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880 lines
28 KiB
880 lines
28 KiB
from __future__ import annotations
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import contextlib
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import dataclasses
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import enum
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import functools
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import logging
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import threading
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import traceback
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import unittest.mock
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import weakref
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from abc import ABC, abstractmethod
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from contextlib import contextmanager
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from typing import (
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Any,
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Callable,
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Dict,
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Generic,
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List,
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NamedTuple,
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Optional,
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Set,
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Tuple,
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TYPE_CHECKING,
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TypeVar,
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)
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import torch
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from torch.utils import _pytree as pytree
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from torch.utils._traceback import CapturedTraceback
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from torch.utils.weak import WeakTensorKeyDictionary
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log = logging.getLogger(__name__)
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if TYPE_CHECKING:
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# Import the following modules during type checking to enable code intelligence features,
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# such as auto-completion in tools like pylance, even when these modules are not explicitly
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# imported in user code.
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import sympy
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"""
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torch._guards is the definitional source of truth for general purpose guard structures.
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An important thing to keep in mind here is the preservation of layering. There should be no dynamo notions,
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and no guard installation notions here.
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"""
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class CompileId(NamedTuple):
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frame_id: int
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# This id is per-frame, and counts how many times we've compiled this
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# frame. This could have been a global id but having this be per-frame
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# gives you a better intuitive sense for how many recompiles have occurred
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# so far.
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frame_compile_id: int
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# TODO: consider also tracking the recompilation count
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def __str__(self):
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return f"{self.frame_id}/{self.frame_compile_id}"
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class TraceId(NamedTuple):
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compile_id: CompileId
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# This starts off as 0, and every time we restart analysis it goes
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# up by one
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attempt: int
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def __str__(self):
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if self.attempt == 0:
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return str(self.compile_id)
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else:
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return f"{self.compile_id}_{self.attempt}"
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class GuardSource(enum.Enum):
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LOCAL = 0
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GLOBAL = 1
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LOCAL_NN_MODULE = 2
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GLOBAL_NN_MODULE = 3
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CONSTANT = 4
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RANDOM_VALUE = 5
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SHAPE_ENV = 6
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LOCAL_FSDP_MODULE = 7
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GLOBAL_FSDP_MODULE = 8
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BACKWARD_STATE = 9
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EPHEMERAL = 10
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SYNTHETIC_LOCAL = 11
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def is_fsdp_module(self) -> bool:
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return self in (GuardSource.GLOBAL_FSDP_MODULE, GuardSource.LOCAL_FSDP_MODULE)
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def is_nn_module(self) -> bool:
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return (
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self
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in (
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GuardSource.GLOBAL_NN_MODULE,
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GuardSource.LOCAL_NN_MODULE,
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)
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or self.is_fsdp_module()
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)
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def is_local(self):
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return self in (
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GuardSource.LOCAL,
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GuardSource.LOCAL_NN_MODULE,
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GuardSource.LOCAL_FSDP_MODULE,
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)
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"""
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Base class for a "GuardBuilder" role.
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The GuardBuilderBase role is to represent a scope within which to build a guard. The name is a little
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confusing, as its not a builder, but for the sake of avoiding a lot of renames and keeping the original reference
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to torchdynamo's GuardBuilder.
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Note: create_fn is invoked with a GuardBuilderBase and a Guard. A GuardBuilder is chosen based
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on GuardSource's select function.
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There is value in keeping this GuardBuilderBase empty to keep layering clean.
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"""
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class GuardBuilderBase:
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pass
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class ShapeGuard(NamedTuple):
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expr: sympy.Expr
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stack: CapturedTraceback
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@dataclasses.dataclass
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class Guard:
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# originating_source is the source that called the make_guard method to
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# construct this guard object. The property name specifies what exactly it
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# is the guard is guarding on. The meaning of the name is dependent on the
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# create_fn; you must look at the use-site inside create_fn to know what
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# name means.
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#
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# That being said, although you might think this is just a "name", name is
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# usually an arbitrary Python expression that will be evaluated with all
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# globals (and locals, if you create a LOCAL guard) to extract the Python
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# object that we want to perform guard tests on. This evaluation
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# typically happens in GuardBuilder.eval. In these cases, name is
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# typically produced by originating_source.name() (not to be confused with
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# GuardSource - the property source).
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#
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# Occasionally, name is not a valid Python expression; sometimes
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# it is meaningless. Example create_fns that are like this include
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# GRAD_MODE and SHAPE_ENV.
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originating_source: Source
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create_fn: Callable[[GuardBuilderBase, Guard], None]
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# Export only. These values are written to at time of guard check_fn creation.
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guard_types: Optional[List[str]] = None
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code_list: Optional[List[str]] = None
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obj_weakref: Optional[object] = None
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guarded_class_weakref: Optional[type] = None
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stack: Optional[CapturedTraceback] = None
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user_stack: Optional[traceback.StackSummary] = None
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_hash: Optional[int] = None
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def __hash__(self):
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if self._hash is None:
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self._hash = hash((self.name, self.source, id(self.create_fn)))
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return self._hash
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def sort_key(self):
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return (
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self.source.value if self.source else -1,
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len(self.name),
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self.name,
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self.inner_create_fn().__code__.co_firstlineno,
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)
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def __lt__(self, other):
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return self.sort_key() < other.sort_key()
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def inner_create_fn(self):
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if isinstance(self.create_fn, functools.partial):
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return self.create_fn.func
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else:
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return self.create_fn
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@property
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def name(self) -> str:
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return self.originating_source.name()
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@property
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def source(self) -> GuardSource:
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return self.originating_source.guard_source()
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@staticmethod
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def weakref_to_str(obj_weakref):
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"""
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This is a workaround of a Python weakref bug.
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`obj_weakref` is instance returned by `weakref.ref`,
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`str(obj_weakref)` is buggy if the original obj overrides __getattr__, e.g:
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class MyConfig(dict):
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def __getattr__(self, x):
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return self[x]
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obj = MyConfig(offset=5)
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obj_weakref = weakref.ref(obj)
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str(obj_weakref) # raise error: KeyError: '__name__'
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"""
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if isinstance(obj_weakref, weakref.ReferenceType):
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obj = obj_weakref()
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if obj is not None:
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return f"<weakref at {hex(id(obj_weakref))}; to '{obj.__class__.__name__}' at {hex(id(obj))}>"
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else:
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return f"<weakref at {hex(id(obj_weakref))}; dead>"
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else:
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return str(obj_weakref)
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def __repr__(self):
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s = f"""
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{self.source.name.lower() if self.source else ""} {repr(self.name)} {self.inner_create_fn().__name__}
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{{
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'guard_types': {self.guard_types},
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'code': {self.code_list},
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'obj_weakref': {self.weakref_to_str(self.obj_weakref)}
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'guarded_class': {self.guarded_class_weakref}
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}}
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"""
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return s
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def __str__(self):
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output = f"Name: {repr(self.name)}\n"
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source = self.source.name.lower() if self.source else ""
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output += f" Source: {source}\n"
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output += f" Create Function: {self.inner_create_fn().__name__}\n"
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output += f" Guard Types: {self.guard_types}\n"
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output += f" Code List: {self.code_list}\n"
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output += f" Object Weakref: {self.weakref_to_str(self.obj_weakref)}\n"
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output += f" Guarded Class Weakref: {self.guarded_class_weakref}\n"
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return output
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def create(self, builder: GuardBuilderBase):
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try:
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return self.create_fn(builder, self)
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except Exception:
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log.error("Error while creating guard:\n%s", str(self).rstrip())
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if self.stack:
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log.error("Created at:\n%s", "".join(self.stack.format()[-4:]).rstrip())
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raise
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def is_nn_module(self):
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return self.source.is_nn_module()
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def is_fsdp_module(self):
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return self.source.is_fsdp_module()
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def is_local(self):
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return self.source.is_local()
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def set_export_info(self, guard_type, guarded_class, code_list, obj_weakref):
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if not self.guard_types:
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self.guard_types = list()
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self.guard_types.append(guard_type)
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assert self.guarded_class_weakref in (
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guarded_class,
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None,
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), "Guarded class id must be identical, or None"
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self.guarded_class_weakref = guarded_class
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if not self.code_list:
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self.code_list = code_list
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else:
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self.code_list.extend(code_list)
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assert self.obj_weakref in (
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obj_weakref,
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None,
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), "Guarded object must be identical, or None"
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self.obj_weakref = obj_weakref
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T = TypeVar("T")
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"""
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Parent structure for guard env expressions.
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A GuardEnvExpr can have any subtype.
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Note: All subtypes must be handled exhaustively in
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torch._dynamo.guards._parse_guard_env_guards to avoid a RuntimeError.
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"""
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@dataclasses.dataclass
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class GuardEnvExpr:
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pass
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"""
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A class representing a pair of duplicate inputs.
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input_pos_a and input_pos_b are input positions we have deduped.
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"""
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@dataclasses.dataclass
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class DuplicateInputs(GuardEnvExpr):
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input_source_a: Source
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input_source_b: Source
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def __post_init__(self):
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assert self.input_source_a != self.input_source_b
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"""
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Checkpointable is an interface for driving state snapshotting, left purposely vague for now.
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copy_graphstate() -> T, a somewhat legacy name, is expected to emit a snapshot of any type that
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can also be taken in at restore_graphstate(T) calls.
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When to snapshot, is, at the moment, an implementation detail of upstream callers. Checkpointable
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does not provide any garuantees around consistency, idempotency, or safety of calling its APIs, yet.
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In the future, it will have a closer coupling to a generic Checkpoint management system.
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"""
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class Checkpointable(ABC, Generic[T]):
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@abstractmethod
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def copy_graphstate(self) -> T:
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...
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@abstractmethod
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def restore_graphstate(self, state: T):
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...
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class GuardsCheckpointState:
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"""
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The GuardCheckpointState - it is the T of Checkpointable[T] for GuardsContext
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"""
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dynamo_guards: Set[Guard] = set()
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def __init__(self, dynamo_guards):
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self.dynamo_guards = dynamo_guards
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def diff(self, other):
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"""
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Produces a delta against another GuardsCheckpointState.
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Returns None if no delta is found, otherwise, return a set() of mismatched
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Guard type objects.
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"""
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r = self.dynamo_guards.difference(other.dynamo_guards)
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if len(r) == 0:
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return None
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return r
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def __eq__(self, other):
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return self.diff(other) is None
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class ModuleContextCheckpointState:
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nn_modules: Dict[str, torch.nn.Module] = {}
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def __init__(self, nn_modules):
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self.nn_modules = nn_modules
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def diff(self, other):
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"""
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Produces a delta against another ModuleContextCheckpointState.
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Returns None if no delta is found, otherwise, return a set() of mismatched
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module key names.
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"""
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r = set(self.nn_modules.keys()).difference(set(other.nn_modules.keys()))
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if len(r) == 0:
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return None
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return r
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def __eq__(self, other):
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return self.diff(other) is None
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class ModuleContext(Checkpointable[ModuleContextCheckpointState]):
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def __init__(self):
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self.nn_modules: Dict[str, Any] = {}
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def copy_graphstate(self):
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return ModuleContextCheckpointState(dict(self.nn_modules))
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def restore_graphstate(self, state):
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assert isinstance(state, ModuleContextCheckpointState)
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self.nn_modules = state.nn_modules
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class GlobalContextCheckpointState:
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global_state: Dict[str, Tuple[Callable, ...]] = {}
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def __init__(self, global_states):
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self.global_state = global_states
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def diff(self, other):
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"""
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Produces a delta against another GlobalContextCheckpointState.
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Returns None if no delta is found, otherwise, return a set() of mismatched
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global key names.
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"""
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r = set(self.global_state.keys()).difference(set(other.global_state.keys()))
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if len(r) == 0:
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return None
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return r
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def __eq__(self, other):
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return self.diff(other) is None
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class GlobalContext(Checkpointable[GlobalContextCheckpointState]):
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"""
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This keeps track of the global torch state during tracing of a function.
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For example, torch.is_grad_enabled.
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"""
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_supported_global_states = {
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"grad_enabled",
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"torch_function_enabled",
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"autocast_enabled",
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"autocast_cpu_enabled",
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"autocast_gpu_dtype",
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"autocast_cpu_dtype",
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"autocast_cache_enabled",
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}
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def __init__(self):
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self.global_state: Dict[str, Tuple[Callable, ...]] = {}
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def copy_graphstate(self):
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return GlobalContextCheckpointState(dict(self.global_state))
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def restore_graphstate(self, state):
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assert isinstance(state, GlobalContextCheckpointState)
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self.global_state = state.global_state
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assert (
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len(self.global_state) == len(self._supported_global_states)
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and set(self.global_state.keys()) == self._supported_global_states
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), "Global state mismatch"
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for func, args in self.global_state.values():
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func(args)
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"""
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A GuardsContext is a checkpointable representation of all the guards in the current tracing
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context. It's lifecycle is bound 1:1 to the tracing context, and it should never be instantiated
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directly outside of it. For passing around internal state representations of this object,
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prefer to extract them with copy_graphstate to produce a GuardsCheckpointState.
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"""
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# Like a Set[Guard] but will record the user stack on all guards at the
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# time they were installed at their destination
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class GuardsSet:
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def __init__(self, inner=None):
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if inner is None:
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inner = set()
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self.inner = inner
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def __iter__(self):
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return iter(self.inner)
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def __len__(self):
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return len(self.inner)
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# Subtraction along with bool is typically used to determine the delta of
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# added guards between checkpoints for higher order ops
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def __sub__(self, other):
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return GuardsSet(self.inner - other.inner)
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def __bool__(self):
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return bool(self.inner)
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def add(self, guard: Guard, *, collect_debug_stack=True, skip=0):
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if guard in self.inner:
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return
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if collect_debug_stack:
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if guard.stack is None:
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guard.stack = CapturedTraceback.extract(skip=1 + skip)
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if guard.user_stack is None:
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guard.user_stack = TracingContext.extract_stack()
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self.inner.add(guard)
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def update(self, *others: Set[Guard]):
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for o in others:
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for g in o:
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self.add(g, skip=1)
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def remove_guards_with_source(self, source):
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"""Delete all guards with a given source"""
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self.inner = {g for g in self.inner if g.originating_source != source}
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class GuardsContext(Checkpointable[GuardsCheckpointState]):
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def __init__(self):
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self.dynamo_guards: GuardsSet = GuardsSet()
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self.aotautograd_guards: List[GuardEnvExpr] = []
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def copy_graphstate(self):
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return GuardsCheckpointState(set(self.dynamo_guards.inner))
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def restore_graphstate(self, state):
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# NB: "steals" the passed in state
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assert isinstance(state, GuardsCheckpointState)
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self.dynamo_guards = GuardsSet(state.dynamo_guards)
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_TLS = threading.local()
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"""
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TracingContext is the source of truth for all currently accumulated information
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needed to trace. Its lifecycle is kept 1:1 when using TorchDynamo, but other systems
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are open to managing their own TracingContext with that in mind.
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The purpose of TracingContext is not to be a dumping ground, or god object, but rather to avoid
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having to plumb complex subsystems across multiple verticals.
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Ex: A common example is guard accumulation between dynamo, shape_env, aot_autograd, and inductor.
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Accessing the current tracing context via
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TracingContext.get() allows users to accumulate their own guards for processing, without needing to know how
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to plumb objects back up to where frame interpretation happened.
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Note that you can end up with multiple TracingContext for a single compilation
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of a frame, as we reset the TracingContext whenever we restart analysis.
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CompileContext is a more overarching context that encompasses multiple restarts.
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"""
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class CompileContext:
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@staticmethod
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def get() -> CompileContext:
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assert _TLS.compile_context is not None
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return _TLS.compile_context
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|
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@staticmethod
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def try_get() -> Optional[CompileContext]:
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return getattr(_TLS, "compile_context", None)
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|
|
def __init__(self, compile_id):
|
|
assert compile_id is None or isinstance(compile_id, CompileId)
|
|
self.compile_id: Optional[CompileId] = compile_id
|
|
self.attempt = 0
|
|
|
|
@staticmethod
|
|
def current_compile_id():
|
|
self = CompileContext.try_get()
|
|
if self is None:
|
|
return None
|
|
return self.compile_id
|
|
|
|
@staticmethod
|
|
def current_trace_id():
|
|
self = CompileContext.try_get()
|
|
if self is None:
|
|
return None
|
|
if self.compile_id is None:
|
|
return None
|
|
return TraceId(self.compile_id, self.attempt)
|
|
|
|
|
|
class TracingContext:
|
|
"""
|
|
Provides the currently installed TracingContext, or None.
|
|
|
|
Note that it is a staticmethod, and invocations outside of `with tracing()` (see below), are valid but
|
|
will return None.
|
|
"""
|
|
|
|
@staticmethod
|
|
def try_get() -> Optional[TracingContext]:
|
|
return getattr(_TLS, "tracing_context", None)
|
|
|
|
@staticmethod
|
|
def get() -> TracingContext:
|
|
if ctx := TracingContext.try_get():
|
|
return ctx
|
|
raise RuntimeError(
|
|
"TracingContext.get() must be called within an ongoing trace."
|
|
)
|
|
|
|
def __init__(self, fake_mode):
|
|
self.guards_context = GuardsContext()
|
|
self.module_context = ModuleContext()
|
|
self.global_context = GlobalContext()
|
|
self.fake_mode = fake_mode
|
|
self.frame_summary_stack = []
|
|
# This is morally part of frame_summary_stack, but it is kept separate
|
|
# for clarity. As we process a frame, this variable gets updated
|
|
# to keep track of what line we are in the function. We make a
|
|
# function call, this gets cleared and the frame location is pushed
|
|
# to frame_summary_stack (prepping this variable for the inner frame's
|
|
# progress)
|
|
self.loc_in_frame = None
|
|
# this is only set after aot_autograd
|
|
self.fw_metadata = None
|
|
self.params_flat = None
|
|
# this is for extended return calling convention from backend
|
|
# compiler to aot_autograd
|
|
# Per output, what the compiler specified stride of the output is,
|
|
# or None if no stride is known. This is always the HINT, it
|
|
# is never a SymInt (it would be better if it was a SymInt, but
|
|
# I can't conveniently get this from Inductor atm. Also, be
|
|
# careful not to accidentally induce guards on the SymInt if
|
|
# you ever do change this in aot_autograd.py; you should check
|
|
# on permutations preferentially.)
|
|
self.output_strides: Optional[List[Optional[List[int]]]] = None
|
|
# When this is True, whenever we encounter an int in Dynamo tracing,
|
|
# we will (1) force unspec it and (2) force it as a size-like unbacked
|
|
# integer. This is currently used when processing certain lists of
|
|
# ints that are known to be size-like and may have 0/1 entries that we
|
|
# must not specialize on.
|
|
self.force_unspec_int_unbacked_size_like = False
|
|
# See note [Tensor Fakification and Symbol Caching]
|
|
self.tensor_to_context = WeakTensorKeyDictionary()
|
|
|
|
# If this true, Aot Autograd will return output Fake Tensors with appropiate
|
|
# meta on the first invocation
|
|
# see note: [Returning Fake Tensors on First AOT Autograd Call]
|
|
self.fakify_first_call = False
|
|
|
|
def clear(self):
|
|
# Look at the note in output_graph.py in function `save_global_state`
|
|
# for the context on clearing global context.
|
|
self.global_context.global_state = {}
|
|
|
|
@staticmethod
|
|
@contextmanager
|
|
def patch(**kwargs):
|
|
prior = {}
|
|
ctx = TracingContext.get()
|
|
|
|
for key in kwargs.keys():
|
|
# KeyError on invalid entry
|
|
prior[key] = getattr(ctx, key)
|
|
for key, val in kwargs.items():
|
|
setattr(ctx, key, val)
|
|
try:
|
|
yield
|
|
finally:
|
|
for key, val in prior.items():
|
|
setattr(ctx, key, val)
|
|
|
|
@staticmethod
|
|
def extract_stack():
|
|
self = TracingContext.try_get()
|
|
if self is None:
|
|
return traceback.StackSummary()
|
|
stack = self.frame_summary_stack
|
|
if self.loc_in_frame is not None:
|
|
stack = stack + [self.loc_in_frame]
|
|
return traceback.StackSummary.from_list(stack)
|
|
|
|
# Call this when you want to call into some code that isn't necessarily
|
|
# associated with the current frame state
|
|
@staticmethod
|
|
@contextlib.contextmanager
|
|
def clear_frame():
|
|
tc = TracingContext.get()
|
|
with unittest.mock.patch.object(
|
|
tc, "frame_summary_stack", []
|
|
), unittest.mock.patch.object(tc, "loc_in_frame", None):
|
|
try:
|
|
yield
|
|
except Exception as e:
|
|
# Prevent real_stack from getting attached
|
|
#
|
|
# The invariant is that if an Exception as real_stack, we've
|
|
# appropriately attached a user stack and we no longer need to
|
|
# attach anything. Because we cannot conveniently interpose
|
|
# when an exception is thrown, we instead interpose everywhere
|
|
# we set what the user stack is set (using the context
|
|
# manager). However, our compiler stack does "tail calls"
|
|
# (when it calls into user compiler), at which point the
|
|
# parent exception frames would incorrectly attach an
|
|
# incorrect frame.
|
|
#
|
|
# However, if, somehow, someone raised an exception with this
|
|
# scope that had a stack (for example, because they are
|
|
# restoring the user stack state appropriately as they process
|
|
# node by node), we should respect it. Thus, we cannot
|
|
# unconditionally set None.
|
|
if not hasattr(e, "real_stack"):
|
|
e.real_stack = None # type: ignore[attr-defined]
|
|
raise
|
|
|
|
@staticmethod
|
|
@contextlib.contextmanager
|
|
def current_frame(frame_summary):
|
|
# frame_summary can be None to solely take advantage of real_stack
|
|
# attachment to thrown exceptions
|
|
tc = TracingContext.get()
|
|
if frame_summary is not None:
|
|
tc.frame_summary_stack.append(frame_summary)
|
|
old = tc.loc_in_frame
|
|
tc.loc_in_frame = None
|
|
try:
|
|
yield
|
|
except Exception as e:
|
|
if not hasattr(e, "real_stack"):
|
|
e.real_stack = tc.extract_stack() # type: ignore[attr-defined]
|
|
raise
|
|
finally:
|
|
if frame_summary is not None:
|
|
tc.frame_summary_stack.pop()
|
|
tc.loc_in_frame = old
|
|
|
|
@staticmethod
|
|
@contextlib.contextmanager
|
|
def report_output_strides():
|
|
tc = TracingContext.try_get()
|
|
if tc is None:
|
|
yield None
|
|
return
|
|
old_output_strides = tc.output_strides
|
|
tc.output_strides = []
|
|
try:
|
|
yield tc.output_strides
|
|
finally:
|
|
tc.output_strides = old_output_strides
|
|
|
|
@staticmethod
|
|
def set_current_loc(filename, lineno, frame_name):
|
|
TracingContext.get().loc_in_frame = traceback.FrameSummary(
|
|
filename, lineno, frame_name
|
|
)
|
|
|
|
|
|
@contextmanager
|
|
def compile_context(context: CompileContext):
|
|
old_context = getattr(_TLS, "compile_context", None)
|
|
_TLS.compile_context = context
|
|
try:
|
|
yield context
|
|
finally:
|
|
_TLS.compile_context = old_context
|
|
|
|
|
|
@contextmanager
|
|
def tracing(context: Optional[TracingContext]):
|
|
"""
|
|
This function installs the passed in tracing context as a dynamic scoped
|
|
global variable.
|
|
|
|
Calls to TracingContext.get() while not under a `with tracing()` context
|
|
will return None.
|
|
"""
|
|
old_context = getattr(_TLS, "tracing_context", None)
|
|
_TLS.tracing_context = context
|
|
try:
|
|
yield context
|
|
except Exception as e:
|
|
if not hasattr(e, "real_stack") and context is not None:
|
|
e.real_stack = context.extract_stack() # type: ignore[attr-defined]
|
|
raise
|
|
finally:
|
|
if (
|
|
context is not None
|
|
and context.fake_mode is not None
|
|
and context.fake_mode.shape_env is not None
|
|
):
|
|
context.fake_mode.shape_env.cleanup()
|
|
_TLS.tracing_context = old_context
|
|
|
|
|
|
# Subclasses can be found in torch/_dynamo/source.py
|
|
# TODO(voz): Consider a toplevel torch/_source.py
|
|
@dataclasses.dataclass(frozen=True)
|
|
class Source:
|
|
def is_dict_key(self):
|
|
return False
|
|
|
|
def is_ephemeral(self):
|
|
return False
|
|
|
|
def reconstruct(self, codegen):
|
|
raise NotImplementedError()
|
|
|
|
def guard_source(self) -> GuardSource:
|
|
raise NotImplementedError()
|
|
|
|
def name(self) -> str:
|
|
raise NotImplementedError()
|
|
|
|
def make_guard(self, fn) -> Guard:
|
|
if self.guard_source() is GuardSource.CONSTANT:
|
|
raise NotImplementedError()
|
|
return Guard(self, fn)
|
|
|
|
def is_nn_module(self) -> bool:
|
|
return self.guard_source().is_nn_module()
|
|
|
|
def subguards_allowed(self):
|
|
"""True if you can guard on attributes of this"""
|
|
return self.guard_source() != GuardSource.SYNTHETIC_LOCAL
|
|
|
|
|
|
# Subclasses can be found in torch/_dynamo/source.py
|
|
@dataclasses.dataclass(frozen=True)
|
|
class ChainedSource(Source):
|
|
base: Source
|
|
|
|
def is_dict_key(self):
|
|
# Recurse until you either hit a ConstDictKey or a Source
|
|
return self.base.is_dict_key()
|
|
|
|
def is_ephemeral(self):
|
|
return self.base.is_ephemeral()
|
|
|
|
|
|
def detect_fake_mode(inputs: Any = None):
|
|
"""
|
|
Attempts to "detect" what the current fake mode is. If there is one ambiently
|
|
available from TracingContext, we preferentially use that. Otherwise, we
|
|
heuristically detect the fake mode via the following sources, in order of
|
|
priority:
|
|
|
|
- Currently active fake mode on stack
|
|
- Fake mode associated with passed in tensors (inputs does not
|
|
have to be flattened)
|
|
"""
|
|
from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
|
|
|
|
fake_modes = []
|
|
|
|
if context := TracingContext.try_get():
|
|
fake_mode = context.fake_mode
|
|
if fake_mode is not None:
|
|
fake_modes.append((fake_mode, "tracing context", 0))
|
|
|
|
from torch.utils._python_dispatch import _get_current_dispatch_mode_stack
|
|
|
|
for i, m in enumerate(reversed(_get_current_dispatch_mode_stack())):
|
|
if isinstance(m, FakeTensorMode):
|
|
fake_modes.append((m, "active fake mode", i))
|
|
|
|
flat_inputs = pytree.tree_leaves(inputs)
|
|
for i, flat_input in enumerate(flat_inputs):
|
|
if isinstance(flat_input, FakeTensor):
|
|
fake_modes.append((flat_input.fake_mode, "fake tensor input", i))
|
|
|
|
if fake_modes:
|
|
fake_mode, desc1, i1 = fake_modes[0]
|
|
for m, desc2, i2 in fake_modes[1:]:
|
|
assert fake_mode is m, (
|
|
f"fake mode ({fake_mode}) from {desc1} {i1} doesn't match mode ({m}) from {desc2} {i2}\n\n"
|
|
f"fake mode from {desc1} {i1} allocated at:\n{fake_mode.stack}\n"
|
|
f"fake mode from {desc2} {i2} allocated at:\n{m.stack}"
|
|
)
|
|
return fake_mode
|
|
else:
|
|
return None
|
|
|
|
|
|
def active_fake_mode():
|
|
"""
|
|
Inspects the dispatch mode stack for an active fake mode and returns it.
|
|
Returns None if no fake mode is active.
|
|
"""
|
|
from torch._subclasses.fake_tensor import FakeTensorMode
|
|
from torch.utils._python_dispatch import _get_current_dispatch_mode_stack
|
|
|
|
for _, m in enumerate(reversed(_get_current_dispatch_mode_stack())):
|
|
if isinstance(m, FakeTensorMode):
|
|
return m
|
|
|
|
return None
|