import collections import logging import math import os import re import types import weakref from inspect import currentframe, getframeinfo from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union from weakref import ReferenceType import numpy as np import sympy import torch from torch._guards import Guard, GuardBuilderBase, GuardSource from torch.fx.experimental.symbolic_shapes import FloorDiv from . import config, convert_frame, mutation_guard from .eval_frame import set_guard_error_hook, set_guard_fail_hook from .exc import unimplemented from .types import GuardedCode, GuardFail, GuardFn # noqa: F401 from .utils import ( dict_const_keys, dict_param_key_ids, guard_failures, istype, orig_code_map, rename_implicit, tuple_iterator_getitem, tuple_iterator_len, ) log = logging.getLogger(__name__) TensorGuards = torch._C._dynamo.guards.TensorGuards check_obj_id = torch._C._dynamo.guards.check_obj_id check_type_id = torch._C._dynamo.guards.check_type_id CLOSURE_VARS = collections.OrderedDict( [ ("___check_type_id", check_type_id), ("___check_obj_id", check_obj_id), ("___is_grad_enabled", torch.is_grad_enabled), ("___odict_getitem", collections.OrderedDict.__getitem__), ("___dict_param_key_ids", dict_param_key_ids), ("___dict_const_keys", dict_const_keys), ("___tuple_iterator_len", tuple_iterator_len), ("___tuple_iterator_getitem", tuple_iterator_getitem), ("__math_isnan", math.isnan), ("inf", float("inf")), ] ) def strip_function_call(name): """ "___odict_getitem(a, 1)" => "a" """ m = re.search(r"([a-z0-9_]+)\(([^(),]+)[^()]*\)", name) if m and m.group(1) != "slice": return strip_function_call(m.group(2)) return strip_getattr_getitem(name) def strip_getattr_getitem(name): """ "a[1]" => "a" "a.foo" => "a" """ return re.split(r"[.\[]", name)[0] class GuardBuilder(GuardBuilderBase): def __init__( self, id_ref: Callable[[Type[object]], str], scope: Optional[Dict[str, object]], guarded_code: "CheckFunctionManager", renames=True, ): self.id_ref = id_ref if scope: if renames: scope = {rename_implicit(k): v for k, v in scope.items()} else: scope = dict() self.scope: Dict[str, object] = scope self.argnames: List[str] = [] # Code is python expression strings generated for each guard self.code: List[str] = [] # Most of the time, we generate Python code in a guard to directly # check various properties. However, tensors are a bit special; # it is too slow to check their properties one-by-one in Python. # Instead, there is a C++ function TensorGuards.check which takes # all of the tensor arguments and checks them all against compile-time # examples entirely in C++. Thus, every time we process a # TENSOR_MATCH guard, we just add another entry to # tensor_check_names/tensor_check_examples, saying "for this local, # check it against this example", and it all ends up getting # swept up into a single call to ___check_tensors. Invariant: # len(tensor_check_names) == len(tensor_check_examples). self.tensor_check_names: List[str] = [] self.tensor_check_examples: List[torch.Tensor] = [] self.tensor_check_ids: Dict[str, int] = {} # TODO: tf is this naming self.guarded_code: CheckFunctionManager = guarded_code def get(self, name: str) -> Any: return eval(name, self.scope, CLOSURE_VARS) def arg_ref(self, guard: Union[str, Guard]) -> str: name: str if isinstance(guard, str): name = guard else: name = guard.name base = strip_getattr_getitem(strip_function_call(name)) if base not in self.argnames: if re.match(r"^\d+$", base): log.warning(f"invalid var name: {guard}") self.argnames.append(base) return name def TYPE_MATCH(self, guard: Guard): # ___check_type_id is same as `id(type(x)) == y` t = type(self.get(guard.name)) obj_id = self.id_ref(t) code = f"___check_type_id({self.arg_ref(guard)}, {obj_id})" self._produce_guard_code(guard, [code]) def ID_MATCH(self, guard: Guard): # ___check_obj_id is same as `id(x) == y` m = re.match(r"^type\((.+)\)$", guard.name) if m: # optional optimization to produce cleaner/faster guard code return self.TYPE_MATCH( Guard(m.group(1), guard.source, GuardBuilder.TYPE_MATCH) ) code = f"___check_obj_id({self.arg_ref(guard)}, {self.id_ref(self.get(guard.name))})" self._produce_guard_code(guard, [code]) def NAME_MATCH(self, guard: Guard): obj = self.get(guard.name) code = f"{self.arg_ref(guard)}.__name__ == {obj.__name__}" self._produce_guard_code(guard, [code]) def HASATTR(self, guard: Guard): m = re.match(r"^(.*)[.]([a-zA-Z0-9_]+)$", guard.name) assert m, f"invalid hasattr check {guard.name}" base, attr = m.group(1, 2) ref = self.arg_ref(base) val = hasattr(self.get(base), attr) code = None if val: code = f"hasattr({ref}, {attr!r})" else: code = f"not hasattr({ref}, {attr!r})" self._produce_guard_code(guard, [code], provided_guarded_object=self.get(base)) def EQUALS_MATCH(self, guard: Guard): ref = self.arg_ref(guard) val = self.get(guard.name) t = type(val) assert istype( val, ( int, float, bool, type(None), str, type, list, tuple, set, slice, frozenset, range, torch.Size, torch.device, torch.dtype, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64, ), ), t.__name__ if istype(val, (torch.device, torch.dtype)): # TODO(jansel): is this slow? perhaps optimize it code = [f"str({ref}) == {str(val)!r}"] self._produce_guard_code(guard, code) return # Special case for nan because float("nan") == float("nan") evaluates to False if istype(val, float) and math.isnan(val): code = list() code.append(f"___check_type_id({ref}, {self.id_ref(t)})") code.append(f"__math_isnan({ref})") self._produce_guard_code(guard, code) return # Add type check to prevent equality check between tensor and non-tensor. code = list() if istype(val, (list, tuple)): self.LIST_LENGTH(guard) for idx, elem in enumerate(val): code.append( f"___check_type_id({ref}[{idx}], {self.id_ref(type(elem))})" ) elif not istype(val, torch.Size): code.append(f"___check_type_id({ref}, {self.id_ref(t)})") if istype(val, torch.Size): val = tuple(val) code.append(f"{ref} == {val!r}") self._produce_guard_code(guard, code) def CONSTANT_MATCH(self, guard: Guard): val = self.get(guard.name) if istype(val, (bool, type(None))): self.ID_MATCH(guard) else: self.EQUALS_MATCH(guard) def NN_MODULE(self, guard: Guard): self.ID_MATCH(guard) ref = self.arg_ref(guard) val = self.get(guard.name) def setup_guard(): assert istype(val.training, bool) self.code.append(f"{ref}.training == {val.training}") if hasattr(val, "training"): # There are cases where a monkeypatched object has a guard made between __new__ and __init__ setup_guard() else: unimplemented(f"Guard setup for uninitialized class {type(val)}") def FUNCTION_MATCH(self, guard: Guard): """things like torch.add and user defined functions""" if guard.is_local(): return self.ID_MATCH(guard) def BUILTIN_MATCH(self, guard: Guard): return self.FUNCTION_MATCH(guard) def PYMODULE_MATCH(self, guard: Guard): return self.FUNCTION_MATCH(guard) def LIST_LENGTH(self, guard): ref = self.arg_ref(guard) value = self.get(guard.name) t = type(value) code = list() code.append(f"___check_type_id({ref}, {self.id_ref(t)})") code.append(f"len({ref}) == {len(value)}") self._produce_guard_code(guard, code) def TUPLE_ITERATOR_LEN(self, guard): ref = self.arg_ref(guard) value = self.get(guard.name) t = type(value) code = list() code.append(f"___check_type_id({ref}, {self.id_ref(t)})") code.append(f"___tuple_iterator_len({ref}) == {tuple_iterator_len(value)}") self._produce_guard_code(guard, code) def DICT_KEYS(self, guard): ref = self.arg_ref(guard) value = self.get(guard.name) t = type(value) code = list() code.append(f"___check_type_id({ref}, {self.id_ref(t)})") param_key_ids = set(dict_param_key_ids(value)) const_keys = set(dict_const_keys(value)) if param_key_ids: code.append(f"___dict_param_key_ids({ref}) == {param_key_ids!r}") code.append(f"___dict_const_keys({ref}) == {const_keys!r}") else: code.append(f"set({ref}.keys()) == {const_keys!r}") self._produce_guard_code(guard, code) def WEAKREF_ALIVE(self, guard): self._produce_guard_code(guard, [f"{self.arg_ref(guard)} is not None"]) def NN_MODULE_PARAM_NAMES(self, guard): ref = self.arg_ref(guard) value = self.get(guard.name) t = type(value) keys = {k for k, v in value.named_parameters()} code = list() code.append(f"___check_type_id({ref}, {self.id_ref(t)})") code.append(f"{{k for k, v in {ref}.named_parameters()}} == {keys!r}") self._produce_guard_code(guard, code) def ODICT_KEYS(self, guard): """OrderedDict keys match""" ref = self.arg_ref(guard) value = self.get(guard.name) t = type(value) code = list() code.append(f"___check_type_id({ref}, {self.id_ref(t)})") code.append(f"str({ref}.keys()) == {str(value.keys())!r}") self._produce_guard_code(guard, code) def OBJECT_MUTATION(self, guard: Guard): mutation_guard.watch(self.get(guard.name), self.guarded_code) def GRAD_MODE(self, guard: Guard): """Guard on the initial grad state""" assert guard.name == "" assert guard.source is GuardSource.GLOBAL code = None if convert_frame.initial_grad_state: code = "___is_grad_enabled()" else: code = "not ___is_grad_enabled()" self._produce_guard_code(guard, [code]) # This is a bit of a crutch for export case for symbolic shape guards. # SYMBOL_MATCH is only ever, and must only ever, be used for setting this value on # the create_fn field for tracking guards in export. def SYMBOL_MATCH(self, guard: Guard): raise AssertionError("this should not actually be called") def TENSOR_MATCH(self, guard: Guard): if guard.is_nn_module(): self.ID_MATCH(guard) else: value = self.get(guard.name) assert isinstance(value, torch.Tensor) tensor_name = self.arg_ref(guard) self.tensor_check_names.append(tensor_name) self.tensor_check_examples.append(value) # STOP - DO NOT USE id_ref FOR TENSORS - TENSOR INVALIDATION RULES DIFFER self.tensor_check_ids[tensor_name] = id(value) # Note: Guard code produced for tensor_match is a little different. # We accumulate tensor names, then do a single install of `___check_tensors`. # See _guards.cpp and TensorGuard for more information. # TODO(voz): Add tensor matching code to export # Note: this is a bit of a special case, and so does not use _produce_guard_code guard.set_export_info( "TENSOR_MATCH", weakref.ref(type(value)), None, weakref.ref(value), ) # A util that appends guarded code, or, in the case of export, adds data onto guards def _produce_guard_code(self, guard, code_list, provided_guarded_object=None): # WARNING: It is important that cur_frame/caller do NOT stay in # the current frame, because they will keep things live longer # than they should. See TestMisc.test_release_module_memory cur_frame = currentframe() assert cur_frame is not None caller = cur_frame.f_back del cur_frame assert caller is not None func_name = getframeinfo(caller)[2] del caller # We use func_name for export, so might as well get a nice defensive check out of it assert func_name in dir( self.__class__ ), f"_produce_guard_code must be called from inside GuardedCode. Called from {func_name}" self.code.extend(code_list) # Not all guards have names, some can be installed globally (see asserts on HAS_GRAD) if provided_guarded_object is None: name_valid = guard.name is not None and guard.name != "" guarded_object = self.get(guard.name) if name_valid else None else: guarded_object = provided_guarded_object guarded_object_type = ( weakref.ref(type(guarded_object)) if guarded_object is not None else None ) obj_ref = None if hasattr(guarded_object.__class__, "__weakref__"): obj_ref = weakref.ref(guarded_object) guard.set_export_info( func_name, guarded_object_type, code_list, obj_ref, ) # NB: Naively, you'd expect this to only be a function that produces # the callable that consistutes the guard. However, there is some # delicate handling for invalidating this check function when the # locals/globals get invalidated, so there's some extra state # we have to hold in this manager class. # # TODO: this object has reference cycle with itself, via check_fn which # references back to CheckFunction via ___guarded_code in closure_vars. # Ideally, there shouldn't be any ref cycle so that guards are # promptly disposed of. class CheckFunctionManager: def __init__( self, output_graph=None, guards: Optional[Set[Guard]] = None, f_locals: Optional[Dict[str, object]] = None, f_globals: Optional[Dict[str, object]] = None, guard_fail_fn: Optional[Callable[[Tuple[str, str]], None]] = None, ): self.valid = True self._weakrefs: List["ReferenceType[object]"] = [] self._seen_ids: Set[int] = set() self.output_graph = output_graph # Note: right overrides left def combine_scopes(left, right): if left is None: return right if right is None: return left return {**left, **right} local_builder = GuardBuilder( self.id_ref, combine_scopes(f_globals, f_locals), self, renames=True ) global_builder = GuardBuilder(self.id_ref, f_globals, self, renames=False) for guard in sorted(guards or [], key=Guard.sort_key): if not config.guard_nn_modules and guard.is_nn_module(): continue guard.create(local_builder, global_builder) self.check_fn = self.compile_check_fn( local_builder, global_builder, guards, guard_fail_fn ) self._seen_ids.clear() def compile_check_fn( self, local_builder, global_builder, guards_out, guard_fail_fn ): assert not (set(local_builder.argnames) & set(global_builder.argnames)) # see parallel handling of ".0" / "___implicit0" in _eval_frame.c largs = [a for a in local_builder.scope.keys() if a == "___implicit0"] largs += [a for a in local_builder.argnames if a != "___implicit0"] largs += ["**___kwargs_ignored"] args = ",".join(largs) code_parts = ( ["___guarded_code.valid"] + local_builder.code + global_builder.code ) # TODO(whc) maybe only the 'check_tensors' one is ambiguous? if so we can be less general.. verbose_code_parts = ( ["___guarded_code.valid"] + local_builder.code + global_builder.code ) tensor_check_names = ( local_builder.tensor_check_names + global_builder.tensor_check_names ) tensor_check_ids = local_builder.tensor_check_ids.copy() tensor_check_ids.update(global_builder.tensor_check_ids) check_tensors_fn = None check_tensors_verbose_fn = None if tensor_check_names: tensor_check_examples = ( local_builder.tensor_check_examples + global_builder.tensor_check_examples ) tensor_guards = TensorGuards( *tensor_check_examples, dynamic_shapes=config.dynamic_shapes ) check_tensors_fn = tensor_guards.check check_tensors_verbose_fn = tensor_guards.check_verbose code_parts.append(f"___check_tensors({', '.join(tensor_check_names)})") verbose_args = ", ".join( tensor_check_names + ["tensor_check_names=tensor_check_names"] ) verbose_code_parts.append(f"___check_tensors_verbose({verbose_args})") # Let's handle ShapeEnv guards. To do this, we will resolve # shape variables to sources from GraphArgs. This must happen after # tensor checks. # NB: self.output_graph can be None in the debug_nops tests if self.output_graph and self.output_graph.shape_env: # NB: use orig_graphargs, as we can have created guards for # inputs that are ultimately unused in the graph, but we # are still on the hook for guarding on them (because, e.g., # Dynamo may have gone down a different conditional branch # because of it.) graphargs = self.output_graph.orig_graphargs expr_as_str = self.output_graph.shape_env.codegen_guards( [a.fake_tensor for a in graphargs if a.is_tensor], [a.source.name() for a in graphargs if a.is_tensor], ) if expr_as_str != "True": code_parts.append(expr_as_str) verbose_code_parts.append(expr_as_str) # TODO: this is a hack guards_out.add( Guard( name="symbolic_shape_expression", source=GuardSource.SHAPE_ENV, create_fn=GuardBuilder.SYMBOL_MATCH, code_list=expr_as_str, ) ) def direct_equality(a, b): return a == b def direct_negation(a, b): return not direct_equality(a, b) code = " and ".join(unique(code_parts)) closure_vars = collections.OrderedDict( [ ("___guarded_code", self), ("___check_tensors", check_tensors_fn), ("___check_tensors_verbose", check_tensors_verbose_fn), ("tensor_check_names", tensor_check_names), ("floor", math.floor), ("ceiling", math.ceil), ("Eq", direct_equality), ("Ne", direct_negation), ("Mod", sympy.Mod), ("FloorDiv", FloorDiv), ] ) closure_vars.update(CLOSURE_VARS) py_code = f"""\ def ___make_guard_fn({','.join(closure_vars.keys())}): return lambda {args}: {code} """ if os.environ.get("TORCHDYNAMO_PRINT_GUARDS", None) == "1": print("GUARDS", code) set_guard_fail_hook(guard_fail_hook) out: Dict[str, Any] = dict() # print("RUNNING PY CODE", py_code) exec(py_code, global_builder.scope, out) guard_fn = out["___make_guard_fn"](*closure_vars.values()) guard_fn.closure_vars = closure_vars # TODO(whc) maybe '.code_parts' was only kept around for the guard callback? so we don't need both guard_fn.code_parts = code_parts guard_fn.verbose_code_parts = verbose_code_parts guard_fn.global_scope = global_builder.scope guard_fn.guard_fail_fn = guard_fail_fn return guard_fn def invalidate(self, ref): # A weakref is no longer valid, self.check_fn should return false self.valid = False def id_ref(self, obj): """add a weakref, return the id""" try: if id(obj) not in self._seen_ids: self._weakrefs.append(weakref.ref(obj, self.invalidate)) self._seen_ids.add(id(obj)) except TypeError: pass # cannot weakref bool object return id(obj) def guard_fail_hook( guard_fn: GuardFn, code: types.CodeType, f_locals: Dict[str, object], last: bool ) -> None: """ called whenever a guard fails. """ if not guard_fn.guard_fail_fn and not last: return scope = {rename_implicit(k): v for k, v in f_locals.items()} scope.update(guard_fn.closure_vars) reason = None for part in guard_fn.verbose_code_parts: fail_reason = eval(part, guard_fn.global_scope, scope) # TODO(whc) hacky for now as not every 'part' in guard_fn.verbose_code_parts # is updated to return a string explaining the failure. if isinstance(fail_reason, str): reason = fail_reason break elif isinstance(fail_reason, bool) and not fail_reason: reason = part break try: if guard_fn.guard_fail_fn is not None: guard_fn.guard_fail_fn( GuardFail(reason or "unknown reason", orig_code_map[code]) ) except Exception as e: log.error( "Failure in guard_fail_fn callback - raising here will cause a NULL Error on guard eval", exc_info=True, ) if last: guard_failures[orig_code_map[code]].append(reason) def guard_error_hook( guard_fn: GuardFn, code: types.CodeType, f_locals: Dict[str, object], last: bool ): print( f"ERROR RUNNING GUARDS {code.co_name} {code.co_filename}:{code.co_firstlineno}" ) print(" ", " and\n ".join(guard_fn.code_parts)) set_guard_error_hook(guard_error_hook) def unique(seq): seen = set() for x in seq: if x not in seen: yield x seen.add(x)