| import collections |
| import dataclasses |
| import enum |
| 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 |
| |
| import numpy as np |
| |
| import sympy |
| |
| import torch |
| 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 .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")), |
| ] |
| ) |
| |
| |
| class GuardSource(enum.Enum): |
| LOCAL = 0 |
| GLOBAL = 1 |
| LOCAL_NN_MODULE = 2 |
| GLOBAL_NN_MODULE = 3 |
| CONSTANT = 4 |
| |
| def select(self, locals_, globals_): |
| if self in (GuardSource.LOCAL, GuardSource.LOCAL_NN_MODULE): |
| return locals_ |
| if self in (GuardSource.GLOBAL, GuardSource.GLOBAL_NN_MODULE): |
| return globals_ |
| raise NotImplementedError() |
| |
| def is_nn_module(self): |
| return self in (GuardSource.GLOBAL_NN_MODULE, GuardSource.LOCAL_NN_MODULE) |
| |
| def is_local(self): |
| return self in (GuardSource.LOCAL, GuardSource.LOCAL_NN_MODULE) |
| |
| |
| @dataclasses.dataclass |
| class Guard: |
| name: str |
| source: GuardSource |
| create_fn: Callable |
| 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[Any] = None |
| guarded_class_weakref: Optional[type] = None |
| |
| def __hash__(self): |
| return hash((self.name, self.source, id(self.create_fn))) |
| |
| def sort_key(self): |
| return ( |
| self.source.value, |
| len(self.name), |
| self.name, |
| self.create_fn.__code__.co_firstlineno, |
| ) |
| |
| def __lt__(self, other): |
| return self.sort_key() < other.sort_key() |
| |
| def __str__(self): |
| s = f""" |
| {self.source.name.lower()} {repr(self.name)} {self.create_fn.__name__} |
| {{ |
| 'guard_types': {self.guard_types}, |
| 'code': {self.code_list}, |
| 'obj_weakref': {self.obj_weakref} |
| 'guarded_class': {self.guarded_class_weakref} |
| }} |
| """ |
| return s |
| |
| def create(self, local_builder: "GuardBuilder", global_builder: "GuardBuilder"): |
| 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_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 |
| |
| |
| 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: |
| def __init__( |
| self, id_ref: Callable, scope: Dict[str, Any], guarded_code, 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 = scope |
| self.argnames: List[str] = [] |
| # Code is python expression strings generated for each guard |
| self.code: List[str] = [] |
| self.tensor_check_names = [] |
| self.tensor_check_ids = {} |
| self.tensor_check_examples = [] |
| self.guarded_code = guarded_code |
| |
| def get(self, name: str): |
| return eval(name, self.scope, CLOSURE_VARS) |
| |
| def arg_ref(self, guard: Guard): |
| 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\((.+)\)$", guard.name) |
| if m: |
| # optional optimization to produce cleaner/faster guard code |
| return self.TYPE_MATCH(Guard(m.group(1), guard.source, None)) |
| |
| code = f"___check_obj_id({self.arg_ref(guard)}, {self.id_ref(self.get(guard.name))})" |
| self._produce_guard_code(guard, [code]) |
| |
| def NAME_MATCH(self, guard: Guard): |
| obj = self.get(guard.name) |
| code = f"{self.arg_ref(guard)}.__name__ == {obj.__name__}" |
| self._produce_guard_code(guard, [code]) |
| |
| def HASATTR(self, guard: Guard): |
| m = re.match(r"^(.*)[.]([a-zA-Z0-9_]+)$", 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]) |
| |
| def TENSOR_MATCH(self, guard: Guard): |
| if guard.is_nn_module(): |
| self.ID_MATCH(guard) |
| else: |
| value = self.get(guard.name) |
| 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): |
| caller = currentframe().f_back |
| func_name = getframeinfo(caller)[2] |
| # 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, |
| ) |
| |
| |
| @dataclasses.dataclass |
| class GuardedCode: |
| code: types.CodeType |
| check_fn: Callable |
| |
| |
| from sympy.printing.str import StrPrinter |
| |
| |
| @dataclasses.dataclass |
| class TensorReference(object): |
| """ |
| TensorReference objects are entirely optional. They are created to give us hints |
| into where the symbolic shape came from. |
| |
| ref_id: The id of the tensor |
| kind: A string tracking where in the tensor this value came from ("size","stride", etc) |
| idx: An index in the structure |
| |
| NOTE - A symbolic shape coming from tensor at id 12345's shape dim 2, would be |
| TensorReference(ref_id=12345, kind="size", idx=2) |
| """ |
| |
| ref_id: Optional[int] = None |
| kind: Optional[str] = None |
| idx: Optional[int] = None |
| # Note - this is untyped because of TypeError: '_SpecialForm' object does not support item assignment |
| # But it is a Optional[Union["sympy.Expr", int]] |
| expr: Optional[object] = None # Populated after association |
| |
| def __hash__(self): |
| return hash((self.ref_id, self.kind, self.idx)) |
| |
| |
| class DynamoGuardPrinter(StrPrinter): |
| @staticmethod |
| def tensor_ref_as_str(tensor_ref, id_to_name_map): |
| if tensor_ref.kind in ("size", "stride"): |
| return f"{id_to_name_map[tensor_ref.ref_id]}.{tensor_ref.kind}()[{tensor_ref.idx}]" |
| return f"{id_to_name_map[tensor_ref.ref_id]}.{tensor_ref.kind}()" |
| |
| def __init__(self, expr_to_tensor_ref, id_to_name_map): |
| super().__init__() |
| self.expr_to_tensor_ref = expr_to_tensor_ref |
| self.id_to_name_map = id_to_name_map |
| |
| def _print_Symbol(self, expr) -> str: |
| assert isinstance(expr, sympy.core.symbol.Symbol) |
| if expr == 0: |
| return "0" |
| if expr == 1: |
| return "1" |
| assert expr in self.expr_to_tensor_ref, f"Unknown expression {expr}" |
| refs = self.expr_to_tensor_ref[expr] |
| if len(refs) == 0: |
| return super()._print_Symbol(expr) |
| tensor_ref = next( |
| iter(refs) |
| ) # Any is fine here, because we install equality guards later |
| return DynamoGuardPrinter.tensor_ref_as_str(tensor_ref, self.id_to_name_map) |
| |
| |
| # 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] = None, |
| f_globals: Optional[Dict] = None, |
| ): |
| self.valid = True |
| self._weakrefs = [] |
| self._seen_ids = 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) |
| self._seen_ids.clear() |
| |
| """ |
| This is a complex bit of logic. The outline here is brief. For a line by line breakdown, see |
| the code comments below. |
| |
| The role of this function is to take the current state of symbolic shape guards, tensor ids in the |
| CURRENT dynamo frame, and tensor names (dynamo's frame agnostic tensor reference mechanism, see TensorCheck and |
| guards.cpp for more info) - and produce executable python expressions for addition to our guarded code components |
| that make their way into check_fn. |
| |
| We DO NOT create guards based on ids. The IDs act as a lookup for the following mapping: |
| |
| dynamo: tensor_name <> tensor_id |
| shape_env: tensor_id <> shape_expr |
| |
| This allows us to then create a tensor_name <> shape_expr association for the current frames guards. |
| """ |
| |
| def _parse_symbolic_shape_expressions(self, tensor_check_names, tensor_check_ids): |
| # Pre join output |
| finished_expressions = [] |
| |
| # A mapping of tensor_ids to tensor names |
| id_to_name_map = {} |
| |
| # We should not have a shape env, or guards if we are not in config.dynamic shapes |
| # But check it anyway. |
| if not config.dynamic_shapes: |
| return None |
| |
| expr_to_tensor_ref = {} |
| guard_printer = DynamoGuardPrinter(expr_to_tensor_ref, id_to_name_map) |
| |
| # tensor_check_names is the primary tensor association mechanism in dynamo. |
| # All other guards installations are driven off of it, so these ones will too. |
| for name in tensor_check_names: |
| tensor_id = tensor_check_ids[name] |
| id_to_name_map[tensor_id] = name |
| |
| if tensor_id in self.output_graph.tensor_id_to_sym_shape_ref: |
| # If we made it here, this tensor_id is relevant to dynamo guard installation |
| # AND was found in the shape_env |
| tensor_ref_set = self.output_graph.tensor_id_to_sym_shape_ref[tensor_id] |
| for tensor_ref in tensor_ref_set: |
| obj_expr = tensor_ref.expr |
| if obj_expr not in expr_to_tensor_ref: |
| expr_to_tensor_ref[obj_expr] = {} |
| expr_to_tensor_ref[obj_expr][tensor_ref] = "" |
| finished_expressions.append(f"isinstance({name}, torch.Tensor)") |
| |
| guard_expression = self.output_graph.shape_env.get_guard_expr() |
| expr_as_str = guard_printer.doprint(guard_expression) |
| # We may get into a state where symbolic shape keys (all should be found in replacements) |
| # Have not been removed from the expression. This is a serious enough error state that we need to assert. |
| for key in self.output_graph.shape_env.var_to_val.keys(): |
| assert str(key) not in expr_as_str, f"Unknown shape symbol {key}. " |
| finished_expressions.append(expr_as_str) |
| |
| for expr in expr_to_tensor_ref.keys(): |
| tensor_refs = expr_to_tensor_ref[expr].keys() |
| equality_candidates = [ |
| DynamoGuardPrinter.tensor_ref_as_str(x, id_to_name_map) |
| for x in tensor_refs |
| ] |
| |
| if len(equality_candidates) > 1: |
| equality_expr = " == ".join(equality_candidates) |
| # breakpoint() |
| finished_expressions.append(equality_expr) |
| |
| # Redundant with code_parts, but allows us to wrap it with parens nicely. |
| if len(finished_expressions) == 0: |
| return None |
| |
| expression = " and ".join(finished_expressions) |
| return f"({expression})" |
| |
| def compile_check_fn(self, local_builder, global_builder): |
| assert not (set(local_builder.argnames) & set(global_builder.argnames)) |
| # see parallel handling of ".0" / "___implicit0" in _eval_frame.c |
| args = [a for a in local_builder.scope.keys() if a == "___implicit0"] |
| args += [a for a in local_builder.argnames if a != "___implicit0"] |
| args += ["**___kwargs_ignored"] |
| args = ",".join(args) |
| |
| 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: |
| symbolic_shape_expression = self._parse_symbolic_shape_expressions( |
| tensor_check_names, tensor_check_ids |
| ) |
| if symbolic_shape_expression: |
| code_parts.append(symbolic_shape_expression) |
| verbose_code_parts.append(symbolic_shape_expression) |
| |
| 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})") |
| |
| 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), |
| ("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() |
| # 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 |
| 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: Callable, code: types.CodeType, f_locals: Dict[str, Any], last: bool |
| ): |
| """ |
| called whenever a guard fails. |
| """ |
| if not last: |
| return |
| scope = {rename_implicit(k): v for k, v in f_locals.items()} |
| scope.update(guard_fn.closure_vars) |
| reasons = [] |
| 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): |
| reasons.append(fail_reason) |
| break |
| elif isinstance(fail_reason, bool) and not fail_reason: |
| reasons.append(part) |
| break |
| guard_failures[orig_code_map[code]].append(reasons) |
| |
| |
| def guard_error_hook( |
| guard_fn: Callable, code: types.CodeType, f_locals: Dict[str, Any], 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) |
