,
)
def strip_ret_annotation(r: Return) -> Return:
return Return(
name=r.name,
type=r.type,
annotation=None,
)
return FunctionSchema(
name=OperatorName(
name=BaseOperatorName(
base=self.name.name.base,
inplace=False,
dunder_method=self.name.name.dunder_method,
),
overload_name="", # stripped
),
arguments=tuple(map(strip_arg_annotation, self.arguments)),
kwarg_only_arguments=tuple(map(strip_arg_annotation, self.kwarg_only_arguments)),
out_arguments=(), # stripped
returns=tuple(map(strip_ret_annotation, self.returns)),
)
def __str__(self) -> str:
all_arguments: List[str] = []
all_arguments.extend(map(str, self.arguments))
if self.kwarg_only_arguments or self.out_arguments:
all_arguments.append('*')
all_arguments.extend(map(str, self.kwarg_only_arguments))
all_arguments.extend(map(str, self.out_arguments))
all_arguments_str = ', '.join(all_arguments)
if len(self.returns) == 1:
returns = str(self.returns[0]) # omit parentheses
else:
returns = '(' + ', '.join(map(str, self.returns)) + ')'
return f'{self.name}({all_arguments_str}) -> {returns}'
# Here is the rest of the data model, described more briefly.
# Simplified version for what actually shows up in built-ins.
# Look at alias_info.h for expanded syntax. If you need the structure,
# you also need to make this structure recursive so it can be lined
# up with the type components too. For primitives this isn't really
# necessary
@dataclass(frozen=True)
class Annotation:
# Typically only has one element. Not actually a set so
# we can conveniently assume it is canonically ordered
alias_set: Tuple[str, ...]
is_write: bool
@staticmethod
def parse(ann: str) -> 'Annotation':
m = re.match(r'^([a-z])(!?)$', ann)
assert m is not None, f'unrecognized alias annotation {ann}'
alias_set = (m.group(1),)
is_write = m.group(2) == '!'
r = Annotation(alias_set=alias_set, is_write=is_write)
assert str(r) == ann, f'{r} != {ann}'
return r
def __str__(self) -> str:
alias_set = '|'.join(self.alias_set)
is_write = '!' if self.is_write else ''
return f'{alias_set}{is_write}'
# The base class for the type system. This is also loosely modeled
# off of jit_type.h, but we've simplified the hierarchy to focus
# in on the aspects of the type system that matter for code generation
# (for example, there's no SingleElementType subclass anymore).
# You never actually construct a Type; usually it's going to be one
# of the subclasses. If Python had ADTs this would be one!
@dataclass(frozen=True)
class Type:
@staticmethod
def parse(t: str) -> 'Type':
r = Type._parse(t)
assert str(r) == t, f'{r} != {t}'
return r
@staticmethod
def _parse(t: str) -> 'Type':
m = re.match(r'^(.+)\?$', t)
if m is not None:
return OptionalType(Type.parse(m.group(1)))
m = re.match(r'^(.+)\[([0-9]+)?\]$', t)
if m is not None:
size = int(m.group(2)) if m.group(2) is not None else None
return ListType(elem=Type.parse(m.group(1)), size=size)
try:
return BaseType(BaseTy[t])
except KeyError:
raise RuntimeError(f"unrecognized type {t}")
def __str__(self) -> str:
raise NotImplementedError
# WARNING: These concepts are not very well-defined. For example,
# is "int?" nullable? How about "int?[]". They are defined
# so we can conveniently generate legacy Declarations.yaml but
# really we should probably just remove these at some point
def is_tensor_like(self) -> bool:
raise NotImplementedError
def is_nullable(self) -> bool:
raise NotImplementedError
def is_list_like(self) -> Optional['ListType']:
raise NotImplementedError
# Base types are simple, atomic types with no further structure
BaseTy = Enum('BaseTy', (
'Generator',
'ScalarType',
'Tensor',
'int',
'Dimname',
'float',
'str',
'bool',
'Layout',
'Device',
'Scalar',
'MemoryFormat',
'QScheme',
'Storage',
'Stream',
'ConstQuantizerPtr', # TODO: rename
))
@dataclass(frozen=True)
class BaseType(Type):
name: BaseTy
def __str__(self) -> str:
return f'{self.name.name}'
def is_tensor_like(self) -> bool:
return self.name == BaseTy.Tensor
def is_nullable(self) -> bool:
return False
def is_list_like(self) -> Optional['ListType']:
return None
# Optional types may be specified, or may also be validly given None
@dataclass(frozen=True)
class OptionalType(Type):
elem: Type
def __str__(self) -> str:
return f'{self.elem}?'
def is_tensor_like(self) -> bool:
return self.elem.is_tensor_like()
def is_nullable(self) -> bool:
return True
def is_list_like(self) -> Optional['ListType']:
return self.elem.is_list_like()
# List types specify that we may have multiples of an element. We
# also support explicit sizes on list types, but these have
# some nontrivial semantics! (However, for C++ API purposes, explicit
# sizes are mostly erased from the type system.)
#
# DANGER WILL ROBINSON: C++ elaboration depends on elem type; e.g.,
# int[] elaborates differently than bool[3]!
@dataclass(frozen=True)
class ListType(Type):
elem: Type
size: Optional[int]
def __str__(self) -> str:
size = f'{self.size}' if self.size else ''
return f'{self.elem}[{size}]'
def is_tensor_like(self) -> bool:
return self.elem.is_tensor_like()
def is_nullable(self) -> bool:
return self.elem.is_nullable()
def is_list_like(self) -> Optional['ListType']:
return self
@dataclass(frozen=True)
class Argument:
# NB: I didn't put kwarg_only as a boolean field here, unlike
# c10::Argument, so that printing works correctly
name: str
type: Type
default: Optional[str]
# The semantics of the annotation field are a little strange.
#
# Alias annotations parametrize Tensors (since Tensors are the only things
# that can alias.) This motivates why I write Tensor(a!)? (and not, for
# example, Tensor?(a!)), because the (a!) describes aliasing on the tensor,
# which may be optional (i.e., the alias annotation should bind first to
# Tensor, before the optional postfix annotation).
#
# However, despite being a property of Tensor, we (and c10::Argument)
# store the annotation at the top level of the Argument, rather than
# inside the embedded Tensor type. In the C++ version of this
# class, we then go through great lengths to mimic the type
# structure in the annotation structure so we can correlate
# annotations with types.
#
# Now, it turns out, in all applications in code generation, the
# structure of annotated types is very simple. So we just hard
# code it here. But if we ever do get anything more complex, this
# model will have to change!
annotation: Optional[Annotation]
@staticmethod
def parse(arg: str) -> 'Argument':
name: str
default: Optional[str]
type_and_annot, name_and_default = arg.rsplit(' ', 1)
if '=' in name_and_default:
name, default = name_and_default.split('=')
else:
name = name_and_default
default = None
# TODO: deduplicate annotation matching with Return
match = re.match(r'Tensor\((.+)\)(.*)', type_and_annot)
annotation: Optional[Annotation]
if match:
# If you update this, make sure the __str__ still works too
assert match.group(2) in ['', '?', '[]'], 'unrecognized alias analysis form with Tensor'
type_s = 'Tensor' + match.group(2)
annotation = Annotation.parse(match.group(1))
else:
type_s = type_and_annot
annotation = None
type = Type.parse(type_s)
r = Argument(
name=name,
type=type,
default=default,
annotation=annotation,
)
assert str(r) == arg, f'{str(r)} != {arg}'
return r
@property
def is_write(self) -> bool:
return self.annotation is not None and self.annotation.is_write
def __str__(self) -> str:
type = f'{self.type}'
if self.annotation:
assert type in ['Tensor', 'Tensor?', 'Tensor[]']
type = type.replace('Tensor', f'Tensor({self.annotation})')
if self.name is None:
return type
else:
mb_default = ''
if self.default:
mb_default = f'={self.default}'
return f"{type} {self.name}{mb_default}"
@dataclass(frozen=True)
class Return:
name: Optional[str]
type: Type
annotation: Optional[Annotation]
@staticmethod
def parse(arg: str) -> 'Return':
name: Optional[str]
if ' ' in arg:
type_and_annot, name = arg.rsplit(' ', 1)
else:
type_and_annot = arg
name = None
match = re.match(r'Tensor\((.+)\)(.*)', type_and_annot)
annotation: Optional[Annotation]
if match:
# If you update this, make sure the __str__ still works too
assert match.group(2) in ['', '?', '[]'], 'unrecognized alias analysis form with Tensor'
type_s = 'Tensor' + match.group(2)
annotation = Annotation.parse(match.group(1))
else:
type_s = type_and_annot
annotation = None
type = Type.parse(type_s)
r = Return(
name=name,
type=type,
annotation=annotation,
)
assert str(r) == arg, f'{str(r)} != {arg}'
return r
@property
def is_write(self) -> bool:
return self.annotation is not None and self.annotation.is_write
def __str__(self) -> str:
type = f'{self.type}'
if self.annotation:
assert type in ['Tensor', 'Tensor?', 'Tensor[]']
type = type.replace('Tensor', f'Tensor({self.annotation})')
if self.name is None:
return type
else:
return f"{type} {self.name}"
# Names that validly are __iXXX__ indicating inplace operations.
# Taken from https://www.python.org/dev/peps/pep-0203/#new-methods
# NB: PyTorch hasn't actually implemented all of these
AUGMENTED_ASSIGNMENT_NAMES = ['add', 'sub', 'mul', 'div', 'mod', 'pow', 'lshift', 'rshift', 'and', 'xor', 'or']
# A BaseOperatorName is what we think of the operator name, without
# the overload name. Unusually, we don't represent this as just a
# string; instead, we directly represent a few important semantic
# bits of information we derive from the string: namely whether
# or not it's inplace (add_) and whether or not it's a double-underscore
# method (__add__)
@dataclass(frozen=True)
class BaseOperatorName:
base: str
inplace: bool
dunder_method: bool
@staticmethod
def parse(op: str) -> 'BaseOperatorName':
assert op != ''
assert not op.endswith('_out'), \
"_out suffix is reserved and not permitted for operator names; " \
"did you mean to specify an out overload name instead?"
m = re.match(r'^__([^_]+)__$', op)
if m is not None:
dunder_method = True
base = m.group(1)
if any(base == f'i{n}' for n in AUGMENTED_ASSIGNMENT_NAMES):
inplace = True
base = base[1:]
else:
inplace = False
# temporary, this is not intrinsically true but
# has been historically true for dunder methods
# we support (but, if we ever got, say, __int__, this would
# be wrong!)
assert base[0] != 'i'
else:
dunder_method = False
base = op
if base[-1] == '_':
inplace = True
base = base[:-1]
else:
inplace = False
r = BaseOperatorName(base=base, inplace=inplace, dunder_method=dunder_method)
assert str(r) == op, f'{str(r)} != {op}'
return r
def __str__(self) -> str:
if self.dunder_method:
i = 'i' if self.inplace else ''
return f'__{i}{self.base}__'
else:
i = '_' if self.inplace else ''
return f'{self.base}{i}'
# Operator name is the base operator name along with the (typically not
# user visible) overload string.
@dataclass(frozen=True)
class OperatorName:
name: BaseOperatorName
overload_name: str
@staticmethod
def parse(op_name: str) -> 'OperatorName':
if '.' in op_name:
name, overload_name = op_name.split('.', 1)
else:
name = op_name
overload_name = ''
r = OperatorName(
name=BaseOperatorName.parse(name),
overload_name=overload_name
)
assert str(r) == op_name, f'{str(r)} != {op_name}'
return r
def __str__(self) -> str:
if self.overload_name:
return f"{self.name}.{self.overload_name}"
else:
return f"{self.name}"
# Helper functions for parsing argument lists (both inputs and returns)
def parse_returns(return_decl: str) -> Tuple[Return, ...]:
"""
Input: '()'
Output: []
"""
if return_decl == '()':
return ()
if return_decl[0] == '(' and return_decl[-1] == ')':
return_decl = return_decl[1:-1]
return tuple(Return.parse(arg) for arg in return_decl.split(', '))
def parse_arguments(args: str) -> Tuple[Tuple[Argument, ...], Tuple[Argument, ...], Tuple[Argument, ...]]:
"""
Input: 'int x, int y, int z'
Output: positional args, kwarg only args
"""
arguments: List[Argument] = []
kwarg_only_arguments: List[Argument] = []
out_arguments: List[Argument] = []
arguments_acc = arguments
# TODO: Use a real parser here; this will get bamboozled
# by signatures that contain things like std::array<bool, 2> (note the space)
for arg in args.split(', '):
if not arg:
continue
if arg == '*':
assert arguments_acc is arguments, "invalid syntax: kwarg-only specifier * can only occur once"
arguments_acc = kwarg_only_arguments
continue
parg = Argument.parse(arg)
# Currently, we rely directly on the invariant that there are NO
# kwarg-only mutating arguments. If you want to relax this,
# we will need a more semantic way of matching that takes
# into account return arguments. In that case, you will have
# to manage out_arguments computation a level up, in
# FunctionSchema. See Note [is_out_fn]
if parg.annotation is not None and parg.annotation.is_write:
if arguments_acc is arguments: