# mypy: ignore-errors # Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. import contextlib import functools from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch import torch.utils._pytree as pytree from torch.fx import Tracer, GraphModule from torch.fx.graph_module import _assign_attr from weakref import WeakKeyDictionary from collections import defaultdict from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode, unset_fake_temporarily, is_fake from torch._dispatch.python import enable_python_dispatcher import torch.fx as fx from torch.fx.node import _side_effectful_need_to_be_preserved_pre_dispatch from torch.fx.passes.shape_prop import _extract_tensor_metadata from contextlib import contextmanager, nullcontext import inspect from dataclasses import dataclass import weakref import operator import traceback from torch.utils._stats import count from torch.utils._traceback import CapturedTraceback import logging from torch._library.fake_class_registry import FakeScriptObject import warnings from torch.overrides import TorchFunctionMode from torch.utils._python_dispatch import ( TorchDispatchMode, _disable_infra_mode, _push_mode, _unset_infra_mode, ) from ._backward_state import BackwardState from .sym_node import SymNode from ._sym_dispatch_mode import SymDispatchMode from torch.fx import Proxy import torch.fx.traceback as fx_traceback from torch import SymInt, SymFloat, SymBool from torch.utils.weak import WeakTensorKeyDictionary, WeakIdKeyDictionary, _WeakHashRef __all__ = ["PythonKeyTracer", "dispatch_trace", "make_fx", "DecompositionInterpreter", "py_sym_types", "get_innermost_proxy_mode"] aten = torch.ops.aten prim = torch.ops.prim log = logging.getLogger(__name__) not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented") CURRENT_DECOMPOSITION_TABLE: Dict[torch._ops.OperatorBase, Callable] = {} CONSTANT_NUMEL_LIMIT = 1 null_ctx_type = type(nullcontext) # We currently convert all SymInt to proxies before we use them. # This could plausibly be handled at the Dynamo level. pytree.register_pytree_node( torch.Size, lambda xs: (list(xs), None), lambda xs, _: tuple(xs), flatten_with_keys_fn=lambda xs: ( [(pytree.SequenceKey(i), x) for i, x in enumerate(xs)], None, ), ) def fake_signature(fn, nargs): """FX gets confused by varargs, de-confuse it""" argnames = ",".join(f"arg{i}" for i in range(nargs)) return eval(f"lambda {argnames}: fn({argnames})", {"fn": fn}) @contextmanager def decompose(decomposition_table): global CURRENT_DECOMPOSITION_TABLE old_decomposition_table = CURRENT_DECOMPOSITION_TABLE CURRENT_DECOMPOSITION_TABLE = decomposition_table try: yield CURRENT_DECOMPOSITION_TABLE finally: CURRENT_DECOMPOSITION_TABLE = old_decomposition_table # ensure we cannot collide with other properties proxy_slot = object() no_default = object() py_sym_types = (SymInt, SymFloat, SymBool) def is_sym_node(node): assert hasattr(node, 'meta'), "All nodes traced with proxy_tensor should have meta" return "val" in node.meta and isinstance(node.meta['val'], py_sym_types) def set_proxy_slot(obj, tracer, proxy): if isinstance(obj, torch.Tensor): # We DO want to clobber proxies whenever we run an inplace operation # on a tensor, and it affects the metadata on the proxy. tracer.tensor_tracker[obj] = proxy elif isinstance(obj, (torch.ScriptObject, FakeScriptObject)): # We DO want to clobber proxies, with a similar rationale as for tensors. tracer.script_object_tracker[obj] = proxy else: # NB: Never clobber pre-existing proxy. Although the proxies # are in principle equivalent, when we do graph partitioning # we need there not to be spurious dependencies on tangent inputs. # This works because primals get their SymInts set first, and # THEN later we allocate tangent inputs. Make sure if a SymInt # is derivable from a primal that we use that. assert isinstance(obj, py_sym_types), type(obj) if obj not in tracer.symnode_tracker: tracer.symnode_tracker[obj] = proxy def has_proxy_slot(obj, tracer): assert isinstance(obj, (torch.Tensor, SymNode)), type(obj) return get_proxy_slot(obj, tracer, False, lambda _: True) # the default argument is what to return if the slot is not set. # the transform argument is handy if you need to extract a subfield from # the successfully looked up result (but NOT the default.) def get_proxy_slot(obj, tracer, default=no_default, transform=lambda x: x): if isinstance(obj, torch.Tensor): tracker = tracer.tensor_tracker elif isinstance(obj, (torch.ScriptObject, FakeScriptObject)): tracker = tracer.script_object_tracker else: assert isinstance(obj, py_sym_types), type(obj) tracker = tracer.symnode_tracker if obj not in tracker: if default is no_default: raise RuntimeError(f"{obj} is not tracked with proxy for {tracer}") return default return transform(tracker[obj]) def snapshot_fake(val): return val.detach() def extract_val(val): if is_fake(val): return snapshot_fake(val) elif isinstance(val, py_sym_types): return val elif isinstance(val, (torch.ScriptObject, FakeScriptObject)): return val elif isinstance(val, BackwardState): return val elif isinstance(val, (list, tuple)): return val.__class__([extract_val(x) for x in val]) elif isinstance(val, torch.Tensor): if not val.is_sparse: # NB: Kinda hacky, but we should try to get val as the metadata # everywhere # TODO: This doesn't properly track storages. A more robust # approach would be to maintain a per-trace FakeTensorMode and # from_real_tensor to create fake values (don't forget to # snapshot_fake) fake_tensor_mode = FakeTensorMode(allow_fallback_kernels=True) with fake_tensor_mode: return torch.empty_strided(val.shape, val.stride(), device=val.device, dtype=val.dtype) else: return None elif isinstance(val, (int, float, bool)): return val # What invariants do we have for the 'val' set on the FX node? It has accurate # metadata... but only for metadata that exists "below" all other subsystems # (most notably autograd, but also vmap, functorch transforms, etc). This means # you can get the dtype, shape, stride, storage, but you CANNOT get requires_grad, # grad_fn, _base (_base actually may be set due to recursive call to # ADInplaceOrView, but you shouldn't rely on it.) def set_meta(proxy, val): proxy.node.meta['val'] = extract_val(val) # Best effort tensor_meta setting; prefer using val! if is_fake(val): proxy.node.meta['tensor_meta'] = _extract_tensor_metadata(val) elif isinstance(val, torch.Tensor) and not val.is_sparse: proxy.node.meta['tensor_meta'] = _extract_tensor_metadata(val) return proxy def thunkify(f, *args, **kwargs): """ Delays computation of f until it's called again Also caches the result """ return functools.lru_cache(1)(functools.partial(f, *args, **kwargs)) def track_tensor(tensor, proxy, *, constant, tracer): def try_set_proxy_slot(outer_s, proxy_callable, *args): assert callable(proxy_callable) if isinstance(outer_s, SymInt): set_proxy_slot(outer_s, tracer, thunkify(proxy_callable, outer_s, *args)) # The basic idea is that we need to associate each tensor/SymInt # with a Proxy. How do we setup this association? We just store # the proxy on the proxy slot of the object, keyed on the tracer # (so that if we have multiple tracers at the same time, they # don't clobber each other.) for i, s in enumerate(tensor.shape): try_set_proxy_slot(s, lambda x, i: set_meta( tracer.create_proxy('call_function', torch.ops.aten.sym_size.int, (proxy, i), {}), x), i) for i, s in enumerate(tensor.stride()): try_set_proxy_slot(s, lambda x, i: set_meta( tracer.create_proxy('call_function', torch.ops.aten.sym_stride.int, (proxy, i), {}), x), i) try_set_proxy_slot(tensor.numel(), lambda x: set_meta( tracer.create_proxy('call_function', torch.ops.aten.sym_numel.default, (proxy,), {}), x)) try_set_proxy_slot(tensor.storage_offset(), lambda x: set_meta( tracer.create_proxy('call_function', torch.ops.aten.sym_storage_offset.default, (proxy,)), x)) set_proxy_slot(tensor, tracer, _ProxyTensor(proxy, constant)) def track_tensor_tree(inner_res, proxy_res, *, constant, tracer): _set_unbacked_bindings(inner_res, proxy_res) def wrap_with_proxy(e, proxy, constant): if isinstance(e, torch.Tensor): track_tensor(e, proxy, tracer=tracer, constant=constant) set_meta(proxy, e) elif isinstance(e, py_sym_types): # NB: eagerly set meta here, so that the numbering is in order set_meta(proxy, e) set_proxy_slot(e, tracer, lambda: proxy) elif isinstance(e, (torch.ScriptObject, FakeScriptObject)): set_proxy_slot(e, tracer, proxy) set_meta(proxy, e) elif isinstance(e, (tuple, list)): if isinstance(proxy, fx.Proxy): set_meta(proxy, e) # example use case: allreduce_ returns ([tensor], work) for idx, ee in enumerate(e): wrap_with_proxy(ee, proxy[idx], get_constant(idx)) elif isinstance(e, dict): # In theory we could support const-prop when proxy-tensor-tracing # operators that returns dicts of tensors, but we have no use case # for it today (since the only op we currently trace that can # return a dict is triton_kernel_wrapper_functional/mutation, # which does not participate in const-prop) assert constant is None if isinstance(proxy, fx.Proxy): set_meta(proxy, e) # example use case: triton_kernel_wrapper takes arguments as kwargs for key, val in e.items(): wrap_with_proxy(val, proxy[key], None) elif isinstance(e, BackwardState): set_meta(proxy, e) e.proxy = proxy else: # intentionally pass on primitives pass def get_constant(idx): if constant is None: return None else: return constant[idx] wrap_with_proxy(inner_res, proxy_res, constant) return inner_res def maybe_disable_fake_tensor_mode(): # TODO: figure out if this API generally makes sense and bake it into the # library return unset_fake_temporarily() @dataclass class _ProxyTensor: proxy: Proxy constant: Optional[torch.Tensor] def fetch_sym_proxy(tracer): def inner(e): n = e.node if n.constant is not None: return n.constant if e.node.expr.is_number: if isinstance(e, SymBool): return bool(e.node.expr) elif isinstance(e, SymInt): return int(e.node.expr) return float(e.node.expr) else: # NB: we REQUIRE all symints to be tracked return get_proxy_slot(e, tracer)() return inner def fetch_object_proxy(tracer): return lambda t: get_proxy_slot(t, tracer, t) HANDLED_TYPES = (torch.Tensor, torch.nn.Parameter, FakeTensor) def proxy_call(proxy_mode, func, pre_dispatch, args, kwargs): unrecognized_types = [] flat_args_kwargs, spec = pytree.tree_flatten((args, kwargs)) def can_handle_tensor(x): r = type(x) in HANDLED_TYPES or has_proxy_slot(x, proxy_mode.tracer) if proxy_mode._allow_fake_constant: r = r or type(x) in (torch._subclasses.FakeTensor,) if not r: unrecognized_types.append(type(x)) return r # If there are any tensor subclasses, we need to handle those tensor subclasses first # TODO: we could use types to test this if not all( can_handle_tensor(x) for x in flat_args_kwargs if isinstance(x, torch.Tensor) ): not_implemented_log.debug( "ProxyTensorMode tensors without proxy had unrecognized subclasses: %s", unrecognized_types, ) return NotImplemented r = maybe_handle_decomp(proxy_mode, func, args, kwargs) if r is not NotImplemented: return r # For pre-autograd tracing, we do not want to run CompositeImplicit decomps. if not pre_dispatch and func not in [ torch.ops.aten.size.default, torch.ops.aten.stride.default, torch.ops.aten.storage_offset.default, ]: with proxy_mode: r = func.decompose(*args, **kwargs) if r is not NotImplemented: return r tracer = proxy_mode.tracer f_flat_args_kwargs = [ ( fetch_object_proxy(tracer)(x) if isinstance(x, (torch.Tensor, torch.ScriptObject, FakeScriptObject)) else x ) for x in flat_args_kwargs ] # If there are SymInts, we also should not consider this constant. # However, fake tensor handling of SymInts is sufficiently broken that # I couldn't write a test for this case all_constant = ( not any( t.constant is None for t in f_flat_args_kwargs if isinstance(t, _ProxyTensor) ) # TODO: maybe constant SymInts should also be allowed? Not sure if # this can happen and not any( isinstance(x, (SymInt, SymFloat, SymBool)) for x in flat_args_kwargs ) ) if torch.Tag.data_dependent_output in func.tags: # Check if all of the Tensor inputs are constants if all_constant: const_flat_args_kwargs = [ t.constant if isinstance(t, _ProxyTensor) else t for t in f_flat_args_kwargs ] const_args, const_kwargs = pytree.tree_unflatten( const_flat_args_kwargs, spec ) with maybe_disable_fake_tensor_mode(): return func(*const_args, **const_kwargs) # If any of the Tensor inputs are "real" (not FakeTensor), we may # incorrectly burn in constants by allowing this access. Raise # an error in this case if proxy_mode._error_on_data_dependent_ops and pytree.tree_all_only( torch.Tensor, lambda t: not is_fake(t), (args, kwargs) ): raise RuntimeError( f"It appears that you're trying to get value out of a tracing tensor with {func} - erroring out! " "It's likely that this is caused by data-dependent control flow or similar. " "It may be possible to trace this with dynamic shapes; try setting tracing_mode='symbolic' " "in your make_fx call." ) proxy_flat_args_kwargs = [ e.proxy if isinstance(e, _ProxyTensor) else e for e in f_flat_args_kwargs ] proxy_flat_args_kwargs = [ ( fetch_sym_proxy(proxy_mode.tracer)(e) if isinstance(e, (SymInt, SymFloat, SymBool)) else e ) for e in proxy_flat_args_kwargs ] proxy_args, proxy_kwargs = pytree.tree_unflatten(proxy_flat_args_kwargs, spec) # When we trace through a torch.tensor invocation, you never actually # see a torch.ops.aten.tensor call. Instead, the way this function is # implemented internally is that we allocate a plain tensor (this is # *guaranteed* to be a plain tensor, we disable all modes when doing # so), and then call at::lift_fresh on it (to give modes a chance to do # their stuff). Furthermore, the tensor argument to lift_fresh is guaranteed # to be freshly allocated, so we want lift_fresh to be a no-op (directly # returning the input argument). # # Here is the basic problem: when we trace this sequence of executions # into an FX graph, what happens to this call sequence? Traditionally, # tensor constants get interned as buffers on the FX GraphModule. But # this is dangerous. Consider: # # x = torch.tensor(1) # x.add_(2) # # Naively, this traces into: # # t = self._tensor_constant0 # initialized to torch.tensor(1) # x = torch.ops.aten.lift_fresh(t) # x.add_(2) # # If lift_fresh returns t directly, the subsequent add_ call will # modify the tensor constant. Really, the problem is we've violated # the invariant the argument to lift is fresh. So what we should # preserve the invariant by replacing lift_fresh with lift_fresh_copy: # # t = self._tensor_constant0 # initialized to torch.tensor(1) # x = torch.ops.aten.lift_fresh_copy(t) # x.add_(2) # # This is what the overload modification does. if func is torch.ops.aten.lift_fresh.default: func = torch.ops.aten.lift_fresh_copy.default proxy_out = proxy_mode.tracer.create_proxy( "call_function", func, proxy_args, proxy_kwargs, name=proxy_mode.tracer.graph._target_to_str(func.overloadpacket.__name__), ) # This makes DCE marginally less likely to DCE inplace operations. # It is not strictly necessary # Kind of a hacky way to test if an op is in-place or not if ( func.overloadpacket.__name__[-1] == "_" and func.overloadpacket.__name__[0] != "_" ): if isinstance(args[0], List): # e.g., c10d::allreduce_ returns a list of tensors as the first element # in the output. for i, a in enumerate(args[0]): a.proxy = proxy_out[0][i] else: args[0].proxy = proxy_out out = func(*args, **kwargs) # In some circumstances, we will be tracing in a situation where a tensor # is *statically* known to be a constant (currently, this only happens if # you run torch.tensor; deterministic factory functions like torch.arange # don't get this treatment). When the tensor in question is small, it's # helpful to due constant propagation in case we call item() (in which # case we can return the constant value that is known, rather than give # an error.) The logic here tests if constant propagation is possible # (because all of the inputs are constant). If so, we disable fake tensor # mode (if it is on) and do true compute on the constant. # # It's worth highlighting that we're making a policy decision here. # There is a potential that the tensor is actually quite large, and we # don't actually want to run the compute. The tensor being quite large # is one of the reasons why factory functions don't get this treatment # (since they can be quite large; if a parameter is initialized to a # constant value it will be!) Similarly, there is also a potential # to run an operator that blows up the size of a small tensor; we don't # protect against this case, but we could force, e.g., only single # element constant computation by testing the numel of the result before # propagating const-ness. Similarly, we don't require the constant to # live on CPU, but we could. any_constant = any( t.constant is not None for t in f_flat_args_kwargs if isinstance(t, _ProxyTensor) ) constant = None # 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 is torch.ops.aten.lift_fresh_copy.default and out.numel() <= CONSTANT_NUMEL_LIMIT ): with maybe_disable_fake_tensor_mode(): constant = args[0].clone() elif ( torch.Tag.nondeterministic_seeded not in func.tags and all_constant and any_constant and pytree.tree_all_only( torch.Tensor, lambda t: t.numel() <= CONSTANT_NUMEL_LIMIT, out ) ): # NB: do NOT include factories as constants with maybe_disable_fake_tensor_mode(): const_flat_args_kwargs = [ t.constant if isinstance(t, _ProxyTensor) else t for t in f_flat_args_kwargs ] const_args, const_kwargs = pytree.tree_unflatten( const_flat_args_kwargs, spec ) constant = func(*const_args, **const_kwargs) else: constant = None track_tensor_tree(out, proxy_out, constant=constant, tracer=tracer) return out class _SymNodeDict: """ Wrapper around a dictionary that will hash SymInts with their nodes """ def __init__(self): self.sym_node_dict = {} def __setitem__(self, key: py_sym_types, value: Any): self.sym_node_dict[key.node] = value def __getitem__(self, key: py_sym_types): return self.sym_node_dict[key.node] def __contains__(self, key: py_sym_types): return key.node in self.sym_node_dict def get(self, key: py_sym_types, default: Any = None): return self.sym_node_dict.get(key.node, default) class PythonKeyTracer(Tracer): def __init__(self): super().__init__(autowrap_modules=()) self.tensor_tracker = WeakTensorKeyDictionary() self.symnode_tracker = _SymNodeDict() # type: ignore[var-annotated] self.script_object_tracker = WeakIdKeyDictionary(dict=None, ref_type=_WeakHashRef) # Stores the torch function that was called during tracing self.torch_fn_metadata = None # Stores the counts for every torch function called. This is to help # distinguish between different calls to the same torch function. self.torch_fn_counts = {} # In general, we don't want to make modules leaves. In principle, users of # this tracer might want to override this in order to turn a couple specific # modules into leaves in the traced graph. def call_module( self, m: torch.nn.Module, forward: Callable[..., Any], args: Tuple[Any, ...], kwargs: Dict[str, Any] ) -> Any: return forward(*args, **kwargs) # We don't want to turn getattr calls into proxies. So we just return the actual value. def getattr(self, attr, attr_val, parameter_proxy_cache): return attr_val def create_arg(self, a: Any): if isinstance(a, torch.nn.Parameter): for n, p in self.root.named_parameters(): if a is p: return self.create_node('get_attr', n, (), {}) qualname: Optional[str] = None if not qualname: qualname = self.get_fresh_qualname("_param_constant") setattr(self.root, qualname, a) return self.create_node('get_attr', qualname, (), {}) elif isinstance(a, (SymInt, SymFloat, SymBool)): assert a.node.constant is not None return a.node.constant return super().create_arg(a) def unwrap_proxy(self, e): if isinstance(e, torch.Tensor): return get_proxy_slot(e, self, e, lambda e: e.proxy) elif isinstance(e, (torch.SymInt, torch.SymFloat, torch.SymBool)): return get_proxy_slot(e, self, e, lambda e: e()) elif isinstance(e, (torch.ScriptObject, FakeScriptObject)): return get_proxy_slot(e, self, e) else: return e @contextmanager def _temp_remove_pre_dispatch_torch_function_mode(): from torch.overrides import _len_torch_function_stack, _pop_mode, _push_mode temp_elements = [] pre_dispatch_mode = None while _len_torch_function_stack() > 0: mode = _pop_mode() if isinstance(mode, PreDispatchTorchFunctionMode): pre_dispatch_mode = mode break else: temp_elements.append(mode) for mode in reversed(temp_elements): _push_mode(mode) try: yield finally: if pre_dispatch_mode is not None: count = len(temp_elements) while count > 0: mode = _pop_mode() count -= 1 temp_elements.append(pre_dispatch_mode) for mode in reversed(temp_elements): _push_mode(mode) @torch._disable_dynamo def dispatch_trace( root: Union[torch.nn.Module, Callable], tracer: Tracer, concrete_args: Optional[Tuple[Any, ...]] = None, ) -> GraphModule: graph = tracer.trace(root, concrete_args) from torch._inductor.fx_passes.dedupe_symint_uses import dedupe_symints dedupe_symints(graph) name = root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__ return fx._lazy_graph_module._make_graph_module(tracer.root, graph, name) def wrap_key(f, tensors, tracer, pre_dispatch: bool): flat_tensors, tensors_spec = pytree.tree_flatten(tensors) @functools.wraps(f) def wrapped(*proxies): flat_proxies, proxies_spec = pytree.tree_flatten(proxies) assert len(flat_proxies) == len(flat_tensors) with disable_proxy_modes_tracing() as m: assert isinstance(m, ProxyTorchDispatchMode) track_tensor_tree(flat_tensors, flat_proxies, constant=None, tracer=tracer) out = f(*tensors) out = pytree.tree_map_only( torch.Tensor, lambda t: get_proxy_slot(t, tracer, t, lambda x: x.proxy), out ) out = pytree.tree_map_only( (torch.ScriptObject, FakeScriptObject), lambda t: get_proxy_slot(t, tracer, t, lambda x: x), out ) out = pytree.tree_map_only( (SymInt, SymFloat, SymBool), lambda t: get_proxy_slot(t, tracer)(), out ) return out return wrapped ORIGINAL_ATEN = None @contextmanager def set_original_aten_op(func): global ORIGINAL_ATEN if ORIGINAL_ATEN is None and fx_traceback.has_preserved_node_meta(): ORIGINAL_ATEN = func fx_traceback.current_meta['original_aten'] = func try: yield finally: ORIGINAL_ATEN = None fx_traceback.current_meta['original_aten'] = None else: yield class TorchFunctionMetadataMode(TorchFunctionMode): def __init__(self, tracer): self.tracer = tracer def __torch_function__(self, func, types, args=(), kwargs=None): kwargs = kwargs or {} self.tracer.torch_fn_metadata = func self.tracer.torch_fn_counts[func] = self.tracer.torch_fn_counts.get(func, 0) + 1 return func(*args, **kwargs) # This mode is **only** used for pre_dispatch tracing. # In particular, we need to make sure that autograd/autocast API's # that do not desugar into dispatcher operators stay in the graph. class PreDispatchTorchFunctionMode(TorchFunctionMode): def __init__(self, tracer): self.tracer = tracer def __torch_function__(self, func, types, args=(), kwargs=None): kwargs = kwargs or {} if func in _side_effectful_need_to_be_preserved_pre_dispatch: # It's for passing the export verifier which needs to verify the meta['val'] # TODO(tmanlaibaatar): we should systematically couple it with expoert verifier, # instead of hardcoding it here. node = self.tracer.create_node("call_function", func, args, {}) if func is torch._C._set_grad_enabled: node.meta['val'] = None return node # Don't actually run the function! We just want to trace the calls # into a graph. We don't actualy want to change global autograd state. return func(*args, **kwargs) class ProxyTorchDispatchMode(TorchDispatchMode): def __init__(self, tracer, tracing_mode, pre_dispatch=False, _allow_fake_constant=False, _error_on_data_dependent_ops=True): dk = torch._C.DispatchKey.PreDispatch if pre_dispatch else None super().__init__(dk) self.tracer = tracer self.tracing_mode = tracing_mode self.enable_tracing = True self.pre_dispatch = pre_dispatch self._allow_fake_constant = _allow_fake_constant self._error_on_data_dependent_ops = _error_on_data_dependent_ops self.sym_mode = ProxySymDispatchMode(tracer) self.trace_state = {} self._managers = [] # Indicates to our torch_dispatch dispatching infra that # this is an "infra" mode with lower dispatching precedence. self._mode_key = torch._C._TorchDispatchModeKey.PROXY # Every time we enter a mode, we maintain a stack telling us what the previous # ProxyTorchDispatchMode state was (if there was any). # This lets us properly reset the state on exit. self.enter_stack: List[Optional[ProxyTorchDispatchMode]] = [] @count def __torch_dispatch__(self, func, types, args=(), kwargs=None): with self.sym_mode.enable(False), set_original_aten_op(func): return self.inner_torch_dispatch(func, types, args, kwargs) def __enter__(self): # sym mode first, then us... m = self.sym_mode.enable(True) self._managers.append(m) m.__enter__() # Stash and store the previous proxy mode (there may or may not be one) maybe_prev_proxy_mode = _unset_infra_mode(torch._C._TorchDispatchModeKey.PROXY) self.enter_stack.append(maybe_prev_proxy_mode) return super().__enter__() def __exit__(self, exc_type, exc_value, traceback): m = self._managers.pop() # ...exit us first, then sym mode b = super().__exit__(exc_type, exc_value, traceback) # Re-enable the previous proxy mode, if there was one. mb_previous_proxy_mode = self.enter_stack.pop() if mb_previous_proxy_mode is not None: _push_mode(mb_previous_proxy_mode) if not b: return m.__exit__(exc_type, exc_value, traceback) else: return m.__exit__(None, None, None) def inner_torch_dispatch(self, func, types, args=(), kwargs=None): if not self.enable_tracing: return func(*args, **kwargs) if func in [prim.device.default]: return func(*args, **kwargs) return proxy_call(self, func, self.pre_dispatch, args, kwargs) class ProxySymDispatchMode(SymDispatchMode): def __init__(self, tracer): super().__init__() self.tracer = tracer # When false, we don't trace operations. If you do this, you MUST # call track_tensor/track_tensor_tree on all results of the operation # to ensure we can adequately track the results self.enable_tracing = True @contextmanager def enable(self, b): old = self.enable_tracing self.enable_tracing = b try: yield finally: self.enable_tracing = old def _compute_proxy(self, func, args, out: Union[SymInt, SymFloat, SymBool]): n_args = tuple( get_proxy_slot(a, self.tracer)().node if isinstance(a, py_sym_types) else a for a in args ) # func doesn't have a __torch_function__ that Proxy can interpose, so # we gotta do it manually n_out = self.tracer.create_node("call_function", func, n_args, {}) p_out = fx.Proxy(n_out, self.tracer) set_meta(p_out, out) return p_out def __sym_dispatch__(self, func, types, args, kwargs): if not self.enable_tracing: return func(*args, **kwargs) # Peephole optimize multiply by one # NB: be careful not to trigger guards here! if func == operator.mul: if isinstance(args[1], int) and args[1] == 1: return args[0] elif isinstance(args[0], int) and args[0] == 1: return args[1] # For speed, we assume there are no nested data structures # (otherwise we could use tree_map) # We also assume there are no keyword arguments. assert not kwargs out = func(*args, **kwargs) # If func returned a constant, we don't need to trace; we have # determined that the result is constant (no matter if the inputs # were symbolic) and it is no longer necessary to trace the # computation. This could occur if func triggered some guards. if isinstance(out, py_sym_types): # Delays tracing out the proxies on this op until we actually need it p_out_thunk = thunkify(self._compute_proxy, func=func, args=args, out=out) set_proxy_slot(out, self.tracer, p_out_thunk) return out # TODO: I'm not sure what the point of this class is; you can just # make_fx through a regular Interpreter class DecompositionInterpreter(torch.fx.Interpreter): def __init__(self, module: torch.fx.GraphModule, new_graph: torch.fx.Graph, decomposition_table=None, **kwargs): super().__init__(module, **kwargs) self.new_graph = new_graph self.tracer = torch.fx.proxy.GraphAppendingTracer(self.new_graph) # Blegh self.tracer.tensor_tracker = WeakTensorKeyDictionary() # type: ignore[attr-defined] self.tracer.symnode_tracker = weakref.WeakKeyDictionary() # type: ignore[attr-defined] self.decomposition_table = decomposition_table if self.decomposition_table is None: self.decomposition_table = {} self.mode = ProxyTorchDispatchMode(self.tracer, tracing_mode="real") # Stores the torch function that was called during tracing self.tracer.torch_fn_metadata = None # Stores the counts for every torch function called. This is to help # distinguish between different calls to the same torch function. self.tracer.torch_fn_counts = {} def placeholder(self, target, args, kwargs): out = super().placeholder(target, args, kwargs) proxy = torch.fx.Proxy(self.new_graph.placeholder(target), self.tracer) track_tensor_tree(out, proxy, constant=None, tracer=self.tracer) # TODO handle case where the first character of target is '*' return out def get_attr(self, target, args, kwargs): out = super().get_attr(target, args, kwargs) proxy = torch.fx.Proxy(self.new_graph.get_attr(target), self.tracer) track_tensor_tree(out, proxy, constant=None, tracer=self.tracer) return out # call_function, call_method, call_module get traced automatically by the outer mode. def output(self, target, args, kwargs): out = super().output(target, args, kwargs) def unwrap(e): return get_proxy_slot(e, self.tracer, e, lambda x: x.proxy.node) self.new_graph.output(pytree.tree_map(unwrap, out)) return out def run(self, *args, **kwargs): # Should enter the mode at least once for being able to restore it later # See: https://github.com/pytorch/pytorch/pull/82549#discussion_r934782025 with decompose(self.decomposition_table), self.mode: return super().run(*args, **kwargs) def wrapper_and_args_for_make_fx(func, args, kwargs): # make_fx doesn't support kwargs, so we need to do this flattening # and then unflatten the args before calling func flat_args, spec = pytree.tree_flatten((args, kwargs)) def wrapped(flat_args): fn_args, fn_kwargs = pytree.tree_unflatten(flat_args, spec) return func(*fn_args, **fn_kwargs) return wrapped, flat_args @contextmanager def disable_autocast_cache(): old_value = torch.is_autocast_cache_enabled() torch.set_autocast_cache_enabled(False) try: yield finally: torch.set_autocast_cache_enabled(old_value) class _ModuleNotInstalledAsSubmoduleError(NameError): pass class _ModuleStackTracer(PythonKeyTracer): r"""Customized version of PythonKeyTracer that retains module stack information in node.meta["nn_module_stack"]. FX symbolic trace actually does this already, but it relies on `self.root` being the actual module being traced. Since make_fx traces a lambda of our creation, things don't work properly. So for this version we hold onto a reference to the original module (scope_root) and use that to match the path. Also when we see, A / \ B C \ / D we want to record the path as A.B.D by recording only one path. See Note [Preserving the nn module stack metadata during export non-strict mode] # noqa: W605 """ def __init__(self, scope_root): super().__init__() self.scope_root = scope_root self.proxy_paths = WeakKeyDictionary() self.proxy_modules = WeakKeyDictionary() self.counter = 0 self.module_id_cache = defaultdict(list) for name, mod in self.scope_root.named_modules(remove_duplicate=False): self.module_id_cache[id(mod)].append(name) self_ = self class AttrProxy: def __init__(self, base, path): self.__class__ = type( base.__class__.__name__, (self.__class__, base.__class__), {}, ) self.__dict__ = base.__dict__ self.__class__.__module__ = base.__class__.__module__ self.__class__.__qualname__ = base.__class__.__qualname__ self_.proxy_paths[self] = path self_.proxy_modules[self] = base def __getattr__(self, name): assert isinstance(self, torch.nn.Module) attr_val = super().__getattr__(name) if isinstance(attr_val, AttrProxy): attr_val = self_.proxy_modules[attr_val] elif not isinstance(attr_val, torch.nn.Module): return attr_val return AttrProxy(attr_val, self_.proxy_paths[self] + "." + name) @property def _modules(self): assert "_modules" in self.__dict__ submodules = self.__dict__["_modules"] assert isinstance(submodules, dict) return { key: AttrProxy(value, self_.proxy_paths[self] + "." + str(key)) for key, value in submodules.items() } self.proxy_type = AttrProxy def path_of_module(self, mod: torch.nn.Module) -> str: """ Use tracked access path during tracing instead of the default BFS behavior. Still use all the possible module paths to verify the result. """ if mod is self.scope_root: return "" if isinstance(mod, self.proxy_type): return self.proxy_paths[mod] try: return Tracer.path_of_module(self, mod) except NameError as e: raise _ModuleNotInstalledAsSubmoduleError from e def getattr(self, attr, attr_val, parameter_proxy_cache): if not isinstance(attr_val, torch.nn.Module) or isinstance(attr_val, torch.fx.GraphModule): return super().getattr(attr, attr_val, parameter_proxy_cache) if isinstance(attr_val, self.proxy_type): return attr_val return self.proxy_type(attr_val, attr) def trace(self, root, concrete_args): res = super().trace(root, concrete_args) # Since we are making AttrProxy mimic the original # submodule, when someone registers a module directly # to the tracer while tracing, the proxy object gets registered # first. So we need to replace the proxy modules with the real ones # This can happen during HOO tracing proxy_module_names_to_be_replaced = [] for name, module in self.root.named_modules(): if module in self.proxy_modules: proxy_module_names_to_be_replaced.append((name, module)) def _delete_proxy_attr(obj, target): # Copied from fx/graph_module.py # Customized it for proxy type atoms = target.split(".") path, target_submod = atoms[:-1], atoms[-1] assert isinstance(obj, torch.nn.Module) mod = obj # Get the parent module for item in path: if not hasattr(mod, item): return False mod = getattr(mod, item) if not isinstance(mod, (self.proxy_type, torch.nn.Module)): return False if not hasattr(mod, target_submod): return False # At least the leaf module should be proxy type. if not isinstance(getattr(mod, target_submod), self.proxy_type): return False delattr(mod, target_submod) return True for (proxy_module_name, proxy_module) in proxy_module_names_to_be_replaced: _delete_proxy_attr(self.root, proxy_module_name) actual_module = self.proxy_modules[proxy_module] _assign_attr(actual_module, self.root, proxy_module_name) return res def call_module(self, m, forward, args, kwargs): """PythonKeyTracer overrides call_module to avoid the scope handling, but we actually want it. """ from torch._dynamo import OptimizedModule # FIXME (tmanlaibaatar) # When we call torch.compile inside HOO, we will end up # invoking a module that is not registered on the root. For # now, we just inline them. But once we start supporting # mark_strict in export, we do need to properly handle this. # Right now, it doesn't matter because current non-strict # use cases don't need to work with HOO. if isinstance(m, (OptimizedModule, GraphModule)): return forward(*args, **kwargs) try: return Tracer.call_module(self, m, forward, args, kwargs) except _ModuleNotInstalledAsSubmoduleError as e: warnings.warn( f"Unable to find the path of the module {m}. " "This might be because the module was not properly registered " "as a submodule, which is not good practice. We will trace " "through the module without recording stack information." ) return forward(*args, **kwargs) def is_leaf_module(self, m, module_qualified_name): return False def create_node(self, *args, **kwargs): ''' Create node and add on metadata. Add nn_module_stack here instead of TracerBase, since calls to make_fx() might not want to record module stack metadata. Add torch_fn by looking at torch_fn_metadata and torch_fn_counts. Add stack_trace by filtering out forward() stack frames. ''' node = super().create_node(*args, **kwargs) # nn_module_stack if node.op not in ["placeholder", "output"]: if "nn_module_stack" not in node.meta: node.meta["nn_module_stack"] = self.module_stack # convert nn_module_stack from Dict[key, (FQN, class)] -> Dict[str, Tuple[str, str]] for key, (fqn, mod_cls) in node.meta["nn_module_stack"].items(): if isinstance(mod_cls, type): node.meta["nn_module_stack"][key] = (fqn, mod_cls.__module__ + "." + mod_cls.__qualname__) # torch_fn if node.op == "call_function" and self.torch_fn_metadata is not None and "torch_fn" not in node.meta: node.meta["torch_fn"] = ( f"{self.torch_fn_metadata.__name__}_{self.torch_fn_counts[self.torch_fn_metadata]}", f"{self.torch_fn_metadata.__class__.__name__}.{self.torch_fn_metadata.__name__}" ) # stack_trace if 'stack_trace' not in node.meta and node.op not in ["placeholder", "output"]: user_frame_summary = CapturedTraceback.extract().summary() if user_frame_summary: # we retain frames from forward() calls, or ops # located in torch/__init__.py (e.g. sym_int, sym_constrain_range, vmap) stack_trace = [frame for frame in user_frame_summary if ( frame.name == 'forward' or frame.filename.endswith('torch/__init__.py') )] # filter out forward() frames from fx/_symbolic_trace.py, export/_trace.py # this is hardcoded, but leads to a much cleaner stack trace stack_trace = [ frame for frame in stack_trace if not ( frame.filename.endswith('fx/_symbolic_trace.py') or frame.filename.endswith('export/_trace.py') ) ] if stack_trace: # empty list for strict mode, dynamo should handle stack_trace stack_trace = traceback.StackSummary.from_list(stack_trace) node.meta["stack_trace"] = ''.join(stack_trace.format()).strip() return node class _MakefxTracer: def __init__( self, decomposition_table: Optional[Dict[Callable, Callable]], tracing_mode: str, _allow_non_fake_inputs: bool, pre_dispatch: bool, record_module_stack: bool, _allow_fake_constant: bool, _error_on_data_dependent_ops: bool ): # Configurations that are used to initialize the context managers and their states. # Should not modify them during tracing. self.decomposition_table: Dict[Callable, Callable] = decomposition_table or {} self.decomposition_table.setdefault(torch.ops.aten.sym_numel.default, torch._decomp.decompositions.sym_numel) self.tracing_mode: str = tracing_mode self._allow_non_fake_inputs: bool = _allow_non_fake_inputs self.pre_dispatch: bool = pre_dispatch self.record_module_stack: bool = record_module_stack self._allow_fake_constant: bool = _allow_fake_constant self._error_on_data_dependent_ops: bool = _error_on_data_dependent_ops # All context managers and their states should be initialized before tracing based on the inputs # and configurations. After tracing, their states should be cleaned except for shape_env. # Remember to specify how to intialize it from user inputs and from parent tracer whenever # adding new modes in _MakefxTracer. self.fake_tensor_mode: Union[null_ctx_type, FakeTensorMode] = nullcontext() self.proxy_mode: Union[null_ctx_type, ProxyTorchDispatchMode] = nullcontext() self.proxy_function_mode: Union[null_ctx_type, PreDispatchTorchFunctionMode] = nullcontext() self.fx_tracer: Union[null_ctx_type, Tracer] = nullcontext() self.python_dispatcher_mode: Union[null_ctx_type, Any] = nullcontext() self.torch_fn_metadata_mode: Union[null_ctx_type, TorchFunctionMetadataMode] = nullcontext() def _checkpoint_modes(self) -> List[Any]: return [ self.fake_tensor_mode, self.proxy_mode, self.proxy_function_mode, self.fx_tracer, self.python_dispatcher_mode, self.torch_fn_metadata_mode ] def _restore_modes( self, prev_fake_tensor_mode: Union[null_ctx_type, FakeTensorMode], prev_proxy_mode: Union[null_ctx_type, ProxyTorchDispatchMode], prev_proxy_function_mode: Union[null_ctx_type, PreDispatchTorchFunctionMode], prev_fx_tracer: Union[null_ctx_type, Tracer], prev_python_dispatcher_mode: Union[null_ctx_type, Any], prev_torch_fn_metadata_mode : Union[null_ctx_type, TorchFunctionMetadataMode], ) -> None: self.fake_tensor_mode = prev_fake_tensor_mode self.proxy_mode = prev_proxy_mode self.proxy_function_mode = prev_proxy_function_mode self.fx_tracer = prev_fx_tracer self.python_dispatcher_mode = prev_python_dispatcher_mode self.torch_fn_metadata_mode = prev_torch_fn_metadata_mode @contextmanager def _init_modes_from_inputs(self, f, args): prev_modes = self._checkpoint_modes() try: # Avoid importing sympy at a module level from .symbolic_shapes import ShapeEnv if hasattr(f, "_orig_mod") and self.record_module_stack: scope_root = f._orig_mod self.fx_tracer = _ModuleStackTracer(scope_root) else: self.fx_tracer = PythonKeyTracer() if self.tracing_mode == "fake": import torch._dynamo fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(args) if fake_tensor_mode is None: import torch._functorch.config as _config with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False): fake_tensor_mode = FakeTensorMode( allow_fallback_kernels=True, allow_non_fake_inputs=self._allow_non_fake_inputs, shape_env=ShapeEnv(), static_shapes=True, ) self.fake_tensor_mode = fake_tensor_mode elif self.tracing_mode == "symbolic": import torch._dynamo fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(args) if fake_tensor_mode is None: shape_env = ShapeEnv() import torch._functorch.config as _config with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False): fake_tensor_mode = FakeTensorMode( allow_fallback_kernels=False, allow_non_fake_inputs=self._allow_non_fake_inputs, shape_env=shape_env) assert fake_tensor_mode.shape_env is not None, "shape_env should be set if tracing with 'symbolic'" self.fake_tensor_mode = fake_tensor_mode else: if not self.tracing_mode == "real": raise AssertionError(f"Unexpected tracing type: {self.tracing_mode}") self._construct_modes_with_fx_tracer(self.fx_tracer) yield finally: self._restore_modes(*prev_modes) def _construct_modes_with_fx_tracer(self, fx_tracer): self.proxy_mode = ProxyTorchDispatchMode( fx_tracer, self.tracing_mode, pre_dispatch=self.pre_dispatch, _allow_fake_constant=self._allow_fake_constant, _error_on_data_dependent_ops=self._error_on_data_dependent_ops ) if self.pre_dispatch: self.proxy_function_mode = PreDispatchTorchFunctionMode(fx_tracer) # pre-autograd tracing uses per-dispatch-key modes, # which requires the python dispatcher if self.tracing_mode == "symbolic" or self.pre_dispatch: self.python_dispatcher_mode = enable_python_dispatcher() self.torch_fn_metadata_mode = TorchFunctionMetadataMode(fx_tracer) @contextmanager def _init_modes_from_parent(self, parent_tracer): # By default, subtracer creates new modes based on parent tracer's config. # However, there are cases where we want to share the same modes with parent tracer # For example, fake_tensor_mode, we want the example value's fake_mode of parent graph and subgraphs to be the same. prev_modes = self._checkpoint_modes() try: self.fake_tensor_mode = parent_tracer.fake_tensor_mode def _create_sub_fx_tracer(parent_tracer): if type(parent_tracer) == PythonKeyTracer: sub_tracer = PythonKeyTracer() elif type(parent_tracer) == _ModuleStackTracer: sub_tracer = _ModuleStackTracer(parent_tracer.scope_root) else: raise RuntimeError(f"Unexpected tracer type: {type(parent_tracer)}.") return sub_tracer self.fx_tracer = _create_sub_fx_tracer(parent_tracer.fx_tracer) self._construct_modes_with_fx_tracer(self.fx_tracer) yield finally: self._restore_modes(*prev_modes) def _trace_inner(self, f, *args): phs = pytree.tree_map(lambda _: fx.PH, args) # type: ignore[attr-defined] def _wrap_fake(args: Tuple[Any]) -> Tuple[Any]: arg_count = 0 def inner_wrap_fake(x): nonlocal arg_count # TODO: it would be nice to line these up with the names # FX will choose for the placeholders, but we don't # actually know what the names will be at this point yet # NB: the Source here is actually meaningless from torch._dynamo.source import ConstantSource source = ConstantSource(f"input{arg_count}") if isinstance(x, torch.Tensor): arg_count += 1 return self.fake_tensor_mode.from_tensor(x, source=source) # type: ignore[attr-defined] # NB: don't match on bools elif type(x) is int and self.tracing_mode == "symbolic": return self.fake_tensor_mode.shape_env.create_symintnode( self.fake_tensor_mode.shape_env.create_symbol(x, source, positive=None), hint=x, source=source ) elif isinstance(x, torch.ScriptObject): return torch._library.fake_class_registry.to_fake_obj(self.fake_tensor_mode, x) assert not isinstance(x, FakeScriptObject), f"ScriptObject {x} has been fakified. Cannot wrap_fake it again." return x wrap_fn_map = { "real": lambda x: x, "fake": inner_wrap_fake, "symbolic": inner_wrap_fake, } return pytree.tree_map(wrap_fn_map[self.tracing_mode], args) def _wrap_func(f, phs): if not hasattr(inspect.unwrap(f), '__code__') or inspect.unwrap(f).__code__.co_flags & inspect.CO_VARARGS: # FX doesn't support varargs, so we gotta fake up a wrapper # TODO: Would be nice to fix this at the source... return fake_signature(f, len(phs)) return f args = _wrap_fake(args) func = _wrap_func(f, phs) # We disable the autocast cache as the autocast cache causes type conversions on parameters to # check a cache, which introduces untracked tensors into the graph # # We also disable tracing by any other tensor proxy-based tracers except the current. The # purpose of `make_fx` is to produce graphmodules as a side effect; its internal execution is # thus irrelevant to any external functional trace. with decompose(self.decomposition_table), self.fake_tensor_mode, self.python_dispatcher_mode, self.proxy_function_mode, \ self.proxy_mode.sym_mode, self.torch_fn_metadata_mode, \ self.proxy_mode, disable_autocast_cache(), _set_make_fx_tracer(self): t = dispatch_trace( wrap_key(func, args, self.fx_tracer, self.pre_dispatch), tracer=self.fx_tracer, concrete_args=tuple(phs) ) # TODO: kind of a bad way to do it, should maybe figure out a better way if self.tracing_mode == "symbolic": t.shape_env = self.fake_tensor_mode.shape_env # type: ignore[assignment] return t def trace(self, f, *args) -> torch.fx.GraphModule: with self._init_modes_from_inputs(f, args): return self._trace_inner(f, *args) def trace_subgraph(self, f, *args): # Create a new tracer based on parent's config sub_tracer = _MakefxTracer( self.decomposition_table, self.tracing_mode, self._allow_non_fake_inputs, self.pre_dispatch, self.record_module_stack, self._allow_fake_constant, self._error_on_data_dependent_ops ) with sub_tracer._init_modes_from_parent(self): return sub_tracer._trace_inner(f, *args) _CURRENT_MAKE_FX_TRACER : Optional[_MakefxTracer] = None @contextmanager def _set_make_fx_tracer(tracer: _MakefxTracer) -> None: global _CURRENT_MAKE_FX_TRACER prev_tracer = _CURRENT_MAKE_FX_TRACER try: _CURRENT_MAKE_FX_TRACER = tracer yield finally: _CURRENT_MAKE_FX_TRACER = prev_tracer def make_fx( f, decomposition_table=None, tracing_mode="real", _allow_non_fake_inputs=False, *, pre_dispatch=False, record_module_stack=False, _allow_fake_constant=False, _error_on_data_dependent_ops=True): assert tracing_mode in ["real", "fake", "symbolic"] make_fx_tracer = _MakefxTracer( decomposition_table, tracing_mode, _allow_non_fake_inputs, pre_dispatch, record_module_stack, _allow_fake_constant, _error_on_data_dependent_ops ) @functools.wraps(f) def wrapped(*args): return make_fx_tracer.trace(f, *args) return wrapped def get_torch_dispatch_modes(): return torch.utils._python_dispatch._get_current_dispatch_mode_stack() def get_innermost_proxy_mode(): return torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.PROXY) @contextlib.contextmanager def disable_proxy_modes_tracing(): return _disable_infra_mode(torch._C._TorchDispatchModeKey.PROXY) def maybe_handle_decomp(proxy_mode, op, args, kwargs): if op in CURRENT_DECOMPOSITION_TABLE: with proxy_mode: return CURRENT_DECOMPOSITION_TABLE[op](*args, **kwargs) return NotImplemented def get_isolated_graphmodule(func, args, kwargs, tracing_mode="real"): """A helper function used to get the GraphModule for the given func. It's expected to be used in the ProxyTensor tracing context. It detaches the args and kwargs from the current tracer so that the trace of the current graph module can be created without any side-effects. """ wrapped, all_args = wrapper_and_args_for_make_fx(func, args, kwargs) with disable_proxy_modes_tracing(): gm = make_fx(wrapped, tracing_mode=tracing_mode)(all_args) return gm def _set_unbacked_bindings(out, out_proxy): """A helper function for setting up unbacked_bindings on the destination FX graph.""" from .symbolic_shapes import compute_unbacked_bindings # Can't use detect_fake_mode here, # # python test/distributed/_tensor/test_dtensor_compile.py -k # test_tp_compile_fullgraph_is_seq_parallel_False # # will fail. Very strange, it probably isn't right for them to be using # two fake modes there... fake_mode = torch._C._get_dispatch_mode( torch._C._TorchDispatchModeKey.FAKE ) if fake_mode and fake_mode.shape_env: if symbol_to_path := compute_unbacked_bindings(fake_mode.shape_env, out): out_proxy.node.meta["unbacked_bindings"] = symbol_to_path