Spaces:
Running
Running
File size: 68,151 Bytes
c61ccee |
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 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 |
# mypy: ignore-errors
import contextlib
import functools
import inspect
import itertools
import logging
import math
import operator
import types
from collections import defaultdict, OrderedDict
from typing import Dict, List
import torch
from torch import sym_float, sym_int
from .. import config, polyfill, variables
from ..exc import (
AttributeMutationError,
unimplemented,
Unsupported,
UserError,
UserErrorType,
)
from ..guards import GuardBuilder, install_guard
from ..replay_record import DummyModule
from ..source import AttrSource, GetItemSource, is_constant_source, TypeSource
from ..utils import (
check_constant_args,
check_numpy_ndarray_args,
check_unspec_python_args,
extract_fake_example_value,
get_fake_value,
guard_if_dyn,
istype,
numpy_operator_wrapper,
proxy_args_kwargs,
tensortype_to_dtype,
)
from .base import MutableLocal, typestr, VariableTracker
from .constant import ConstantVariable
from .ctx_manager import EventVariable, StreamVariable
from .dicts import (
ConstDictVariable,
DefaultDictVariable,
DictView,
is_hashable,
SetVariable,
)
from .lists import (
BaseListVariable,
ListIteratorVariable,
ListVariable,
SizeVariable,
TupleIteratorVariable,
TupleVariable,
)
from .tensor import (
FakeItemVariable,
SymNodeVariable,
TensorVariable,
UnspecializedPythonVariable,
)
from .user_defined import UserDefinedVariable
log = logging.getLogger(__name__)
IN_PLACE_DESUGARING_MAP = {
operator.iadd: operator.add,
operator.isub: operator.sub,
operator.imul: operator.mul,
operator.ifloordiv: operator.floordiv,
operator.itruediv: operator.truediv,
operator.imod: operator.mod,
operator.imatmul: operator.imatmul,
operator.ilshift: operator.lshift,
operator.irshift: operator.rshift,
operator.ipow: operator.pow,
operator.iand: operator.and_,
operator.ior: operator.or_,
operator.ixor: operator.xor,
}
def _polyfill_call_impl(name):
"""Create a BuiltinVariable.call_{name} method that inlines through polyfill.{name}"""
def call_fn(self, tx, *args, **kwargs):
return tx.inline_user_function_return(
variables.UserFunctionVariable(fn), args, kwargs
)
fn = getattr(polyfill, name)
call_fn.__name__ = f"call_{name}"
return call_fn
class BuiltinVariable(VariableTracker):
_SENTINEL = object()
@classmethod
def create_with_source(cls, value, source):
install_guard(source.make_guard(GuardBuilder.BUILTIN_MATCH))
return BuiltinVariable(value, source=source)
@staticmethod
@functools.lru_cache(None)
def _constant_fold_functions():
fns = {
abs,
all,
any,
bool,
callable,
chr,
divmod,
float,
getattr,
int,
len,
max,
min,
ord,
pow,
repr,
round,
str,
str.format,
sum,
type,
operator.abs,
operator.pos,
operator.neg,
operator.not_,
operator.truth,
operator.invert,
operator.pow,
operator.mul,
operator.matmul,
operator.floordiv,
operator.truediv,
operator.mod,
operator.add,
operator.sub,
operator.getitem,
operator.length_hint,
operator.lshift,
operator.rshift,
operator.and_,
operator.or_,
operator.xor,
operator.ipow,
operator.imul,
operator.imatmul,
operator.ifloordiv,
operator.itruediv,
operator.imod,
operator.iadd,
operator.isub,
operator.ilshift,
operator.irshift,
operator.iand,
operator.ixor,
operator.ior,
operator.index,
}
fns.update(x for x in math.__dict__.values() if isinstance(x, type(math.sqrt)))
return fns
def can_constant_fold_through(self):
return self.fn in self._constant_fold_functions()
@staticmethod
@functools.lru_cache(None)
def _fx_graph_functions():
fns = {
operator.abs,
operator.pos,
operator.neg,
operator.not_,
operator.invert,
operator.pow,
operator.mul,
operator.matmul,
operator.floordiv,
operator.truediv,
operator.mod,
operator.add,
operator.lt,
operator.gt,
operator.ge,
operator.le,
operator.ne,
operator.eq,
operator.sub,
operator.getitem,
operator.length_hint,
operator.lshift,
operator.rshift,
operator.and_,
operator.or_,
operator.xor,
operator.ipow,
operator.imul,
operator.imatmul,
operator.ifloordiv,
operator.itruediv,
operator.imod,
operator.iadd,
operator.isub,
operator.ilshift,
operator.irshift,
operator.iand,
operator.ixor,
operator.ior,
}
return fns
@staticmethod
@functools.lru_cache(None)
def _binops():
# function -> ([forward name, reverse name, in-place name], in-place op)
fns = {
operator.add: (["__add__", "__radd__", "__iadd__"], operator.iadd),
operator.sub: (["__sub__", "__rsub__", "__isub__"], operator.isub),
operator.mul: (["__mul__", "__rmul__", "__imul__"], operator.imul),
operator.truediv: (
["__truediv__", "__rtruediv__", "__itruediv__"],
operator.itruediv,
),
operator.floordiv: (
["__floordiv__", "__rfloordiv__", "__ifloordiv__"],
operator.ifloordiv,
),
operator.mod: (["__mod__", "__rmod__", "__imod__"], operator.imod),
pow: (["__pow__", "__rpow__", "__ipow__"], operator.ipow),
operator.pow: (["__pow__", "__rpow__", "__ipow__"], operator.ipow),
operator.lshift: (
["__lshift__", "__rlshift__", "__ilshift__"],
operator.ilshift,
),
operator.rshift: (
["__rshift__", "__rrshift__", "__irshift__"],
operator.irshift,
),
# NB: The follow binary operators are not supported for now, since the
# corresponding magic methods aren't defined on SymInt / SymFloat:
# operator.matmul
# divmod
# operator.and_
# operator.or_
# operator.xor
}
return fns
@staticmethod
@functools.lru_cache(None)
def _binop_handlers():
# Multiple dispatch mechanism defining custom binop behavior for certain type
# combinations. Handlers are attempted in order, and will be used if the type checks
# match. They are expected to have the signature:
# fn(tx, arg0: VariableTracker, arg1: VariableTracker, options) -> VariableTracker
# Override table contains: op_fn -> [list of handlers]
op_handlers = {}
for (
op,
(magic_method_names, in_place_op),
) in BuiltinVariable._binops().items():
op_handlers[op] = []
op_handlers[in_place_op] = []
forward_name, reverse_name, inplace_name = magic_method_names
# User-defined args (highest precedence)
def user_defined_handler(
tx,
a,
b,
options,
forward_name=forward_name,
reverse_name=reverse_name,
):
# Manually handle reversing logic if needed (e.g. call __radd__)
# TODO: If we expand this to handle tensor args, we need to manually
# handle cases like this:
#
# class A(int):
# def __radd__(self, other):
# print("woof")
# torch.randn(3) + A(3)
#
# In this example, A.__radd__() is not called -> nothing is printed, because
# Tensor.__add__ only does a subtype test against int, ignoring the subclass.
# To be fully correct, we should not call A.__radd__() here, and there may be
# other cases to reason about and add exceptions for.
if isinstance(a, UserDefinedVariable):
return a.call_method(tx, forward_name, [b], {})
else:
return b.call_method(tx, reverse_name, [a], {})
op_handlers[op].append(
((UserDefinedVariable, VariableTracker), user_defined_handler)
)
op_handlers[op].append(
((VariableTracker, UserDefinedVariable), user_defined_handler)
)
def user_defined_inplace_handler(
tx, a, b, options, forward_name=inplace_name
):
return a.call_method(tx, forward_name, [b], {})
op_handlers[in_place_op].append(
((UserDefinedVariable, VariableTracker), user_defined_inplace_handler)
)
op_handlers[in_place_op].append(
((VariableTracker, UserDefinedVariable), user_defined_inplace_handler)
)
# Dynamic shape args
def dynamic_handler(tx, a, b, options, fn=op):
from .builder import wrap_fx_proxy
return wrap_fx_proxy(
tx,
tx.output.create_proxy(
"call_function", fn, *proxy_args_kwargs([a, b], {})
),
**options,
)
op_handlers[op].append(
((SymNodeVariable, VariableTracker), dynamic_handler)
)
op_handlers[op].append(
((VariableTracker, SymNodeVariable), dynamic_handler)
)
# NB: Prefer out-of-place op when calling in-place op to generate valid graph
op_handlers[in_place_op].append(
((SymNodeVariable, VariableTracker), dynamic_handler)
)
op_handlers[in_place_op].append(
((VariableTracker, SymNodeVariable), dynamic_handler)
)
# Special cases - lower precedence but still prefer these over constant folding
# List-like addition (e.g. [1, 2] + [3, 4])
def tuple_add_handler(tx, a, b, options):
return TupleVariable(a.items + list(b.unpack_var_sequence(tx)), **options)
def size_add_handler(tx, a, b, options):
return SizeVariable(a.items + list(b.unpack_var_sequence(tx)), **options)
list_like_addition_handlers = [
# NB: Prefer the tuple-specific logic over base logic because of
# some SizeVariable weirdness. Specifically, the tuple-specific logic
# drops the subclass type (e.g. SizeVariable) and returns TupleVariables.
(
(SizeVariable, SizeVariable),
size_add_handler,
),
(
(TupleVariable, TupleVariable),
tuple_add_handler,
),
(
(TupleVariable, ConstantVariable),
tuple_add_handler,
),
(
(ConstantVariable, TupleVariable),
lambda tx, a, b, options: TupleVariable(
list(a.unpack_var_sequence(tx)) + b.items, **options
),
),
(
(BaseListVariable, BaseListVariable),
lambda tx, a, b, options: type(a)(a.items + b.items, **options),
),
]
op_handlers[operator.add].extend(list_like_addition_handlers)
def list_iadd_handler(tx, a, b, _):
if not a.mutable_local or not b.has_unpack_var_sequence(tx):
# Handler doesn't apply
return None
seq = b.unpack_var_sequence(tx)
tx.output.side_effects.mutation(a)
a.items.extend(seq)
return a
list_like_iadd_handlers = [
(
(ListVariable, VariableTracker),
list_iadd_handler,
),
(
(TupleVariable, TupleVariable),
tuple_add_handler,
),
(
(TupleVariable, ConstantVariable),
tuple_add_handler,
),
]
op_handlers[operator.iadd].extend(list_like_iadd_handlers)
# List-like expansion (e.g. [1, 2, 3] * 3)
def expand_list_like(tx, lst, const, options):
return lst.__class__(
items=lst.items * const.as_python_constant(),
mutable_local=MutableLocal(),
**options,
)
list_like_expansion_handlers = [
((ListVariable, ConstantVariable), expand_list_like),
((TupleVariable, ConstantVariable), expand_list_like),
(
(ConstantVariable, ListVariable),
lambda tx, a, b, options: expand_list_like(tx, b, a, options),
),
(
(ConstantVariable, TupleVariable),
lambda tx, a, b, options: expand_list_like(tx, b, a, options),
),
]
op_handlers[operator.mul].extend(list_like_expansion_handlers)
return op_handlers
@staticmethod
def _find_binop_handler(op, a, b):
handlers = BuiltinVariable._binop_handlers()
if op not in handlers:
return None
# Return first handler that matches the type checks
for (type1, type2), handler in handlers[op]:
if isinstance(a, type1) and isinstance(b, type2):
return handler
return None
def can_insert_in_graph(self):
return self.fn in self._fx_graph_functions()
def __init__(self, fn, **kwargs):
super().__init__(**kwargs)
self.fn = fn
def __str__(self):
if self.fn is None:
name = "None"
else:
name = self.fn.__name__
return f"{self.__class__.__name__}({name})"
def python_type(self):
return type(self.fn)
def as_python_constant(self):
return self.fn
def as_proxy(self):
DTYPE = {
bool: torch.bool,
int: torch.int64,
float: torch.float64,
}
if self.fn in DTYPE:
return DTYPE[self.fn]
return super().as_proxy()
def reconstruct(self, codegen):
name = self.fn.__name__
assert self.fn.__module__ == "builtins"
assert name not in codegen.tx.f_globals, "shadowed global"
codegen.append_output(codegen.create_load_global(name, False, add=True))
def constant_args(self, *args, **kwargs):
return check_constant_args(args, kwargs)
def tensor_args(self, *args, **kwargs):
return any(
isinstance(i, variables.TensorVariable)
for i in itertools.chain(args, kwargs.values())
) and not any(
isinstance(i, variables.GetAttrVariable)
for i in itertools.chain(args, kwargs.values())
)
def python_and_tensor_constant_only(self, *args, **kwargs):
tensor_args = []
non_tensor_args = []
for i in itertools.chain(args, kwargs.values()):
if isinstance(i, variables.TensorVariable):
tensor_args.append(i)
else:
non_tensor_args.append(i)
return all(
is_constant_source(t.source) if t.source is not None else False
for t in tensor_args
) and self.constant_args(*non_tensor_args)
def unspec_python_args(self, *args, **kwargs):
return check_unspec_python_args(args, kwargs)
@staticmethod
def unwrap_unspec_args_kwargs(args, kwargs):
return [x.as_python_constant() for x in args], {
k: v.as_python_constant() for k, v in kwargs.items()
}
def has_constant_handler(self, args, kwargs):
constant_args = check_constant_args(args, kwargs)
unspec_python_args = self.unspec_python_args(*args, **kwargs)
return self.can_constant_fold_through() and (
constant_args or unspec_python_args
)
def call_function(
self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
) -> "VariableTracker":
from . import UserFunctionVariable
from .builder import wrap_fx_proxy, wrap_fx_proxy_cls
args = [v.realize() for v in args]
kwargs = {k: v.realize() for k, v in kwargs.items()}
assert isinstance(args, (list, tuple))
assert isinstance(kwargs, dict)
tensor_args = self.tensor_args(*args, **kwargs)
# args[0] is list and args[1] is unspec
if self.fn is operator.getitem and not isinstance(
args[0], variables.TensorVariable
):
tensor_args = False
if (
self.can_insert_in_graph()
and tensor_args
and not (
self.fn is operator.getitem
and isinstance(args[0], ConstDictVariable)
and isinstance(args[1], variables.TensorVariable)
)
):
try:
fn = self.fn
# Constant fold for constant tensor and python constants
if tensor_args and self.python_and_tensor_constant_only(
*args, **kwargs
):
from ..bytecode_transformation import unique_id
from .functions import invoke_and_store_as_constant
return invoke_and_store_as_constant(
tx, fn, unique_id(fn.__name__), args, kwargs
)
if self.fn in IN_PLACE_DESUGARING_MAP and isinstance(
args[0], variables.ConstantVariable
):
# In-place operators like += usually mustate tensor
# values, but in the edge case of immutable values they
# re-bind the variable.
#
# The easiest way to keep the graph consistent in this
# scenario is to de-sugar eagerly.
fn, args = IN_PLACE_DESUGARING_MAP[self.fn], [args[0], args[1]]
if self.fn is operator.getitem and isinstance(args[1], SymNodeVariable):
# Standard indexing will force specialization due to
# __index__. Rewrite as a regular torch op which will
# trace fine
fn, args = torch.select, [
args[0],
variables.ConstantVariable.create(0),
args[1],
]
# Interaction between ndarray and tensors:
# We prefer the tensor op whenever there are tensors involved
if check_numpy_ndarray_args(args, kwargs) and not any(
type(arg) == variables.TensorVariable for arg in args
):
proxy = tx.output.create_proxy(
"call_function",
numpy_operator_wrapper(self.fn),
*proxy_args_kwargs(args, kwargs),
)
return wrap_fx_proxy_cls(variables.NumpyNdarrayVariable, tx, proxy)
proxy = tx.output.create_proxy(
"call_function",
fn,
*proxy_args_kwargs(args, kwargs),
)
if any(isinstance(arg, FakeItemVariable) for arg in args):
return wrap_fx_proxy_cls(
FakeItemVariable,
tx,
proxy,
)
elif self.unspec_python_args(*args, **kwargs):
_args, _kwargs = self.unwrap_unspec_args_kwargs(args, kwargs)
raw_value = self.fn(*_args, **_kwargs)
need_unwrap = any(
x.need_unwrap
for x in itertools.chain(args, kwargs.values())
if isinstance(x, variables.UnspecializedPythonVariable)
)
return wrap_fx_proxy_cls(
UnspecializedPythonVariable,
tx,
proxy,
raw_value=raw_value,
need_unwrap=need_unwrap,
)
elif all(isinstance(x, SymNodeVariable) for x in args):
return SymNodeVariable.create(tx, proxy, None)
else:
# Work around for vision_maskrcnn due to precision difference
# specialize the dividend when float divide by tensor
if self.fn is operator.truediv and isinstance(
args[0], variables.UnspecializedPythonVariable
):
args[0] = args[0].convert_to_constant(tx)
return wrap_fx_proxy(tx, proxy)
except NotImplementedError:
unimplemented(f"partial tensor op: {self} {args} {kwargs}")
# Handle cases like int(torch.seed())
# Also handle sym_float to sym_int cases
if self.fn in (int, float) and isinstance(
args[0], (SymNodeVariable, variables.TensorVariable)
):
if isinstance(args[0], variables.TensorVariable):
item = args[0].call_method(tx, "item", [], {})
else:
item = args[0]
fn_ = sym_int if self.fn is int else sym_float
out = wrap_fx_proxy(
tx=tx,
proxy=tx.output.create_proxy(
"call_function",
fn_,
(item.as_proxy(),),
{},
),
)
return out
# Handle `str` on a user defined function
if self.fn == str and args and isinstance(args[0], (UserFunctionVariable)):
return variables.ConstantVariable.create(value=str(args[0].fn))
# Handle binary ops (e.g. __add__ / __radd__, __iadd__, etc.)
# NB: Tensor args are handled above and not here
if len(kwargs) == 0 and len(args) == 2:
# Try to find a handler for the arg types; otherwise, fall through to constant handler
binop_handler = BuiltinVariable._find_binop_handler(
self.fn, args[0], args[1]
)
if binop_handler:
res = binop_handler(tx, args[0], args[1], {})
if res is not None:
return res
handler = getattr(self, f"call_{self.fn.__name__}", None)
if handler:
try:
result = handler(tx, *args, **kwargs)
if result is not None:
return result
except TypeError:
# Check if binding is bad. inspect signature bind is expensive.
# So check only when handler call fails.
try:
inspect.signature(handler).bind(tx, *args, **kwargs)
except TypeError as e:
has_constant_handler = self.has_constant_handler(args, kwargs)
if not has_constant_handler:
log.warning(
"incorrect arg count %s %s and no constant handler",
handler,
e,
)
unimplemented(f"invalid handler args {handler} {args} {kwargs}")
else:
raise
except Unsupported as exc:
has_constant_handler = self.has_constant_handler(args, kwargs)
if not has_constant_handler:
raise
# Actually, we will handle this just fine
exc.remove_from_stats()
# NB: call to has_constant_handler is deliberately delayed post generic
# handler because has_constant_handler calls as_python_constant
# internally which realizes LazyVariableTracker for ConstantVariables,
# unnecessarily putting guards on objects which might not actually be used.
has_constant_handler = self.has_constant_handler(args, kwargs)
if has_constant_handler:
from .builder import SourcelessBuilder
# constant fold
return SourcelessBuilder()(
tx,
self.as_python_constant()(
*[x.as_python_constant() for x in args],
**{k: v.as_python_constant() for k, v in kwargs.items()},
),
)
return super().call_function(tx, args, kwargs)
def call_method(
self,
tx,
name,
args: "List[VariableTracker]",
kwargs: "Dict[str, VariableTracker]",
) -> "VariableTracker":
if self.fn == dict and name == "fromkeys":
return BuiltinVariable.call_custom_dict_fromkeys(tx, dict, *args, **kwargs)
if self.fn == itertools.chain and name == "from_iterable":
assert len(args) == 1
assert len(kwargs) == 0
obj = args[0]
items = []
for item in obj.unpack_var_sequence(tx):
items.extend(item.unpack_var_sequence(tx))
return variables.TupleVariable(items)
return super().call_method(tx, name, args, kwargs)
def _call_min_max(self, tx, *args):
if len(args) == 1 and args[0].has_unpack_var_sequence(tx):
# expand iterable
items = args[0].unpack_var_sequence(tx)
return self._call_min_max_seq(tx, items)
elif len(args) == 2:
return self._call_min_max_binary(tx, args[0], args[1])
elif len(args) > 2:
return self._call_min_max_seq(tx, args)
def _call_min_max_seq(self, tx, items):
assert len(items) > 0
if len(items) == 1:
return items[0]
return functools.reduce(functools.partial(self._call_min_max_binary, tx), items)
def _call_min_max_binary(self, tx, a, b):
if self.tensor_args(a, b):
if not isinstance(a, variables.TensorVariable):
a, b = b, a
assert isinstance(a, variables.TensorVariable)
# result of an item call is a scalar convert to a tensor
if isinstance(a, FakeItemVariable):
a = variables.TorchInGraphFunctionVariable(torch.tensor).call_function(
tx, [a], {}
)
# Dynamic input does not get resolved, rather, gets stored as call_function
if isinstance(a, SymNodeVariable) or isinstance(b, SymNodeVariable):
from .builder import wrap_fx_proxy_cls
return wrap_fx_proxy_cls(
type(a),
tx=tx,
proxy=tx.output.create_proxy(
"call_function",
self.fn,
*proxy_args_kwargs([a, b], {}),
),
)
# convert min/max to torch ops
if b.is_python_constant():
if isinstance(a, variables.NumpyNdarrayVariable):
import numpy as np
fn = variables.NumpyVariable(np.clip)
else:
fn = variables.TorchInGraphFunctionVariable(torch.clamp)
kwargs = {"min": b} if (self.fn is max) else {"max": b}
result = fn.call_function(tx, [a], kwargs)
else:
if isinstance(a, variables.NumpyNdarrayVariable):
import numpy as np
fn = {max: np.maximum, min: np.minimum}[self.fn]
fn = variables.NumpyVariable(fn)
else:
fn = {max: torch.maximum, min: torch.minimum}[self.fn]
fn = variables.TorchInGraphFunctionVariable(fn)
result = fn.call_function(tx, [a, b], {})
# return unspec if both a, b are unspec or const
if all(
isinstance(
i,
(
variables.UnspecializedPythonVariable,
variables.ConstantVariable,
),
)
for i in [a, b]
):
if any(isinstance(val, FakeItemVariable) for val in [a, b]):
return variables.FakeItemVariable.from_tensor_variable(result)
if b.is_python_constant():
raw_b = b.as_python_constant()
else:
raw_b = b.raw_value
if self.fn is max:
raw_res = max(a.raw_value, raw_b)
else:
raw_res = min(a.raw_value, raw_b)
need_unwrap = any(
x.need_unwrap
for x in [a, b]
if isinstance(x, variables.UnspecializedPythonVariable)
)
return variables.UnspecializedPythonVariable.from_tensor_variable(
result, raw_res, need_unwrap
)
# otherwise return tensor
else:
return result
elif isinstance(a, SymNodeVariable) or isinstance(b, SymNodeVariable):
fn = torch.sym_max if self.fn is max else torch.sym_min
proxy = tx.output.create_proxy(
"call_function", fn, *proxy_args_kwargs([a, b], {})
)
return SymNodeVariable.create(tx, proxy, None)
call_min = _call_min_max
call_max = _call_min_max
def call_abs(self, tx, arg: "VariableTracker"):
# Call arg.__abs__()
abs_method = BuiltinVariable(getattr).call_function(
tx, [arg, ConstantVariable.create("__abs__")], {}
)
return abs_method.call_function(tx, [], {})
def call_pos(self, tx, arg: "VariableTracker"):
# Call arg.__pos__()
pos_method = BuiltinVariable(getattr).call_function(
tx, [arg, ConstantVariable.create("__pos__")], {}
)
return pos_method.call_function(tx, [], {})
def call_round(self, tx, arg, *args, **kwargs):
# Call arg.__round__()
round_method = BuiltinVariable(getattr).call_function(
tx, [arg, ConstantVariable.create("__round__")], {}
)
return round_method.call_function(tx, args, kwargs)
def call_range(self, tx, *args):
if self.unspec_python_args(*args) or self.constant_args(*args):
return variables.RangeVariable(args)
elif self._dynamic_args(*args):
args = [
variables.ConstantVariable.create(guard_if_dyn(arg)) for arg in args
]
return variables.RangeVariable(args)
# None no-ops this handler and lets the driving function proceed
return None
def _dynamic_args(self, *args, **kwargs):
return any(isinstance(x, SymNodeVariable) for x in args) or any(
isinstance(x, SymNodeVariable) for x in kwargs.values()
)
def call_slice(self, tx, *args):
return variables.SliceVariable(args)
def _dyn_proxy(self, tx, *args, **kwargs):
from .builder import wrap_fx_proxy
return wrap_fx_proxy(
tx,
tx.output.create_proxy(
"call_function", self.fn, *proxy_args_kwargs(args, kwargs)
),
)
def _call_iter_tuple_list(self, tx, obj=None, *args, **kwargs):
if self._dynamic_args(*args, **kwargs):
return self._dyn_proxy(tx, *args, **kwargs)
if isinstance(obj, variables.IteratorVariable):
# For non-list iterators, we will guard on vars that
# determine the control flow
return obj
cls = variables.BaseListVariable.cls_for(self.fn)
if obj is None:
return cls(
[],
mutable_local=MutableLocal(),
)
elif obj.has_unpack_var_sequence(tx):
if obj.source and not is_constant_source(obj.source):
if isinstance(obj, TupleIteratorVariable):
install_guard(
obj.source.make_guard(GuardBuilder.TUPLE_ITERATOR_LEN)
)
else:
install_guard(obj.source.make_guard(GuardBuilder.SEQUENCE_LENGTH))
return cls(
list(obj.unpack_var_sequence(tx)),
mutable_local=MutableLocal(),
)
def call_iter(self, tx, obj, *args, **kwargs):
# Handle the case where we are iterating over a tuple, list or iterator
ret = self._call_iter_tuple_list(tx, obj, *args, **kwargs)
if ret is None:
# If the object doesn't implement a __iter__ method, it will be an error in eager mode when calling iter on it anyway.
# If the object implements a __iter__ method, inlining effectively forwards the call to another iter call
# (e.g. when __iter__ just returns iter(self.list)) or return a user-defined iterator.
return obj.call_method(tx, "__iter__", args, kwargs)
return ret
call_tuple = _call_iter_tuple_list
call_list = _call_iter_tuple_list
def call_callable(self, tx, arg):
from .functions import BaseUserFunctionVariable
if isinstance(
arg, (variables.UserDefinedClassVariable, BaseUserFunctionVariable)
):
return variables.ConstantVariable.create(True)
elif isinstance(arg, UserDefinedVariable):
return variables.ConstantVariable.create(callable(arg.value))
elif isinstance(arg, (ConstantVariable, SymNodeVariable, TensorVariable)):
return variables.ConstantVariable.create(False)
def call_cast(self, _, *args, **kwargs):
if len(args) == 2:
return args[1]
unimplemented(f"unsupported args to builtin cast(): {args} {kwargs}")
def call_dict(self, tx, *args, **kwargs):
return BuiltinVariable.call_custom_dict(tx, dict, *args, **kwargs)
@staticmethod
def call_custom_dict(tx, user_cls, *args, **kwargs):
if not kwargs:
if not args:
args = ({},)
assert len(args) == 1
arg = args[0]
if isinstance(arg, dict):
return ConstDictVariable(arg, user_cls, mutable_local=MutableLocal())
elif isinstance(arg, variables.ConstDictVariable):
return arg.clone(user_cls=user_cls, mutable_local=MutableLocal())
elif isinstance(
arg,
(
ListVariable,
TupleVariable,
ListIteratorVariable,
),
):
items = dict(
x.unpack_var_sequence(tx) for x in arg.unpack_var_sequence(tx)
)
return ConstDictVariable(items, user_cls, mutable_local=MutableLocal())
elif not args and kwargs:
items = {ConstantVariable.create(k): v for k, v in kwargs.items()}
return variables.ConstDictVariable(
items, user_cls=user_cls, mutable_local=MutableLocal()
)
unimplemented(f"{user_cls.__name__}(): {args} {kwargs}")
@staticmethod
def call_custom_dict_fromkeys(tx, user_cls, *args, **kwargs):
assert user_cls in {dict, OrderedDict, defaultdict}
if kwargs:
# Only `OrderedDict.fromkeys` accepts `value` passed by keyword
assert user_cls is OrderedDict
assert len(args) == 1 and len(kwargs) == 1 and "value" in kwargs
args = (*args, kwargs.pop("value"))
if len(args) == 0:
raise UserError(TypeError, "fromkeys expected at least 1 argument, got 0")
if len(args) == 1:
args = (*args, ConstantVariable.create(None))
assert len(args) == 2
arg, value = args
DictVariableType = (
ConstDictVariable if user_cls is not defaultdict else DefaultDictVariable
)
if isinstance(arg, dict):
arg = [ConstantVariable.create(k) for k in arg.keys()]
return DictVariableType(
dict.fromkeys(arg, value), user_cls, mutable_local=MutableLocal()
)
elif arg.has_unpack_var_sequence(tx) and all(
is_hashable(v) for v in arg.unpack_var_sequence(tx)
):
keys = arg.unpack_var_sequence(tx)
return DictVariableType(
dict.fromkeys(keys, value), user_cls, mutable_local=MutableLocal()
)
unimplemented(f"{user_cls.__name__}.fromkeys(): {args} {kwargs}")
def call_set(self, tx, *args, **kwargs):
# Can we merge this implementation and call_dict's one?
assert not kwargs
if not args:
return SetVariable([], mutable_local=MutableLocal())
assert len(args) == 1
arg = args[0]
if isinstance(arg, variables.SetVariable):
return arg.clone(mutable_local=MutableLocal())
elif arg.has_unpack_var_sequence(tx):
items = arg.unpack_var_sequence(tx)
return SetVariable(items, mutable_local=MutableLocal())
else:
unimplemented(f"set(): {args} {kwargs}")
def call_zip(self, tx, *args, **kwargs):
if kwargs:
assert len(kwargs) == 1 and "strict" in kwargs
if all(x.has_unpack_var_sequence(tx) for x in args):
unpacked = [arg.unpack_var_sequence(tx) for arg in args]
if kwargs.pop("strict", False) and len(unpacked) > 0:
if not all(len(u) == len(unpacked[0]) for u in unpacked):
raise UserError(
ValueError,
"zip() has one argument of len differing from others",
)
items = [variables.TupleVariable(list(item)) for item in zip(*unpacked)]
return variables.TupleVariable(items)
def call_enumerate(self, tx, *args):
if len(args) == 1:
start = 0
else:
assert len(args) == 2
assert isinstance(args[1], variables.ConstantVariable)
start = args[1].as_python_constant()
if args[0].has_unpack_var_sequence(tx):
items = [
variables.TupleVariable(
[variables.ConstantVariable.create(idx), var],
)
for idx, var in enumerate(args[0].unpack_var_sequence(tx), start)
]
return variables.TupleVariable(items)
def call_len(self, tx, *args, **kwargs):
return args[0].call_method(tx, "__len__", args[1:], kwargs)
def call_getitem(self, tx, *args, **kwargs):
return args[0].call_method(tx, "__getitem__", args[1:], kwargs)
def call_isinstance(self, tx, arg, isinstance_type):
try:
arg_type = arg.python_type()
except NotImplementedError:
unimplemented(
f"isinstance({arg}, {isinstance_type}): can't determine type of {arg}"
)
isinstance_type = isinstance_type.as_python_constant()
if isinstance(arg, variables.TensorVariable) and arg.dtype is not None:
def _tensor_isinstance(tensor_var, tensor_type):
def check_type(ty):
if ty not in tensortype_to_dtype:
return issubclass(arg.python_type(), ty)
dtypes = tensortype_to_dtype[ty]
return arg.dtype in dtypes
if type(tensor_type) is tuple:
return any(check_type(ty) for ty in tensor_type)
else:
return check_type(tensor_type)
return variables.ConstantVariable.create(
_tensor_isinstance(arg, isinstance_type)
)
# UserDefinedObject with C extensions can have torch.Tensor attributes,
# so break graph.
if isinstance(arg, variables.UserDefinedObjectVariable) and isinstance(
arg.value, types.MemberDescriptorType
):
unimplemented(
f"isinstance called on UserDefinedClass {arg} {isinstance_type}"
)
# handle __instancecheck__ defined in user class
if (
isinstance(arg, variables.UserDefinedObjectVariable)
and "__instancecheck__" in isinstance_type.__class__.__dict__
):
return variables.ConstantVariable.create(
isinstance_type.__class__.__instancecheck__(isinstance_type, arg.value)
)
try:
val = issubclass(arg_type, isinstance_type)
except TypeError:
val = arg_type is isinstance_type
return variables.ConstantVariable.create(val)
def call_issubclass(self, tx, left_ty, right_ty):
"""Checks if first arg is subclass of right arg"""
left_ty = left_ty.as_python_constant()
right_ty = right_ty.as_python_constant()
return variables.ConstantVariable(issubclass(left_ty, right_ty))
def call_super(self, tx, a, b):
return variables.SuperVariable(a, b)
def call_next(self, tx, arg):
if isinstance(
arg, (variables.ListIteratorVariable, variables.IteratorVariable)
):
val, next_iter = arg.next_variables(tx)
return val
elif isinstance(arg, variables.BaseListVariable):
return arg.items[0]
def call_hasattr(self, tx, obj, attr):
if attr.is_python_constant():
name = attr.as_python_constant()
return obj.call_hasattr(tx, name)
def call_map(self, tx, fn, seq):
if seq.has_unpack_var_sequence(tx):
items = [fn.call_function(tx, [x], {}) for x in seq.unpack_var_sequence(tx)]
return variables.TupleVariable(items)
def call_sum(self, tx, seq, start=_SENTINEL):
# Special case for sum on tuple of floats and ints
if isinstance(seq, (variables.ListVariable, variables.TupleVariable)) and all(
isinstance(x, variables.ConstantVariable)
and isinstance(x.value, (int, float))
for x in seq.items
):
if start is self._SENTINEL:
return variables.ConstantVariable.create(
sum(x.value for x in seq.items),
)
if isinstance(start, variables.ConstantVariable) and isinstance(
start.value, (int, float)
):
return variables.ConstantVariable.create(
sum((x.value for x in seq.items), start=start.value),
)
if seq.has_unpack_var_sequence(tx):
if start is self._SENTINEL:
start = variables.ConstantVariable.create(0)
items = seq.unpack_var_sequence(tx)
return BuiltinVariable(functools.reduce).call_function(
tx,
[
BuiltinVariable(operator.add),
variables.TupleVariable(items),
start,
],
{},
)
def call_reduce(self, tx, function, iterable, initial=_SENTINEL):
if iterable.has_unpack_var_sequence(tx):
items = iterable.unpack_var_sequence(tx)
if initial is self._SENTINEL:
value, items = items[0], items[1:]
else:
value = initial
for element in items:
value = function.call_function(tx, [value, element], {})
return value
def call_getattr(
self, tx, obj: VariableTracker, name_var: VariableTracker, default=None
):
from .. import trace_rules
from . import (
ConstantVariable,
GetAttrVariable,
PythonModuleVariable,
TorchInGraphFunctionVariable,
UserFunctionVariable,
)
from .builder import SourcelessBuilder, VariableBuilder
name = name_var.as_python_constant()
if not name_var.is_python_constant():
unimplemented("non-const getattr() name")
if tx.output.side_effects.is_attribute_mutation(obj):
try:
# re-read a pending side effect?
return tx.output.side_effects.load_attr(obj, name)
except KeyError:
pass
if default is not None:
hasattr_var = self.call_hasattr(tx, obj, name_var)
assert hasattr_var.as_python_constant() in (True, False)
if not hasattr_var.as_python_constant():
return default
options = {}
if obj.source:
source = AttrSource(obj.source, name)
options["source"] = source
else:
source = None
if name == "__bases__":
try:
value = obj.as_python_constant()
if isinstance(value, type):
bases = value.__bases__
if source is not None:
tuple_args = [
VariableBuilder(tx, GetItemSource(source, i))(b)
for i, b in enumerate(bases)
]
else:
tuple_args = [SourcelessBuilder()(tx, b) for b in bases]
return variables.TupleVariable(tuple_args, **options)
except NotImplementedError:
pass
if isinstance(obj, variables.NNModuleVariable):
return obj.var_getattr(tx, name)
elif isinstance(
obj,
(
variables.TensorVariable,
variables.NamedTupleVariable,
variables.ConstantVariable,
variables.UserDefinedClassVariable,
variables.UserDefinedObjectVariable,
),
):
try:
return obj.var_getattr(tx, name)
except NotImplementedError:
return GetAttrVariable(obj, name, **options)
elif isinstance(obj, TorchInGraphFunctionVariable):
# Get OpOverload from an OpOverloadPacket, e.g., torch.ops.aten.add.default.
member = getattr(obj.value, name)
if isinstance(
member, (torch._ops.OpOverloadPacket, torch._ops.OpOverload)
) and trace_rules.is_aten_op_or_tensor_method(member):
return TorchInGraphFunctionVariable(member, **options)
elif isinstance(obj, (PythonModuleVariable, DummyModule)):
if obj.is_torch:
member = getattr(obj.value, name)
else:
member = obj.value.__dict__[name]
if config.replay_record_enabled:
tx.exec_recorder.record_module_access(obj.value, name, member)
if source is not None:
return VariableBuilder(tx, source)(member)
else:
return SourcelessBuilder()(tx, member)
elif istype(obj, UserFunctionVariable) and name in ("__name__", "__module__"):
return ConstantVariable.create(getattr(obj.fn, name))
else:
try:
return obj.var_getattr(tx, name)
except NotImplementedError:
return GetAttrVariable(obj, name, **options)
def call_setattr(
self, tx, obj: VariableTracker, name_var: VariableTracker, val: VariableTracker
):
from .distributed import PlacementVariable
if isinstance(
obj,
(
variables.DataClassVariable,
variables.CustomizedDictVariable,
PlacementVariable,
),
):
return obj.call_method(tx, "__setattr__", [name_var, val], {})
elif (
tx.output.side_effects.is_attribute_mutation(obj)
and name_var.is_python_constant()
):
name = name_var.as_python_constant()
if isinstance(obj, variables.TensorVariable):
from .builder import wrap_fx_proxy
if name == "requires_grad":
# TODO(voz): Make it work properly
unimplemented(
"mutating requires_grad can introduce a new leaf from non-leaf or vice versa in "
"the middle of the graph, which aot_autograd does not currently know how to handle. "
)
if name == "data":
# Remove the old reference in tracked fakes - if we don't do this
# new .data value size and shape differences will cause
# tracked fakes to produce incorrect guards. This is sound because the TensorVariable
# coming out of set_() below will be a new one, and get
# installed in tracked fakes.
to_remove = []
for tf in tx.output.tracked_fakes:
if tf.source == obj.source:
to_remove.append(tf)
for tf in to_remove:
tx.output.tracked_fakes.remove(tf)
# Step 1 - disable grads
with dynamo_disable_grad(tx), torch.no_grad():
# Step 2 - call `set_`
out = wrap_fx_proxy(
tx,
tx.output.create_proxy(
"call_function",
torch.Tensor.set_,
*proxy_args_kwargs([obj, val], {}),
),
)
# Step 3 - drop the version counter - this is a step required to get
# .data setting to play correctly with the autograd engine.
# Esentially, dynamo is trying to faithful preserve the (absurd)
# behavior of .data= from eager mode
def _lower_version_count_by_1(x):
version = x._version
if version > 0:
version = version - 1
torch._C._autograd._unsafe_set_version_counter(x, version)
return x
tx.output.create_proxy(
"call_function",
_lower_version_count_by_1,
(out.as_proxy(),),
{},
)
_lower_version_count_by_1(obj.as_proxy().node.meta["example_value"])
# This handles options prop, guards and ends with a clone
# Step 4 - replace all reference to the current object with the new one
return out
tx.output.side_effects.store_attr(obj, name, val)
return val
elif isinstance(obj, variables.UserDefinedObjectVariable):
unimplemented(
f"setattr(UserDefinedObjectVariable) {type(obj.value).__setattr__}"
)
elif isinstance(obj, variables.NNModuleVariable):
if not tx.output.is_root_tracer():
raise AttributeMutationError(
"Can't inplace modify module params/buffers inside HigherOrderOp"
)
if name_var.is_python_constant() and isinstance(
val, variables.TensorVariable
):
assigning_fake_val = get_fake_value(val.as_proxy().node, tx)
try:
getattr_var = obj.var_getattr(tx, name_var.as_python_constant())
except AttributeError:
getattr_var = None
if isinstance(getattr_var, variables.TensorVariable):
# get_fake_val will get the same fake tensor
existing_fake_attr = get_fake_value(getattr_var.as_proxy().node, tx)
# same tensor identiy, setattr is a no-op
mod_setattr = inspect.getattr_static(obj.module_type, "__setattr__")
if (
existing_fake_attr is assigning_fake_val
and mod_setattr is torch.nn.Module.__setattr__
):
return getattr_var
obj.convert_to_unspecialized(tx)
# FIXME (tmanlaibaatar) this is utter hack to unblock HuggingFace export
# Export generally doesn't want to allow mutations on objects directly,
# but we don't have good way to do this rn. For now, we make it an undefined
# behaviour and just set attributes directly on the PretrainedConfig object
# for now.
elif isinstance(obj, variables.dicts.HFPretrainedConfigVariable) and tx.export:
if name_var.is_python_constant() and isinstance(
val, variables.ConstantVariable
):
setattr(
obj.obj, name_var.as_python_constant(), val.as_python_constant()
)
return ConstantVariable(None)
def call_delattr(self, tx, obj: VariableTracker, name_var: VariableTracker):
return self.call_setattr(tx, obj, name_var, variables.DeletedVariable())
def call_type(self, tx, obj: VariableTracker):
from .builder import SourcelessBuilder, VariableBuilder
try:
py_type = obj.python_type()
except NotImplementedError as error:
raise UserError(
UserErrorType.INVALID_INPUT,
str(error),
case_name="unknown_python_type",
) from None
if obj.source is None:
return SourcelessBuilder()(tx, py_type)
else:
return VariableBuilder(tx, TypeSource(obj.source))(py_type)
def call_reversed(self, tx, obj: VariableTracker):
if obj.has_unpack_var_sequence(tx):
items = list(reversed(obj.unpack_var_sequence(tx)))
return variables.TupleVariable(items)
def call_sorted(self, tx, obj: VariableTracker, **kwargs):
if (
obj.has_unpack_var_sequence(tx)
and not isinstance(obj, variables.TensorVariable)
and all(x.is_python_constant() for x in obj.unpack_var_sequence(tx))
):
function = kwargs.pop("key", None)
reverse = kwargs.pop(
"reverse", ConstantVariable.create(False)
).as_python_constant()
assert len(kwargs) == 0
if function:
items = sorted(
obj.unpack_var_sequence(tx),
key=lambda x: function.call_function(
tx, [x], {}
).as_python_constant(),
reverse=reverse,
)
else:
items = sorted(
obj.unpack_var_sequence(tx),
key=lambda x: x.as_python_constant(),
reverse=reverse,
)
return variables.ListVariable(items)
def call_chain(self, tx, *args):
if all(obj.has_unpack_var_sequence(tx) for obj in args):
items = []
for obj in args:
items.extend(obj.unpack_var_sequence(tx))
return variables.TupleVariable(items)
def call_islice(self, tx, iterable, *args):
if iterable.has_unpack_var_sequence(tx) and all(
x.is_python_constant() for x in args
):
const_args = [x.as_python_constant() for x in args]
items = iterable.unpack_var_sequence(tx)
items = list(itertools.islice(items, *const_args))
return variables.TupleVariable(items)
# neg is a constant fold function, so we only get here if constant fold is not valid
def call_neg(self, tx, a):
if isinstance(a, SymNodeVariable):
return SymNodeVariable.create(
tx,
(operator.neg)(a.as_proxy()),
sym_num=None,
)
# None no-ops this handler and lets the driving function proceed
return None
def call_format(self, tx, _format_string, *args, **kwargs):
format_string = _format_string.as_python_constant()
return variables.StringFormatVariable.create(format_string, args, kwargs)
def call_id(self, tx, *args):
if len(args) > 0 and isinstance(args[0], variables.NNModuleVariable):
nn_mod_variable = args[0]
mod = tx.output.get_submodule(nn_mod_variable.module_key)
return variables.ConstantVariable.create(id(mod))
else:
unimplemented(f"call_id with args {args}")
def call_deepcopy(self, tx, x):
unimplemented(f"copy.deepcopy {repr(x)}")
def _comparison(self, tx, left, right):
"""
Used to implement comparison operators for different types.
For example, list1 < list2 is implemented differently from tensor1 < tensor2
"""
from . import (
BaseListVariable,
ConstantVariable,
NNModuleVariable,
TensorVariable,
UserDefinedObjectVariable,
UserFunctionVariable,
)
from .lists import SizeVariable
from .tensor import (
supported_const_comparison_ops,
supported_tensor_comparison_ops,
)
op = self.fn
def _unimplemented():
unimplemented(f"comparison {typestr(left)} {op} {typestr(right)}")
if (
all(
isinstance(x, (NNModuleVariable, ConstantVariable))
for x in [left, right]
)
and op in supported_const_comparison_ops.values()
):
left = (
tx.output.get_submodule(left.module_key)
if isinstance(left, NNModuleVariable)
else left.as_python_constant()
)
right = (
tx.output.get_submodule(right.module_key)
if isinstance(right, NNModuleVariable)
else right.as_python_constant()
)
return ConstantVariable.create(op(left, right))
if isinstance(left, UserFunctionVariable):
if op not in supported_const_comparison_ops.values():
_unimplemented()
if not isinstance(right, UserFunctionVariable):
_unimplemented()
return ConstantVariable.create(op(left.fn, right.fn))
# Note, we have a rare BaseListVariable subtype mismatch with valid comparison
# x = torch.randn([3, 3])
# x.size() == (3, 3) # True
# (3, 3) == x.size() # True
if isinstance(left, (SizeVariable, TupleVariable)) and isinstance(
right, (TupleVariable, SizeVariable)
):
return BaseListVariable.list_compare(tx, op, left, right)
if isinstance(left, BaseListVariable):
if not type(left) == type(right): # Mismatch in BaseListVariable subclasses
_unimplemented()
return BaseListVariable.list_compare(tx, op, left, right)
# If they implement set semantics (e.g. SetVariable or DictKeys)
if hasattr(left, "set_items") and hasattr(right, "set_items"):
return ConstantVariable.create(op(left.set_items, right.set_items))
if isinstance(left, TensorVariable) or isinstance(right, TensorVariable):
from .builder import wrap_fx_proxy_cls
if op in [operator.is_, operator.is_not]:
is_result = (
isinstance(left, TensorVariable)
and isinstance(right, TensorVariable)
and id(extract_fake_example_value(left.as_proxy().node))
== id(extract_fake_example_value(right.as_proxy().node))
)
if op is operator.is_:
return ConstantVariable.create(is_result)
else:
return ConstantVariable.create(not is_result)
if op not in supported_tensor_comparison_ops.values():
_unimplemented()
if (
isinstance(left, TensorVariable)
and isinstance(right, TensorVariable)
and (left.size and right.size) is not None
and left.size != right.size
):
try:
torch.broadcast_shapes(left.size, right.size)
except RuntimeError:
# not broadcastable, can't be compared
_unimplemented()
tensor_cls = left if isinstance(left, TensorVariable) else right
proxy = tx.output.create_proxy(
"call_function", op, (left.as_proxy(), right.as_proxy()), {}
)
return wrap_fx_proxy_cls(
type(tensor_cls), # handle Ndarrays and Tensors
tx,
proxy,
)
if isinstance(left, SymNodeVariable) or isinstance(right, SymNodeVariable):
if op not in supported_tensor_comparison_ops.values():
_unimplemented()
proxy = tx.output.create_proxy(
"call_function", op, (left.as_proxy(), right.as_proxy()), {}
)
return SymNodeVariable.create(
tx,
proxy,
sym_num=None,
)
if isinstance(left, UserDefinedObjectVariable) and isinstance(
right, UserDefinedObjectVariable
):
return ConstantVariable.create(op(left.value, right.value))
if isinstance(left, (StreamVariable, EventVariable)) or isinstance(
right, (StreamVariable, EventVariable)
):
if type(left) == type(right) and op is operator.eq:
return ConstantVariable(op(left.value, right.value))
if isinstance(right, ConstantVariable) or isinstance(
left, ConstantVariable
):
return ConstantVariable(op(left.value, right.value))
if op.__name__.startswith("is_"):
# If the two objects are of different type, we can safely return False and True for `is` and `is not`, respectively
if type(left) is not type(right):
return ConstantVariable.create(op.__name__ != "is_")
if isinstance(left, BuiltinVariable) and isinstance(right, BuiltinVariable):
return ConstantVariable.create(op(left.fn, right.fn))
_unimplemented()
def call_and_(self, tx, a, b):
# Rely on constant_handler
if isinstance(a, ConstantVariable) and isinstance(b, ConstantVariable):
return None
if isinstance(a, (SymNodeVariable, ConstantVariable)) and isinstance(
b, (SymNodeVariable, ConstantVariable)
):
return SymNodeVariable.create(
tx,
tx.output.create_proxy(
"call_function", operator.and_, *proxy_args_kwargs([a, b], {})
),
sym_num=None,
)
if hasattr(a, "set_items") and hasattr(b, "set_items"):
return SetVariable(list(a.set_items & b.set_items))
# None no-ops this handler and lets the driving function proceed
def call_or_(self, tx, a, b):
# Rely on constant_handler
if isinstance(a, ConstantVariable) and isinstance(b, ConstantVariable):
return None
if isinstance(a, (SymNodeVariable, ConstantVariable)) and isinstance(
b, (SymNodeVariable, ConstantVariable)
):
return SymNodeVariable.create(
tx,
tx.output.create_proxy(
"call_function", operator.or_, *proxy_args_kwargs([a, b], {})
),
sym_num=None,
)
if hasattr(a, "set_items") and hasattr(b, "set_items"):
return SetVariable(list(a.set_items | b.set_items))
# None no-ops this handler and lets the driving function proceed
return None
def call_not_(self, tx, a):
if isinstance(a, SymNodeVariable):
return SymNodeVariable.create(
tx,
tx.output.create_proxy(
"call_function", operator.not_, *proxy_args_kwargs([a], {})
),
sym_num=None,
)
# Unwrap the underlying ConstDictVariable
if isinstance(a, DictView):
a = a.dv_dict
if isinstance(a, (ListVariable, ConstDictVariable)):
return ConstantVariable.create(len(a.items) == 0)
return None
call_eq = _comparison
call_gt = _comparison
call_lt = _comparison
call_ge = _comparison
call_le = _comparison
call_ne = _comparison
call_is_ = _comparison
call_is_not = _comparison
call_all = _polyfill_call_impl("all")
call_any = _polyfill_call_impl("any")
@contextlib.contextmanager
def dynamo_disable_grad(tx):
from . import GradModeVariable
org_value = torch.is_grad_enabled()
gmv = GradModeVariable.create(tx, False)
try:
gmv.enter(tx)
yield
finally:
gmv.exit(tx)
|