import contextlib import functools import itertools import sys import warnings import weakref from dataclasses import dataclass from functools import partial from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union import torch from torch._ops import OpOverload from torch._subclasses.meta_utils import MetaConverter, WeakTensorRefKey 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 TorchDispatchMode from torch.utils._pytree import PyTree, tree_flatten, tree_map pytree = torch.utils._pytree T = TypeVar("T") TensorWeakRef = Any aten = torch.ops.aten CONSTANT_NUMEL_LIMIT = 1 @dataclass class UnsupportedFakeTensorException(RuntimeError): reason: str @dataclass class DynamicOutputShapeException(RuntimeError): func: OpOverload @dataclass class DataDependentOutputException(RuntimeError): func: OpOverload _device_not_kwarg_ops = ( aten._resize_output_.default, aten._nested_tensor_from_tensor_list.default, aten._nested_tensor_from_tensor_list.out, aten.pin_memory.default, aten.is_pinned.default, aten.to.device, aten.to.prim_Device, aten._pin_memory.default, aten._pin_memory.out, aten._resize_output.default, aten._resize_output.out, ) # this op is never actually used _non_kwarg_device_constructors = (aten._list_to_tensor,) def contains_tensor_types(type): tensor_type = torch._C.TensorType.get() return type.isSubtypeOf(tensor_type) or any( contains_tensor_types(e) for e in type.containedTypes() ) _like_tensor_constructors = ( aten.empty_like.default, aten.empty_like.out, aten.full_like.default, aten.full_like.out, aten.ones_like.default, aten.ones_like.out, aten.rand_like.default, aten.rand_like.out, aten.randn_like.default, aten.randn_like.out, aten.randint_like.default, aten.randint_like.out, aten.randint_like.low_dtype, aten.randint_like.low_dtype_out, aten.zeros_like.default, aten.zeros_like.out, aten.new_empty.default, aten.new_empty.out, aten.new_empty_strided.default, aten.new_empty_strided.out, aten.new_full.default, aten.new_full.out, aten.new_zeros.default, aten.new_zeros.out, aten.new_ones.default, aten.new_ones.out, ) @functools.lru_cache(None) def _is_tensor_constructor(func: OpOverload): assert isinstance(func, OpOverload) schema = func._schema if any(contains_tensor_types(arg.type) for arg in schema.arguments): return False # TODO: no real reason to restrict multiple outputs return ( len(schema.returns) == 1 and schema.returns[0].type is torch._C.TensorType.get() ) @functools.lru_cache(None) def get_schema_info(func): return torch._C._SchemaInfo(func._schema) # type: ignore[attr-defined] def tree_flatten_only(ty: Type[T], pytree: PyTree): flat_vals, _ = tree_flatten(pytree) 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 `WeakTensorRefKey` to # hold a weak reference for all memoized tensors. class FakeTensorConverter(object): tensor_memo: weakref.WeakValueDictionary meta_converter: MetaConverter constant_storage_mapping: Dict[StorageWeakRef, List[TensorWeakRef]] def __init__(self): # FakeTensors store the FakeTensorMode which in turn stores a # FakeTensor, so we need to hold a weak reference to the FakeTensor # otherwise we would induce a circular reference self.tensor_memo = weakref.WeakValueDictionary() 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.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.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 WeakTensorRefKey(t) in self.tensor_memo: out = self.tensor_memo[WeakTensorRefKey(t)] out._fix_weakref() return out return None def set_tensor_memo(self, t, v): th = WeakTensorRefKey(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): maybe_memo = self._get_memo(t) if maybe_memo is not None: return maybe_memo existing_device = t.device # not yet supported in metatensors if t.is_quantized: raise UnsupportedFakeTensorException("quantized nyi in meta tensors") with no_dispatch(): meta_t = self.meta_converter(t, shape_env=shape_env) if meta_t.device.type != "meta": raise UnsupportedFakeTensorException("meta converter nyi") out = FakeTensor( fake_mode, meta_t, existing_device, constant=t if make_constant else None, ) out.requires_grad_(t.requires_grad) if make_constant: self.add_constant_storage_mapping(out) if type(t) is torch.nn.Parameter: assert not make_constant out = torch.nn.Parameter(out, requires_grad=out.requires_grad) # type: ignore[assignment] with warnings.catch_warnings(): warnings.filterwarnings("ignore", "The .grad attribute of a Tensor") grad_not_none = t.grad is not None if grad_not_none: out.grad = self.from_real_tensor(fake_mode, t.grad) self.set_tensor_memo(t, out) return out def from_meta_and_device(self, fake_mode, t, device): 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 # There are two ways to call this. First, you can have manually constructed # a meta tensor and you need to turn it into a fake tensor. In that case, # pass a meta tensor and a device argument. Alternately, you can have a # real tensor that you need to convert into a fake tensor; in that case, # omit the device. # # The disallowed case: if you specify the device, it MUST be a meta tensor. # However, 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, device=None, *, make_constant=False, shape_env=None ): if device is None: return self.from_real_tensor( fake_mode, t, make_constant, shape_env=shape_env ) else: assert make_constant is False assert t.device.type == "meta" return self.from_meta_and_device(fake_mode, t, device) op_implementations = [] def register_op_impl(run_impl_check: Union[Callable[[OpOverload], bool], OpOverload]): def impl_decorator(op_impl): global op_implementations if isinstance(run_impl_check, OpOverload): op_implementations.append((lambda func: func == run_impl_check, op_impl)) else: op_implementations.append((run_impl_check, op_impl)) return op_impl return impl_decorator @register_op_impl( lambda func: (_is_tensor_constructor(func) or func in _like_tensor_constructors) ) def constructors(fake_mode, func, *args, **kwargs): assert func not in _non_kwarg_device_constructors _, new_kwargs = normalize_function( func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True ) if func in _like_tensor_constructors: default_device = new_kwargs["input"].device # TODO: file issue args = (new_kwargs.pop("input"),) else: # cpu is default device if none is specified default_device = torch.device("cpu") args = () out_device = new_kwargs.pop("device", None) out_device = out_device if out_device is not None else default_device new_kwargs["device"] = torch.device("meta") r = func(*args, **new_kwargs) return FakeTensor(fake_mode, r, out_device) @register_op_impl(lambda func: func in (aten.to.prim_Device, aten.to.device)) def non_kwarg_to(fake_mode, func, *args, **kwargs): _, new_kwargs = normalize_function( func, args, kwargs, normalize_to_only_use_kwargs=True ) input_device = new_kwargs["device"] out_device = input_device if input_device else new_kwargs["input"].device new_kwargs["device"] = torch.device("meta") r = func(*args, **new_kwargs) return fake_mode.fake_tensor_converter(fake_mode, r, out_device) # Dont default to default device handling, # since the device of `the_template` is ignored @register_op_impl(aten.resize_as_.default) def resize_as_(fake_mode, func, *args, **kwargs): return func(*args, **kwargs) @register_op_impl(aten._sparse_coo_tensor_with_dims_and_tensors.default) def _sparse_coo_tensor_with_dims_and_tensors(fake_mode, func, *args, **kwargs): # TODO: remove me return constructors(fake_mode, func, *args, **kwargs) # _to_copy fails when run with FakeTensors to cuda device # TODO: debug @register_op_impl(aten._to_copy.default) def to_copy(fake_mode, func, *args, **kwargs): _, new_kwargs = normalize_function( func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True ) input_device = new_kwargs.pop("device", None) out_device = input_device if input_device else new_kwargs["input"].device with no_dispatch(), in_kernel_invocation_manager(fake_mode): input = new_kwargs.pop("input").to("meta") return FakeTensor(fake_mode, aten._to_copy(input, **new_kwargs), out_device) # index.Tensor data-dependent in only some conditions @register_op_impl( lambda func: torch.Tag.dynamic_output_shape in func.tags # type: ignore[attr-defined] and func != aten.index.Tensor ) def dyn_shape(fake_mode, func, *args, **kwargs): raise DynamicOutputShapeException(func) @register_op_impl( lambda func: torch.Tag.data_dependent_output in func.tags # type: ignore[attr-defined] ) def data_dep(fake_mode, func, *args, **kwargs): if fake_mode.throw_on_data_dependent_ops: raise DataDependentOutputException(func) return NotImplemented # Bool Indices get Expanded as Masks # See: IndexingUtils.h:expandTensors def check_no_bool_index_tensors(func, self, indices): for index in indices: if index is not None and index.dtype in (torch.bool, torch.uint8): raise DynamicOutputShapeException(func) def run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs): _, new_kwargs = normalize_function( func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True ) out_device = new_kwargs["input"].device with in_kernel_invocation_manager(fake_mode): out = func(*args, **kwargs) return FakeTensor(fake_mode, out, out_device) # Dont default to default device handling, # Since op can take in non-zero sized cpu # index tensors with cuda self @register_op_impl(aten.index.Tensor) def index_tensor(fake_mode, func, *args, **kwargs): # dynamic shape op if indices are bool/uint8 check_no_bool_index_tensors(func, *args, **kwargs) return run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs) # takes in multiple-devices, dont default to default device handling @register_op_impl(aten.index_put.default) def index_put(fake_mode, func, *args, **kwargs): return run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs) # same with index_put, but return the input @register_op_impl(aten.index_put_.default) def index_put_(fake_mode, func, *args, **kwargs): with in_kernel_invocation_manager(fake_mode): out = func(*args, **kwargs) _, new_kwargs = normalize_function( func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True ) return new_kwargs["input"] @register_op_impl(lambda fn: fn in _device_not_kwarg_ops) def nyi(fake_mode, func, *args, **kwargs): assert func not in _device_not_kwarg_ops, f"NYI: {func}" # 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. @contextlib.contextmanager def in_kernel_invocation_manager(fake_mode): # See: note [Fake Tensor Dispatch Keys] meta_in_tls = torch._C._meta_in_tls_dispatch_include() prev = fake_mode.in_kernel_invocation fake_mode.in_kernel_invocation = True if not meta_in_tls: torch._C._add_meta_to_tls_dispatch_include() try: yield finally: fake_mode.in_kernel_invocation = prev if not meta_in_tls: torch._C._remove_meta_from_tls_dispatch_include() class FakeTensor(torch.Tensor): fake_device: torch.device fake_mode: "FakeTensorMode" constant: Optional[torch.Tensor] # 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 @staticmethod def __new__(cls, fake_mode, elem, device, constant=None): return torch.Tensor._make_subclass( cls, elem, elem.requires_grad, dispatch_device=True, device_for_backend_keys=device, ) def __init__( self, fake_mode, elem, device: Union[torch.device, str], constant: Optional[torch.Tensor] = None, ): 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 cuda device. if device.type == "cuda" and device.index is None: device = torch.device(f"cuda:{torch.cuda.current_device()}") self.fake_device = device self.fake_mode = fake_mode self.constant = constant @staticmethod def from_tensor(t, fake_mode): return fake_mode.from_tensor(t) # TODO: resolve error in default __repr__ def __repr__(self): with in_kernel_invocation_manager(self.fake_mode): self_repr = super().__repr__() return f"FakeTensor({self_repr}, {self.fake_device})" @classmethod 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. if any(not issubclass(t, FakeTensor) and t is not torch.Tensor for t in types): return NotImplemented fake_mode = None for arg in itertools.chain(tree_flatten(args)[0], tree_flatten(kwargs)[0]): if isinstance(arg, FakeTensor): if fake_mode is None: fake_mode = arg.fake_mode else: assert fake_mode is arg.fake_mode, "Mixing modes NYI" assert fake_mode is not None with fake_mode: # type: ignore[attr-defined] return func(*args, **kwargs) @staticmethod def _find_common_device(func, args, kwargs): # 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 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}" ) tree_map(merge_devices, args) tree_map(merge_devices, kwargs) # 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 ( torch._C._should_allow_numbers_as_tensors( func.name().split("::")[-1].split(".")[0] ) and common_device is None ): common_device = torch.device("cpu") assert common_device is not None, f"Could not find common device for {func}" return common_device __torch_function__ = torch._C._disabled_torch_function_impl # 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 incraese. class FakeTensorMode(TorchDispatchMode): def __init__( self, *, allow_fallback_kernels=True, allow_meta=False, throw_on_data_dependent_ops=False, ): self.allow_fallback_kernels = allow_fallback_kernels self.fake_tensor_converter = FakeTensorConverter() self.allow_meta = allow_meta # TODO: delete arg and default to true. waiting on dynamo perf regression testing self.throw_on_data_dependent_ops = throw_on_data_dependent_ops # [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 def __torch_dispatch__(self, func, types, args=(), kwargs=None): kwargs = kwargs if kwargs else {} 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 flat_arg_fake_tensors = tree_flatten_only(FakeTensor, (args, kwargs)) flat_symints = tree_flatten_only(torch.SymIntNode, (args, kwargs)) has_symbolic_sizes = ( any([i._has_symbolic_sizes_strides for i in flat_arg_fake_tensors]) or len(flat_symints) > 0 ) converter = self.fake_tensor_converter # If this is a lift, 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 if func in self.lift_fns: out = func(*args, **kwargs) if self.may_turn_const(out): with no_dispatch(): return converter(self, out.clone(), make_constant=True) with no_dispatch(): flat_arg_tensors = tree_flatten_only(torch.Tensor, (args, kwargs)) # 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. if self.check_for_subclass(flat_arg_tensors): 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 and type(args[0]) is torch.Tensor ), f"{args} {kwargs}" return converter(self, args[0]) if self.check_for_non_fake(flat_arg_tensors): raise Exception( "Invoking operators with non-Fake Tensor inputs in FakeTensorMode is not yet supported. " f"Please convert all Tensors to FakeTensors first. Found in {func}(*{args}, **{kwargs})" ) # 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 # type: ignore[attr-defined] and torch.Tag.inplace_view not in func.tags # type: ignore[attr-defined] and all_constant and len(flat_arg_fake_tensors) != 0 and not has_symbolic_sizes ): with no_dispatch(): const_args, const_kwargs = pytree.tree_map_only( FakeTensor, lambda t: t.constant, (args, kwargs) ) out = func(*const_args, **const_kwargs) all_constant = pytree.tree_all_only( torch.Tensor, lambda t: self.may_turn_const(t), out ) 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 tree_flatten_only(torch.Tensor, out): converter.invalidate_constant_aliases(ten) # we are falling through to running non constant tensors, any input constant that # is written to must be invalidated self.invalidate_written_to_constants(func, flat_arg_fake_tensors, args, kwargs) # IDK: feels bad man, sym_numel on as_strided infinite loops otherwise if ( has_symbolic_sizes and func not in self.functions_with_cpp_meta_impl_that_support_symint ): # TODO: Find better approach for this # Avoid circular import from torch._decomp import decomposition_table from torch._meta_registrations import meta_table with no_dispatch(): if func == aten.size.default: sys.stderr.write( "Trying to call aten.size on a tensor with symbolic shapes. " "It's likely that this is from calling tensor.shape in C++" ) # We do this to allow for better error localization with `TORCH_SHOW_CPP_STACKTRACES=1` return None with self: if func in meta_table: r = meta_table[func](*args, **kwargs) return r if func in decomposition_table: return decomposition_table[func](*args, **kwargs) # 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 if ( "prims::" in func._schema.name and len(flat_arg_fake_tensors) != 0 and hasattr(func, "prim_meta_impl") ): with self: return func.prim_meta_impl(*args, **kwargs) if has_symbolic_sizes: if func not in self.functions_with_cpp_meta_impl_that_support_symint: raise RuntimeError( f"{func} - couldn't find symbolic meta function/decomposition" ) with no_dispatch(): # 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: if run_impl_check(func): op_impl_out = op_impl(self, func, *args, **kwargs) if op_impl_out != NotImplemented: return op_impl_out # run kernel registered to meta for func, which include # python meta registrations, prims, decomps, and c++ meta fns (structured kernels) try: with in_kernel_invocation_manager(self): r = func(*args, **kwargs) except NotImplementedError as not_implemented_error: # no meta kernel registered, fallback to kernel for the device if not self.allow_fallback_kernels: raise not_implemented_error return run_fallback_kernel( self, func, args, kwargs, not_implemented_error ) return self.wrap_meta_outputs_with_default_device_logic( r, func, args, kwargs ) # [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_arg_tensors): return any( not isinstance(x, FakeTensor) and type(x) is not torch.Tensor and type(x) is not torch.nn.Parameter for x in flat_arg_tensors ) def check_for_non_fake(self, flat_arg_tensors): return any( isinstance(x, torch.Tensor) and not isinstance(x, FakeTensor) for x in flat_arg_tensors ) def wrap_meta_outputs_with_default_device_logic(self, r, func, args, kwargs): wrap = self.gen_wrap_fn(func, args, kwargs) # if device is specified, use that if kwargs.get("device", None): return tree_map(partial(wrap, device=kwargs["device"]), r) return tree_map(partial(wrap), r) def gen_wrap_fn(self, func, args, kwargs): converter = self.fake_tensor_converter # Lazily initialized, in case there are no tensor returns common_device = None def wrap(e, device=None): nonlocal common_device if isinstance(e, torch.Tensor) and not isinstance(e, FakeTensor): if common_device is None: common_device = FakeTensor._find_common_device(func, args, kwargs) return converter(self, e, device or common_device) else: return e return wrap @property def functions_with_cpp_meta_impl_that_support_symint(self): return [ aten.empty_strided.default, aten.as_strided.default, aten.zeros.default, aten.detach.default, ] @property def lift_fns(self): return (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 isinstance(t, FakeTensor) ) 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) if any_constant and get_schema_info(func).is_mutable(): schema_info = get_schema_info(func) _, 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 ( isinstance(v, FakeTensor) 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, shape_env=None): return self.fake_tensor_converter(self, tensor, shape_env=shape_env) # NB: returns fake tensors def run_fallback_kernel(fake_mode, func, args, kwargs, 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: # type: ignore[attr-defined] raise orig_not_implemented_exception with no_dispatch(): inp_impls = {} def to_real_tensor(e): if isinstance(e, FakeTensor): 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 args = tree_map(to_real_tensor, args) kwargs = tree_map(to_real_tensor, kwargs) r = func(*args, **kwargs) tensor_impls = set() storages = set() for e in tree_flatten((args, kwargs))[0]: if isinstance(e, torch.Tensor): if not e.is_sparse: storages.add(e.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 for e in tree_flatten(r)[0]: if id(e) not in inp_impls and ( isinstance(e, torch.Tensor) and not e.is_sparse and e.storage()._cdata in storages ): raise orig_not_implemented_exception def map_out(e): 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 tree_map(map_out, r) # 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]), **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) memo[id(tensor)] = out return out else: with torch._C.DisableTorchFunction(): return func(*args, **kwargs)