790 lines
25 KiB
Python
790 lines
25 KiB
Python
from __future__ import annotations
|
|
|
|
import contextlib
|
|
|
|
import dataclasses
|
|
import enum
|
|
import functools
|
|
import logging
|
|
import threading
|
|
import traceback
|
|
import unittest.mock
|
|
import weakref
|
|
from abc import ABC, abstractmethod
|
|
from contextlib import contextmanager
|
|
from typing import (
|
|
Any,
|
|
Callable,
|
|
Dict,
|
|
Generic,
|
|
List,
|
|
NamedTuple,
|
|
Optional,
|
|
Set,
|
|
Tuple,
|
|
TypeVar,
|
|
)
|
|
|
|
import torch
|
|
from torch.utils._traceback import CapturedTraceback
|
|
|
|
log = logging.getLogger(__name__)
|
|
|
|
import sympy
|
|
|
|
"""
|
|
torch._guards is the definitional source of truth for general purpose guard structures.
|
|
|
|
An important thing to keep in mind here is the preservation of layering. There should be no dynamo notions,
|
|
and no guard installation notions here.
|
|
"""
|
|
|
|
|
|
class CompileId(NamedTuple):
|
|
frame_id: int
|
|
# This id is per-frame, and counts how many times we've compiled this
|
|
# frame. This could have been a global id but having this be per-frame
|
|
# gives you a better intuitive sense for how many recompiles have occurred
|
|
# so far.
|
|
frame_compile_id: int
|
|
# TODO: consider also tracking the recompilation count
|
|
|
|
def __str__(self):
|
|
return f"{self.frame_id}/{self.frame_compile_id}"
|
|
|
|
|
|
class GuardSource(enum.Enum):
|
|
LOCAL = 0
|
|
GLOBAL = 1
|
|
LOCAL_NN_MODULE = 2
|
|
GLOBAL_NN_MODULE = 3
|
|
CONSTANT = 4
|
|
RANDOM_VALUE = 5
|
|
SHAPE_ENV = 6
|
|
LOCAL_FSDP_MODULE = 7
|
|
GLOBAL_FSDP_MODULE = 8
|
|
|
|
def select(self, locals_, globals_):
|
|
# SHAPE_ENV counts as locals, because the guard expressions
|
|
# created by shape env can reference f_locals
|
|
#
|
|
# RANDOM_VALUE counts as locals, because what we do is we run
|
|
# Python RNG and assign it to a temporary, and then perform
|
|
# guard tests on that temporary
|
|
if self in (
|
|
GuardSource.LOCAL,
|
|
GuardSource.LOCAL_NN_MODULE,
|
|
GuardSource.LOCAL_FSDP_MODULE,
|
|
GuardSource.SHAPE_ENV,
|
|
GuardSource.RANDOM_VALUE,
|
|
):
|
|
return locals_
|
|
if self in (
|
|
GuardSource.GLOBAL,
|
|
GuardSource.GLOBAL_NN_MODULE,
|
|
GuardSource.GLOBAL_FSDP_MODULE,
|
|
):
|
|
return globals_
|
|
raise NotImplementedError(str(self))
|
|
|
|
def is_fsdp_module(self) -> bool:
|
|
return self in (GuardSource.GLOBAL_FSDP_MODULE, GuardSource.LOCAL_FSDP_MODULE)
|
|
|
|
def is_nn_module(self) -> bool:
|
|
return (
|
|
self
|
|
in (
|
|
GuardSource.GLOBAL_NN_MODULE,
|
|
GuardSource.LOCAL_NN_MODULE,
|
|
)
|
|
or self.is_fsdp_module()
|
|
)
|
|
|
|
def is_local(self):
|
|
return self in (
|
|
GuardSource.LOCAL,
|
|
GuardSource.LOCAL_NN_MODULE,
|
|
GuardSource.LOCAL_FSDP_MODULE,
|
|
)
|
|
|
|
|
|
"""
|
|
Base class for a "GuardBuilder" role.
|
|
|
|
The GuardBuilderBase role is to represent a scope within which to build a guard. The name is a little
|
|
confusing, as its not a builder, but for the sake of avoiding a lot of renames and keeping the original reference
|
|
to torchdynamo's GuardBuilder.
|
|
|
|
Note: create_fn is invoked with a GuardBuilderBase and a Guard. A GuardBuilder is chosen based
|
|
on GuardSource's select function.
|
|
|
|
There is value in keeping this GuardBuilderBase empty to keep layering clean.
|
|
"""
|
|
|
|
|
|
class GuardBuilderBase:
|
|
pass
|
|
|
|
|
|
class ShapeGuard(NamedTuple):
|
|
expr: sympy.Expr
|
|
stack: CapturedTraceback
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class Guard:
|
|
# originating_source is the source that called the make_guard method to
|
|
# construct this guard object. The property name specifies what exactly it
|
|
# is the guard is guarding on. The meaning of the name is dependent on the
|
|
# create_fn; you must look at the use-site inside create_fn to know what
|
|
# name means.
|
|
#
|
|
# That being said, although you might think this is just a "name", name is
|
|
# usually an arbitrary Python expression that will be evaluated with all
|
|
# globals (and locals, if you create a LOCAL guard) to extract the Python
|
|
# object that we want to perform guard tests on. This evaluation
|
|
# typically happens in GuardBuilder.eval. In these cases, name is
|
|
# typically produced by originating_source.name() (not to be confused with
|
|
# GuardSource - the property source).
|
|
#
|
|
# Occasionally, name is not a valid Python expression; sometimes
|
|
# it is meaningless. Example create_fns that are like this include
|
|
# GRAD_MODE and SHAPE_ENV.
|
|
originating_source: Source
|
|
create_fn: Callable[[GuardBuilderBase, Guard], None]
|
|
is_volatile: bool = False
|
|
|
|
# Export only. These values are written to at time of guard check_fn creation.
|
|
guard_types: Optional[List[str]] = None
|
|
code_list: Optional[List[str]] = None
|
|
obj_weakref: Optional[object] = None
|
|
guarded_class_weakref: Optional[type] = None
|
|
|
|
stack = None
|
|
user_stack = None
|
|
|
|
def __hash__(self):
|
|
return hash((self.name, self.source, id(self.create_fn)))
|
|
|
|
def sort_key(self):
|
|
return (
|
|
self.source.value if self.source else -1,
|
|
len(self.name),
|
|
self.name,
|
|
self.inner_create_fn().__code__.co_firstlineno,
|
|
)
|
|
|
|
def __lt__(self, other):
|
|
return self.sort_key() < other.sort_key()
|
|
|
|
def inner_create_fn(self):
|
|
if isinstance(self.create_fn, functools.partial):
|
|
return self.create_fn.func
|
|
else:
|
|
return self.create_fn
|
|
|
|
@property
|
|
def name(self) -> str:
|
|
return self.originating_source.name()
|
|
|
|
@property
|
|
def source(self) -> GuardSource:
|
|
return self.originating_source.guard_source()
|
|
|
|
@staticmethod
|
|
def weakref_to_str(obj_weakref):
|
|
"""
|
|
This is a workaround of a Python weakref bug.
|
|
|
|
`obj_weakref` is instance returned by `weakref.ref`,
|
|
`str(obj_weakref)` is buggy if the original obj overrides __getattr__, e.g:
|
|
|
|
class MyConfig(dict):
|
|
def __getattr__(self, x):
|
|
return self[x]
|
|
|
|
obj = MyConfig(offset=5)
|
|
obj_weakref = weakref.ref(obj)
|
|
str(obj_weakref) # raise error: KeyError: '__name__'
|
|
"""
|
|
if isinstance(obj_weakref, weakref.ReferenceType):
|
|
obj = obj_weakref()
|
|
if obj is not None:
|
|
return f"<weakref at {hex(id(obj_weakref))}; to '{obj.__class__.__name__}' at {hex(id(obj))}>"
|
|
else:
|
|
return f"<weakref at {hex(id(obj_weakref))}; dead>"
|
|
else:
|
|
return str(obj_weakref)
|
|
|
|
def __repr__(self):
|
|
s = f"""
|
|
{self.source.name.lower() if self.source else ""} {repr(self.name)} {self.inner_create_fn().__name__}
|
|
{{
|
|
'guard_types': {self.guard_types},
|
|
'code': {self.code_list},
|
|
'obj_weakref': {self.weakref_to_str(self.obj_weakref)}
|
|
'guarded_class': {self.guarded_class_weakref}
|
|
}}
|
|
"""
|
|
return s
|
|
|
|
def __str__(self):
|
|
output = f"Name: {repr(self.name)}\n"
|
|
source = self.source.name.lower() if self.source else ""
|
|
output += f" Source: {source}\n"
|
|
output += f" Create Function: {self.inner_create_fn().__name__}\n"
|
|
output += f" Guard Types: {self.guard_types}\n"
|
|
output += f" Code List: {self.code_list}\n"
|
|
output += f" Object Weakref: {self.weakref_to_str(self.obj_weakref)}\n"
|
|
output += f" Guarded Class Weakref: {self.guarded_class_weakref}\n"
|
|
return output
|
|
|
|
def create(self, local_builder: GuardBuilderBase, global_builder: GuardBuilderBase):
|
|
return self.create_fn(self.source.select(local_builder, global_builder), self)
|
|
|
|
def is_nn_module(self):
|
|
return self.source.is_nn_module()
|
|
|
|
def is_fsdp_module(self):
|
|
return self.source.is_fsdp_module()
|
|
|
|
def is_local(self):
|
|
return self.source.is_local()
|
|
|
|
def set_export_info(self, guard_type, guarded_class, code_list, obj_weakref):
|
|
if not self.guard_types:
|
|
self.guard_types = list()
|
|
|
|
self.guard_types.append(guard_type)
|
|
|
|
assert self.guarded_class_weakref in (
|
|
guarded_class,
|
|
None,
|
|
), "Guarded class id must be identical, or None"
|
|
self.guarded_class_weakref = guarded_class
|
|
|
|
if not self.code_list:
|
|
self.code_list = code_list
|
|
else:
|
|
self.code_list.extend(code_list)
|
|
|
|
assert self.obj_weakref in (
|
|
obj_weakref,
|
|
None,
|
|
), "Guarded object must be identical, or None"
|
|
self.obj_weakref = obj_weakref
|
|
|
|
|
|
T = TypeVar("T")
|
|
|
|
"""
|
|
Parent structure for guard env expressions.
|
|
A GuardEnvExpr can have any subtype.
|
|
Note: All subtypes must be handled exhaustively in
|
|
torch._dynamo.guards._parse_guard_env_guards to avoid a RuntimeError.
|
|
"""
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class GuardEnvExpr:
|
|
pass
|
|
|
|
|
|
"""
|
|
A class representing a pair of duplicate inputs.
|
|
input_pos_a and input_pos_b are input positions we have deduped.
|
|
"""
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class DuplicateInputs(GuardEnvExpr):
|
|
input_source_a: Source
|
|
input_source_b: Source
|
|
|
|
def __post_init__(self):
|
|
assert self.input_source_a != self.input_source_b
|
|
|
|
|
|
"""
|
|
Checkpointable is an interface for driving state snapshotting, left purposely vague for now.
|
|
|
|
copy_graphstate() -> T, a somewhat legacy name, is expected to emit a snapshot of any type that
|
|
can also be taken in at restore_graphstate(T) calls.
|
|
|
|
When to snapshot, is, at the moment, an implementation detail of upstream callers. Checkpointable
|
|
does not provide any garuantees around consistency, idempotency, or safety of calling its APIs, yet.
|
|
|
|
In the future, it will have a closer coupling to a generic Checkpoint management system.
|
|
"""
|
|
|
|
|
|
class Checkpointable(ABC, Generic[T]):
|
|
@abstractmethod
|
|
def copy_graphstate(self) -> T:
|
|
...
|
|
|
|
@abstractmethod
|
|
def restore_graphstate(self, state: T):
|
|
...
|
|
|
|
|
|
"""
|
|
The GuardCheckpointState - it is the T of Checkpointable[T] for GuardsContext
|
|
"""
|
|
|
|
|
|
class GuardsCheckpointState:
|
|
dynamo_guards: Set[Guard] = set()
|
|
|
|
def __init__(self, dynamo_guards):
|
|
self.dynamo_guards = dynamo_guards
|
|
|
|
"""
|
|
Produces a delta against another GuardsCheckpointState.
|
|
|
|
Returns None if no delta is found, otherwise, return a set() of mismatched
|
|
Guard type objects.
|
|
"""
|
|
|
|
def diff(self, other):
|
|
r = self.dynamo_guards.difference(other.dynamo_guards)
|
|
if len(r) == 0:
|
|
return None
|
|
return r
|
|
|
|
def __eq__(self, other):
|
|
return self.diff(other) is None
|
|
|
|
|
|
class ModuleContextCheckpointState:
|
|
nn_modules: Dict[str, torch.nn.Module] = {}
|
|
|
|
def __init__(self, nn_modules):
|
|
self.nn_modules = nn_modules
|
|
|
|
"""
|
|
Produces a delta against another ModuleContextCheckpointState.
|
|
|
|
Returns None if no delta is found, otherwise, return a set() of mismatched
|
|
module key names.
|
|
"""
|
|
|
|
def diff(self, other):
|
|
r = set(self.nn_modules.keys()).difference(set(other.nn_modules.keys()))
|
|
if len(r) == 0:
|
|
return None
|
|
return r
|
|
|
|
def __eq__(self, other):
|
|
return self.diff(other) is None
|
|
|
|
|
|
class ModuleContext(Checkpointable[ModuleContextCheckpointState]):
|
|
def __init__(self):
|
|
self.nn_modules: Dict[str, torch.nn.Module] = {}
|
|
|
|
def copy_graphstate(self):
|
|
return ModuleContextCheckpointState(dict(self.nn_modules))
|
|
|
|
def restore_graphstate(self, state):
|
|
assert isinstance(state, ModuleContextCheckpointState)
|
|
self.nn_modules = state.nn_modules
|
|
|
|
|
|
class GlobalContextCheckpointState:
|
|
global_state: Dict[str, Tuple[Callable, ...]] = {}
|
|
|
|
def __init__(self, global_states):
|
|
self.global_state = global_states
|
|
|
|
"""
|
|
Produces a delta against another GlobalContextCheckpointState.
|
|
|
|
Returns None if no delta is found, otherwise, return a set() of mismatched
|
|
global key names.
|
|
"""
|
|
|
|
def diff(self, other):
|
|
r = set(self.global_state.keys()).difference(set(other.global_state.keys()))
|
|
if len(r) == 0:
|
|
return None
|
|
return r
|
|
|
|
def __eq__(self, other):
|
|
return self.diff(other) is None
|
|
|
|
|
|
class GlobalContext(Checkpointable[GlobalContextCheckpointState]):
|
|
"""
|
|
This keeps track of the global torch state during tracing of a function.
|
|
For example, torch.is_grad_enabled.
|
|
"""
|
|
|
|
_supported_global_states = {
|
|
"grad_enabled",
|
|
"torch_function_enabled",
|
|
"autocast_enabled",
|
|
"autocast_cpu_enabled",
|
|
"autocast_gpu_dtype",
|
|
"autocast_cpu_dtype",
|
|
"autocast_cache_enabled",
|
|
}
|
|
|
|
def __init__(self):
|
|
self.global_state: Dict[str, Tuple[Callable, ...]] = {}
|
|
|
|
def copy_graphstate(self):
|
|
return GlobalContextCheckpointState(dict(self.global_state))
|
|
|
|
def restore_graphstate(self, state):
|
|
assert isinstance(state, GlobalContextCheckpointState)
|
|
self.global_state = state.global_state
|
|
assert (
|
|
len(self.global_state) == len(self._supported_global_states)
|
|
and set(self.global_state.keys()) == self._supported_global_states
|
|
), "Global state mismatch"
|
|
for func, args in self.global_state.values():
|
|
func(args)
|
|
|
|
|
|
"""
|
|
A GuardsContext is a checkpointable representation of all the guards in the current tracing
|
|
context. It's lifecycle is bound 1:1 to the tracing context, and it should never be instantiated
|
|
directly outside of it. For passing around internal state representations of this object,
|
|
prefer to extract them with copy_graphstate to produce a GuardsCheckpointState.
|
|
"""
|
|
|
|
|
|
# Like a Set[Guard] but will record the user stack on all guards at the
|
|
# time they were installed at their destination
|
|
class GuardsSet:
|
|
def __init__(self, inner=None):
|
|
if inner is None:
|
|
inner = set()
|
|
self.inner = inner
|
|
|
|
def __iter__(self):
|
|
return iter(self.inner)
|
|
|
|
def __len__(self):
|
|
return len(self.inner)
|
|
|
|
# Subtraction along with bool is typically used to determine the delta of
|
|
# added guards between checkpoints for higher order ops
|
|
def __sub__(self, other):
|
|
return GuardsSet(self.inner - other.inner)
|
|
|
|
def __bool__(self):
|
|
return bool(self.inner)
|
|
|
|
def add(self, guard: Guard, *, skip=0):
|
|
if guard in self.inner:
|
|
return
|
|
if guard.stack is None:
|
|
guard.stack = CapturedTraceback.extract(skip=1 + skip)
|
|
if guard.user_stack is None:
|
|
guard.user_stack = TracingContext.extract_stack()
|
|
self.inner.add(guard)
|
|
|
|
def update(self, *others: Set[Guard]):
|
|
for o in others:
|
|
for g in o:
|
|
self.add(g, skip=1)
|
|
|
|
|
|
class GuardsContext(Checkpointable[GuardsCheckpointState]):
|
|
def __init__(self):
|
|
self.dynamo_guards: GuardsSet = GuardsSet()
|
|
self.aotautograd_guards: List[GuardEnvExpr] = []
|
|
|
|
def copy_graphstate(self):
|
|
return GuardsCheckpointState(set(self.dynamo_guards.inner))
|
|
|
|
def restore_graphstate(self, state):
|
|
# NB: "steals" the passed in state
|
|
assert isinstance(state, GuardsCheckpointState)
|
|
self.dynamo_guards = GuardsSet(state.dynamo_guards)
|
|
|
|
|
|
_TLS = threading.local()
|
|
|
|
"""
|
|
TracingContext is the source of truth for all currently accumulated information
|
|
needed to trace. Its lifecycle is kept 1:1 when using TorchDynamo, but other systems
|
|
are open to managing their own TracingContext with that in mind.
|
|
|
|
The purpose of TracingContext is not to be a dumping ground, or god object, but rather to avoid
|
|
having to plumb complex subsystems across multiple verticals.
|
|
|
|
Ex: A common example is guard accumulation between dynamo, shape_env, aot_autograd, and inductor.
|
|
Accessing the current tracing context via
|
|
TracingContext.get() allows users to accumulate their own guards for processing, without needing to know how
|
|
to plumb objects back up to where frame interpretation happened.
|
|
|
|
Note that you can end up with multiple TracingContext for a single compilation
|
|
of a frame, as we reset the TracingContext whenever we restart analysis.
|
|
CompileContext is a more overarching context that encompasses multiple restarts.
|
|
"""
|
|
|
|
|
|
class CompileContext:
|
|
@staticmethod
|
|
def get() -> Optional[CompileContext]:
|
|
return getattr(_TLS, "compile_context", None)
|
|
|
|
def __init__(self, compile_id):
|
|
assert compile_id is None or isinstance(compile_id, CompileId)
|
|
self.compile_id = compile_id
|
|
|
|
@staticmethod
|
|
def current_compile_id():
|
|
self = CompileContext.get()
|
|
if self is None:
|
|
return None
|
|
return self.compile_id
|
|
|
|
|
|
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 get() -> Optional[TracingContext]:
|
|
return getattr(_TLS, "tracing_context", None)
|
|
|
|
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
|
|
|
|
@staticmethod
|
|
def extract_stack():
|
|
self = TracingContext.get()
|
|
if self is None:
|
|
return traceback.StackSummary()
|
|
stack = list(self.frame_summary_stack)
|
|
if self.loc_in_frame is not None:
|
|
stack.append(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()
|
|
assert (
|
|
tc is not None
|
|
), "Frame context manager must be called within an ongoing trace."
|
|
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()
|
|
assert (
|
|
tc is not None
|
|
), "Frame context manager must be called within an ongoing trace."
|
|
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.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):
|
|
tc = TracingContext.get()
|
|
assert (
|
|
tc is not None
|
|
), "Loc context manager must be called within an ongoing trace."
|
|
tc.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: 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 reconstruct(self, codegen):
|
|
raise NotImplementedError()
|
|
|
|
def guard_source(self) -> GuardSource:
|
|
raise NotImplementedError()
|
|
|
|
def name(self) -> str:
|
|
raise NotImplementedError()
|
|
|
|
def make_guard(self, fn, is_volatile=False) -> Guard:
|
|
if self.guard_source() is GuardSource.CONSTANT:
|
|
raise NotImplementedError()
|
|
return Guard(self, fn, is_volatile)
|
|
|
|
def is_nn_module(self) -> bool:
|
|
return self.guard_source().is_nn_module()
|
|
|
|
|
|
# Subclasses can be found in torch/_dynamo/source.py
|
|
# Note - there is an odd exception to this invariant of a single base,
|
|
# see class SuperSource
|
|
@dataclasses.dataclass(frozen=True)
|
|
class ChainedSource(Source):
|
|
base: Source
|
|
|
|
|
|
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
|
|
from torch.utils._pytree import tree_flatten
|
|
|
|
fake_modes = []
|
|
|
|
context = TracingContext.get()
|
|
if context is not None:
|
|
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, _ = tree_flatten(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
|