# 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 collections.abc import KeysView from typing import Dict, List, TYPE_CHECKING import torch from torch import sym_float, sym_int from torch.utils._python_dispatch import is_traceable_wrapper_subclass from .. import config, 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_or_constant_args, check_unspec_python_args, does_not_override_dict_iter_methods, extract_fake_example_value, get_fake_value, guard_if_dyn, is_wrapper_or_member_descriptor, istype, numpy_operator_wrapper, proxy_args_kwargs, tensortype_to_dtype, ) from .base import MutableLocal, VariableTracker from .constant import ConstantVariable from .ctx_manager import EventVariable, StreamVariable from .dicts import ( ConstDictVariable, DefaultDictVariable, DictView, FrozensetVariable, is_hashable, SetVariable, ) from .lists import ( BaseListVariable, ListIteratorVariable, ListVariable, SizeVariable, TupleIteratorVariable, TupleVariable, ) from .tensor import ( FakeItemVariable, supported_comparison_ops, SymNodeVariable, TensorVariable, UnspecializedPythonVariable, ) from .user_defined import UserDefinedObjectVariable, UserDefinedVariable if TYPE_CHECKING: from torch._dynamo.symbolic_convert import InstructionTranslator 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, } class BuiltinVariable(VariableTracker): _SENTINEL = object() _nonvar_fields = { "fn", *VariableTracker._nonvar_fields, } @classmethod def create_with_source(cls, value, source): install_guard(source.make_guard(GuardBuilder.BUILTIN_MATCH)) return cls(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, } from .tensor import supported_comparison_ops fns.update(supported_comparison_ops.values()) 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) -> VariableTracker from .dicts import DictKeys, SetVariable from .functions import BaseUserFunctionVariable, UserFunctionVariable from .nn_module import NNModuleVariable from .tensor import supported_const_comparison_ops from .torch import BaseTorchVariable from .user_defined import ( UserDefinedClassVariable, UserDefinedObjectVariable, UserDefinedVariable, ) # 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, *, 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: "InstructionTranslator", a, b, *, 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: "InstructionTranslator", a, b, *, 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], {}) ), ) 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: "InstructionTranslator", a, b): return TupleVariable([*a.items, *b.unpack_var_sequence(tx)]) def size_add_handler(tx: "InstructionTranslator", a, b): return SizeVariable([*a.items, *b.unpack_var_sequence(tx)]) 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: TupleVariable( [*a.unpack_var_sequence(tx), *b.items], ), ), ( ( ListVariable, (BaseListVariable, ConstantVariable, ListIteratorVariable), ), lambda tx, a, b: ListVariable( [*a.items, *b.unpack_var_sequence(tx)], mutable_local=MutableLocal() ), ), ( (BaseListVariable, BaseListVariable), lambda tx, a, b: type(a)([*a.items, *b.items]), ), ] op_handlers[operator.add].extend(list_like_addition_handlers) def list_iadd_handler(tx: "InstructionTranslator", 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: "InstructionTranslator", lst, const): if isinstance(lst, ConstantVariable): lst, const = const, lst return lst.__class__( items=lst.items * const.as_python_constant(), mutable_local=MutableLocal(), ) list_like_expansion_handlers = [ ((ListVariable, ConstantVariable), expand_list_like), ((TupleVariable, ConstantVariable), expand_list_like), ((ConstantVariable, ListVariable), expand_list_like), ((ConstantVariable, TupleVariable), expand_list_like), ] op_handlers[operator.mul].extend(list_like_expansion_handlers) size_or_tuple = (SizeVariable, TupleVariable) has_set_items = (SetVariable, DictKeys) def create_cmp_op_handlers(op): def compare_by_value(tx: "InstructionTranslator", a, b): return ConstantVariable(op(a.value, b.value)) result = [((ConstantVariable, ConstantVariable), compare_by_value)] if op in supported_const_comparison_ops.values(): # Tensor is None, List is not None, etc none_result = op(object(), None) if op.__name__.startswith("is_"): def never(tx: "InstructionTranslator", a, b): return ConstantVariable(none_result) obj_op_none = never none_op_obj = never else: def obj_op_none( tx: "InstructionTranslator", a, b: ConstantVariable ): if b.value is None or b.value is True or b.value is False: return ConstantVariable(none_result) def none_op_obj( tx: "InstructionTranslator", a: ConstantVariable, b ): if a.value is None or a.value is True or a.value is False: return ConstantVariable(none_result) types_that_are_never_none = ( TensorVariable, SymNodeVariable, NNModuleVariable, BaseListVariable, UserDefinedVariable, BaseUserFunctionVariable, ConstDictVariable, BaseTorchVariable, ) result.extend( [ ( (types_that_are_never_none, ConstantVariable), obj_op_none, ), ( (ConstantVariable, types_that_are_never_none), none_op_obj, ), ] ) def list_compare_nocheck(tx: "InstructionTranslator", left, right): return BaseListVariable.list_compare(tx, op, left, right) def list_compare_check(tx: "InstructionTranslator", left, right): if type(left) is not type( right ): # Mismatch in BaseListVariable subclasses unimplemented(f"{op.__name__}({left}, {right})") return BaseListVariable.list_compare(tx, op, left, right) def compare_set_items(tx: "InstructionTranslator", left, right): return ConstantVariable(op(left.set_items, right.set_items)) def compare_via_method(tx: "InstructionTranslator", left, right): return left.call_method(tx, f"__{op.__name__}__", [right], {}) if op.__name__.startswith("is_"): compare_user_defined = compare_by_value else: compare_user_defined = compare_via_method op_var = BuiltinVariable(op) result.extend( [ ( ( (UserFunctionVariable, BuiltinVariable), (UserFunctionVariable, BuiltinVariable), ), lambda tx, a, b: ConstantVariable(op(a.fn, b.fn)), ), ( ( NNModuleVariable, NNModuleVariable, ), lambda tx, a, b: ConstantVariable( op( tx.output.get_submodule(a.module_key), tx.output.get_submodule(b.module_key), ) ), ), ((size_or_tuple, size_or_tuple), list_compare_nocheck), ( (variables.BaseListVariable, variables.BaseListVariable), list_compare_check, ), ((has_set_items, has_set_items), compare_set_items), ( (UserDefinedObjectVariable, UserDefinedObjectVariable), compare_user_defined, ), ( (UserDefinedClassVariable, UserDefinedClassVariable), compare_user_defined, ), ( ( (StreamVariable, EventVariable, ConstantVariable), (StreamVariable, EventVariable, ConstantVariable), ), compare_by_value, ), ( (TensorVariable, VariableTracker), op_var._comparison_with_tensor, ), ( (VariableTracker, TensorVariable), op_var._comparison_with_tensor, ), ( (SymNodeVariable, VariableTracker), op_var._comparison_with_symnode, ), ( (VariableTracker, SymNodeVariable), op_var._comparison_with_symnode, ), ] ) if op.__name__.startswith("is_"): def handle_is(tx: "InstructionTranslator", left, right): # 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_") result.append(((VariableTracker, VariableTracker), handle_is)) return result for op in supported_comparison_ops.values(): assert callable(op) assert op not in op_handlers op_handlers[op] = create_cmp_op_handlers(op) return op_handlers @staticmethod def _find_binop_handler(op, a_type, b_type): handlers = BuiltinVariable._binop_handlers().get(op) if handlers is None: return None matches = [] for (type1, type2), handler in handlers: if issubclass(a_type, type1) and issubclass(b_type, type2): matches.append(handler) return matches def can_insert_in_graph(self): return self.fn in self._fx_graph_functions() def __init__(self, fn, **kwargs) -> None: super().__init__(**kwargs) self.fn = fn def __str__(self) -> str: if self.fn is None: name = "None" else: name = self.fn.__name__ return f"{self.__class__.__name__}({name})" 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): any_tensor = False for arg in args: if isinstance(arg, variables.GetAttrVariable): return False any_tensor = any_tensor or isinstance(arg, variables.TensorVariable) return any_tensor def tensor_args_type(self, arg_types): any_tensor = False for arg_type in arg_types: if issubclass(arg_type, variables.GetAttrVariable): return False any_tensor = any_tensor or issubclass(arg_type, variables.TensorVariable) return any_tensor 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) @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): return self.can_constant_fold_through() and check_unspec_or_constant_args( args, kwargs ) @staticmethod def _make_handler(fn, arg_types: List[type], has_kwargs: bool): from .builder import SourcelessBuilder from .lazy import LazyVariableTracker obj = BuiltinVariable(fn) handlers = [] if any(issubclass(t, LazyVariableTracker) for t in arg_types): return lambda tx, args, kwargs: obj.call_function( tx, [v.realize() for v in args], kwargs ) if inspect.isclass(fn) and issubclass(fn, Exception): def create_exception_class_object( tx: "InstructionTranslator", args, kwargs ): if fn is AssertionError and not all( isinstance(x, variables.ConstantVariable) and isinstance(x.value, str) for x in args ): unimplemented("assert with non-string message") return variables.ExceptionVariable(fn, args, **kwargs) return create_exception_class_object if obj.can_insert_in_graph() and not ( fn is operator.getitem and not issubclass(arg_types[0], variables.TensorVariable) ): if obj.tensor_args_type(arg_types): return obj._handle_insert_op_in_graph elif has_kwargs: # need runtime check for kwargs handlers.append(obj._handle_insert_op_in_graph) # Handle binary ops (e.g. __add__ / __radd__, __iadd__, etc.) # NB: Tensor args are handled above and not here if len(arg_types) == 2 and not has_kwargs: # Try to find a handler for the arg types; otherwise, fall through to constant handler binop_handlers = BuiltinVariable._find_binop_handler(fn, *arg_types) if not binop_handlers: pass elif len(binop_handlers) == 1: (binop_handler,) = binop_handlers handlers.append(lambda tx, args, _: binop_handler(tx, *args)) else: def call_binop_handlers(tx: "InstructionTranslator", args, _): for fn in binop_handlers: rv = fn(tx, *args) if rv: return rv handlers.append(call_binop_handlers) self_handler = getattr(obj, f"call_{fn.__name__}", None) if self_handler: def call_self_handler(tx: "InstructionTranslator", args, kwargs): try: result = self_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(self_handler).bind(tx, *args, **kwargs) except TypeError as e: has_constant_handler = obj.has_constant_handler(args, kwargs) if not has_constant_handler: log.warning( "incorrect arg count %s %s and no constant handler", self_handler, e, ) unimplemented( f"invalid handler args {self_handler} {args} {kwargs}" ) else: raise except Unsupported as exc: has_constant_handler = obj.has_constant_handler(args, kwargs) if not has_constant_handler: raise # Actually, we will handle this just fine exc.remove_from_stats() handlers.append(call_self_handler) if obj.can_constant_fold_through(): builder = SourcelessBuilder.create if ( all(issubclass(x, ConstantVariable) for x in arg_types) and not has_kwargs ): def constant_fold_handler(tx: "InstructionTranslator", args, kwargs): # fast path try: res = fn( *[x.as_python_constant() for x in args], ) except Exception as exc: unimplemented(f"constant fold exception: {repr(exc)}") return builder(tx, res) else: def constant_fold_handler(tx: "InstructionTranslator", args, kwargs): # path with a runtime check if check_unspec_or_constant_args(args, kwargs): try: res = fn( *[x.as_python_constant() for x in args], **{ k: v.as_python_constant() for k, v in kwargs.items() }, ) except Exception as exc: unimplemented(f"constant fold exception: {repr(exc)}") return builder(tx, res) handlers.append(constant_fold_handler) error_msg = f"builtin: {fn.__name__} {arg_types} {has_kwargs}" if len(handlers) == 0: return lambda *args: unimplemented(error_msg) elif len(handlers) == 1: (handler,) = handlers def builtin_dispatch(tx: "InstructionTranslator", args, kwargs): rv = handler(tx, args, kwargs) if rv: return rv unimplemented(error_msg) else: def builtin_dispatch(tx: "InstructionTranslator", args, kwargs): for fn in handlers: rv = fn(tx, args, kwargs) if rv: return rv unimplemented(error_msg) return builtin_dispatch def _handle_insert_op_in_graph(self, tx: "InstructionTranslator", args, kwargs): from .builder import wrap_fx_proxy, wrap_fx_proxy_cls if kwargs and not self.tensor_args(*args, *kwargs.values()): return fn = self.fn try: # Constant fold for constant tensor and python constants if 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 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[fn], [args[0], args[1]] if 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(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 check_unspec_python_args(args, kwargs): _args, _kwargs = self.unwrap_unspec_args_kwargs(args, kwargs) raw_value = 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 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}") call_function_handler_cache = {} def call_function( self, tx: "InstructionTranslator", args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]", ) -> "VariableTracker": if kwargs: kwargs = {k: v.realize() for k, v in kwargs.items()} key = (self.fn, *(type(x) for x in args), True) else: key = (self.fn, *(type(x) for x in args)) handler = self.call_function_handler_cache.get(key) if not handler: self.call_function_handler_cache[key] = handler = self._make_handler( self.fn, [type(x) for x in args], bool(kwargs) ) return handler(tx, args, kwargs) def call_method( self, tx, name, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]", ) -> "VariableTracker": if self.fn is object and name == "__setattr__": assert len(args) == 3 assert len(kwargs) == 0 obj, name_var, val = args obj = obj.realize() if ( isinstance(obj, UserDefinedObjectVariable) and tx.output.side_effects.is_attribute_mutation(obj) and name_var.is_python_constant() ): return obj.method_setattr_standard(tx, name_var, val) if self.fn is object and name == "__new__": assert len(args) == 1 assert len(kwargs) == 0 return tx.output.side_effects.track_object_new_from_user_defined_class( args[0] ) if self.fn is dict and name == "fromkeys": return BuiltinVariable.call_custom_dict_fromkeys(tx, dict, *args, **kwargs) return super().call_method(tx, name, args, kwargs) def _call_int_float(self, tx: "InstructionTranslator", arg): # Handle cases like int(torch.seed()) # Also handle sym_float to sym_int cases if isinstance(arg, (SymNodeVariable, variables.TensorVariable)): if isinstance(arg, variables.TensorVariable): item = arg.call_method(tx, "item", [], {}) else: item = arg fn_ = sym_int if self.fn is int else sym_float from torch._dynamo.variables.builder import wrap_fx_proxy return wrap_fx_proxy( tx=tx, proxy=tx.output.create_proxy( "call_function", fn_, (item.as_proxy(),), {}, ), ) call_int = _call_int_float call_float = _call_int_float def call_str(self, tx: "InstructionTranslator", arg): # Handle `str` on a user defined function or object if isinstance(arg, (variables.UserFunctionVariable)): return variables.ConstantVariable.create(value=str(arg.fn)) elif isinstance(arg, (variables.UserDefinedObjectVariable)): # Check if object has __str__ method if hasattr(arg.value, "__str__"): str_method = arg.value.__str__ elif hasattr(arg.value, "__repr__"): # account for __repr__ functions when __str__ is absent str_method = arg.value.__repr__ else: unimplemented("user defined object has no __str__ or __repr__ method") if type(arg.value).__str__ is object.__str__: # Rely on the object str method try: return variables.ConstantVariable.create(value=str_method()) except AttributeError: # Graph break return elif is_wrapper_or_member_descriptor(str_method): unimplemented(f"{type(arg.value)} has a C/C++ based str method") else: # Overrides for custom str method # Pass method as function to call tx.inline_user_function_return bound_method = str_method.__func__ try: # Only supports certain function types user_func_variable = variables.UserFunctionVariable(bound_method) except AssertionError as e: # Won't be able to do inline the str method, return to avoid graph break log.warning("Failed to create UserFunctionVariable: %s", e) return # Inline the user function return tx.inline_user_function_return(user_func_variable, [arg], {}) def _call_min_max(self, tx: "InstructionTranslator", *args): if len(args) == 1 and args[0].has_force_unpack_var_sequence(tx): items = args[0].force_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: "InstructionTranslator", 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: "InstructionTranslator", a, b): if a is None or b is None: # a or b could be none if we reduce and _call_min_max_binary failed # to return something return 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: "InstructionTranslator", 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: "InstructionTranslator", arg: "VariableTracker"): # Call arg.__pos__() pos_method = BuiltinVariable(getattr).call_function( tx, [arg, ConstantVariable.create("__pos__")], {} ) return pos_method.call_function(tx, [], {}) def call_index(self, tx: "InstructionTranslator", arg: "VariableTracker"): if isinstance(arg, variables.TensorVariable): unimplemented("unsupported index(tensor)") arg = guard_if_dyn(arg) constant_value = operator.index(arg) return variables.ConstantVariable.create(constant_value) def call_round(self, tx: "InstructionTranslator", 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: "InstructionTranslator", *args): if check_unspec_or_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: "InstructionTranslator", *args): return variables.SliceVariable(args) def _dyn_proxy(self, tx: "InstructionTranslator", *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) ), ) # NOTE must handle IteratorVariable separately! def _call_iter_tuple_list( self, tx: "InstructionTranslator", obj=None, *args, **kwargs ): assert not isinstance(obj, variables.IteratorVariable) if self._dynamic_args(*args, **kwargs): return self._dyn_proxy(tx, *args, **kwargs) 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: if ( getattr(obj, "source", False) and isinstance(obj, ConstDictVariable) and not istype(obj, SetVariable) ): tx.output.guard_on_key_order.add(obj.source.name()) install_guard(obj.source.make_guard(GuardBuilder.SEQUENCE_LENGTH)) return cls( list(obj.unpack_var_sequence(tx)), mutable_local=MutableLocal(), ) def _call_tuple_list(self, tx, obj=None, *args, **kwargs): if isinstance(obj, variables.IteratorVariable): cls = variables.BaseListVariable.cls_for(self.fn) return cls( list(obj.force_unpack_var_sequence(tx)), mutable_local=MutableLocal(), ) else: return self._call_iter_tuple_list(tx, obj, *args, **kwargs) def call_iter(self, tx: "InstructionTranslator", obj, *args, **kwargs): if isinstance(obj, variables.IteratorVariable): ret = obj else: # 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_tuple_list call_list = _call_tuple_list def call_callable(self, tx: "InstructionTranslator", arg): from .functions import BaseUserFunctionVariable from .nn_module import NNModuleVariable if isinstance( arg, ( variables.UserDefinedClassVariable, BaseUserFunctionVariable, NNModuleVariable, ), ): return variables.ConstantVariable.create(True) elif isinstance(arg, UserDefinedVariable): return variables.ConstantVariable.create(callable(arg.value)) elif isinstance( arg, ( ConstantVariable, SymNodeVariable, TensorVariable, ListVariable, TupleVariable, ListIteratorVariable, ), ): 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: "InstructionTranslator", *args, **kwargs): return BuiltinVariable.call_custom_dict(tx, dict, *args, **kwargs) @staticmethod def call_custom_dict(tx: "InstructionTranslator", user_cls, *args, **kwargs): from .builder import SourcelessBuilder 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, variables.IteratorVariable, ), ): items = dict( x.force_unpack_var_sequence(tx) for x in arg.force_unpack_var_sequence(tx) ) return ConstDictVariable(items, user_cls, mutable_local=MutableLocal()) elif isinstance(arg, variables.MutableMappingVariable): # This is applicable for user defined objects which seem like dict, but are not really dicts. For # example, TensorDict derives from MutableMapping. For such cases, we can directly inline the .items # method and create a new dict. if does_not_override_dict_iter_methods(type(arg.value)): # These are implemeted in C, so we will have to manually construct the items if tx.output.side_effects.has_pending_mutation(arg): unimplemented( f"{user_cls.__name__}.items(): {args} {kwargs} - object is mutated" ) new_dict = dict(arg.value.items()) return SourcelessBuilder.create(tx, new_dict) else: func_var = arg.var_getattr(tx, "items") if not isinstance(func_var, variables.UserFunctionVariable): unimplemented(f"{user_cls.__name__}.items(): {args} {kwargs}") out = tx.inline_user_function_return(func_var, args, kwargs) if isinstance(out, ConstDictVariable): return out return BuiltinVariable(user_cls).call_custom_dict(tx, user_cls, out) 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: "InstructionTranslator", 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_force_unpack_var_sequence(tx): keys = arg.force_unpack_var_sequence(tx) if all(is_hashable(v) for v in keys): return DictVariableType( dict.fromkeys(keys, value), user_cls, mutable_local=MutableLocal() ) unimplemented(f"{user_cls.__name__}.fromkeys(): {args} {kwargs}") def call_set(self, tx: "InstructionTranslator", *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_force_unpack_var_sequence(tx): items = arg.force_unpack_var_sequence(tx) return SetVariable(items, mutable_local=MutableLocal()) elif isinstance(arg, variables.UserDefinedObjectVariable) and isinstance( arg.value, KeysView ): iter_fn = arg.var_getattr(tx, "__iter__") if isinstance(iter_fn, variables.UserMethodVariable): out = tx.inline_user_function_return(iter_fn, args, kwargs) if isinstance(out, SetVariable): return out return BuiltinVariable(set).call_set(tx, out) else: unimplemented(f"set(): {args} {kwargs}") else: unimplemented(f"set(): {args} {kwargs}") def call_frozenset(self, tx: "InstructionTranslator", *args, **kwargs): assert not kwargs if not args: return FrozensetVariable([]) assert len(args) == 1 arg = args[0] if isinstance(arg, variables.FrozensetVariable): return FrozensetVariable([x.vt for x in arg.set_items]) elif arg.has_unpack_var_sequence(tx): items = arg.unpack_var_sequence(tx) return FrozensetVariable(items) else: unimplemented(f"frozenset(): {args} {kwargs}") def call_zip(self, tx: "InstructionTranslator", *args, **kwargs): if kwargs: assert len(kwargs) == 1 and "strict" in kwargs strict = kwargs.pop("strict", False) args = [ arg.unpack_var_sequence(tx) if arg.has_unpack_var_sequence(tx) else arg for arg in args ] return variables.ZipVariable(args, strict=strict, mutable_local=MutableLocal()) def call_len(self, tx: "InstructionTranslator", *args, **kwargs): return args[0].call_method(tx, "__len__", args[1:], kwargs) def call_getitem(self, tx: "InstructionTranslator", *args, **kwargs): return args[0].call_method(tx, "__getitem__", args[1:], kwargs) def call_isinstance(self, tx: "InstructionTranslator", 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: example_val = arg.as_proxy().node.meta["example_value"] if ( is_traceable_wrapper_subclass(example_val) and ty is torch.nn.parameter.Parameter ): # N.B: we are calling isinstance directly on the example value. # torch.nn.Parameter has a meta-class that overrides __isinstance__, # the isinstance check here allows us to invoke that logic. return isinstance(example_val, ty) else: 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: "InstructionTranslator", left_ty, right_ty): """Checks if first arg is subclass of right arg""" try: left_ty_py = left_ty.as_python_constant() right_ty_py = right_ty.as_python_constant() except NotImplementedError: unimplemented( f"call_issubclass args not constant left_ty: {left_ty}, right_ty: {right_ty}" ) return variables.ConstantVariable(issubclass(left_ty_py, right_ty_py)) def call_super(self, tx: "InstructionTranslator", a, b): return variables.SuperVariable(a, b) def call_next(self, tx: "InstructionTranslator", arg: VariableTracker): try: return arg.next_variable(tx) except Unsupported as ex: if isinstance(arg, variables.BaseListVariable): ex.remove_from_stats() return arg.items[0] raise def call_hasattr(self, tx: "InstructionTranslator", obj, attr): if attr.is_python_constant(): name = attr.as_python_constant() if isinstance(obj, variables.BuiltinVariable): return variables.ConstantVariable(hasattr(obj.fn, name)) return obj.call_hasattr(tx, name) def call_map(self, tx: "InstructionTranslator", fn, *seqs): seqs = [ seq.unpack_var_sequence(tx) if seq.has_unpack_var_sequence(tx) else seq for seq in seqs ] return variables.MapVariable(fn, seqs, mutable_local=MutableLocal()) def call_filter(self, tx: "InstructionTranslator", fn, seq): if seq.has_unpack_var_sequence(tx): seq_unpacked = seq.unpack_var_sequence(tx) try: items = list( filter( lambda x: fn.call_function(tx, [x], {}).as_python_constant(), seq_unpacked, ) ) return variables.TupleVariable(items) except NotImplementedError: return def call_sum(self, tx: "InstructionTranslator", 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_force_unpack_var_sequence(tx): if start is self._SENTINEL: start = variables.ConstantVariable.create(0) items = seq.force_unpack_var_sequence(tx) return BuiltinVariable(functools.reduce).call_function( tx, [ BuiltinVariable(operator.add), variables.TupleVariable(items), start, ], {}, ) def call_reduce( self, tx: "InstructionTranslator", function, iterable, initial=_SENTINEL ): if iterable.has_force_unpack_var_sequence(tx): items = iterable.force_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: "InstructionTranslator", obj: VariableTracker, name_var: VariableTracker, default=None, ): from .. import trace_rules from . import ( ConstantVariable, GetAttrVariable, 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): if isinstance(obj, variables.UnspecializedNNModuleVariable): if ( name in ( "named_parameters", "parameters", "named_buffers", "buffers", "named_modules", "modules", ) and obj.is_state_mutated and tx.output.side_effects.has_pending_mutation(obj) ): unimplemented( f"pending mutation on nn module, so graph breaking at {name!r} call" ) if tx.output.side_effects.has_pending_mutation_of_attr(obj, name): return tx.output.side_effects.load_attr(obj, name) 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 in {"__bases__", "__base__", "__flags__"}: try: value = obj.as_python_constant() if isinstance(value, type): if name == "__bases__": 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.create(tx, b) for b in bases ] return variables.TupleVariable(tuple_args, **options) if name == "__base__": base = value.__base__ if source is not None: return VariableBuilder(tx, source)(base) return SourcelessBuilder.create(tx, base) if name == "__flags__": return ConstantVariable.create(value.__flags__) except NotImplementedError: pass if isinstance(obj, variables.NNModuleVariable): return obj.var_getattr(tx, name) elif isinstance( obj, ( variables.TensorVariable, variables.NamedTupleVariable, variables.ConstantVariable, variables.DistributedVariable, 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, DummyModule): # TODO(mlazos) - Do we need this? if obj.is_torch or name not in obj.value.__dict__: 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.create(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: "InstructionTranslator", obj: VariableTracker, name_var: VariableTracker, val: VariableTracker, ): if isinstance( obj, ( variables.CustomizedDictVariable, variables.PlacementVariable, variables.UserDefinedObjectVariable, ), ): 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. # Essentially, dynamo is trying to faithfully 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) if name == "_grad": tx.output.side_effects.store_attr(obj, "grad", 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: "InstructionTranslator", obj: VariableTracker, name_var: VariableTracker, ): return self.call_setattr(tx, obj, name_var, variables.DeletedVariable()) def call_type(self, tx: "InstructionTranslator", 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.create(tx, py_type) else: return VariableBuilder(tx, TypeSource(obj.source))(py_type) def call_reversed(self, tx: "InstructionTranslator", 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: "InstructionTranslator", obj: VariableTracker, **kwargs): if obj.has_force_unpack_var_sequence(tx) and not isinstance( obj, variables.TensorVariable ): unpacked = obj.force_unpack_var_sequence(tx) if not all(x.is_python_constant() for x in unpacked): return function = kwargs.pop("key", None) reverse = kwargs.pop( "reverse", ConstantVariable.create(False) ).as_python_constant() assert len(kwargs) == 0 if function: items = sorted( unpacked, key=lambda x: function.call_function( tx, [x], {} ).as_python_constant(), reverse=reverse, ) else: items = sorted( unpacked, key=lambda x: x.as_python_constant(), reverse=reverse, ) return variables.ListVariable(items) # neg is a constant fold function, so we only get here if constant fold is not valid def call_neg(self, tx: "InstructionTranslator", 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: "InstructionTranslator", _format_string, *args, **kwargs): format_string = _format_string.as_python_constant() return variables.StringFormatVariable.create(format_string, args, kwargs) def call_id(self, tx: "InstructionTranslator", *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)) elif len(args) == 1 and isinstance( args[0], variables.UserDefinedObjectVariable ): install_guard(args[0].source.make_guard(GuardBuilder.ID_MATCH)) constant_result = id(args[0].value) return variables.ConstantVariable.create(constant_result) elif len(args) == 1 and isinstance(args[0], TensorVariable): tensor_variable = args[0] return tensor_variable.call_id(tx) else: unimplemented(f"call_id with args {args}") def call_deepcopy(self, tx: "InstructionTranslator", x): unimplemented(f"copy.deepcopy {repr(x)}") def _comparison_with_tensor(self, tx: "InstructionTranslator", left, right): from .builder import wrap_fx_proxy_cls from .tensor import supported_tensor_comparison_op_values op = self.fn 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_op_values: unimplemented(f"{op.__name__}({left}, {right})") 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(f"{op.__name__}({left}, {right})") 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, ) def _comparison_with_symnode(self, tx: "InstructionTranslator", left, right): from .tensor import supported_tensor_comparison_op_values op = self.fn if op not in supported_tensor_comparison_op_values: unimplemented(f"{op.__name__}({left}, {right})") proxy = tx.output.create_proxy( "call_function", op, (left.as_proxy(), right.as_proxy()), {} ) return SymNodeVariable.create( tx, proxy, sym_num=None, ) def call_and_(self, tx: "InstructionTranslator", 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: "InstructionTranslator", 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: "InstructionTranslator", 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 def call_contains( self, tx: "InstructionTranslator", a: VariableTracker, b: VariableTracker ): return a.call_method(tx, "__contains__", [b], {}) @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)