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# mypy: allow-untyped-defs
from typing import Optional
import torch.fx
from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
from torch.fx import Node
from torch.fx._compatibility import compatibility
from torch.fx.experimental.proxy_tensor import py_sym_types, snapshot_fake
from torch.fx.node import map_aggregate
__all__ = ["FakeTensorProp"]
@compatibility(is_backward_compatible=False)
class FakeTensorProp(torch.fx.Interpreter):
"""
Execute an FX graph Node-by-Node and record a fake tensor representing
the metadata for the node. Unlike ShapeProp, (1) this propagation
is cheap--it does the propagation with meta tensors which do not actually
store data, and (2) the fake tensors have much more fine grained information,
e.g., they have accurate alias information that can be consulted by looking
at the storages.
Args:
module (GraphModule): The module to be executed
mode (Optional[FakeTensorMode]): The dispatch mode used to execute computation indicated by each FX Node.
"""
def __init__(
self, module: torch.fx.GraphModule, mode: Optional[FakeTensorMode] = None
):
super().__init__(module)
if mode is None:
mode = FakeTensorMode()
self._mode = mode
mode.epoch += 1
mode.reset_nt_tensor_id_counter()
def run_node(self, n: Node):
from torch.fx.experimental.symbolic_shapes import (
compute_unbacked_bindings,
rebind_unbacked,
)
result = super().run_node(n)
rebind_unbacked(self._mode.shape_env, n, result)
def extract_val(obj):
if isinstance(obj, FakeTensor):
return snapshot_fake(obj)
elif isinstance(obj, torch.Tensor):
# TODO: How is it possible that we get a non fake tensor? We
# should be running under the mode...
return snapshot_fake(self._mode.from_tensor(obj, static_shapes=True))
elif isinstance(obj, py_sym_types):
return obj
else:
return None
meta = map_aggregate(result, extract_val)
if meta is not None:
n.meta["val"] = meta
if (shape_env := self._mode.shape_env) and (
symbol_to_path := compute_unbacked_bindings(shape_env, result)
):
n.meta["unbacked_bindings"] = symbol_to_path
return result
def propagate(self, *args):
fake_args = [
self._mode.from_tensor(a) if isinstance(a, torch.Tensor) else a
for a in args
]
return self.propagate_dont_convert_inputs(*fake_args)
def propagate_dont_convert_inputs(self, *args):
with self._mode:
return super().run(*args)
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