# mypy: ignore-errors import contextlib import functools import logging import os import traceback import weakref from collections import defaultdict from dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Type, TYPE_CHECKING, TypeVar from weakref import ReferenceType import torch import torch._custom_op import torch._logging from torch._C._functorch import is_functorch_wrapped_tensor from torch._guards import Source from torch._ops import OpOverload from torch._prims_common import suggest_memory_format from torch._subclasses.meta_utils import ( assert_eq, assert_metadata_eq, is_sparse_any, is_sparse_compressed, MetaConverter, ) from torch._utils import render_call from torch.fx.operator_schemas import normalize_function from torch.multiprocessing.reductions import StorageWeakRef from torch.overrides import TorchFunctionMode from torch.utils._mode_utils import no_dispatch from torch.utils._python_dispatch import ( is_traceable_wrapper_subclass, TorchDispatchMode, ) from torch.utils._pytree import PyTree, tree_map from torch.utils._stats import count from torch.utils.weak import WeakIdRef if TYPE_CHECKING: from torch.fx.experimental.symbolic_shapes import ShapeEnv DimList = List log = logging.getLogger(__name__) # TODO: Hack to unblock https://github.com/pytorch/pytorch/pull/108186 # Proper fix tracked by https://github.com/pytorch/pytorch/issues/120105 try: not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented") except ValueError as e: if "'not_implemented' not registered" in str(e): import logging as not_implemented_log else: raise e pytree = torch.utils._pytree T = TypeVar("T") TensorWeakRef = Any aten = torch._ops.ops.aten CONSTANT_NUMEL_LIMIT = 1 RECURSION_COUNT = 0 # Small helper that increments recursion count, and # resets it when the object goes out of scope. Useful # if you don't want to increase indentation which is # what a context manager would do. class IncrementRecursionCount: def __init__(self): global RECURSION_COUNT RECURSION_COUNT += 1 def __del__(self): global RECURSION_COUNT RECURSION_COUNT -= 1 @dataclass class UnsupportedFakeTensorException(RuntimeError): reason: str @dataclass class DynamicOutputShapeException(RuntimeError): func: OpOverload @dataclass class DataDependentOutputException(RuntimeError): func: OpOverload @dataclass class UnsupportedOperatorException(RuntimeError): func: OpOverload def ordered_set(*items): return dict.fromkeys(items, True) @contextlib.contextmanager def unset_fake_temporarily(): old = torch._C._unset_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) try: yield old finally: if old is not None: torch._C._set_dispatch_mode(old) def is_fake(x): if isinstance(x, FakeTensor): return True if is_traceable_wrapper_subclass(x): attrs, _ = type(x).__tensor_flatten__(x) flattened_tensors = [getattr(x, attr) for attr in attrs] # need to recurse because we could have nested subclasses all_fake = all(is_fake(x) for x in flattened_tensors) any_fake = any(is_fake(x) for x in flattened_tensors) assert all_fake == any_fake, "got mixed fake and real tensors!" return all_fake elif isinstance(x, torch.Tensor) and torch._is_functional_tensor(x): reapply_views = torch._C._functionalization_reapply_views_tls() unwrapped = torch._C._functorch._unwrap_functional_tensor(x, reapply_views) return is_fake(unwrapped) elif isinstance(x, torch.Tensor) and is_functorch_wrapped_tensor(x): unwrapped = torch._C._functorch.get_unwrapped(x) return is_fake(unwrapped) return False def maybe_get_fake_mode(t): if isinstance(t, FakeTensor): return t.fake_mode if is_traceable_wrapper_subclass(t): inner_tensor_names, _ = t.__tensor_flatten__() modes = [ maybe_get_fake_mode(getattr(t, t_name)) for t_name in inner_tensor_names ] m = modes[0] assert all(m is x for x in modes) return m elif isinstance(t, torch.Tensor) and torch._is_functional_tensor(t): reapply_views = torch._C._functionalization_reapply_views_tls() unwrapped = torch._C._functorch._unwrap_functional_tensor(t, reapply_views) return maybe_get_fake_mode(unwrapped) elif isinstance(t, torch.Tensor) and is_functorch_wrapped_tensor(t): unwrapped = torch._C._functorch.get_unwrapped(t) return maybe_get_fake_mode(unwrapped) return None @functools.lru_cache(None) def get_schema_info(func): return torch._C._SchemaInfo(func._schema) # type: ignore[attr-defined] # many of the decompositions registered to torch/_prims do not at the moment model # aliasing or strides, so as an incremental step, just enable the decompositions in # torch/_decomp/decompositions.py. # decomps are used for aot autograd tracing so we would like to unify on their # implementation and add additional testing to them @functools.lru_cache(None) def torch_decomp_decompositions(func): from torch._decomp import decomposition_table decompositions = torch._decomp.decompositions # Note that the function in the decomposition table might be # different from the one in the module because of the difference # in out handling in aten API and torch public API return decomposition_table[func].__module__.startswith( "torch._decomp" ) and decomposition_table[func].__name__ in dir(decompositions) def tree_flatten_only(ty: Type[T], tree: PyTree): flat_vals = pytree.tree_leaves(tree) return [elem for elem in flat_vals if isinstance(elem, ty)] # Similar to `MetaConverter`, this is a class for converting # multiple tensors into fake tensors which share the same view/storage # structure. Like `MetaConverter`, it uses `WeakIdRef` to # hold a weak reference for all memoized tensors. class FakeTensorConverter: @property def tensor_memo(self): return self.meta_converter.tensor_memo meta_converter: MetaConverter constant_storage_mapping: Dict[StorageWeakRef, List[ReferenceType]] def __init__(self): self.meta_converter = MetaConverter() # map from to storage to corresponding constant tensors self.constant_storage_mapping = {} def add_constant_storage_mapping(self, fake_tensor): # when you have a constant, aliased tensor: # const_tensor.add_(torch.rand([1])) # all aliases of it must become no longer const assert isinstance(fake_tensor, FakeTensor) and fake_tensor.constant is not None weak_st = StorageWeakRef(fake_tensor.constant._typed_storage()) # we need a map from a weak storage to all of its corresponding # constant tensors. python doesn't have the weak value equivalent # of defaultdict(list), so we are using a WeakValueDictionary as one if weak_st not in self.constant_storage_mapping: self.constant_storage_mapping[weak_st] = [] self.constant_storage_mapping[weak_st].append(weakref.ref(fake_tensor)) def invalidate_constant_aliases(self, tensor): assert not isinstance(tensor, FakeTensor) weak_st = StorageWeakRef(tensor._typed_storage()) if weak_st not in self.constant_storage_mapping: return for weak_tensor_ref in self.constant_storage_mapping[weak_st]: ten = weak_tensor_ref() if ten is not None: ten._fix_weakref() ten.constant = None del self.constant_storage_mapping[weak_st] def _get_memo(self, t): if WeakIdRef(t) in self.tensor_memo: out = self.tensor_memo[WeakIdRef(t)] out._fix_weakref() return out return None def set_tensor_memo(self, t, v): th = WeakIdRef(t) # hold a weak ref to self, otherwise it will be kept alive # by the del_ten closure self_weak_ref = weakref.ref(self) def del_ten(): self_ref = self_weak_ref() if self_ref is None: return # on shutdown, th may not be in memo self_ref.tensor_memo.pop(th, None) weakref.finalize(t, del_ten) self.tensor_memo[th] = v def from_real_tensor( self, fake_mode, t, make_constant=False, shape_env=None, *, source=None, symbolic_context=None, memoized_only=False, ): # see note [Tensor Fakification and Symbol Caching] if not symbolic_context and not source and shape_env: if tracing_context := torch._guards.TracingContext.try_get(): if t in tracing_context.tensor_to_context: symbolic_context = tracing_context.tensor_to_context[t] source = symbolic_context.tensor_source maybe_memo = self._get_memo(t) if maybe_memo is not None: return maybe_memo if memoized_only: return None existing_device = t.device # not yet supported in metatensors if t.is_quantized: raise UnsupportedFakeTensorException("quantized nyi in meta tensors") if type(t) is torch.nn.Parameter: assert not make_constant def mk_fake_tensor(make_meta_t): # NB: don't use in_kernel_invocation_manager. to # ensure FakeTensor can internally do constant computation # as necessary. Invocation manager is "more correct" as # it works for more operators in make_meta_t, but # invariant is that make_meta_t only calls factories # for which it is not strictly necessary to use the # invocation manager (I think!) with no_dispatch(): return FakeTensor( fake_mode, make_meta_t(), existing_device, constant=t if make_constant else None, ) out = self.meta_converter( t, shape_env=shape_env, callback=mk_fake_tensor, source=source, symbolic_context=symbolic_context, ) if out is NotImplemented: raise UnsupportedFakeTensorException("meta converter nyi") if make_constant: self.add_constant_storage_mapping(out) # NB: meta_converter set the memo return out # If you specify the device, it MUST be a meta tensor. def from_meta_and_device(self, fake_mode, t, device): assert ( t.device.type == "meta" ), f"tensor's device must be `meta`, got {t.device.type} instead" maybe_memo = self._get_memo(t) if maybe_memo is not None: return maybe_memo out = FakeTensor(fake_mode, t, device) self.set_tensor_memo(t, out) return out # You can have a real tensor that you need to convert into a fake tensor. # If you have a meta tensor already, call from_meta_and_device. # # You're allowed to pass a meta tensor to be turned into a fake # tensor; although an odd thing to do, this can occur if you're doing # cross ref testing and the inner test is already operating on meta tensors. def __call__( self, fake_mode, t, *, make_constant=False, shape_env=None, source=None, symbolic_context=None, memoized_only=False, ): return self.from_real_tensor( fake_mode, t, make_constant, shape_env=shape_env, source=source, symbolic_context=symbolic_context, memoized_only=memoized_only, ) @functools.lru_cache(None) def init_cuda_context(): # Backward will error with cuda Fake Tensors if no cuda tensors have been initialized first if torch.cuda.is_available(): torch.empty(1, device="cuda") if torch.version.hip is None else torch.zeros( 1, device="cuda" ) @contextlib.contextmanager def in_kernel_invocation_manager(fake_mode): # See: note [Fake Tensor Dispatch Keys] prev_in_kernel = fake_mode.in_kernel_invocation meta_in_tls = torch._C._meta_in_tls_dispatch_include() assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" guard = torch._C._DisableTorchDispatch() # type: ignore[attr-defined] fake_mode.in_kernel_invocation = True torch._C._set_meta_in_tls_dispatch_include(True) try: yield finally: fake_mode.in_kernel_invocation = prev_in_kernel torch._C._set_meta_in_tls_dispatch_include(prev_in_kernel) del guard # Return if the function allows Python numbers to bind to Tensors def should_allow_numbers_as_tensors(func: OpOverload): return torch._C._should_allow_numbers_as_tensors( func.name().split("::")[-1].split(".")[0] ) class FakeTensorConfig: debug = os.environ.get("TORCH_FAKE_TENSOR_DEBUG", "0") == "1" class FakeTensor(torch.Tensor): """ Meta tensors give you the ability to run PyTorch code without having to actually do computation through tensors allocated on a `meta` device. Because the device is `meta`, meta tensors do not model device propagation. FakeTensor extends MetaTensors to also carry an additional `fake_device` which tracks devices that would have been used. """ fake_device: torch.device fake_mode: "FakeTensorMode" constant: Optional[torch.Tensor] # This memorizes the unbacked SymInt representing the number of nonzero # elements in this tensor. This is helpful if you do something like # x[mask] and y[mask]; mask.nonzero() gets repeatedly called and should # give a consistent unbacked SymInt. It needs to be invalidated in the # same way constant is. # TODO: Generalize this as needed, e.g., into a trie of memos _nonzero_memo: Optional[torch.SymInt] _nonzero_memo_vc: Optional[int] # Indicates to our torch_dispatch dispatching infra that # this is an "infra" mode with lower dispatching precedence. _mode_key = torch._C._TorchDispatchModeKey.FAKE @property def nonzero_memo(self): if self._nonzero_memo is None: return None # Version counter based tracking isn't 100% sound but it's close # enough if self._nonzero_memo_vc != self._version: self._nonzero_memo = None return None return self._nonzero_memo @property def device(self): if self.fake_mode.in_kernel_invocation: return torch.device("meta") else: return self.fake_device # Note: [Fake Tensor Dispatch Keys] # In order to model the behavior of device-specific autocast # and autograd logic, we update the dispatch keys of FakeTensors # to reflect their fake device. This includes the BackendComponent # (DispatchKey::Meta -> DispatchKey::CUDA), and also the BackendComponent # related Autocast and Autograd keys. __torch__dispatch__ sits below # Autocast and Autograd, and is only invoked when we are at the # kernel for the BackendComponent. Then, we add Meta to the # thread-local dispatch include set to hit the meta kernel # instead of the kernel of the BackendComponent for the fake device. # The `device_for_backend_keys` does that below # NOTE: this probably will not do the right thing for backends # that have dispatch keys which are higher than the "meta" key: # https://github.com/pytorch/pytorch/blob/main/c10/core/DispatchKey.h#L189 # We don't support named tensors; graph break @property def names(self): raise UnsupportedFakeTensorException( "torch.compile doesn't support named tensors" ) @staticmethod def __new__(cls, fake_mode, elem, device, constant=None): self = torch.Tensor._make_subclass( cls, elem, elem.requires_grad, dispatch_device=True, device_for_backend_keys=device, ) assert elem.device.type == "meta", elem.device.type device = device if isinstance(device, torch.device) else torch.device(device) # NB: it is fine, if a little confusing, for device to be meta # (we are faking a meta tensor in that case). However, it often # indicates some sort of confusion (e.g., you accidentally passed # in a meta tensor when you should have passed in the real tensor). # So by default we disallow meta, and if you are working in a situation # where it is helpful (e.g., crossref testing) you can turn it back # on if not fake_mode.allow_meta: assert device.type != "meta" # normalize device. if device.type == "cuda": init_cuda_context() if ( device.type in ["cuda", "hpu", "xpu", torch._C._get_privateuse1_backend_name()] and device.index is None ): device = torch.device( f"{device.type}:{getattr(torch, device.type).current_device()}" ) self.fake_device = device # type: ignore[attr-defined] self.fake_mode = fake_mode # type: ignore[attr-defined] self.constant = constant # type: ignore[attr-defined] self._nonzero_memo = None # type: ignore[attr-defined] self._nonzero_memo_vc = None # type: ignore[attr-defined] if FakeTensorConfig.debug: import traceback self._debug_trace = traceback.extract_stack() # type: ignore[attr-defined] return self # In some circumstances, a conventional torch.Tensor constructor # will get rewritten to call into FakeTensor. We must provide an # __init__ method that can accept the Python interpreters initialization # in such a situation; we must also be able to handle direct fake # tensor construction via FakeTensor(). # # In particular, the __init__ call will look funny in the following case: # # with FakeTensorMode(): # x = torch.Tensor([1, 2, 3]) # # this desugars into: # # with FakeTensorMode(): # x = torch.Tensor.__new__([1, 2, 3]) # # NB: x is a fake tensor, because of the mode! # x.__init__([1, 2, 3]) # not the normal fake tensor args! # def __init__(self, *args, **kwargs): super().__init__() @staticmethod def from_tensor(t, fake_mode): return fake_mode.from_tensor(t) @classmethod @count def __torch_dispatch__(cls, func, types, args=(), kwargs=None): # need to handle here to avoid infinite recursion # see [in_kernel_invocation] if func == torch.ops.prim.device.default: assert len(args) == 1 and isinstance(args[0], FakeTensor) if args[0].fake_mode.in_kernel_invocation: return torch.device("meta") else: return args[0].fake_device # Because fake mode can return NotImplemented (if it sees a subclass # it doesn't know how to deal with), this test here is important # because the next dispatch after a fake mode will attempt to use # subclasses of tensors to dispatch, and any FakeTensor arguments # will be considered eligible. unrecognized_types = [ t for t in types if not issubclass(t, FakeTensor) and t is not torch.Tensor ] if unrecognized_types: not_implemented_log.debug( "FakeTensor unrecognized subclass(es): %s", unrecognized_types ) return NotImplemented fake_mode = None for arg in pytree.arg_tree_leaves(*args, **kwargs): if isinstance(arg, FakeTensor): fake_mode = arg.fake_mode break assert fake_mode is not None # If the fake mode is already active, don't try to reapply it! # NotImplemented is the right thing to return here, because the # typical situation this can occur is if ProxyTensorMode returned a # NotImplemented because of a not implemented subclass; we may have # unluckily attempted to hit FakeTensor's dispatch first, # NotImplemented lets us keep chaining until we find the actual # subclass maybe_cur_fake_mode = torch._C._get_dispatch_mode( torch._C._TorchDispatchModeKey.FAKE ) if maybe_cur_fake_mode: not_implemented_log.debug( "FakeTensor mode already active: %s in %s", fake_mode, maybe_cur_fake_mode, ) return NotImplemented with fake_mode: # type: ignore[attr-defined] return func(*args, **kwargs) @staticmethod def _find_common_device(func, flat_args) -> Tuple[torch.device, bool]: # Returns: (common_device, has_scalar_only_inputs) # cpu - zero-dim tensors can be called in cuda kernels, # so overwrite the common_device if it the only existing # device comes from a cpu zero-dim tensor common_device = None has_scalar_only_inputs = False is_cpu_zero_dim = None def cpu_zero_dim(t): return t.device.type == "cpu" and t.dim() == 0 def merge_devices(t): nonlocal common_device nonlocal is_cpu_zero_dim if not isinstance(t, FakeTensor): return if common_device is None: common_device = t.device is_cpu_zero_dim = cpu_zero_dim(t) return t_is_cpu_zero_dim = cpu_zero_dim(t) if t.device == common_device: if is_cpu_zero_dim: is_cpu_zero_dim = t_is_cpu_zero_dim return # mismatching devices ! # if current tensor is cpu 0 dim, defer to existing device if t_is_cpu_zero_dim: return # current device is from cpu 0 dim tensor, overwrite if is_cpu_zero_dim: common_device = t.device is_cpu_zero_dim = t_is_cpu_zero_dim return # mismatching devices of non-zero dim tensors, throw # This might be valid behavior and need to be explicitly modeled, e.g. reshape_as raise RuntimeError( f"Unhandled FakeTensor Device Propagation for {func}, found two different devices {common_device}, {t.device}" ) for arg in flat_args: merge_devices(arg) # some functions that allow Python numbers to bind to Tensors # if we have failed to find a device, and we're running one of these operators, # we must have scalar only inputs if should_allow_numbers_as_tensors(func) and common_device is None: # ops with scalar only inputs always have result on cpu has_scalar_only_inputs = True common_device = torch.device("cpu") assert common_device is not None, f"Could not find common device for {func}" return common_device, has_scalar_only_inputs # We must handle tolist in a special way for FakeTensors here in the case # where tolist is called from torch dispatch for tensor subclasses. # Ordinarily, if a program calls .tolist compiling still works because there is # special handling in dynamo, but for tensor subclasses if .tolist is called # inside torch dispatch, the .tolist call may be directly on a FakeTensor. # This would result in an error since wrapper subclasses don't have storage. # To avoid this, we handle the FakeTensor case by (1) specializing on the size # of the tensor to create the output Python list, and (2) creating unbacked # symints for each element of the list. def tolist(self): assert self.dim() == 1, "NYI for higher dims" shape_env = self.fake_mode.shape_env out = [] # Specialize on the length of the list for _ in range(self.shape[0]): s = shape_env.create_unbacked_symint() # max value? torch._constrain_as_size(s, min=2) out.append(s) return out @dataclass(frozen=True) class TensorMetadata: """ The Tensor metadata relevant to hashing FakeTensors when caching. """ dtype: torch.dtype shape: torch.Size stride: Tuple[Any, ...] device: torch.device layout: torch.layout memory_format: Optional[torch.memory_format] storage_offset: int requires_grad: bool is_quantized: bool is_conj: bool is_neg: bool is_inference: bool is_sparse: bool # read: is sparse COO is_coalesced: Optional[bool] dense_dim: Optional[int] sparse_dim: Optional[int] def extract_tensor_metadata(t: torch.Tensor) -> "TensorMetadata": """ Extract the TensorMetadata of a tensor. """ memory_format = suggest_memory_format(t) if is_sparse_any(t) or not t.is_contiguous(memory_format=memory_format): memory_format = None return TensorMetadata( dtype=t.dtype, shape=t.shape, stride=t.stride() if t.layout == torch.strided else (), device=t.device, layout=t.layout, memory_format=memory_format, storage_offset=t.storage_offset(), requires_grad=t.requires_grad, is_quantized=t.is_quantized, is_conj=t.is_conj(), is_neg=t.is_neg(), is_inference=t.is_inference(), is_sparse=t.is_sparse, is_coalesced=t.is_coalesced() if t.is_sparse else None, dense_dim=t.dense_dim() if t.is_sparse else None, sparse_dim=t.sparse_dim() if t.is_sparse else None, ) @dataclass(frozen=True) class _ShapeEnvSettings: """ Encapsulates all shape env settings that could potentially affect FakeTensor dispatch. Used when creating dispatch cache keys. """ allow_scalar_outputs: bool allow_dynamic_output_shape_ops: bool assume_static_by_default: bool specialize_zero_one: bool duck_shape: bool def __init__(self, env: "ShapeEnv"): # Initialize this way because the class is frozen (to enable hashing): object.__setattr__(self, "allow_scalar_outputs", env.allow_scalar_outputs) object.__setattr__( self, "allow_dynamic_output_shape_ops", env.allow_dynamic_output_shape_ops ) object.__setattr__( self, "assume_static_by_default", env.assume_static_by_default ) object.__setattr__(self, "specialize_zero_one", env.specialize_zero_one) object.__setattr__(self, "duck_shape", env.duck_shape) class _DispatchCacheKey(list): """ Key for the FakeTensor dispatch cache. Inspired by (copied from) _HashedSeq from the functools.lru_cache implementation. """ __slots__ = "hashvalue" # noqa: PLC0205 def __init__(self, tup, hash=hash): self[:] = tup self.hashvalue = hash(tup) def __hash__(self): return self.hashvalue @dataclass(frozen=True) class _DispatchCacheEntry: """ Entry type for the FakeTensor dispatch cache. Accounts for two possibilities: 1) The op is inplace, and a hit means we need to alias the argument at a given index. 2) We need to synthesize a new FakeTensor given tensor metadata. For view ops, we further capture the index of the arg to alias. """ inplace_idx: Optional[int] = None metadata: Optional[TensorMetadata] = None view_idx: Optional[int] = None @dataclass(frozen=True) class _BypassDispatchCache(Exception): """ Signals cases that should skip FakeTensor caching. """ reason: str @dataclass(frozen=True) class DispatchCacheInfo: """ Information about the state of the FakeTensor dispatch cache. """ hits: int misses: int bypasses: Dict[str, int] size: int # We keep one instantiation of `fake_tensor_converter` active # for the duration of `with FakeTensorMode()`. # This allows accurate storage aliasing across invocation of # different operators. While this will keep all freshly allocated # tensors alive during `FakeTensorMode`, there will no be no # new allocations of Tensors which have non-meta storage so # memory should not significantly increase. class FakeTensorMode(TorchDispatchMode): cache: Dict[_DispatchCacheKey, _DispatchCacheEntry] = {} cache_hits: int = 0 cache_misses: int = 0 cache_bypasses = defaultdict(int) def __init__( self, *, allow_fallback_kernels=True, allow_non_fake_inputs=False, shape_env=None, static_shapes=None, ): log.debug("create_mode 0x%x", id(self)) self.allow_fallback_kernels = allow_fallback_kernels self.fake_tensor_converter = FakeTensorConverter() if static_shapes is not None: self.static_shapes = static_shapes else: self.static_shapes = shape_env is None import torch._dynamo.config import torch._functorch.config self.allow_meta = torch._functorch.config.fake_tensor_allow_meta self.cache_enabled = torch._dynamo.config.fake_tensor_cache_enabled self.cache_crosscheck_enabled = ( torch._dynamo.config.fake_tensor_cache_crosscheck_enabled ) # A flag that controls, whether we want to invoke ops on mix of # real weights/global variables and fake inputs self.allow_non_fake_inputs = allow_non_fake_inputs # [in_kernel_invocation] # when FakeTensor is invoked in user code, .device should return # the fake_device of the tensor so that code such as as `if x.is_cuda` # or torch.zeros([10, 10], device=x.device) continues to execute as if # the FakeTensor were real. However, within kernel execution, we return # the `Meta` device because all computation within the kernels should # behave as if the Tensors are on meta devices. Kernels should allocate # new tensors on meta devices, and checks like `is_meta` should return true. # within python refs, we always return the real device by defining # the device property self.in_kernel_invocation = False # True if we enter'ed and actually enabled fake tensor mode, # false if it was a no-op. Not thread safe but neither is # in_kernel_invocation # If another fake mode was already active when we enter, we also stash it here. # That way when we exit, we know to re-enable the previous fake mode. self.enter_stack: List[Tuple[bool, Optional[FakeTensorMode]]] = [] self.shape_env = shape_env self.stack = "".join(traceback.format_stack()) # Indicates to our torch_dispatch dispatching infra that # this is an "infra" mode with lower dispatching precedence. self._mode_key = torch._C._TorchDispatchModeKey.FAKE # Typically, there is only one fake tensor mode and you test for it by # doing an isinstance test. However, in some situations, there might be # TWO fake tensor modes. The canonical example of this is exporting # a fake model: there is an outer fake mode created by the user, and # an inner fake mode created by Dynamo. The two phase process is required # because the outer fake mode typically won't have a ShapeEnv, even if # the user is interested in exporting with dynamic shapes (so the inner # fake mode will actually have a ShapeEnv and swap in symbolic sizes.) # # In this case, it's insufficient to test only one FakeTensor: you need # to distinguish between our fake tensor and other fake tensors. That's # what this function does. def is_our_fake(self, t): return isinstance(t, FakeTensor) and t.fake_mode is self @count def __torch_dispatch__(self, func, types, args=(), kwargs=None): # FakeTensorMode should not be set when we're inside of it. assert ( torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) is None ), func try: return self.dispatch(func, types, args, kwargs) except TypeError: log.exception("fake tensor raised TypeError") raise # No-op if FakeTensorMode is already in use def __enter__(self): maybe_prev_fake_mode = torch._C._unset_dispatch_mode(self._mode_key) if self is not maybe_prev_fake_mode: self.enter_stack.append((True, maybe_prev_fake_mode)) return super().__enter__() else: # no-op (still need to re-set the fake mode though since we unset it) torch._C._set_dispatch_mode(self) self.enter_stack.append((False, None)) return self def __exit__(self, a, b, c): live, maybe_prev_fake_mode = self.enter_stack.pop() if live: out = super().__exit__(a, b, c) # Re-enable the previous fake mode, if there was one. if maybe_prev_fake_mode is not None: torch._C._set_dispatch_mode(maybe_prev_fake_mode) @classmethod def cache_info(cls) -> DispatchCacheInfo: """ Query the state of the dispatch cache. """ return DispatchCacheInfo( FakeTensorMode.cache_hits, FakeTensorMode.cache_misses, dict(FakeTensorMode.cache_bypasses), len(FakeTensorMode.cache), ) @classmethod def cache_clear(cls): """ Clear the dispatch cache. """ cls.cache_hits = 0 cls.cache_misses = 0 cls.cache_bypasses.clear() cls.cache.clear() def _cached_dispatch_impl( self, func: OpOverload, types: Tuple[Any, ...], args: Tuple[Any, ...], kwargs: Dict[str, Any], ): """ Lookup a cache entry for the given arguments. If none exists, dispatch and cache the result (if the result is eligible for caching). """ output = unassigned = object() try: key = self._cache_key(func, args, kwargs) entry = FakeTensorMode.cache.get(key, None) if entry is not None: output = self._output_from_cache_entry(entry, func, args) FakeTensorMode.cache_hits += 1 if self.cache_crosscheck_enabled: # For debugging / testing: Validate that the output synthesized # from the cache matches the output created by normal dispatch. self._crosscheck_cache_output(output, func, types, args, kwargs) else: output = self._dispatch_impl(func, types, args, kwargs) entry = self._make_cache_entry(key, func, args, kwargs, output) FakeTensorMode.cache[key] = entry FakeTensorMode.cache_misses += 1 except _BypassDispatchCache as e: FakeTensorMode.cache_bypasses[e.reason] += 1 if output is unassigned: output = self._dispatch_impl(func, types, args, kwargs) return output def _cache_key( self, func: OpOverload, args: Tuple[Any, ...], kwargs: Dict[str, Any], ) -> _DispatchCacheKey: """ Create a cache key given the dispatch args. Raises _BypassDispatchCache for any situation that precludes caching. """ # Avoid caching for any ops that would require a more sophisticated # caching implementation, e.g., data dependent ops or ops that modify # the inputs. if torch.Tag.data_dependent_output in func.tags: raise _BypassDispatchCache("data dependent output") if torch.Tag.dynamic_output_shape in func.tags: raise _BypassDispatchCache("dynamic output shape") if torch.Tag.inplace_view in func.tags: raise _BypassDispatchCache("inplace view") if func == aten._unsafe_view.default: raise _BypassDispatchCache("unsafe view") if func in self.lift_fns: raise _BypassDispatchCache("lift") if not torch._library.utils.is_builtin(func): raise _BypassDispatchCache("non-builtin") # In order to handle storage aliasing, we need to establish the alias # for any view op on a cache hit. But CompositeImplicitAutograd ops may # or may not alias the input, so just punt on caching these. if func.is_view and torch._C._dispatch_has_kernel_for_dispatch_key( func.name(), torch._C.DispatchKey.CompositeImplicitAutograd ): raise _BypassDispatchCache("CompositeImplicitAutograd") key_values = ( func, # Translate any FakeTensor args to metadata. self._prep_args_for_hash(args) if args else (), self._prep_args_for_hash(kwargs) if kwargs else (), # Capture the default_dtype mode since that can affect the output tensor, # e.g., when operating on constant float values. torch.get_default_dtype(), # Capture the current device to support, e.g., cache tensor creation, # where there isn't necessarily a tensor to take the device from. torch._C._get_default_device(), # We want to create tensors from cached metadata only when the inference # mode is the same. torch.is_inference_mode_enabled(), # Shape env settings could affect behavior. One example seen in the wild: # Disasllowing dynamic shapes can introduce a DynamicOutputShapeException # where it wasn't seen on a previous instance of the same op. _ShapeEnvSettings(self.shape_env) if self.shape_env else None, ) return _DispatchCacheKey(key_values) def _prep_args_for_hash(self, args: Any) -> Any: """ Translate the provided args into a form suitable for caching at FakeTensor dispatch, i.e., convert unhashable types like lists & dicts into tuples and convert FakeTensors into metadata. Raises _BypassDispatchCache to signal unsupported cases that should bypass caching. """ if isinstance(args, dict): args = list(args.keys()) + list(args.values()) result = [] for arg in args: if isinstance(arg, FakeTensor): if not self.is_our_fake(arg): raise _BypassDispatchCache("not our fake") if arg._has_symbolic_sizes_strides: raise _BypassDispatchCache("symbolic shape") if arg.constant is not None: raise _BypassDispatchCache("constant attribute") if arg.is_sparse: raise _BypassDispatchCache("sparse tensor") if is_sparse_compressed(arg): raise _BypassDispatchCache("sparse compressed tensor") result.append(extract_tensor_metadata(arg)) elif isinstance(arg, torch.Tensor): raise _BypassDispatchCache("non-fake tensor") elif isinstance(arg, (torch.SymBool, torch.SymInt, torch.SymFloat)): raise _BypassDispatchCache("symbolic shape") elif isinstance(arg, (list, tuple, dict)): result.extend(self._prep_args_for_hash(arg)) else: # It's important to capture the type of the arg since, e.g., 1 and 1.0 # hash to the same value, but can produce different dtypes for the # output tensor. result.append((type(arg), arg)) return tuple(result) def _make_cache_entry( self, key: _DispatchCacheKey, func: OpOverload, args: Tuple[Any, ...], kwargs: Dict[str, Any], output: FakeTensor, ) -> _DispatchCacheEntry: """ Make a cache entry object for the given 'output' Tensor. Raises _BypassDispatchCache if the output tensor has characteristics that prevent caching it. """ # Some ops return tuples of Tensors, but it's rare, so avoid # the complexity of caching other types. if not isinstance(output, FakeTensor): raise _BypassDispatchCache("non-FakeTensor output") # Avoid caching FakeTensors with constants attached since those # can be invalidated. if output.constant is not None: raise _BypassDispatchCache("constant attribute") # TODO: support caching sparse outputs? if output.is_sparse: raise _BypassDispatchCache("sparse output") if is_sparse_compressed(output): raise _BypassDispatchCache("sparse compressed output") # Can an in-place op really reference a kwarg? If so, then we need # to extend the implementation to handle it. for kval in kwargs.values(): if id(kval) == id(output): raise _BypassDispatchCache("kwarg aliases output") # If this is an in-place op, the entry records which input arg is aliased. for idx in range(len(args)): if id(args[idx]) == id(output): return _DispatchCacheEntry( inplace_idx=idx, metadata=None, view_idx=None ) # Otherwise, create an entry that records the output tensor's metadata. view_idx = None if func.is_view: idxs = [i for i, t in enumerate(args) if isinstance(t, torch.Tensor)] assert len(idxs) == 1 view_idx = idxs[0] metadata = extract_tensor_metadata(output) entry = _DispatchCacheEntry( inplace_idx=None, metadata=metadata, view_idx=view_idx ) # N.B.: Some checks for bypassing the cache would be performed on the # output tensor synthesized from the cached metadata. As an optimization, # we can synthesize a tensor here and do the checks on that instance. # This approach keeps the (more frequent) cache-hit path as lightweight # as possible. synth_output = self._output_from_cache_entry(entry, func, args) # Make sure the dispatch_key_set from the synthesized output tensor will # be the same. synth_key_set = torch._C._dispatch_key_set(synth_output) key_set = torch._C._dispatch_key_set(output) if synth_key_set != key_set: raise _BypassDispatchCache("dispatch_key_set mismatch") return entry def _output_from_cache_entry( self, entry: _DispatchCacheEntry, func: OpOverload, args: Tuple[Any, ...] ) -> FakeTensor: """ Create a new FakeTensor from the cache entry. """ if entry.inplace_idx is not None: # This is an in-place op; return the aliased arg. return args[entry.inplace_idx] # Synthesize a new FakeTensor with the cached metadata. metadata = entry.metadata assert not metadata.is_sparse empty = torch.empty_strided( metadata.shape, metadata.stride, dtype=metadata.dtype, layout=metadata.layout, device="meta", requires_grad=metadata.requires_grad, ) if metadata.is_conj: torch._C._set_conj(empty, True) if metadata.is_neg: torch._C._set_neg(empty, True) if func.is_view: # For view ops, the storage should be the same as the tensor input. storage = args[entry.view_idx].untyped_storage() with in_kernel_invocation_manager(self): empty.set_( storage, metadata.storage_offset, metadata.shape, metadata.stride ) elif metadata.storage_offset != 0: storage = empty.untyped_storage() with in_kernel_invocation_manager(self): empty.set_( storage, metadata.storage_offset, metadata.shape, metadata.stride ) return FakeTensor(self, empty, metadata.device) def _crosscheck_cache_output( self, output: FakeTensor, func: OpOverload, types: Tuple[Any, ...], args: Tuple[Any, ...], kwargs: Dict[str, Any], ): """ Helper to validate that the output synthesized from the cache matches the output created by normal dispatch. """ try: true_output = self._dispatch_impl(func, types, args, kwargs) except Exception as e: raise RuntimeError( f"FakeTensor cache crosscheck failure: func={func}, " f"args={args}, kwargs={kwargs}: Dispatch raised={e}" ) from e try: assert_metadata_eq(assert_eq, true_output, output) except Exception as e: raise RuntimeError( f"FakeTensor cache crosscheck failure: func={func}, " f"args={args}, kwargs={kwargs}" ) from e def dispatch(self, func, types, args=(), kwargs=None): kwargs = kwargs or {} with no_dispatch(): log.debug("%s %s %s", func, args, kwargs) if func in _DISPATCH_META_HANDLERS: return _DISPATCH_META_HANDLERS[func](args) if log.getEffectiveLevel() <= logging.DEBUG: log.debug( "%sFakeTensorMode.__torch_dispatch__: %s", " " * RECURSION_COUNT, func ) # NOTE: incr is intentionally unused for a RAII pattern incr = IncrementRecursionCount() # Some attribute queries that can be serviced directly # See Note [is_coalesced is dispatched] if func in _DISPATCH_HANDLE_DIRECTLY: # NB: no_dispatch is ok here too, this func is very simple with in_kernel_invocation_manager(self): return func(*args, **kwargs) if self.cache_enabled: return self._cached_dispatch_impl(func, types, args, kwargs) else: return self._dispatch_impl(func, types, args, kwargs) def _dispatch_impl(self, func, types, args, kwargs): flat_args, args_spec = pytree.tree_flatten((args, kwargs)) flat_arg_fake_tensors = [ t for t in flat_args if isinstance(t, FakeTensor) and self.is_our_fake(t) ] has_symbolic_sizes = any( i._has_symbolic_sizes_strides for i in flat_arg_fake_tensors ) or any(isinstance(a, torch.SymInt) for a in flat_args) converter = self.fake_tensor_converter def maybe_to_constant(t): if isinstance(t, FakeTensor) and self.is_our_fake(t): return t.constant else: return t # To constant propagate through these functions: # 1, If this is a lift due to a torch.tensor call, # the input tensor is guaranteed to be a # constant, so we keep a copy of the original argument along so # we can query it if we're asked to item() it at some later point. # (Note that you can always call a lift fn manually, so we do # have to check if there are any fake tensors!) # 2, Some functions that allow Python numbers to bind to Tensors, e.g, torch.div if (func in self.lift_fns and not flat_arg_fake_tensors) or ( should_allow_numbers_as_tensors(func) and not has_symbolic_sizes and not flat_arg_fake_tensors ): assert all( t.constant is not None for t in flat_arg_fake_tensors ), f"{func} should not have fake inputs without constants" const_flat_args = [maybe_to_constant(a) for a in flat_args] const_args, const_kwargs = pytree.tree_unflatten(const_flat_args, args_spec) out = func(*const_args, **const_kwargs) if type(out) is torch.Tensor and self.may_turn_const(out): # NB: not in_kernel_invocation_manager because we're doing real # compute here # NB: no_dispatch() here is VERY DANGEROUS (like, segfault # dangerous) if this is actually a wrapper subclass tensor, # therefore the exact type test above with no_dispatch(): out = out.clone() return converter(self, out, make_constant=True) # See [subclass inputs] below # NB: If you're seeing a mysterious infinite loop involving fake # tensor, it might be related to this line. Though I'm not sure # how you'll know to read this comment, as this line won't show up # in the stack trace. unrecognized_types = self.check_for_subclass(flat_args) if unrecognized_types: not_implemented_log.debug( "FakeTensorMode unrecognized subclass(es): %s", unrecognized_types ) return NotImplemented # if we are in the dispatch mode, we will enter this function even if the inputs # are not FakeTensors. For now, throw if any non-Fake Tensor inputs # and just support constructors. # this is generated from torch.tensor(), which does not use the # dispatcher, to allow wrapper subclasses to wrap the new tensor if func in self.lift_fns: assert len(kwargs) == 0 and len(args) == 1, f"{args} {kwargs}" if type(args[0]) is torch.Tensor: return converter(self, args[0]) # Recompute flat_arg_fake_tensors here again in case some of the inputs # were real tensors and fakified in validate_and_convert_non_fake_tensors (flat_args, flat_arg_fake_tensors) = self.validate_and_convert_non_fake_tensors( func, converter, flat_args, args_spec ) del args, kwargs # Invalidated # The current constant handling only support tracing systems # (aot autograd, torchdynamo) where each operation is run consecutively. # Because each operation is run in order, we can trace out and support # sequences like: x = torch.tensor(0.); y = x.add_(1) # Whenver a constant is written to but with inputs that cannot be evaluated # statically, such as random_(), we invalidate all constants that alias the input # We will rely on functionalization for use of fake tensors constants as persistent # objects on an FX Graph. # We dispatch size/stride/numel on the FakeTensor not its constant, so bail on inplace_view all_constant = all(e.constant is not None for e in flat_arg_fake_tensors) if ( torch.Tag.nondeterministic_seeded not in func.tags and torch.Tag.inplace_view not in func.tags and all_constant and len(flat_arg_fake_tensors) != 0 and not has_symbolic_sizes ): const_flat_args = [maybe_to_constant(a) for a in flat_args] const_args, const_kwargs = pytree.tree_unflatten(const_flat_args, args_spec) # NB: not in_kernel_invocation_manager(self) as we want to do REAL # compute with no_dispatch(): out = func(*const_args, **const_kwargs) flat_out = pytree.tree_leaves(out) flat_out_tensors = [t for t in flat_out if isinstance(t, torch.Tensor)] all_constant = all(self.may_turn_const(t) for t in flat_out_tensors) if all_constant: return pytree.tree_map_only( torch.Tensor, lambda t: converter(self, t, make_constant=True), out, ) # we weren't able to turn outputs to constants, # so invalidate all constants that might be aliases of the outputs for ten in flat_out_tensors: converter.invalidate_constant_aliases(ten) # we are falling through to running non constant tensors, any input constant that # is written to must be invalidated args, kwargs = pytree.tree_unflatten(flat_args, args_spec) self.invalidate_written_to_constants(func, flat_arg_fake_tensors, args, kwargs) # Try for fastpath if has_symbolic_sizes: fast_impl = get_fast_op_impls().get(func) if fast_impl is not None: return fast_impl(self, *args, **kwargs) # If there's a Python meta, prefer that over the decomposition from torch._decomp import meta_table as meta_table if func not in meta_table and not self.cpp_meta_supports_symint(func): from torch._decomp import decomposition_table # Prefer Python decompositions over C++ ones if func in decomposition_table and ( has_symbolic_sizes or ( # TODO: Remove these exclusions, so that we can remove # this leg entirely torch_decomp_decompositions(func) and all(not e.is_sparse for e in flat_arg_fake_tensors) ) ): with self: return decomposition_table[func](*args, **kwargs) with self: # Decomposes CompositeImplicitAutograd ops r = func.decompose(*args, **kwargs) if r is not NotImplemented: return r # prims already wrap FakeTensor inputs to FakeTensor outputs # and do device logic, we dont need do anything but run them # and ensure that Meta kernels are dispatched to (see) # Fake Tensor Dispatch Keys # TODO - we should be use the prim aten impl # TODO - fix prims complex ops if ( "prims::" in func._schema.name and hasattr(func, "prim_meta_impl") and not stride_incorrect_op(func) ): with self: return func.prim_meta_impl(*args, **kwargs) # Users can register FakeTensor rules for custom operators # Call them if they exist. maybe_abstract_impl = torch._library.simple_registry.singleton.find( func.name() ).abstract_impl.kernel if maybe_abstract_impl: ctx = torch._library.abstract_impl.AbstractImplCtx(self.shape_env, func) with torch._library.abstract_impl.set_ctx_getter(lambda: ctx), self: result = maybe_abstract_impl(*args, **kwargs) return result # special handling for funcs registered through `register_op_impl`, # e.g., manipulating args on constructor calls to construct meta tensors # and then afterwards wrapping them to a FakeTensor for run_impl_check, op_impl in op_implementations_checks: if run_impl_check(func): op_impl_out = op_impl(self, func, *args, **kwargs) if op_impl_out != NotImplemented: return op_impl_out def maybe_run_unsafe_fallback(error=None): # We infer the meta of a custom ops that return None to just # return None. custom ops are not allowed to mutate metadata # of their inputs, so this is safe. if can_generate_trivial_abstract_impl(func): return None # no meta kernel registered, fallback to kernel for the device if has_symbolic_sizes or not self.can_run_unsafe_fallback(func): raise UnsupportedOperatorException(func) if error is None: error = UnsupportedOperatorException(func) return run_fallback_kernel(self, func, flat_args, args_spec, error) # Optimization: If there is no Meta kernel, it takes a surprisingly long # amount of time to catch the NotImplementedError, so we check it here. if not has_meta(func): return maybe_run_unsafe_fallback() # run kernel registered to meta for func, which include # python meta registrations, prims, decomps, and c++ meta fns (structured kernels) # It's possible that the kernel will return NotImplementedError try: with in_kernel_invocation_manager(self): r = func(*args, **kwargs) except NotImplementedError as not_implemented_error: return maybe_run_unsafe_fallback(not_implemented_error) return self.wrap_meta_outputs_with_default_device_logic( r, func, flat_args, device=kwargs.get("device") ) # WARNING: DO NOT add any additional namespaces/operators here if they refer to operators # outside of the pytorch/pytorch library! Any pre-existing things here # are either in the pytorch/pytorch library or have been grandfathered in. # The fallback does not always work and MAY CRASH and emit unreadable error messages # so it should not be allowed by default. _can_run_unsafe_fallback_allowed_namespaces = ordered_set( "debugprims", "prims", "aten", "xla", "vision", "torchtext", "torchaudio", "quantized", ) def can_run_unsafe_fallback(self, func: OpOverload): if not self.allow_fallback_kernels: return False # It's OK to try the fallback for built-in ops (e.g. aten, prims) # because we control and test these but the fallback leads to unexpected behavior # in user-defined custom ops return ( func.namespace in self._can_run_unsafe_fallback_allowed_namespaces or func.name() == "fbgemm::gmm" ) # [subclass inputs] # Suppose we enable fake tensor mode. This means that fake tensor # mode will run first. But what if we do an operation that # involves a tensor subclass that will desugar into normal tensor # operations? Without returning NotImplemented, fake tensor mode will run first, # decide that a conversion was made (since there was a non fake # tensor argument), and report an error that converting non # fake tensor is not supported. What we actually wanted to happen # was to give the subclass a chance to figure out what it wants to # before erroring out. Returning NotImplemented here allows this. def check_for_subclass(self, flat_args): def check(x): return ( isinstance(x, torch.Tensor) and not isinstance(x, FakeTensor) and type(x) is not torch.Tensor and type(x) is not torch.nn.Parameter ) return [type(x) for x in flat_args if check(x)] def validate_and_convert_non_fake_tensors( self, func, converter, flat_args, args_spec ): """ Checks if the list of tensors are fake tensors. If not, try to convert them to fake tensors. Returns the original args, kwargs, and a flattened list of (args, kwargs) that are fake tensors. """ flat_arg_fake_tensors = [] def validate(x): if not isinstance(x, torch.Tensor): return x nonlocal flat_arg_fake_tensors if not self.is_our_fake(x): if torch.Tag.inplace_view in func.tags: args, kwargs = pytree.tree_unflatten(flat_args, args_spec) raise Exception( f"Can't call metadata mutating ops on non-Fake Tensor inputs. Found in {render_call(func, args, kwargs)}" ) if not self.allow_non_fake_inputs: if isinstance(x, FakeTensor) and x.fake_mode is not self: raise AssertionError("Mixing fake modes NYI") args, kwargs = pytree.tree_unflatten(flat_args, args_spec) raise Exception( f"Please convert all Tensors to FakeTensors first or instantiate FakeTensorMode " f"with 'allow_non_fake_inputs'. Found in {render_call(func, args, kwargs)}" ) x = converter(self, x) flat_arg_fake_tensors.append(x) return x validated_args = [validate(a) for a in flat_args] return validated_args, flat_arg_fake_tensors def wrap_meta_outputs_with_default_device_logic(self, r, func, flat_args, device): converter = self.fake_tensor_converter # Lazily initialized, in case there are no tensor returns common_device = None has_scalar_only_inputs = False def wrap(e): nonlocal common_device nonlocal has_scalar_only_inputs if isinstance(e, torch.Tensor) and common_device is None: ( common_device, has_scalar_only_inputs, ) = FakeTensor._find_common_device(func, flat_args) if self.is_our_fake(e): torch._check( e.device == common_device, lambda: f"FakeTensor is wrapped to wrong device, found {e.device}, expected {common_device}", ) if ( isinstance(e, torch.Tensor) and not self.is_our_fake(e) and converter is not None ): if has_scalar_only_inputs: # Under FakeTensorMode, op accepts scalar only inputs, such as aten.add/sub/mul/div, # returns a real scalar tensor on CPU. See TensorMeta() in _prims/__init__.py for details. # We thus directly convert real tensor to fake tensor. return converter(self, e) else: return converter.from_meta_and_device( self, e, device or common_device ) else: return e return tree_map(wrap, r) _cpp_meta_supports_symint = ordered_set( aten.empty.memory_format, aten.empty_strided.default, aten.as_strided_scatter.default, aten.as_strided.default, aten.as_strided_.default, aten.zeros.default, aten.detach.default, aten.view_as_real.default, aten.view_as_complex.default, aten.set_.source_Storage_storage_offset, aten._sparse_coo_tensor_with_dims_and_tensors.default, ) def cpp_meta_supports_symint(self, func): if torch.Tag.view_copy in func.tags: return True return func in self._cpp_meta_supports_symint lift_fns = ordered_set(aten.lift_fresh.default, aten.lift_fresh_copy.default) def may_turn_const(self, t): return ( t.numel() <= CONSTANT_NUMEL_LIMIT and not t.is_sparse and not self.is_our_fake(t) and not t.device.type == "meta" ) def invalidate_written_to_constants( self, func, flat_arg_fake_tensors, args, kwargs ): any_constant = any(e.constant is not None for e in flat_arg_fake_tensors) schema_info = get_schema_info(func) if any_constant and schema_info.is_mutable(): _, new_kwargs = normalize_function( func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True ) for k, v in new_kwargs.items(): k = k if (k != "input" or schema_info.has_argument(k)) else "self" if ( self.is_our_fake(v) and schema_info.is_mutable(k) and v.constant is not None ): self.fake_tensor_converter.invalidate_constant_aliases(v.constant) def from_tensor( self, tensor, *, static_shapes=None, source: Optional[Source] = None, symbolic_context=None, # Setting this flag will force FakeTensorMode to return `None` if attempting to convert a tensor we have not # seen before. memoized_only=False, ): shape_env = self.shape_env if static_shapes is None: static_shapes = self.static_shapes if static_shapes: assert ( symbolic_context is None ), "cannot set both static_shapes and symbolic_context" shape_env = None # see note [Tensor Fakification and Symbol Caching] if not symbolic_context and not source and not static_shapes: if tracing_context := torch._guards.TracingContext.try_get(): if tensor in tracing_context.tensor_to_context: symbolic_context = tracing_context.tensor_to_context[tensor] source = symbolic_context.tensor_source return self.fake_tensor_converter( self, tensor, shape_env=shape_env, source=source, symbolic_context=symbolic_context, memoized_only=memoized_only, ) # NB: returns fake tensors def run_fallback_kernel( fake_mode, func, flat_args, args_spec, orig_not_implemented_exception ): # these should all be supported, just to be safe # avoid fallback for operators which inplace modify metadata # because the input fake tensors would be umodified if torch.Tag.inplace_view in func.tags: raise orig_not_implemented_exception inp_impls = {} # Don't use in_kernel_invocation_manager(fake_mode) as we want to do # REAL compute (not with meta device) with no_dispatch(): def to_real_tensor(e): if fake_mode.is_our_fake(e): out = torch.zeros_like(e, device=e.fake_device) if e.is_sparse: out._coalesced_(e.is_coalesced()) inp_impls[id(out)] = e return out return e flat_args = [to_real_tensor(a) for a in flat_args] args, kwargs = pytree.tree_unflatten(flat_args, args_spec) r = func(*args, **kwargs) tensor_impls = set() storages = set() for e in flat_args: if isinstance(e, torch.Tensor): if not e.is_sparse: storages.add(e._typed_storage()._cdata) # TODO: also check metadata change on inputs # proper aliasing/metadata relationship between outputs and inputs will # not be set up, bc of conversion to device, unless we can reuse an # input impl def map_out(e): if id(e) not in inp_impls and ( isinstance(e, torch.Tensor) and not e.is_sparse and e._typed_storage()._cdata in storages ): raise orig_not_implemented_exception if isinstance(e, torch.Tensor): if id(e) in inp_impls: return inp_impls[id(e)] else: return fake_mode.fake_tensor_converter(fake_mode, e) else: return e return pytree.tree_map(map_out, r) def can_generate_trivial_abstract_impl(op: torch._ops.OpOverload) -> bool: assert isinstance(op, torch._ops.OpOverload) if torch._library.utils.is_builtin(op): # We control the built-ins. These may (in rare cases) # do input metadata mutation (which we have banned on custom ops) return False schema = op._schema # It's suspicious if the op is not mutable but returns nothing, so we return False out of an abundance of caution if not schema.is_mutable: return False if len(schema.returns) > 0: return False # If the op returns nothing, then it has a trivial abstract impl. return True # Just for use to allow copying a module to fake tensors, # does not apply elsewhere class FakeCopyMode(TorchFunctionMode): def __init__(self, fake_mode): self.fake_mode = fake_mode def __torch_function__(self, func, types, args=(), kwargs=None): kwargs = kwargs if kwargs else {} # clone will get called in Parameter deepcopy if func == torch._C.TensorBase.clone: return func( self.fake_mode.from_tensor(args[0], static_shapes=True), **kwargs ) elif func == torch.Tensor.__deepcopy__: assert len(args) == 2 and len(kwargs) == 0 tensor, memo = args if id(tensor) in memo: return memo[id(tensor)] out = self.fake_mode.from_tensor(tensor, static_shapes=True) memo[id(tensor)] = out return out else: with torch._C.DisableTorchFunctionSubclass(): return func(*args, **kwargs) def _device_handler(args): # NB: Don't use is_our_fake, just serve the fake information # as is. Notice we don't use 'self'; we use args[0].fake_mode # because they may not be the same. It would also be possible # to return NotImplemented here, in which case the FakeTensor # handler on args[0] would handle it, but we're being nice and # short-circuiting quickly. assert len(args) == 1 and isinstance(args[0], FakeTensor) if args[0].fake_mode.in_kernel_invocation: return torch.device("meta") else: return args[0].fake_device _DISPATCH_META_HANDLERS = { torch.ops.prim.device.default: _device_handler, torch.ops.aten.size.default: lambda args: tuple(int(s) for s in args[0].size()), torch.ops.aten.stride.default: lambda args: tuple(int(s) for s in args[0].stride()), torch.ops.aten.storage_offset.default: lambda args: int(args[0].storage_offset()), } _DISPATCH_HANDLE_DIRECTLY = ordered_set( torch.ops.aten.is_coalesced.default, torch.ops.aten.dense_dim.default, torch.ops.aten.sparse_dim.default, ) from torch._subclasses.fake_impls import ( # noqa: F401 _device_not_kwarg_ops, # noqa: F401 _is_tensor_constructor, # noqa: F401 _like_tensor_constructors, # noqa: F401 contains_tensor_types, # noqa: F401 get_fast_op_impls, has_meta, op_implementations_checks, stride_incorrect_op, )