""" Variable tracking implementations for list-like data structures in Dynamo. This module provides specialized variable tracking for various collection types: - Lists and list subclasses (including torch.nn.ModuleList, ParameterList) - Tuples and named tuples - Ranges and slices - Collections.deque - torch.Size with special proxy handling The implementations support both mutable and immutable collections, iteration, and common sequence operations. Each collection type has a dedicated Variable class that handles its unique behaviors while integrating with Dynamo's variable tracking system. """ import collections import inspect import operator import sys from collections.abc import Sequence from typing import Any, Optional, TYPE_CHECKING import torch import torch.fx from .. import graph_break_hints, polyfills, variables from ..bytecode_transformation import ( create_build_tuple, create_call_function, create_instruction, create_rot_n, ) from ..exc import raise_observed_exception, unimplemented from ..source import AttrSource, NamedTupleFieldsSource from ..utils import ( cmp_name_to_op_mapping, cmp_name_to_op_str_mapping, get_fake_value, guard_if_dyn, iter_contains, Lit, namedtuple_fields, odict_values, raise_args_mismatch, range_iterator, set_example_value, ) from .base import ValueMutationNew, VariableTracker from .constant import ConstantVariable from .functions import UserFunctionVariable, UserMethodVariable from .iter import IteratorVariable if TYPE_CHECKING: from torch._dynamo.codegen import PyCodegen from torch._dynamo.symbolic_convert import InstructionTranslator class BaseListVariable(VariableTracker): @staticmethod def cls_for_instance(obj: Any) -> type["BaseListVariable"]: return BaseListVariable.cls_for(type(obj)) @staticmethod def cls_for(obj: Any) -> type: return { iter: ListIteratorVariable, list: ListVariable, slice: SliceVariable, torch.Size: SizeVariable, tuple: TupleVariable, odict_values: ListVariable, torch.nn.ParameterList: ListVariable, torch.nn.ModuleList: ListVariable, collections.deque: DequeVariable, }[obj] def __init__( self, items: list[VariableTracker], **kwargs: Any, ) -> None: super().__init__(**kwargs) assert isinstance(items, list) assert all(isinstance(x, VariableTracker) for x in items) self.items: list[VariableTracker] = items def _as_proxy(self) -> list[Any]: return [x.as_proxy() for x in self.items] def modified( self, items: list[VariableTracker], **kwargs: Any ) -> "BaseListVariable": return type(self)(items, **kwargs) @property def value(self) -> Any: return self.as_python_constant() def debug_repr_helper(self, prefix: str, suffix: str) -> str: return prefix + ", ".join(i.debug_repr() for i in self.items) + suffix def as_python_constant(self) -> Any: return self.python_type()([x.as_python_constant() for x in self.items]) def as_proxy(self) -> Any: assert self.python_type() is not SizeVariable return self.python_type()(self._as_proxy()) def getitem_const( self, tx: "InstructionTranslator", arg: VariableTracker ) -> VariableTracker: from .tensor import SymNodeVariable if isinstance(arg, SymNodeVariable): index = arg.sym_num else: index = arg.as_python_constant() if isinstance(index, slice): if index.step == 0: msg = ConstantVariable.create("slice step cannot be zero") raise_observed_exception(ValueError, tx, args=[msg]) # Set source to None because slicing a list gives a new local return self.clone( items=self.items[index], source=None, mutation_type=ValueMutationNew() if self.mutation_type else None, ) else: assert isinstance(index, (int, torch.SymInt)) try: return self.items[index] except IndexError: raise_observed_exception( IndexError, tx, args=["list index out of range"] ) def unpack_var_sequence(self, tx: "InstructionTranslator") -> list[VariableTracker]: return list(self.items) def call_tree_map_branch( self, tx: "InstructionTranslator", tree_map_fn: UserFunctionVariable, map_fn: VariableTracker, rest: Sequence[VariableTracker], tree_map_kwargs: dict[str, VariableTracker], ) -> VariableTracker: if not isinstance(self, (ListVariable, TupleVariable)): return self._tree_map_fallback( tx, tree_map_fn, map_fn, rest, tree_map_kwargs ) other_lists: list[BaseListVariable] = [] for candidate in rest: if ( not isinstance(candidate, BaseListVariable) or len(candidate.items) != len(self.items) or self.python_type() != candidate.python_type() ): return self._tree_map_fallback( tx, tree_map_fn, map_fn, rest, tree_map_kwargs ) other_lists.append(candidate) new_items: list[VariableTracker] = [] for idx, item in enumerate(self.items): sibling_leaves = [candidate.items[idx] for candidate in other_lists] new_items.append( item.call_tree_map( tx, tree_map_fn, map_fn, sibling_leaves, tree_map_kwargs, ) ) return self.clone( items=new_items, source=None, mutation_type=ValueMutationNew(), ) def call_method( self, tx: "InstructionTranslator", name: str, args: list[VariableTracker], kwargs: dict[str, VariableTracker], ) -> VariableTracker: if name == "__getitem__": if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) if args[0].is_tensor(): value = get_fake_value(args[0].as_proxy().node, tx) if value.constant is not None and value.constant.numel() == 1: value = variables.ConstantVariable.create(value.constant.item()) else: unimplemented( gb_type="Indexing list with non-scalar tensor", context=f"call_method {self} {name} {args} {kwargs}", explanation=( "Attempted to index list-like object with tensor with > 1 element." ), hints=[*graph_break_hints.USER_ERROR], ) else: value = args[0] if value.python_type() not in (int, slice): msg = f"indices must be integers or slices, not {value.python_type()}" raise_observed_exception(TypeError, tx, args=[ConstantVariable(msg)]) return self.getitem_const(tx, value) elif name == "__contains__": if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) return iter_contains(self.unpack_var_sequence(tx), args[0], tx) elif name == "index": if not len(args): raise_args_mismatch( tx, name, "0 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) return tx.inline_user_function_return( VariableTracker.build(tx, polyfills.index), [self] + list(args), kwargs, ) elif name == "count": if len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) return VariableTracker.build(tx, operator.countOf).call_function( tx, [self, args[0]], kwargs, ) elif name in ("__add__", "__iadd__"): if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) if type(self) is not type(args[0]): tp_name = self.python_type_name() other = args[0].python_type_name() msg_vt = ConstantVariable.create( f'can only concatenate {tp_name} (not "{other}") to {tp_name}' ) raise_observed_exception(TypeError, tx, args=[msg_vt]) if name == "__add__": return type(self)(self.items + args[0].items, source=self.source) # type: ignore[attr-defined] else: self.items += args[0].items # type: ignore[attr-defined] return self elif name in ("__mul__", "__imul__"): if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) if not (args[0].is_python_constant() and args[0].python_type() is int): msg_vt = ConstantVariable.create( f"can't multiply sequence by non-int type of '{args[0].python_type_name()}'" ) raise_observed_exception(TypeError, tx, args=[msg_vt]) val = args[0].as_python_constant() if name == "__mul__": return type(self)(self.items * val, source=self.source) else: self.items *= val return self elif name in cmp_name_to_op_mapping: if len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) left = self right = args[0] # TODO this type check logic mirrors the following # https://github.com/python/cpython/blob/a1c52d1265c65bcf0d9edf87e143843ad54f9b8f/Objects/object.c#L991-L1007 # But we should probably move it up the stack to so that we don't # need to duplicate it for different VTs. if not isinstance(left, BaseListVariable) or not isinstance( right, BaseListVariable ): if name == "__eq__": return variables.BuiltinVariable(operator.is_).call_function( tx, (left, right), {} ) elif name == "__ne__": return variables.BuiltinVariable(operator.is_not).call_function( tx, (left, right), {} ) else: op_str = cmp_name_to_op_str_mapping[name] left_ty = left.python_type_name() right_ty = right.python_type_name() msg = f"{op_str} not supported between instances of '{left_ty}' and '{right_ty}'" raise_observed_exception(TypeError, tx, args=[msg]) return variables.UserFunctionVariable(polyfills.list_cmp).call_function( tx, [variables.BuiltinVariable(cmp_name_to_op_mapping[name]), left, right], {}, ) elif name == "__iter__": return ListIteratorVariable(self.items, mutation_type=ValueMutationNew()) return super().call_method(tx, name, args, kwargs) class RangeVariable(BaseListVariable): def __init__(self, items: Sequence[VariableTracker], **kwargs: Any) -> None: items_to_map = items start = variables.ConstantVariable.create(0) stop = None step = variables.ConstantVariable.create(1) if len(items_to_map) == 1: (stop,) = items_to_map elif len(items_to_map) == 2: start, stop = items_to_map elif len(items_to_map) == 3: start, stop, step = items_to_map else: raise AssertionError def maybe_as_int(x: VariableTracker) -> VariableTracker: return ( ConstantVariable.create(int(x.as_python_constant())) if x.is_python_constant() else x ) # cast each argument to an integer start = maybe_as_int(start) step = maybe_as_int(step) stop = maybe_as_int(stop) assert stop is not None super().__init__([start, stop, step], **kwargs) def debug_repr(self) -> str: return self.debug_repr_helper("range(", ")") def python_type(self) -> type: return range def start(self) -> Any: return self.items[0].as_python_constant() def stop(self) -> Any: return self.items[1].as_python_constant() def step(self) -> Any: return self.items[2].as_python_constant() def range_length(self) -> int: lo = self.start() hi = self.stop() step = self.step() assert step != 0 if step > 0 and lo < hi: return 1 + (hi - 1 - lo) // step elif step < 0 and lo > hi: return 1 + (lo - 1 - hi) // (0 - step) else: return 0 def _get_slice_indices(self, length: int, slice: slice) -> list[int]: step_is_negative = 0 if slice.step is None: step = 1 step_is_negative = False else: step = slice.step step_is_negative = slice.step < 0 # Find lower and upper bounds for start and stop. if step_is_negative: lower = -1 upper = length + lower else: lower = 0 upper = length # Compute start if slice.start is None: start = upper if step_is_negative else lower else: start = slice.start if start < 0: start += length if start < lower: start = lower else: if start > upper: start = upper # Compute stop. if slice.stop is None: stop = lower if step_is_negative else upper else: stop = slice.stop if stop < 0: stop += length if stop < lower: stop = lower else: if stop > upper: stop = upper return [start, stop, step] def apply_index(self, index: int) -> VariableTracker: length = self.range_length() if index < 0: index = length + index if index < 0 or index >= length: tx = torch._dynamo.symbolic_convert.InstructionTranslator.current_tx() raise_observed_exception( IndexError, tx, args=[ConstantVariable("range object index out of range")], ) return variables.ConstantVariable.create(self.start() + (index * self.step())) def apply_slice(self, slice: slice) -> "RangeVariable": (slice_start, slice_stop, slice_step) = self._get_slice_indices( self.range_length(), slice ) def compute_item(index: int) -> int: return self.start() + (index * self.step()) sub_step = self.step() * slice_step sub_start = compute_item(slice_start) sub_stop = compute_item(slice_stop) result = RangeVariable( [ variables.ConstantVariable.create(x) for x in [sub_start, sub_stop, sub_step] ], mutation_type=ValueMutationNew() if self.mutation_type else None, ) return result def as_python_constant(self) -> range: return range(*[x.as_python_constant() for x in self.items]) def getitem_const( self, tx: "InstructionTranslator", arg: VariableTracker ) -> VariableTracker: # implementations mimics https://github.com/python/cpython/blob/main/Objects/rangeobject.c index = arg.as_python_constant() if isinstance(index, slice): return self.apply_slice(index) elif isinstance(index, int): return self.apply_index(index) else: msg = ConstantVariable("range indices must be integers or slices") raise_observed_exception(TypeError, tx, args=[msg]) def as_proxy(self) -> range: return self.python_type()(*self._as_proxy()) def unpack_var_sequence( self, tx: Optional["InstructionTranslator"] = None ) -> list[VariableTracker]: return [variables.ConstantVariable.create(x) for x in self.as_python_constant()] def reconstruct(self, codegen: "PyCodegen") -> None: assert "range" not in codegen.tx.f_globals codegen.add_push_null( lambda: codegen.append_output(codegen.create_load_python_module(range)) # type: ignore[arg-type] ) codegen.foreach(self.items) codegen.extend_output(create_call_function(3, False)) def call_obj_hasattr( self, tx: "InstructionTranslator", name: str ) -> ConstantVariable: if self.python_type() is range: return variables.ConstantVariable.create(name in range.__dict__) return super().call_obj_hasattr(tx, name) def range_equals(self, other: "RangeVariable") -> bool: r0, r1 = self, other if ( self.range_length() != r1.range_length() or self.range_length() == 0 or r0.start() != r1.start() ): return False if self.range_length() == 1: return True return r0.step() == r1.step() def range_count(self, x: VariableTracker) -> int: # Based on CPython # https://github.com/guilhermeleobas/cpython/blob/baefaa6cba1d69efd2f930cdc56bca682c54b139/Objects/rangeobject.c#L442-L486 x = x.as_python_constant() if type(x) not in (bool, int, float): return 0 start, stop, step = self.start(), self.stop(), self.step() if step == 0: return 0 in_range = (start <= x < stop) if step > 0 else (stop < x <= start) if in_range: re = ((x - start) % step) == 0 return int(re) return 0 def call_method( self, tx: "InstructionTranslator", name: str, args: list[VariableTracker], kwargs: dict[str, VariableTracker], ) -> VariableTracker: if name == "__iter__": if not all(var.is_python_constant() for var in self.items): # Can't represent a `range_iterator` without well defined bounds return variables.misc.DelayGraphBreakVariable( msg="Cannot create range_iterator: bounds (start, stop, step) must be fully defined as concrete constants.", ) return RangeIteratorVariable( self.start(), self.stop(), self.step(), self.range_length() ) elif name == "__len__": length = self.range_length() if length > sys.maxsize: raise_observed_exception(OverflowError, tx) return ConstantVariable.create(self.range_length()) elif name in ("count", "__contains__"): return ConstantVariable(self.range_count(*args)) elif name == "__getitem__": return self.getitem_const(tx, *args) elif name in cmp_name_to_op_mapping: other = args[0] pt = other.python_type() if name not in ("__eq__", "__ne__"): # ranges are only comparable to other ranges msg = f"{name} not supported between instances of 'range' and '{pt}'" raise_observed_exception( TypeError, tx, args=[ConstantVariable.create(msg)], ) if pt is not range: return ConstantVariable.create(NotImplemented) if isinstance(other, RangeVariable): cmp = self.range_equals(other) else: cmp = False # Two ranges are equal if they produce the same sequence of values if name == "__eq__": return ConstantVariable(cmp) else: return ConstantVariable(not cmp) return super().call_method(tx, name, args, kwargs) def var_getattr(self, tx: "InstructionTranslator", name: str) -> VariableTracker: fields = ["start", "stop", "step"] if name in fields: return self.items[fields.index(name)] return super().var_getattr(tx, name) def is_python_hashable(self): return True def get_python_hash(self): l = self.range_length() start = self.start() step = self.step() return hash((l, start, step)) def is_python_equal(self, other): if not isinstance(other, variables.RangeVariable): return False return ( self.start() == other.start() and self.step() == other.step() and self.stop() == other.stop() ) class CommonListMethodsVariable(BaseListVariable): """ Implement methods common to List and other List-like things """ def call_method( self, tx: "InstructionTranslator", name: str, args: list[VariableTracker], kwargs: dict[str, VariableTracker], ) -> VariableTracker: from .tensor import SymNodeVariable if name == "append" and self.is_mutable(): if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) (arg,) = args tx.output.side_effects.mutation(self) self.items.append(arg) return ConstantVariable.create(None) elif name == "extend" and self.is_mutable(): if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) if not args[0].has_force_unpack_var_sequence(tx): msg = ConstantVariable.create(f"{type(args[0])} object is not iterable") raise_observed_exception(TypeError, tx, args=[msg]) (arg,) = args arg.force_apply_to_var_sequence( tx, lambda item: self.call_method(tx, "append", [item], {}) ) return ConstantVariable.create(None) elif name == "insert" and self.is_mutable(): if kwargs or len(args) != 2: raise_args_mismatch( tx, name, "2 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) idx, value = args if isinstance(idx, SymNodeVariable): const_idx = idx.evaluate_expr() else: const_idx = idx.as_python_constant() tx.output.side_effects.mutation(self) self.items.insert(const_idx, value) return ConstantVariable.create(None) elif name == "pop" and self.is_mutable(): if kwargs or len(args) > 1: raise_args_mismatch( tx, name, "at most 1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) if len(self.items) == 0: msg = ConstantVariable.create("pop from empty list") raise_observed_exception(IndexError, tx, args=[msg]) if len(args): idx = args[0].as_python_constant() if idx > len(self.items): msg = ConstantVariable.create("pop index out of range") raise_observed_exception(IndexError, tx, args=[msg]) tx.output.side_effects.mutation(self) return self.items.pop(*[a.as_python_constant() for a in args]) elif name == "clear" and self.is_mutable(): if args or kwargs: raise_args_mismatch( tx, name, "0 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) tx.output.side_effects.mutation(self) self.items.clear() return ConstantVariable.create(None) elif ( name == "__setitem__" and self.is_mutable() and args and ( args[0].is_python_constant() or isinstance(args[0], SymNodeVariable) or ( isinstance(args[0], SliceVariable) and all( s.is_python_constant() or isinstance(s, SymNodeVariable) for s in args[0].items ) ) ) ): if kwargs: raise_args_mismatch(tx, name, "0 kwargs", f"{len(kwargs)} kwargs") key, value = args tx.output.side_effects.mutation(self) if isinstance(key, SymNodeVariable): self.items[key.evaluate_expr()] = value elif isinstance(key, SliceVariable): if key.is_python_constant(): self.items[key.as_python_constant()] = list(value.items) # type: ignore[attr-defined] else: items_slice = slice( *[ ( s.evaluate_expr() if isinstance(s, SymNodeVariable) else s.as_python_constant() ) for s in key.items ] ) self.items[items_slice] = list(value.items) # type: ignore[attr-defined] else: self.items[key.as_python_constant()] = value return ConstantVariable.create(None) elif name == "__delitem__" and self.is_mutable(): if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) tx.output.side_effects.mutation(self) if args[0].is_python_constant() and isinstance( args[0].as_python_constant(), (int, slice) ): if isinstance(args[0], SymNodeVariable): idx = args[0].evaluate_expr() else: idx = args[0].as_python_constant() try: self.items.__delitem__(idx) except (IndexError, ValueError) as exc: raise_observed_exception( type(exc), tx, args=list(map(ConstantVariable.create, exc.args)), ) else: msg = ConstantVariable.create( f"list indices must be integers or slices, not {args[0].python_type_name()}" ) raise_observed_exception(TypeError, tx, args=[msg]) return ConstantVariable.create(None) elif name == "copy": # List copy() doesn't have args and kwargs if args or kwargs: raise_args_mismatch( tx, name, "0 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) items_lst: list[VariableTracker] = list(self.items) return self.modified(items_lst, mutation_type=ValueMutationNew()) elif name == "reverse" and self.is_mutable(): if args or kwargs: raise_args_mismatch( tx, name, "0 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) self.items.reverse() tx.output.side_effects.mutation(self) return ConstantVariable.create(None) elif name == "remove" and self.is_mutable(): if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) idx = self.call_method(tx, "index", args, kwargs) self.call_method(tx, "pop", [idx], {}) return ConstantVariable.create(None) else: return super().call_method(tx, name, args, kwargs) class ListVariable(CommonListMethodsVariable): def python_type(self) -> type: return list def __repr__(self) -> str: return f"{self.__class__.__name__}(length={len(self.items)})" def debug_repr(self) -> str: return self.debug_repr_helper("[", "]") def reconstruct(self, codegen: "PyCodegen") -> None: codegen.foreach(self.items) codegen.append_output(create_instruction("BUILD_LIST", arg=len(self.items))) def call_method( self, tx: "InstructionTranslator", name: str, args: list[VariableTracker], kwargs: dict[str, VariableTracker], ) -> VariableTracker: from .tensor import SymNodeVariable if name == "__setitem__" and self.is_mutable(): if kwargs or len(args) != 2: raise_args_mismatch( tx, name, "2 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) key, value = args if not key.is_python_constant(): # probably will graph-break super().call_method(tx, name, args, kwargs) tx.output.side_effects.mutation(self) if isinstance(key, SliceVariable): if not value.has_force_unpack_var_sequence(tx): msg = ConstantVariable.create("can only assign an iterable") raise_observed_exception(TypeError, tx, args=[msg]) key_as_const = key.as_python_constant() if key_as_const.step == 0: msg = ConstantVariable.create("slice step cannot be zero") raise_observed_exception(ValueError, tx, args=[msg]) value_unpack = value.force_unpack_var_sequence(tx) try: self.items[key_as_const] = value_unpack except Exception as exc: raise_observed_exception( type(exc), tx, args=list(map(ConstantVariable.create, exc.args)), ) else: if isinstance(key, SymNodeVariable): key = key.evaluate_expr() else: key = key.as_python_constant() try: self.items[key] = value except (IndexError, TypeError) as e: raise_observed_exception( type(e), tx, args=list(map(ConstantVariable.create, e.args)) ) return ConstantVariable.create(None) if name == "sort" and self.is_mutable(): if len(args) != 0: raise_args_mismatch(tx, name, "0 args", f"{len(args)} args") key_fn_var = kwargs.pop("key", ConstantVariable.create(None)) reverse = kwargs.pop( "reverse", ConstantVariable.create(False) ).as_python_constant() if len(kwargs) != 0: raise_args_mismatch(tx, name, "0 kwargs", f"{len(kwargs)} kwargs") if key_fn_var.is_constant_none(): keys = self.items.copy() else: keys = [key_fn_var.call_function(tx, [x], {}) for x in self.items] if not all(k.is_python_constant() for k in keys): first_non_constant_key = None for k in keys: if not k.is_python_constant(): first_non_constant_key = k assert first_non_constant_key is not None try: python_type = str(first_non_constant_key.python_type()) except NotImplementedError: python_type = "unknown" unimplemented( gb_type="sort with non-constant keys", context=str(first_non_constant_key), explanation=( f"Cannot perform sort with non-constant key. " f"First non-constant key type: {python_type}. " f"Most notably, we cannot sort with Tensor or SymInt keys, but we can " f"sort ints." ), hints=["Use something else as the key."], ) tx.output.side_effects.mutation(self) sorted_items_with_keys = sorted( ( ( x, k.as_python_constant(), -i if reverse else i, # extra key to ensure stable sort ) for i, (k, x) in enumerate(zip(keys, self.items)) ), key=operator.itemgetter(1, 2), reverse=reverse, ) self.items[:] = [x for x, *_ in sorted_items_with_keys] return ConstantVariable.create(None) if name == "__init__" and self.is_mutable(): if kwargs: raise_args_mismatch(tx, name, "0 kwargs", f"{len(kwargs)} kwargs") if len(args) == 0: return ConstantVariable.create(None) elif len(args) == 1 and args[0].has_force_unpack_var_sequence(tx): (arg,) = args tx.output.side_effects.mutation(self) self.items[:] = arg.force_unpack_var_sequence(tx) return ConstantVariable.create(None) return super().call_method(tx, name, args, kwargs) def var_getattr(self, tx: "InstructionTranslator", name: str) -> VariableTracker: if name == "__class__": source = AttrSource(self.source, name) if self.source else None class_type = self.python_type() if class_type is list: return variables.BuiltinVariable(class_type, source=source) else: return variables.UserDefinedClassVariable(class_type, source=source) return super().var_getattr(tx, name) def call_obj_hasattr( self, tx: "InstructionTranslator", name: str ) -> ConstantVariable: if self.python_type() is not list: return super().call_obj_hasattr(tx, name) return variables.ConstantVariable.create(hasattr([], name)) def is_python_hashable(self): return False class DequeVariable(CommonListMethodsVariable): def __init__( self, items: list[VariableTracker], maxlen: Optional[VariableTracker] = None, **kwargs: Any, ) -> None: if maxlen is None: maxlen = ConstantVariable.create(None) assert maxlen.is_python_constant(), ( f"maxlen must be a constant, got: {maxlen.debug_repr()}" ) self.maxlen = maxlen items = list(items) if self.maxlen.as_python_constant() is not None: items = items[-maxlen.as_python_constant() :] super().__init__(items, **kwargs) def python_type(self) -> type: return collections.deque def debug_repr(self) -> str: if self.maxlen.as_python_constant() is None: return self.debug_repr_helper( "deque([", "], maxlen=" + self.maxlen.debug_repr() + ")" ) return self.debug_repr_helper("deque([", "])") def as_python_constant(self) -> collections.deque[Any]: return self.python_type()( [x.as_python_constant() for x in self.items], maxlen=self.maxlen.as_python_constant(), ) def reconstruct(self, codegen: "PyCodegen") -> None: codegen.add_push_null( lambda: codegen.append_output( codegen.create_load_python_module(collections.deque) # type: ignore[arg-type] ) ) codegen.foreach(self.items) codegen.extend_output([create_instruction("BUILD_LIST", arg=len(self.items))]) codegen(self.maxlen) codegen.extend_output(codegen.create_call_function_kw(2, ("maxlen",), False)) def var_getattr(self, tx: "InstructionTranslator", name: str) -> VariableTracker: if name == "maxlen": return self.maxlen return super().var_getattr(tx, name) def call_method( self, tx: "InstructionTranslator", name: str, args: list[VariableTracker], kwargs: dict[str, VariableTracker], ) -> VariableTracker: if ( name == "__setitem__" and self.is_mutable() and args and args[0].is_python_constant() ): if kwargs or len(args) != 2: raise_args_mismatch( tx, name, "2 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) key, value = args assert key.is_python_constant() assert isinstance(key.as_python_constant(), int) tx.output.side_effects.mutation(self) self.items[key.as_python_constant()] = value return ConstantVariable.create(None) maxlen = self.maxlen.as_python_constant() if maxlen is not None: slice_within_maxlen = slice(-maxlen, None) else: slice_within_maxlen = None if ( name == "extendleft" and self.is_mutable() and len(args) > 0 and args[0].has_force_unpack_var_sequence(tx) ): if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) # NOTE this is inefficient, but the alternative is to represent self.items # as a deque, which is a more intrusive change. args[0].force_apply_to_var_sequence( tx, lambda item: self.call_method(tx, "appendleft", [item], {}) ) slice_within_maxlen = slice(None, maxlen) result = ConstantVariable.create(None) elif name == "popleft" and self.is_mutable(): if kwargs or len(args) > 0: raise_args_mismatch( tx, name, "0 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) tx.output.side_effects.mutation(self) result, *self.items[:] = self.items elif name == "appendleft" and len(args) > 0 and self.is_mutable(): if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) tx.output.side_effects.mutation(self) self.items[:] = [args[0], *self.items] slice_within_maxlen = slice(None, maxlen) result = ConstantVariable.create(None) elif name == "insert" and len(args) > 0 and self.is_mutable(): if kwargs or len(args) != 2: raise_args_mismatch( tx, name, "2 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) if maxlen is not None and len(self.items) == maxlen: raise_observed_exception( IndexError, tx, args=["deque already at its maximum size"] ) result = super().call_method(tx, name, args, kwargs) else: result = super().call_method(tx, name, args, kwargs) if ( slice_within_maxlen is not None and maxlen is not None and len(self.items) > maxlen ): self.items[:] = self.items[slice_within_maxlen] return result def call_obj_hasattr( self, tx: "InstructionTranslator", name: str ) -> ConstantVariable: if self.python_type() is collections.deque: return variables.ConstantVariable.create(name in collections.deque.__dict__) return super().call_obj_hasattr(tx, name) class TupleVariable(BaseListVariable): def python_type(self) -> type[tuple]: # type: ignore[type-arg] return tuple def __repr__(self) -> str: return f"{self.__class__.__name__}(length={len(self.items)})" def debug_repr(self) -> str: return self.debug_repr_helper("(", ")") def reconstruct(self, codegen: "PyCodegen") -> None: codegen.foreach(self.items) codegen.append_output(create_build_tuple(len(self.items))) def var_getattr(self, tx: "InstructionTranslator", name: str) -> VariableTracker: if name == "__class__": source = AttrSource(self.source, name) if self.source else None class_type = self.python_type() if class_type is tuple: return variables.BuiltinVariable(class_type, source=source) else: return variables.UserDefinedClassVariable(class_type, source=source) return super().var_getattr(tx, name) def call_obj_hasattr( self, tx: "InstructionTranslator", name: str ) -> ConstantVariable: if self.python_type() is not tuple: return super().call_obj_hasattr(tx, name) return variables.ConstantVariable.create(hasattr((), name)) def is_python_hashable(self): return all(item.is_python_hashable() for item in self.items) def get_python_hash(self): items = tuple(x.get_python_hash() for x in self.items) return hash(items) def is_python_equal(self, other): return isinstance(other, variables.TupleVariable) and all( a.is_python_equal(b) for (a, b) in zip(self.items, other.items) ) class SizeVariable(TupleVariable): """torch.Size(...)""" _nonvar_fields = { "proxy", *TupleVariable._nonvar_fields, } def __init__( self, items: list[VariableTracker], proxy: Optional[torch.fx.Proxy] = None, **kwargs: Any, ) -> None: self.proxy = proxy super().__init__(items, **kwargs) def debug_repr(self) -> str: return self.debug_repr_helper("torch.Size([", "])") def python_type(self) -> type: return torch.Size def as_proxy(self) -> Any: if self.proxy is not None: return self.proxy # torch.Size needs special handling. Normally, we pun a list-like # container to directly contain Proxy/Node objects from FX, and FX # knows to look inside containers (via map_aggregate). But torch.Size # is weird; although it subclasses from tuple, it doesn't allow # members which aren't int-like (rejecting Proxy and Node). This # means we can't use the normal representation trick # torch.Size([proxy0, proxy1]). I looked into seeing if I could # relax torch.Size in PyTorch proper, but if torch.Size constructor # sees a type that it doesn't recognize, it will try to call # __index__() on it, so there is no BC way to actually change this # behavior (though it occurs to me that I could have just added a # YOLO no checking alternate constructor.) # # To work around this problem, I represent a torch.Size proxy as # a straight up proxy, that would have been constructed by taking # the constituent proxies as arguments. This trick can be generally # used for any construct that we need a proxy for but we can't # directly represent as an aggregate; I don't see very many examples # of this in torchdynamo though! # Look for a proxy. If there are none, do the legacy behavior tracer = None proxies = self._as_proxy() for proxy in proxies: if isinstance(proxy, torch.fx.Proxy): tracer = proxy.tracer break if tracer is None: return torch.Size(proxies) proxy = tracer.create_proxy("call_function", torch.Size, (proxies,), {}) set_example_value( proxy.node, torch.Size( [ p.node.meta["example_value"] if not isinstance(p, int) else p for p in proxies ] ), ) return proxy def reconstruct(self, codegen: "PyCodegen") -> None: codegen.add_push_null(lambda: codegen.load_import_from("torch", "Size")) codegen.foreach(self.items) build_torch_size = [ create_build_tuple(len(self.items)), ] + create_call_function(1, False) codegen.extend_output(build_torch_size) def unpack_var_sequence(self, tx: "InstructionTranslator") -> list[VariableTracker]: return list(self.items) def numel(self, tx: "InstructionTranslator") -> VariableTracker: from .builtin import BuiltinVariable from .tensor import SymNodeVariable const_result = 1 sym_sizes = [] for v in self.items: if v.is_python_constant(): const_result *= v.as_python_constant() else: assert isinstance(v, SymNodeVariable), type(v) # Delay proxy calls until we know it will be necessary sym_sizes.append(v) result = ConstantVariable.create(const_result) if sym_sizes and const_result == 1: # Skip multiplying by 1 result, *sym_sizes = sym_sizes if not sym_sizes or const_result == 0: return result mul = BuiltinVariable(operator.mul) for v in sym_sizes: result = mul.call_function(tx, [result, v], {}) return result def call_method( self, tx: "InstructionTranslator", name: str, args: list[VariableTracker], kwargs: dict[str, VariableTracker], ) -> VariableTracker: if name == "__getitem__": if kwargs or len(args) != 1: raise_args_mismatch( tx, name, "1 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) out = self.get_item_dyn(tx, args[0]) return out elif name == "numel": if args or kwargs: raise_args_mismatch( tx, name, "0 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) return self.numel(tx) return super().call_method(tx, name, args, kwargs) def get_item_dyn( self, tx: "InstructionTranslator", arg: VariableTracker ) -> VariableTracker: from .tensor import SymNodeVariable if isinstance(arg, SymNodeVariable): index = arg.sym_num else: index = arg.as_python_constant() if isinstance(index, slice): return SizeVariable(self.items[index]) else: assert isinstance(index, (int, torch.SymInt)) return self.items[index] def call_obj_hasattr( self, tx: "InstructionTranslator", name: str ) -> ConstantVariable: return variables.ConstantVariable.create(hasattr(torch.Size, name)) class NamedTupleVariable(TupleVariable): _nonvar_fields = { "tuple_cls", "dynamic_attributes", *TupleVariable._nonvar_fields, } def __init__( self, items: list[VariableTracker], tuple_cls: type, dynamic_attributes: Optional[dict[str, VariableTracker]] = None, **kwargs: Any, ) -> None: super().__init__(items, **kwargs) self.tuple_cls = tuple_cls self.dynamic_attributes = dynamic_attributes if dynamic_attributes else {} def is_namedtuple(self) -> bool: return isinstance(getattr(self.tuple_cls, "_fields", None), tuple) and callable( getattr(self.tuple_cls, "_make", None) ) def is_structseq(self) -> bool: return not self.is_namedtuple() def fields(self) -> tuple[str, ...]: return namedtuple_fields(self.tuple_cls) def debug_repr(self) -> str: if self.is_structseq(): # StructSequenceType(iterable) return repr(self.tuple_cls([Lit(x.debug_repr()) for x in self.items])) # NamedTupleType(*iterable) return repr(self.tuple_cls(*(Lit(x.debug_repr()) for x in self.items))) def python_type(self) -> type: return self.tuple_cls def as_python_constant(self) -> Any: if self.is_structseq(): # StructSequenceType(iterable) result = self.python_type()([x.as_python_constant() for x in self.items]) else: # NamedTupleType(*iterable) result = self.python_type()(*[x.as_python_constant() for x in self.items]) # Apply dynamic attributes if any were set if self.dynamic_attributes: for attr_name, attr_value in self.dynamic_attributes.items(): # Convert VariableTracker to Python constant if needed if hasattr(attr_value, "as_python_constant"): python_value = attr_value.as_python_constant() else: raise NotImplementedError( "Can not convert dynamic attribute without python constant value to python constant." ) setattr(result, attr_name, python_value) return result def as_proxy(self) -> Any: assert self.python_type() is not SizeVariable if self.is_structseq(): # StructSequenceType(iterable) return self.python_type()(self._as_proxy()) # NamedTupleType(*iterable) return self.python_type()(*self._as_proxy()) def reconstruct(self, codegen: "PyCodegen") -> None: # Always reconstruct the NamedTuple normally first # Constructors: # StructSequenceType(iterable) # NamedTupleType(*iterable) # NamedTupleType._make(iterable) if self.is_structseq(): create_fn = self.tuple_cls else: create_fn = self.tuple_cls._make # type: ignore[attr-defined] codegen.add_push_null( lambda: codegen.append_output( codegen.create_load_const_unchecked(create_fn) ) ) codegen.foreach(self.items) codegen.extend_output( [ create_build_tuple(len(self.items)), ] + create_call_function(1, False) ) for name, value in self.dynamic_attributes.items(): codegen.dup_top() codegen(value) codegen.extend_output(create_rot_n(2)) codegen.store_attr(name) def _is_method_overridden(self, method_name: str) -> bool: """Checks if a method is overridden in the NamedTuple subclass. Args: method_name (str): The name of the method to check. Returns: bool: True if the method is overridden in the subclass, False otherwise. Raises: ValueError: If the NamedTuple class does not inherit from both Tuple and Object. """ if len(self.tuple_cls.__mro__) < 3: raise ValueError("NamedTuple should inherit from Tuple and Object.") if getattr(self.tuple_cls, method_name, None) == getattr( self.tuple_cls.__mro__[-3], method_name, None ): return False return True def call_method( self, tx: "InstructionTranslator", name: str, args: list[VariableTracker], kwargs: dict[str, VariableTracker], ) -> VariableTracker: if name == "__setattr__": if kwargs or len(args) != 2: raise_args_mismatch( tx, name, "2 args and 0 kwargs", f"{len(args)} args and {len(kwargs)} kwargs", ) attr, value = args attr = attr.as_python_constant() if ( # structseq is immutable self.is_structseq() # namedtuple directly created by `collections.namedtuple` is immutable or self.tuple_cls.__bases__ == (tuple,) # fields are immutable or attr in self.fields() ): raise_observed_exception(AttributeError, tx) # Subclass of namedtuple type can have dynamic attributes tx.output.side_effects.mutation(self) if self.source: tx.output.side_effects.store_attr(self, attr, value) self.dynamic_attributes[attr] = value return ConstantVariable.create(None) elif name == "_replace": # NamedTuple._replace should create a new instance with replaced fields if args: raise_args_mismatch(tx, name, "0 args", f"{len(args)} args") # Get the field names for validation fields = self.fields() # Start with current items (copy them) new_items = list(self.items) # Replace fields specified in kwargs for field_name, new_value in kwargs.items(): if field_name not in fields: raise_observed_exception( ValueError, tx, args=[ ConstantVariable.create( f"Got unexpected field name: '{field_name}'" ) ], ) # Replace the item at the field's index field_index = fields.index(field_name) new_items[field_index] = new_value return NamedTupleVariable(new_items, self.tuple_cls) return super().call_method(tx, name, args, kwargs) def getitem_const( self, tx: "InstructionTranslator", arg: VariableTracker ) -> VariableTracker: if isinstance(arg, SliceVariable): # slicing a namedtuple produces a tuple return TupleVariable( self.items[arg.as_python_constant()], source=None, ) return super().getitem_const(tx, arg) def var_getattr(self, tx: "InstructionTranslator", name: str) -> VariableTracker: def check_and_create_method() -> Optional[VariableTracker]: method = inspect.getattr_static(self.tuple_cls, name, None) if isinstance(method, classmethod): # We need the unbounded cls method to avoid the inline __self__ return UserMethodVariable( method.__func__, variables.UserDefinedClassVariable(self.tuple_cls), ) elif isinstance(method, staticmethod): # pyrefly: ignore[bad-argument-type] return UserFunctionVariable(method.__func__) elif inspect.isfunction(method): return UserMethodVariable(method, self) else: return None # Avoid UserMethodVariable fallback precisely when methods NamedTuple methods have not been overwritten. if ( name == "_replace" and not self._is_method_overridden("_replace") and not self._is_method_overridden("__getattr__") ): # Return a BuiltinVariable for the _replace method # Get the actual _replace method from the tuple class actual_replace_method = getattr(self.tuple_cls, "_replace", None) if actual_replace_method: from ..source import AttrSource source = AttrSource(self.source, name) if self.source else None return variables.GetAttrVariable(self, name, source=source) # Fallback if _replace doesn't exist (shouldn't happen for proper NamedTuples) return super().var_getattr(tx, name) if name == "_fields": result_source = NamedTupleFieldsSource(self.source) if self.source else None return VariableTracker.build(tx, self.fields(), source=result_source) if name in self.dynamic_attributes: return self.dynamic_attributes[name] fields = self.fields() if name not in fields: method = check_and_create_method() if not method: return super().var_getattr(tx, name) return method return self.items[fields.index(name)] def call_obj_hasattr( self, tx: "InstructionTranslator", name: str ) -> ConstantVariable: return variables.ConstantVariable.create( name in self.dynamic_attributes or hasattr(self.tuple_cls, name) ) class SliceVariable(VariableTracker): def __init__( self, items: Sequence[VariableTracker], tx: Optional["InstructionTranslator"] = None, **kwargs: Any, ) -> None: items_to_map = items start, stop, step = [variables.ConstantVariable.create(None)] * 3 if len(items_to_map) == 1: (stop,) = items_to_map elif len(items_to_map) == 2: start, stop = items_to_map elif len(items_to_map) == 3: start, stop, step = items_to_map else: raise AssertionError # Convert TensorVariable to SymIntVariable by calling .item() # This decomposes a[:t] to u=t.item(); a[:u] at the dynamo level if start.is_tensor(): assert tx is not None, ( "tx is required when slice indices are TensorVariables" ) start = start.call_method(tx, "item", [], {}) if stop.is_tensor(): assert tx is not None, ( "tx is required when slice indices are TensorVariables" ) stop = stop.call_method(tx, "item", [], {}) if step.is_tensor(): assert tx is not None, ( "tx is required when slice indices are TensorVariables" ) step = step.call_method(tx, "item", [], {}) self.items = (start, stop, step) super().__init__(**kwargs) def debug_repr(self) -> str: return "slice(" + ", ".join(i.debug_repr() for i in self.items) + ")" def as_proxy(self) -> slice: return slice(*[x.as_proxy() for x in self.items]) def python_type(self) -> type: return slice def as_python_constant(self) -> slice: return slice(*[guard_if_dyn(x) for x in self.items]) def reconstruct(self, codegen: "PyCodegen") -> None: codegen.foreach(self.items) codegen.append_output(create_instruction("BUILD_SLICE", arg=len(self.items))) def var_getattr(self, tx: "InstructionTranslator", name: str) -> VariableTracker: if name in cmp_name_to_op_mapping: return variables.GetAttrVariable(self, name) fields = ["start", "stop", "step"] if name not in fields: unimplemented( gb_type="Unsupported attribute for slice() object", context=f"var_getattr {self} {name}", explanation=f"Expected attribute to be one of {','.join(fields)} " f"but got {name}", hints=[*graph_break_hints.USER_ERROR], ) return self.items[fields.index(name)] class ListIteratorVariable(IteratorVariable): _nonvar_fields = { "index", *IteratorVariable._nonvar_fields, } def __init__( self, items: list[VariableTracker], index: int = 0, **kwargs: Any ) -> None: super().__init__(**kwargs) assert isinstance(items, list) # Removing this check as it slows things down too much # https://github.com/pytorch/pytorch/pull/87533#issuecomment-1287574492 # assert all(isinstance(x, VariableTracker) for x in items) self.items = items self.index = index self.is_exhausted = False def __repr__(self) -> str: return f"{self.__class__.__name__}(length={len(self.items)}, index={repr(self.index)})" def next_variable(self, tx: "InstructionTranslator") -> VariableTracker: assert self.is_mutable() old_index = self.index if old_index >= len(self.items) or self.is_exhausted: self.is_exhausted = True raise_observed_exception(StopIteration, tx) tx.output.side_effects.mutation(self) self.index += 1 return self.items[old_index] def call_obj_hasattr( self, tx: "InstructionTranslator", name: str ) -> ConstantVariable: return variables.ConstantVariable.create(hasattr(iter([]), name)) def python_type(self) -> type: return type(iter([])) def as_python_constant(self) -> Any: if self.index > 0: raise NotImplementedError return iter([x.as_python_constant() for x in self.items]) def has_unpack_var_sequence(self, tx: "InstructionTranslator") -> bool: return True def unpack_var_sequence(self, tx: "InstructionTranslator") -> list[VariableTracker]: if self.is_exhausted: return [] self.is_exhausted = True return list(self.items[self.index :]) def force_unpack_var_sequence( self, tx: "InstructionTranslator" ) -> list[VariableTracker]: return self.unpack_var_sequence(tx) def reconstruct(self, codegen: "PyCodegen") -> None: if not self.is_exhausted: remaining_items = self.items[self.index :] else: remaining_items = [] codegen.foreach(remaining_items) codegen.extend_output( [ create_build_tuple(len(remaining_items)), create_instruction("GET_ITER"), ] ) class TupleIteratorVariable(ListIteratorVariable): pass class RangeIteratorVariable(IteratorVariable): # only needed for isinstance(..., range_iterator) to work _nonvar_fields = { "iter_obj", } def __init__( self, start: int, stop: int, step: int, len_: int, **kwargs: Any ) -> None: super().__init__(**kwargs) self.start = start self.stop = stop self.step = step self.len = len_ def call_method( self, tx: "InstructionTranslator", name: str, args: list[VariableTracker], kwargs: dict[str, VariableTracker], ) -> VariableTracker: if name == "__next__": return self.next_variable(tx) elif name == "__iter__": return self return super().call_method(tx, name, args, kwargs) def call_obj_hasattr( self, tx: "InstructionTranslator", name: str ) -> ConstantVariable: if self.python_type() is range_iterator: ri = iter(range(0)) return ConstantVariable(hasattr(ri, name)) return super().call_obj_hasattr(tx, name) def next_variable(self, tx: "InstructionTranslator") -> VariableTracker: if self.len <= 0: raise_observed_exception(StopIteration, tx) self.len -= 1 current = self.start self.start += self.step return ConstantVariable.create(current) def python_type(self) -> type: return range_iterator def reconstruct(self, codegen: "PyCodegen") -> None: codegen.add_push_null( lambda: codegen.append_output(codegen.create_load_python_module(range)) # type: ignore[arg-type] ) codegen.append_output(codegen.create_load_const(self.start)) codegen.append_output(codegen.create_load_const(self.stop)) codegen.append_output(codegen.create_load_const(self.step)) codegen.extend_output(create_call_function(3, False)) codegen.append_output(create_instruction("GET_ITER"))