torch/_dynamo/variables/base.py

# mypy: ignore-errors

import collections
from enum import Enum
from typing import Any, Callable, Dict, List

from .. import variables
from ..current_scope_id import current_scope_id
from ..exc import unimplemented
from ..source import AttrSource, Source
from ..utils import istype


class MutableLocalSource(Enum):
    """
    If the VariableTracker.mutable_local represents a Variable that:
    - already existed that Dynamo began tracking while introspection (Existing)
    - is a new variable that is created during Dynamo introspection (Local)
    """

    Existing = 0
    Local = 1


class MutableLocalBase:
    """
    Base class for Variable.mutable_local
    """

    def __init__(self, typ: MutableLocalSource):
        # In HigherOrderOperator tracing, we need to distinguish
        # between MutableLocals inside the HigherOrderOperator and
        # ones outside it. For example, it is not safe to mutate
        # `a` in the following example because it was constructed
        # in a different scope.
        #
        # def f(x):
        #     a = 1
        #     def g(x):
        #         nonlocal a
        #         a = 2
        #         return x
        #     return wrap(g, x) + a
        #
        # We use self.scope to distinguish this.
        # scope == 0: The object was an existing variable
        # scope == 1: The object was created while Dynamo
        #             was introspecting a function
        #             (and no HigherOrderOps were involved)
        # scope >= 2: The object was created through
        #             Dynamo introspection of a HigherOrderOp.
        #             The exact number corresponds to the level
        #             of nested HigherOrderOps.
        if typ is MutableLocalSource.Existing:
            self.scope = 0
        elif typ is MutableLocalSource.Local:
            self.scope = current_scope_id()
        else:
            unimplemented(f"Unsupported MutableLocalSource: {typ}")


class MutableLocal(MutableLocalBase):
    """
    Marker used to indicate this (list, iter, etc) was constructed in
    local scope and can be mutated safely in analysis without leaking
    state.
    """

    def __init__(self):
        super().__init__(MutableLocalSource.Local)

    def __hash__(self):
        return id(self)

    def __eq__(self, other):
        return self is other


def _is_top_level_scope(scope_id):
    return scope_id == 1


def is_side_effect_safe(m: MutableLocalBase):
    scope_id = current_scope_id()

    # In the top-level scope (if no HigherOrderOperators are involved),
    # we are allowed to modify variables created in this scope as well
    # as existing variables.
    if _is_top_level_scope(scope_id):
        return True
    # Otherwise, only allow local mutation of variables created in the current scope
    return m.scope == scope_id


class VariableTrackerMeta(type):
    all_subclasses = []

    def __instancecheck__(cls, instance) -> bool:
        """Make isinstance work with LazyVariableTracker"""
        if type.__instancecheck__(
            variables.LazyVariableTracker, instance
        ) and cls not in (
            VariableTracker,
            variables.LazyVariableTracker,
        ):
            instance = instance.realize()
        return type.__instancecheck__(cls, instance)

    def __init__(cls, name, bases, attrs):
        super().__init__(name, bases, attrs)
        VariableTrackerMeta.all_subclasses.append(cls)


class VariableTracker(metaclass=VariableTrackerMeta):
    """
    Base class for tracked locals and stack values

    VariableTracker instances are immutable and should be copied in
    order to change them.
    """

    # fields to leave unmodified in apply()
    _nonvar_fields = {
        "value",
        "guards",
        "source",
        "mutable_local",
        "parents_tracker",
        "user_code_variable_name",
    }

    def clone(self, **kwargs):
        """Shallow copy with some (optional) changes"""
        args = dict(self.__dict__)
        args.update(kwargs)
        return self.__class__(**args)

    @classmethod
    def visit(
        cls,
        fn: Callable[["VariableTracker"], None],
        value,
        cache=None,
    ):
        """
        Walk value and call fn on all the VariableTracker instances
        """
        if cache is None:
            cache = dict()

        idx = id(value)
        if idx in cache:
            return
        # save `value` to keep it alive and ensure id() isn't reused
        cache[idx] = value

        if isinstance(value, VariableTracker):
            value = value.unwrap()
            fn(value)
            value = value.unwrap()  # calling fn() might have realized it
            nonvars = value._nonvar_fields
            for key, subvalue in value.__dict__.items():
                if key not in nonvars:
                    cls.visit(fn, subvalue, cache)
        elif istype(value, (list, tuple)):
            for subvalue in value:
                cls.visit(fn, subvalue, cache)
        elif istype(value, (dict, collections.OrderedDict)):
            for subvalue in value.values():
                cls.visit(fn, subvalue, cache)

    def __repr__(self):
        return f"{self.__class__.__name__}()"

    def debug_repr(self):
        # Intended to be overridden to provide more info
        try:
            return repr(self.as_python_constant())
        except NotImplementedError:
            return repr(self)

    def python_type(self):
        """
        Abstract method to be implemented by subclasses of VariableTracker.

        This method should return the type represented by the instance of the subclass.
        The purpose is to provide a standardized way to retrieve the Python type information
        of the variable being tracked.

        Returns:
            type: The Python type (such as int, str, list, etc.) of the variable tracked by
                the subclass. If the type cannot be determined or is not relevant,
                leaving it undefined or invoking super() is always sound.

        Note:
            This is an abstract method and may be overridden in subclasses.

        Example:
            class SetVariable(VariableTracker):
                def python_type(self):
                    return set

        Raises:
            NotImplementedError: If the method is not implemented in a subclass.
        """
        raise NotImplementedError(f"{self} has no type")

    def as_python_constant(self):
        """For constants"""
        raise NotImplementedError(f"{self} is not a constant")

    def guard_as_python_constant(self):
        """Similar to as_python_constant(), but add ID_MATCH guards to try to force things to become constants"""
        try:
            return self.as_python_constant()
        except NotImplementedError as e:
            unimplemented(str(e))

    def is_python_constant(self):
        try:
            self.as_python_constant()
            return True
        except NotImplementedError:
            return False

    def make_guard(self, fn):
        if self.source:
            return self.source.make_guard(fn)
        raise NotImplementedError

    def const_getattr(self, tx, name: str) -> Any:
        """getattr(self, name) returning a python constant"""
        raise NotImplementedError

    def var_getattr(self, tx, name: str) -> "VariableTracker":
        """getattr(self, name) returning a new variable"""
        value = self.const_getattr(tx, name)
        if not variables.ConstantVariable.is_literal(value):
            raise NotImplementedError
        source = None
        if self.source:
            source = AttrSource(self.source, name)
        return variables.ConstantVariable.create(value, source=source)

    def is_proxy(self):
        try:
            self.as_proxy()
            return True
        except NotImplementedError:
            return False

    def as_proxy(self):
        raise NotImplementedError(str(self))

    def maybe_fx_node(self):
        try:
            proxy = self.as_proxy()
            import torch.fx

            if isinstance(proxy, torch.fx.Proxy):
                return proxy.node
            return None
        except NotImplementedError:
            return None

    def reconstruct(self, codegen):
        raise NotImplementedError

    def can_reconstruct(self, tx):
        """If it is possible to reconstruct the Python object this
        VariableTracker represents."""
        assert tx is tx.output.root_tx, "Only root tx can reconstruct"
        try:
            from ..codegen import PyCodegen

            cg = PyCodegen(tx)
            self.reconstruct(cg)
            return True
        except NotImplementedError:
            return False

    def unpack_var_sequence(self, tx) -> List["VariableTracker"]:
        raise NotImplementedError

    def has_unpack_var_sequence(self, tx) -> bool:
        try:
            self.unpack_var_sequence(tx)
            return True
        except NotImplementedError:
            return False

    def inspect_parameter_names(self) -> List[str]:
        unimplemented(f"inspect_parameter_names: {self}")

    def call_hasattr(self, tx, name: str) -> "VariableTracker":
        unimplemented(f"hasattr {self.__class__.__name__} {name}")

    def call_function(
        self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
    ) -> "VariableTracker":
        unimplemented(f"call_function {self} {args} {kwargs}")

    def call_method(
        self,
        tx,
        name,
        args: "List[VariableTracker]",
        kwargs: "Dict[str, VariableTracker]",
    ) -> "VariableTracker":
        if name == "__len__" and self.has_unpack_var_sequence(tx):
            assert not (args or kwargs)
            return variables.ConstantVariable.create(len(self.unpack_var_sequence(tx)))
        elif (
            name == "__getattr__"
            and len(args) == 1
            and args[0].is_python_constant()
            and not kwargs
        ):
            return self.var_getattr(tx, args[0].as_python_constant())
        unimplemented(f"call_method {self} {name} {args} {kwargs}")

    def set_name_hint(self, name):
        pass

    def realize(self) -> "VariableTracker":
        """Used by LazyVariableTracker to build the real VariableTracker"""
        return self

    def unwrap(self) -> "VariableTracker":
        """Used by LazyVariableTracker to return the real VariableTracker if it already exists"""
        return self

    def is_realized(self):
        """Used by LazyVariableTracker to indicate an unrealized node"""
        return True

    def next_variable(self, tx):
        unimplemented(f"next({self})")

    def is_strict_mode(self, tx):
        return tx.strict_checks_fn and tx.strict_checks_fn(self)

    def __init__(
        self,
        *,
        source: Source = None,
        mutable_local: MutableLocal = None,
    ):
        super().__init__()
        self.source = source
        self.mutable_local = mutable_local


def typestr(*objs):
    if len(objs) == 1:
        (obj,) = objs
        if isinstance(obj, VariableTracker):
            return str(obj)
        else:
            return type(obj).__name__
    else:
        return " ".join(map(typestr, objs))