# mypy: allow-untyped-defs """ Built-in function and type variable tracking for TorchDynamo's symbolic execution. This module contains variable tracker classes for Python built-in functions, types, and operations during graph compilation. It handles symbolic execution of: - Built-in functions (len, getattr, isinstance, etc.) - Type constructors (int, float, str, list, dict, etc.) - Built-in operators and methods - Special Python constructs (super, hasattr, etc.) Key classes: - BuiltinVariable: Tracks built-in functions and handles their execution - TypeVariable: Manages type constructor calls and type checking - SuperVariable: Handles super() calls in class hierarchies These variable trackers ensure that built-in Python operations are correctly handled during symbolic execution, either by executing them directly when safe or by creating appropriate graph nodes when needed. """ import contextlib import functools import inspect import itertools import logging import math import operator import sys import types import typing import unittest from collections import defaultdict, OrderedDict from collections.abc import KeysView, Sequence from typing import Callable, TYPE_CHECKING, Union import torch from torch import sym_float, sym_int from torch._subclasses.meta_utils import is_sparse_any from torch.utils._python_dispatch import is_traceable_wrapper_subclass from .. import config, graph_break_hints, polyfills, variables from ..exc import ( AttributeMutationError, ObservedAttributeError, raise_observed_exception, unimplemented_v2, Unsupported, UserError, UserErrorType, ) from ..guards import GuardBuilder, install_guard from ..replay_record import DummyModule from ..source import ( AttrSource, GetItemSource, GlobalSource, is_constant_source, TypeSource, ) from ..utils import ( check_constant_args, check_numpy_ndarray_args, check_unspec_or_constant_args, check_unspec_python_args, cmp_name_to_op_mapping, dict_methods, extract_fake_example_value, get_fake_value, guard_if_dyn, is_tensor_getset_descriptor, is_wrapper_or_member_descriptor, istype, numpy_operator_wrapper, proxy_args_kwargs, str_methods, tensortype_to_dtype, ) from .base import AsPythonConstantNotImplementedError, ValueMutationNew, VariableTracker from .constant import ConstantVariable from .ctx_manager import EventVariable, StreamVariable from .dicts import ( ConstDictVariable, DefaultDictVariable, DictViewVariable, 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: # Cyclic dependency... from torch._dynamo.codegen import PyCodegen 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, } _HandlerCallback = Callable[ ["InstructionTranslator", typing.Any, typing.Any], VariableTracker ] _TrackersType = Union[type[VariableTracker], tuple[type[VariableTracker], ...]] polyfill_fn_mapping = { operator.eq: polyfills.cmp_eq, operator.ne: polyfills.cmp_ne, operator.lt: polyfills.cmp_lt, operator.le: polyfills.cmp_le, operator.gt: polyfills.cmp_gt, operator.ge: polyfills.cmp_ge, } class BuiltinVariable(VariableTracker): """ A VariableTracker that represents a built-in value (functions and operators). A lot of the code here assumes it will be a function object. The BuiltinVariable class wraps Python built-in functions (like len, isinstance, etc.) and operators (like +, -, *, etc.) to enable symbolic execution during tracing. This allows Dynamo to properly handle these operations when converting Python code to FX graphs while maintaining correct semantics and enabling optimizations. """ _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.cache 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.cache 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.length_hint, operator.lshift, operator.rshift, operator.and_, operator.or_, operator.xor, operator.ipow, operator.imul, operator.imatmul, operator.ifloordiv, operator.itruediv, operator.getitem, operator.imod, operator.iadd, operator.isub, operator.ilshift, operator.irshift, operator.iand, operator.ixor, operator.ior, } return fns @staticmethod @functools.cache def _binops() -> dict[ Callable[..., object], tuple[list[str], Callable[..., object]] ]: # function -> ([forward name, reverse name, in-place name], in-place op) fns: dict[Callable[..., object], tuple[list[str], Callable[..., object]]] = { 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.cache 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 .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: dict[ Callable[..., object], list[ tuple[ tuple[ type[VariableTracker], _TrackersType, ], _HandlerCallback, ] ], ] = {} 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: list[ tuple[ tuple[ type[VariableTracker], _TrackersType, ], _HandlerCallback, ] ] = [ # 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, ), ( (SizeVariable, TupleVariable), size_add_handler, ), ( (TupleVariable, 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)], mutation_type=ValueMutationNew(), ), ), ( (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 a.is_immutable() 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: list[ tuple[ tuple[type[VariableTracker], type[VariableTracker]], _HandlerCallback, ] ] = [ ( (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(), mutation_type=ValueMutationNew(), ) list_like_expansion_handlers: list[ tuple[ tuple[type[VariableTracker], type[VariableTracker]], _HandlerCallback, ] ] = [ ((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) def create_cmp_op_handlers(op): def compare_by_value(tx: "InstructionTranslator", a, b): return ConstantVariable(op(a.value, b.value)) result: list[ tuple[ tuple[ _TrackersType, _TrackersType, ], _HandlerCallback, ] ] = [((ConstantVariable, ConstantVariable), compare_by_value)] if op in polyfill_fn_mapping: # For constants, speedup the comparison instead of using # polyfill. Removing this line causes major regression for pr # time benchmark - add_loop_eager. result = [((ConstantVariable, ConstantVariable), compare_by_value)] op_var = BuiltinVariable(op) # Special handling of SymNode variable result.extend( [ ( (SymNodeVariable, VariableTracker), op_var._comparison_with_symnode, ), ( (VariableTracker, SymNodeVariable), op_var._comparison_with_symnode, ), ] ) def handler(tx, a, b): return tx.inline_user_function_return( VariableTracker.build(tx, polyfill_fn_mapping[op]), [a, b], {} ) result.append(((VariableTracker, VariableTracker), handler)) return result result = [((ConstantVariable, ConstantVariable), compare_by_value)] if op in supported_const_comparison_ops.values() and op.__name__.startswith( "is_" ): # Tensor is None, List is not None, etc none_result = op(object(), None) def never(tx: "InstructionTranslator", a, b): return ConstantVariable(none_result) obj_op_none = never none_op_obj = never 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, ), ] ) 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), ) ), ), ( (UserDefinedObjectVariable, UserDefinedObjectVariable), compare_by_value, ), ( (UserDefinedClassVariable, UserDefinedClassVariable), compare_by_value, ), ( ( (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, ), ] ) 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_") if left is right: return ConstantVariable.create(op(left, right)) if ( istype(left, variables.ExceptionVariable) and istype(right, variables.ExceptionVariable) and left.exc_type is not right.exc_type ): return ConstantVariable.create(op(left, right)) 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 __repr__(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: "PyCodegen"): 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, 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 .lazy import LazyVariableTracker obj = BuiltinVariable(fn) handlers: list[_HandlerCallback] = [] 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) # GeneratorExit doesn't inherit from Exception # >>> issubclass(GeneratorExit, Exception) # False or fn is GeneratorExit ): 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_v2( gb_type="assert with non-string message", context=str(args), explanation="Dynamo only supports asserts with string messages", hints=[*graph_break_hints.SUPPORTABLE], ) 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_v2( gb_type="invalid call to builtin op handler", context=f"invalid args to {self_handler}: {args} {kwargs}", explanation=f"Encountered TypeError when trying to handle op {fn.__name__}", hints=[*graph_break_hints.DIFFICULT], ) 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(): 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: raise_observed_exception( type(exc), tx, args=list(map(ConstantVariable.create, exc.args)), ) except AsPythonConstantNotImplementedError as exc: unimplemented_v2( gb_type="constant fold exception", context=f"attempted to run function {fn} with arguments {args}", explanation="Encountered exception when attempting to constant fold.", hints=[*graph_break_hints.DYNAMO_BUG], from_exc=exc, ) return VariableTracker.build(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 AsPythonConstantNotImplementedError as exc: unimplemented_v2( gb_type="constant fold exception", context=f"attempted to run function {fn} with arguments {args}", explanation="Encountered exception when attempting to constant fold.", hints=[*graph_break_hints.DYNAMO_BUG], from_exc=exc, ) except Exception as exc: raise_observed_exception( type(exc), tx, args=list(map(ConstantVariable.create, exc.args)), ) return VariableTracker.build(tx, res) handlers.append(constant_fold_handler) def call_unimplemented_v2(args): real_arg_types = [arg.python_type_name() for arg in args] unimplemented_v2( gb_type="Failed to trace builtin operator", context=f"builtin {fn.__name__} {arg_types} {has_kwargs}", explanation=f"Dynamo does not know how to trace builtin operator `{fn.__name__}` " f"with argument types {real_arg_types} (has_kwargs {has_kwargs})", hints=[ f"Avoid calling builtin `{fn.__name__}` with argument types {real_arg_types}. " f"Consider using an equivalent alternative function/method to `{fn.__name__}`.", "If you are attempting to call a logging function (e.g. `print`), " "you can try adding it to `torch._dynamo.config.reorderable_logging_functions`.", "Please report an issue to PyTorch.", ], ) if len(handlers) == 0: return lambda tx, args, kwargs: call_unimplemented_v2(args) elif len(handlers) == 1: (handler,) = handlers def builtin_dispatch(tx: "InstructionTranslator", args, kwargs): rv = handler(tx, args, kwargs) if rv: return rv call_unimplemented_v2(args) else: def builtin_dispatch(tx: "InstructionTranslator", args, kwargs): for fn in handlers: rv = fn(tx, args, kwargs) if rv: return rv call_unimplemented_v2(args) 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 # insert handling for torch function here from .builder import SourcelessBuilder from .torch_function import ( BUILTIN_TO_TENSOR_FN_MAP, BUILTIN_TO_TENSOR_RFN_MAP, can_dispatch_torch_function, dispatch_torch_function, ) if can_dispatch_torch_function(tx, args, kwargs): # Only remap the fn to tensor methods if we aren't exporting # export serde does not handle method descriptors today if not tx.export: # Use sourceless builder, we built the map ourselves if not isinstance(args[0], TensorVariable): if self.fn in BUILTIN_TO_TENSOR_RFN_MAP: func = BUILTIN_TO_TENSOR_RFN_MAP[self.fn] else: func = BUILTIN_TO_TENSOR_FN_MAP[self.fn] tmp = args[0] # swap args and call reverse version of func args[0] = args[1] args[1] = tmp else: func = BUILTIN_TO_TENSOR_FN_MAP[self.fn] else: func = self.fn fn_var = SourcelessBuilder.create(tx, func) return dispatch_torch_function(tx, fn_var, args, kwargs) 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) if ( fn is operator.eq and len(args) == 2 and isinstance(args[0], variables.TensorVariable) ): # Dynamo expects `__eq__` str while operator.eq gives just `eq` # TODO - supporting all comparison operators could also work but # it fails lots of tests because graph str changes. return args[0].call_method(tx, "__eq__", args[1:], kwargs) 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].as_python_constant() return wrap_fx_proxy(tx, proxy) except NotImplementedError: unimplemented_v2( gb_type="unimplemented builtin op on tensor arguments", context=f"partial tensor op: {self} {args} {kwargs}", explanation=f"Dynamo does not know how to trace builtin operator {self.fn} with tensor arguments", hints=[*graph_break_hints.SUPPORTABLE], ) call_function_handler_cache: dict[ tuple[object, ...], Callable[ [ "InstructionTranslator", Sequence[VariableTracker], dict[str, VariableTracker], ], VariableTracker, ], ] = {} def call_function( self, tx: "InstructionTranslator", args: Sequence["VariableTracker"], kwargs: "dict[str, VariableTracker]", ) -> "VariableTracker": key: tuple[object, ...] 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 name == "__new__": # Supported __new__ methods if self.fn is object and len(args) == 1: assert len(kwargs) == 0 return tx.output.side_effects.track_new_user_defined_object( self, args[0], args[1:] ) if self.fn is dict and len(args) == 1 and not kwargs: dict_vt = ConstDictVariable({}, dict, mutation_type=ValueMutationNew()) if isinstance(args[0], BuiltinVariable) and args[0].fn is dict: return dict_vt # We don't have to set the underlying dict_vt in # UserDefinedDictVariable because it will be set to empty # ConstDictVariableTracker in the constructor. return tx.output.side_effects.track_new_user_defined_object( self, args[0], args[1:], ) if ( self.fn is tuple and len(args) == 2 and args[1].has_unpack_var_sequence(tx) and not kwargs ): if isinstance(args[0], BuiltinVariable) and args[0].fn is tuple: init_args = args[1].unpack_var_sequence(tx) return variables.TupleVariable( init_args, mutation_type=ValueMutationNew() ) return tx.output.side_effects.track_new_user_defined_object( self, args[0], args[1:], ) if self.fn is list: list_vt = ListVariable([], mutation_type=ValueMutationNew()) if isinstance(args[0], BuiltinVariable) and args[0].fn is list: return list_vt return tx.output.side_effects.track_new_user_defined_object( self, args[0], args[1:], ) if self.fn is object and name == "__init__": # object.__init__ is a no-op return variables.ConstantVariable(None) if self.fn is dict and name == "fromkeys": return BuiltinVariable.call_custom_dict_fromkeys(tx, dict, *args, **kwargs) if self.fn is dict: resolved_fn = getattr(self.fn, name) if resolved_fn in dict_methods: if isinstance(args[0], variables.UserDefinedDictVariable): return args[0]._dict_vt.call_method(tx, name, args[1:], kwargs) elif isinstance(args[0], variables.ConstDictVariable): return args[0].call_method(tx, name, args[1:], kwargs) if self.fn is str and len(args) >= 1: resolved_fn = getattr(self.fn, name) if resolved_fn in str_methods: if isinstance(args[0], ConstantVariable): return args[0].call_method(tx, name, args[1:], 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_bool(self, tx: "InstructionTranslator", arg): # Emulate `PyBool_Type.tp_vectorcall` which boils down to `PyObject_IsTrue`. # https://github.com/python/cpython/blob/3.12/Objects/object.c#L1674-L1697 if isinstance(arg, SymNodeVariable): # Note that we delay specializing on symbolic values to avoid # unnecessary guards. Specialization will happen later if, e.g., the # resulting boolean is used for branching. if isinstance(arg.sym_num, torch.SymBool): return arg # Emulate `nb_bool` of int/float objects # - https://github.com/python/cpython/blob/3.12/Objects/longobject.c#L4940-L4944 # - https://github.com/python/cpython/blob/3.12/Objects/floatobject.c#L878-L882 assert istype(arg.sym_num, (torch.SymInt, torch.SymFloat)) return SymNodeVariable.create(tx, arg.as_proxy() != 0) # TODO handle more cases and merge this with this with `generic_jump`. 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_v2( gb_type="failed to call str() on user defined object", context=str(arg), explanation="User defined object has no __str__ or __repr__ method", hints=[*graph_break_hints.USER_ERROR], ) 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_v2( gb_type="Attempted to a str() method implemented in C/C++", context="", explanation=f"{type(arg.value)} has a C/C++ based str method. This is not supported.", hints=["Write the str method in Python"], ) else: # Overrides for custom str method # Pass method as function to call tx.inline_user_function_return bound_method = str_method.__func__ # type: ignore[attr-defined] 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], {}) elif isinstance(arg, (variables.ExceptionVariable,)): if len(arg.args) == 0: value = f"{arg.exc_type}" else: value = ", ".join(a.as_python_constant() for a in arg.args) return variables.ConstantVariable.create(value=value) 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(): fn: VariableTracker 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 np_fn = {max: np.maximum, min: np.minimum}[self.fn] fn = variables.NumpyVariable(np_fn) else: torch_fn = {max: torch.maximum, min: torch.minimum}[self.fn] fn = variables.TorchInGraphFunctionVariable(torch_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): py_fn = torch.sym_max if self.fn is max else torch.sym_min proxy = tx.output.create_proxy( "call_function", py_fn, *proxy_args_kwargs([a, b], {}) ) return SymNodeVariable.create(tx, proxy, None) elif isinstance(a, ConstantVariable) and isinstance(b, ConstantVariable): value = self.fn( a.as_python_constant(), b.as_python_constant(), ) return ConstantVariable(value) 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_v2( gb_type="unsupported index(Tensor)", context="", explanation="Dynamo does not support tracing builtin index() on a Tensor", hints=[], ) 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 = tuple( 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( [], mutation_type=ValueMutationNew(), ) 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) if isinstance(obj, variables.MappingProxyVariable): # This could be an overguarding, but its rare to iterate # through a mapping proxy and not use the keys. install_guard( obj.source.make_guard(GuardBuilder.MAPPING_KEYS_CHECK) ) elif not isinstance(obj, variables.UnspecializedNNModuleVariable): # Prevent calling __len__ method for guards, the tracing # of __iter__ will insert the right guards later. install_guard( obj.source.make_guard(GuardBuilder.SEQUENCE_LENGTH) ) return cls( list(obj.unpack_var_sequence(tx)), mutation_type=ValueMutationNew(), ) def _call_iter_tuple_generator(self, tx, obj, *args, **kwargs): cls = variables.BaseListVariable.cls_for(self.fn) return cls( list(obj.force_unpack_var_sequence(tx)), # exhaust generator mutation_type=ValueMutationNew(), ) 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)), mutation_type=ValueMutationNew(), ) elif isinstance(obj, variables.LocalGeneratorObjectVariable): return self._call_iter_tuple_generator(tx, obj, *args, **kwargs) 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, FunctoolsPartialVariable from .nn_module import NNModuleVariable if isinstance( arg, ( variables.UserDefinedClassVariable, BaseUserFunctionVariable, FunctoolsPartialVariable, 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_v2( gb_type="bad args to builtin cast()", context=f"got args {args} {kwargs}", explanation="Dynamo expects exactly 2 args to builtin cast().", hints=["Ensure your call to cast() has exactly 2 arguments."], ) 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): return tx.inline_user_function_return( VariableTracker.build(tx, polyfills.construct_dict), [VariableTracker.build(tx, user_cls), *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") # type: ignore[arg-type] 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, mutation_type=ValueMutationNew() ) 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, mutation_type=ValueMutationNew(), ) unimplemented_v2( gb_type="failed to call dict.fromkeys()", context=f"{user_cls.__name__}.fromkeys(): {args} {kwargs}", explanation=f"Failed to call {user_cls.__name__}.fromkeys() because " "arguments could not be automatically converted to a list, " "or some dict key is not hashable.", hints=[ "Manually convert the argument to a list.", "Ensure all keys are hashable.", ], ) 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([], mutation_type=ValueMutationNew()) if len(args) != 1: raise_observed_exception( TypeError, tx, args=[ ConstantVariable.create( f"set() takes 1 positional argument but {len(args)} were given" ) ], ) arg = args[0] if isinstance(arg, variables.SetVariable): return arg.clone(mutation_type=ValueMutationNew()) elif arg.has_force_unpack_var_sequence(tx): items = arg.force_unpack_var_sequence(tx) return SetVariable(items, mutation_type=ValueMutationNew()) 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) raise_observed_exception( TypeError, tx, args=[ConstantVariable.create("failed to construct builtin set()")], ) def call_frozenset(self, tx: "InstructionTranslator", *args, **kwargs): assert not kwargs if not args: return FrozensetVariable([]) if len(args) != 1: raise_observed_exception( TypeError, tx, args=[ ConstantVariable.create( f"frozenset() takes 1 positional argument but {len(args)} were given" ) ], ) 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) raise_observed_exception( TypeError, tx, args=[ConstantVariable.create("failed to construct builtin frozenset()")], ) 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, mutation_type=ValueMutationNew() ) def call_len(self, tx: "InstructionTranslator", *args, **kwargs): try: return args[0].call_method(tx, "__len__", args[1:], kwargs) except AttributeError as e: raise_observed_exception(type(e), tx, args=list(e.args)) 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_v2( gb_type="builtin isinstance() cannot determine type of argument", context=f"isinstance({arg}, {isinstance_type})", explanation=f"Dynamo doesn't have a rule to determine the type of argument {arg}", hints=[*graph_break_hints.DYNAMO_BUG], ) 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_v2( gb_type="isinstance() called on user defined object with C extensions", context=f"isinstance({arg}, {isinstance_type})", explanation="User-defined object with C extensions can have torch.Tensor " "attributes; intentionally graph breaking.", hints=[*graph_break_hints.SUPPORTABLE], ) # 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) ) if isinstance(arg, variables.UserDefinedExceptionClassVariable): return ConstantVariable.create(isinstance(arg_type, isinstance_type)) isinstance_type_tuple: tuple[type, ...] if isinstance(isinstance_type, type) or callable( # E.g. isinstance(obj, typing.Sequence) getattr(isinstance_type, "__instancecheck__", None) ): isinstance_type_tuple = (isinstance_type,) elif sys.version_info >= (3, 10) and isinstance( isinstance_type, types.UnionType ): isinstance_type_tuple = isinstance_type.__args__ elif isinstance(isinstance_type, tuple) and all( isinstance(tp, type) or callable(getattr(tp, "__instancecheck__", None)) for tp in isinstance_type ): isinstance_type_tuple = isinstance_type else: raise_observed_exception( TypeError, tx, args=[ "isinstance() arg 2 must be a type, a tuple of types, or a union" ], ) try: # NB: `isinstance()` does not call `__subclasscheck__` but use `__instancecheck__`. # But usually `isinstance(obj, type_info)` and `issubclass(type(obj), type_info)` gives # the same result. # WARNING: This might run arbitrary user code `__subclasscheck__` and we did not trace # through it. This is a limitation of the current implementation. # Usually `__subclasscheck__` and `__instancecheck__` can be constant fold through, it # might not be a big issue and we trade off it for performance. val = issubclass(arg_type, isinstance_type_tuple) except TypeError: val = arg_type in isinstance_type_tuple 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_v2( gb_type="issubclass() with non-constant arguments", context=f"issubclass({left_ty}, {right_ty})", explanation="issubclass() with non-constant arguments not supported.", hints=[ "Make sure your arguments are types.", *graph_break_hints.USER_ERROR, ], ) # WARNING: This might run arbitrary user code `__subclasscheck__`. # See the comment in call_isinstance above. 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_obj_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, mutation_type=ValueMutationNew()) def call_filter(self, tx: "InstructionTranslator", fn, seq): seq = seq.unpack_var_sequence(tx) if seq.has_unpack_var_sequence(tx) else seq return variables.FilterVariable(fn, seq, mutation_type=ValueMutationNew()) def call_getattr( self, tx: "InstructionTranslator", obj: VariableTracker, name_var: VariableTracker, default=None, ): if not name_var.is_python_constant(): unimplemented_v2( gb_type="getattr() with non-constant name argument", context=f"getattr({obj}, {name_var}, {default})", explanation="getattr() with non-constant name argument is not supported", hints=["Ensure the name argument of getattr() is a string"], ) name = name_var.as_python_constant() # See NOTE [Tensor "grad" and "_grad" attr] if isinstance(obj, TensorVariable) and name == "_grad": name = "grad" 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_v2( gb_type="getattr() on nn.Module with pending mutation", context=f"getattr({obj}, {name}, {default})", explanation="Intentionally graph breaking on getattr() on a nn.Module " "with a pending mutation", hints=[], ) 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 source = obj.source and AttrSource(obj.source, name) if name in {"__bases__", "__base__", "__flags__"}: try: value = obj.as_python_constant() if isinstance(value, type): if name == "__bases__": tuple_args = [ VariableTracker.build( tx, b, source and GetItemSource(source, i) ) for i, b in enumerate(value.__bases__) ] return variables.TupleVariable(tuple_args, source=source) if name == "__base__": return VariableTracker.build(tx, value.__base__, source) 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, ), ): if ( isinstance(obj, variables.UserDefinedObjectVariable) and issubclass(obj.value.__class__, unittest.TestCase) and config.enable_trace_unittest and name in ( "assertRaisesRegex", "assertNotWarns", "assertWarnsRegex", "assertDictEqual", "assertSequenceEqual", "assertWarns", ) ): unimplemented_v2( gb_type="Failed to trace unittest method", context=f"function: unittest.TestCase.{name}", explanation=f"Dynamo does not know how to trace unittest method `{name}` ", hints=[ f"Avoid calling `TestCase.{name}`. " "Please report an issue to PyTorch.", ], ) if isinstance(obj, TensorVariable): fake_val = obj.proxy.node.meta["example_value"] if ( isinstance(fake_val, torch.Tensor) and is_sparse_any(fake_val) and (not tx.export or not config.capture_sparse_compute) ): unimplemented_v2( gb_type="Attempted to wrap sparse Tensor", context="", explanation="torch.compile does not support sparse Tensors", hints=[*graph_break_hints.SUPPORTABLE], ) try: return obj.var_getattr(tx, name) except NotImplementedError: return variables.GetAttrVariable(obj, name, source=source) elif isinstance(obj, variables.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 torch._dynamo.trace_rules.is_aten_op_or_tensor_method(member): return variables.TorchInGraphFunctionVariable(member, source=source) elif name in cmp_name_to_op_mapping: return variables.GetAttrVariable(obj, name, source=source) 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) # type: ignore[arg-type, union-attr] return VariableTracker.build(tx, member, source) elif istype(obj, variables.UserFunctionVariable) and name in ( "__name__", "__module__", ): return ConstantVariable.create(getattr(obj.fn, name)) else: try: return obj.var_getattr(tx, name) except NotImplementedError: return variables.GetAttrVariable(obj, name, source=source) def call_setattr( self, tx: "InstructionTranslator", obj: VariableTracker, name_var: VariableTracker, val: VariableTracker, ): if isinstance( obj, ( variables.PlacementVariable, variables.NamedTupleVariable, variables.UserDefinedObjectVariable, variables.NestedUserFunctionVariable, variables.ExceptionVariable, ), ): 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 # Some special handling for tensor attributes. if name == "requires_grad": # TODO(voz): Make it work properly unimplemented_v2( gb_type="setattr() on Tensor.requires_grad", context=f"setattr({obj}, {name}, {val})", explanation="setattr() on Tensor.requires_grad not supported. " "Mutating requires_grad can introduce a new leaf from non-leaf or vice versa in " "the middle of the graph, which AOTAutograd does not currently know how to handle.", hints=[*graph_break_hints.SUPPORTABLE], ) elif name == "data": # See comments on `test_set_data_on_scoped_tensor` for plans # to support this. if obj.source is None: unimplemented_v2( gb_type="Failed to mutate tensor data attribute", context=f"setattr({obj}, {name}, {val})", explanation="Dyanmo only supports mutating `.data`" " of tensor created outside `torch.compile` region", hints=[ "Don't mutate `.data` on this tensor, or move " "the mutation out of `torch.compile` region", ], ) # 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 = [ tf for tf in tx.output.tracked_fakes if tf.source == obj.source ] 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 elif name in ("_grad", "grad"): # NOTE: [Tensor "grad" and "_grad" attr] # _grad and grad share the same setter/getter, see # THPVariable_properties, and here we make sure setting one # enables reading `val` from the other, by routing all # read/write to `grad`. name = "grad" elif is_tensor_getset_descriptor(name): # Attribute like `torch.Tensor.real` has special setters we # don't yet support; it's not as simple adding an entry to # the side effect mapping. unimplemented_v2( gb_type="Failed to set tensor attribute", context=f"setattr({obj}, {name}, {val})", explanation="Dyanmo doesn't support setting these tensor attributes", hints=[ f"Don't mutate attribute '{name}' on tensors, or " "move the mutation out of `torch.compile` region", ], ) tx.output.side_effects.store_attr(obj, name, val) return val 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, ObservedAttributeError): 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 identity, 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) def call_delattr( self, tx: "InstructionTranslator", obj: VariableTracker, name_var: VariableTracker, ): return obj.call_method(tx, "__delattr__", [name_var], {}) def call_type(self, tx: "InstructionTranslator", obj: VariableTracker): try: py_type = obj.python_type() except NotImplementedError as error: raise UserError( UserErrorType.INVALID_INPUT, str(error), case_name="unknown_python_type", ) from None source = obj.source and TypeSource(obj.source) if ( source is None and isinstance(obj, variables.UserDefinedObjectVariable) and obj.cls_source ): source = obj.cls_source if py_type is torch.Tensor: # In some cases torch isn't available in globals name = tx.output.install_global_by_id("", torch) source = AttrSource(GlobalSource(name), "Tensor") return VariableTracker.build(tx, py_type, source) 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: VariableTracker, ): if obj.has_force_unpack_var_sequence(tx) and not isinstance( obj, variables.TensorVariable ): list_var = variables.ListVariable( obj.force_unpack_var_sequence(tx), mutation_type=ValueMutationNew(), ) list_var.call_method(tx, "sort", [], kwargs) return list_var # 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() format_string = str(format_string) 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.UserDefinedClassVariable, variables.UserDefinedObjectVariable), ): if args[0].source: 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) elif istype(args[0], variables.UserFunctionVariable): return variables.ConstantVariable.create(id(args[0].fn)) elif istype(args[0], variables.SkipFunctionVariable): return variables.ConstantVariable.create(id(args[0].value)) elif istype(args[0], variables.FunctoolsPartialVariable): return variables.ConstantVariable.create(id(args[0].fake_value)) else: unimplemented_v2( gb_type="id() with unsupported args", context=str(args), explanation=f"Dynamo doesn't know how to trace id() call with args {args}", hints=[ "Supported args are Tensors, and functions/nn.Modules/user-defined objects " "from outside the compiled region.", *graph_break_hints.SUPPORTABLE, ], ) def call_deepcopy(self, tx: "InstructionTranslator", x): unimplemented_v2( gb_type="copy.deepcopy()", context=f"copy.deepcopy({x})", explanation="Dynamo does not support copy.deepcopy()", hints=[ "Avoid calling copy.deepcopy()", *graph_break_hints.SUPPORTABLE, ], ) 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_v2( gb_type="unsupported Tensor comparison op", context=f"{op.__name__}({left}, {right})", explanation=f"Dynamo does not support the comparison op {op.__name__} " f"with Tensor arguments {left}, {right}", hints=[*graph_break_hints.SUPPORTABLE], ) 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_v2( gb_type="failed to broadcast when attempting Tensor comparison op", context=f"{op.__name__}({left}, {right})", explanation=f"Dynamo was unable to broad cast the arguments {left}, {right} " f"when attempting to trace the comparison op {op.__name__}.", hints=[*graph_break_hints.USER_ERROR], ) 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_v2( gb_type="unsupported SymNode comparison op", context=f"{op.__name__}({left}, {right})", explanation=f"Dynamo does not support the comparison op {op.__name__} " f"with SymNode arguments {left}, {right}", hints=[*graph_break_hints.SUPPORTABLE], ) # This is seen in inspect signature where we check if the value is a default value if isinstance(right, variables.UserDefinedClassVariable): return variables.ConstantVariable(op(object(), None)) 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 call_iand = call_and_ 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)) # This call looks like `{"one": torch.ones(1)} | {"two": torch.ones(2)}`. if isinstance(a, ConstDictVariable): return a.call_method(tx, "__or__", args=[b], kwargs={}) # None no-ops this handler and lets the driving function proceed return None call_ior = call_or_ 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, DictViewVariable): 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 gmv = GradModeVariable.create(tx, False) try: gmv.enter(tx) yield finally: gmv.exit(tx)