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# mypy: allow-untyped-defs
import operator
import traceback
import typing
from contextlib import nullcontext
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
import torch
from functorch.experimental.control_flow import _unstack_pytree
from torch import fx
from torch._dispatch.python import enable_python_dispatcher
from torch._export.pass_infra.node_metadata import NodeMetadata
from torch._export.pass_infra.proxy_value import ProxyValue
from torch._subclasses import FakeTensor, UnsupportedFakeTensorException
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.fx import traceback as fx_traceback
from torch.fx.experimental.proxy_tensor import PythonKeyTracer
from torch.fx.graph import CodeGen
from torch.fx.passes.infra.pass_base import PassBase, PassResult
from torch.fx.passes.shape_prop import _extract_tensor_metadata, TensorMetadata
from torch.utils import _pytree as pytree
from torch.fx.experimental.symbolic_shapes import PropagateUnbackedSymInts, compute_unbacked_bindings
__all__ = ["_ExportPassBaseDeprecatedDoNotUse"]
Argument = Any
Value = Any
Fn = Callable[..., Any]
PassType = Callable[[torch.fx.GraphModule], Optional[PassResult]]
_TORCH_SYM_OPS: Set[Callable] = {
torch.sym_int,
torch.sym_float,
torch.sym_ite,
torch.sym_max,
torch.sym_min,
torch.sym_not,
torch.sym_sqrt,
}
class ExportPassBaseError(RuntimeError):
pass
class _ExportPassBaseDeprecatedDoNotUse(PassBase):
"""
Interpreter-based pass class to help users maintain the IR spec while writing
transformations.
"""
@staticmethod
def _create_dummy_node_metadata():
return NodeMetadata({"stack_trace": "".join(traceback.format_stack(limit=1))})
class ExportTracer(PythonKeyTracer):
def __init__(self, callback: "_ExportPassBaseDeprecatedDoNotUse", codegen: CodeGen) -> None:
super().__init__()
self.callback = callback
self.root = torch.nn.Module()
self.graph = torch.fx.Graph()
self.graph.set_codegen(codegen)
self.tensor_attrs: Dict[str, torch.Tensor] = {} # type: ignore[assignment]
self.fake_tensor_mode: Optional[FakeTensorMode] = None
self.submodules: Dict[torch.nn.Module, str] = {}
def trace(self) -> None: # type: ignore[override]
raise ExportPassBaseError("ExportTracer doesn't support trace().")
def create_arg(self, a: Argument) -> torch.fx.Node:
if isinstance(a, torch.nn.Module):
if a not in self.submodules:
name_submodule = f"submodule_{len(self.submodules)}"
self.root.add_module(name_submodule, a)
self.submodules[a] = name_submodule
elif isinstance(a, FakeTensor):
if not hasattr(a, "constant") or a.constant is None:
raise ExportPassBaseError(f"Cannot add {a} to graph.")
a = a.constant
node = super().create_arg(a)
if (
isinstance(a, torch.Tensor)
and isinstance(node, torch.fx.Node)
and node.op == "get_attr"
):
self.set_metadata(node, a)
self.callback.on_attr(ProxyValue(a, node))
return node
def set_metadata(
self, node: torch.fx.Node, value: Argument,
) -> None:
# propagate the fake tensor or sym nodes
def make_val(
x: Argument,
) -> Union[FakeTensor, torch.SymInt, torch.SymFloat, torch.SymBool, int, float, bool, str, None]:
if isinstance(x, FakeTensor):
return x
elif isinstance(x, torch.Tensor):
if x.is_quantized:
# TODO (tmanlaibaatar) properly support Quantized FakeTensor
x = torch.dequantize(x)
try:
assert self.fake_tensor_mode is not None
# TODO we should allocate static shapes
# for param/buffer values
if isinstance(x, torch.nn.Parameter):
fake_tensor = self.fake_tensor_mode.from_tensor(
x, static_shapes=True
)
else:
fake_tensor = self.fake_tensor_mode.from_tensor(x)
except UnsupportedFakeTensorException:
# TODO: This is just a workaround to get over the
# x.as_subclass error
print(
"Fakeifying a Tensor subclass is not supported \
right now. Instead a TensorMetadata is used."
)
fake_tensor = None
return fake_tensor
elif isinstance(x, (torch.SymInt, torch.SymFloat, torch.SymBool, int, float, bool, str)):
return x
else:
return None
node.meta["val"] = pytree.tree_map(make_val, value)
# Set the tensor_metadata for values that do not have a corresponding FakeTensor
def make_tensor_meta(x: Argument) -> Optional[TensorMetadata]:
if not isinstance(x, FakeTensor) and isinstance(x, torch.Tensor):
if x.is_quantized:
# TODO (tmanlaibaatar) properly support Quantized FakeTensor
x = torch.dequantize(x)
try:
assert self.fake_tensor_mode is not None
_ = self.fake_tensor_mode.from_tensor(x)
tensor_meta = None
except UnsupportedFakeTensorException:
# TODO: This is just a workaround to get over the
# x.as_subclass error
tensor_meta = _extract_tensor_metadata(x)
return tensor_meta
else:
return None
node.meta["tensor_meta"] = pytree.tree_map(make_tensor_meta, value)
class ExportInterpreter(fx.Interpreter):
def __init__(self, callback: "_ExportPassBaseDeprecatedDoNotUse", gm: fx.GraphModule) -> None:
super().__init__(gm)
self.callback = callback
self.node: torch.fx.Node = next(iter(gm.graph.nodes))
def placeholder(
self,
target: str, # type: ignore[override]
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
) -> ProxyValue:
arg = super().placeholder(target, args, kwargs)
return self.callback.placeholder(target, arg, NodeMetadata(self.node.meta))
def output(
self,
target: torch.fx.node.Target,
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
) -> ProxyValue:
return self.callback.output(args[0], NodeMetadata(self.node.meta)).data
def call_function(
self,
target: torch.fx.node.Target,
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
) -> ProxyValue:
meta = NodeMetadata(self.node.meta)
if target == operator.getitem:
value, key = args
return self.callback.call_getitem(value, key, meta)
elif getattr(target, "__module__", None) in {"_operator", "builtins", "math"}:
assert callable(target)
return self.callback.call_sym(target, args, meta)
elif target in _TORCH_SYM_OPS:
assert callable(target)
return self.callback.call_sym(target, args, meta)
elif isinstance(target, (torch._ops.OpOverload, torch._ops.OpOverloadPacket)):
return self.callback.call_operator(
target,
args,
kwargs,
meta,
)
elif target == torch.ops.higher_order.cond:
pred, true_fn, false_fn, inputs = args
return self.callback.call_cond(pred, true_fn, false_fn, inputs, meta)
elif target == torch.ops.higher_order.map_impl:
f, mapped_args, operands = args # type: ignore[assignment]
return self.callback.call_map(f, mapped_args, operands, meta)
# For other unregistered HigherOrderOps, just interpret them blindly
elif isinstance(target, torch._ops.HigherOrderOperator):
return self.callback._fx(
"call_function",
target,
args,
kwargs,
meta,
)
else:
raise ExportPassBaseError(f"Unsupported target type: {target}")
def get_attr(
self, target: str, args: Tuple[Argument, ...], kwargs: Dict[str, Argument] # type: ignore[override]
) -> Argument:
return super().get_attr(target, args, kwargs)
def call_module(
self,
target: torch.fx.node.Target,
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
) -> None:
raise ExportPassBaseError("call_module is not supported.")
def call_method(
self, target: str, args: Tuple[Argument, ...], kwargs: Dict[str, Argument] # type: ignore[override]
) -> None:
raise ExportPassBaseError("call_method is not supported.")
def run_node(self, n: torch.fx.Node) -> Argument:
self.node = n
self.callback.node_debug_str = n.format_node()
return super().run_node(n)
def __init__(self) -> None:
self.interpreter = PropagateUnbackedSymInts(
torch.fx.GraphModule(torch.nn.Module(), torch.fx.Graph())
)
self.tracer = self.ExportTracer(self, CodeGen())
self.fake_tensor_mode: Optional[FakeTensorMode] = None
self._initialized = True
self.node_debug_str: typing.Optional[str] = None
def _fx(
self,
kind: str,
target: torch.fx.node.Target,
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
meta: NodeMetadata,
) -> ProxyValue:
args_data, kwargs_data = pytree.tree_map_only(
ProxyValue, lambda x: x.data, (args, kwargs)
)
res_data = getattr(self.interpreter, kind)(target, args_data, kwargs_data)
args_proxy, kwargs_proxy = pytree.tree_map_only(
ProxyValue, lambda x: x.proxy, (args, kwargs)
)
name = None
if isinstance(target, torch._ops.OpOverload):
name = self.tracer.graph._target_to_str(target.overloadpacket.__name__)
res_proxy = self.tracer.create_proxy(kind, target, args_proxy, kwargs_proxy, name=name)
res_proxy.node.meta.update(meta.data)
if self.fake_tensor_mode and (shape_env := self.fake_tensor_mode.shape_env):
if symbol_to_path := compute_unbacked_bindings(shape_env, res_data):
res_proxy.node.meta["unbacked_bindings"] = symbol_to_path
self.tracer.set_metadata(res_proxy.node, res_data)
return ProxyValue(res_data, res_proxy)
def inputs(self, graph_module: torch.fx.GraphModule) -> List[Argument]:
# TODO(angelayi): Update this with what we decide to do for metadata in
# the exported graph module
if (args := graph_module.meta.get("args", None)) is not None:
return list(args)
def extract_input(node: torch.fx.Node) -> Optional[FakeTensor]:
if "val" in node.meta:
fake = node.meta["val"]
if hasattr(fake, "constant") and fake.constant is not None:
return fake.constant
return fake
elif tensor_meta := node.meta.get("tensor_meta"):
assert self.fake_tensor_mode is not None
return FakeTensor(
self.fake_tensor_mode,
torch.empty(
tensor_meta.shape,
dtype=tensor_meta.dtype,
device="meta",
requires_grad=tensor_meta.requires_grad,
memory_format=tensor_meta.memory_format,
),
torch.device("cpu"),
)
elif len(node.users) == 0:
return None
raise ExportPassBaseError(
f"Cannot construct an input for graph module: {graph_module}.",
)
return [
extract_input(node)
for node in graph_module.graph.nodes
if node.op == "placeholder"
]
def on_attr(self, attr: ProxyValue) -> None:
pass
def placeholder(self, name: str, arg: Argument, meta: NodeMetadata) -> ProxyValue:
arg_proxy = self.tracer.create_proxy("placeholder", name, (), {})
arg_proxy.node.meta = meta.data
self.tracer.set_metadata(arg_proxy.node, arg)
return ProxyValue(arg, arg_proxy)
def call_operator(
self,
op,
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
meta: NodeMetadata,
) -> ProxyValue:
return self._fx("call_function", op, args, kwargs, meta)
def call_sym(
self,
target: Fn,
args: Tuple[Argument, ...],
meta: NodeMetadata,
) -> ProxyValue:
return self._fx("call_function", target, args, {}, meta)
def call_cond(
self,
pred: ProxyValue,
true_fn: torch.fx.GraphModule,
false_fn: torch.fx.GraphModule,
inputs: List[Argument],
meta: NodeMetadata,
) -> ProxyValue:
true_branch = self.call_submodule(true_fn, tuple(inputs))
false_branch = self.call_submodule(false_fn, tuple(inputs))
assert true_branch is not None
assert false_branch is not None
return self._fx(
"call_function",
torch.ops.higher_order.cond,
(pred, true_branch.graph_module, false_branch.graph_module, list(inputs)),
{},
meta,
)
def call_map(
self,
f: torch.fx.GraphModule,
mapped_args: List[ProxyValue],
operands: List[ProxyValue],
meta: NodeMetadata,
) -> ProxyValue:
xs = _unstack_pytree([arg.data for arg in mapped_args])[0]
f_branch = self.call_submodule(f, tuple(xs + [arg.data for arg in operands]))
assert f_branch is not None
return self._fx(
"call_function",
torch.ops.higher_order.map_impl,
(f_branch.graph_module, mapped_args, operands),
{},
meta,
)
def call_getitem(
self, value: ProxyValue, key: int, meta: NodeMetadata
) -> ProxyValue:
return self._fx("call_function", operator.getitem, (value, key), {}, meta)
def output(self, results: List[Argument], meta: NodeMetadata) -> ProxyValue:
return self._fx("output", "output", (results,), {}, meta)
def call_submodule(
self, graph_module: fx.GraphModule, inputs: Tuple[Argument, ...]
) -> PassResult:
prev_tracer, self.tracer = self.tracer, self.ExportTracer(
self, graph_module.graph._codegen
)
self.tracer.fake_tensor_mode = prev_tracer.fake_tensor_mode
interpreter = self.ExportInterpreter(self, graph_module)
prev_interpreter, self.interpreter = self.interpreter, torch.fx.Interpreter( # type: ignore[assignment]
torch.fx.GraphModule(torch.nn.Module(), torch.fx.Graph())
)
inputs_data = pytree.tree_map_only(ProxyValue, lambda x: x.data, inputs)
with fx_traceback.preserve_node_meta():
interpreter.run(*inputs_data)
new_graph_module = torch.fx.GraphModule(self.tracer.root, self.tracer.graph)
self.tracer = prev_tracer
self.interpreter = prev_interpreter
return PassResult(
new_graph_module,
True,
)
def call(self, graph_module: fx.GraphModule) -> PassResult:
if not getattr(self, "_initialized", False):
raise ExportPassBaseError(
"ExportPass is not initialized with __init__().",
)
inputs = self.inputs(graph_module)
fake_tensor_mode = None
for i in inputs:
if isinstance(i, FakeTensor):
assert (
fake_tensor_mode is None or fake_tensor_mode is i.fake_mode
), "Multiple fake tensor mode detected."
fake_tensor_mode = i.fake_mode
if fake_tensor_mode is None:
self.tracer.fake_tensor_mode = FakeTensorMode(allow_non_fake_inputs=True)
fake_tensor_mode = nullcontext() # type: ignore[assignment]
dispatcher_mode = nullcontext() # type: ignore[assignment]
else:
fake_tensor_mode.allow_non_fake_inputs = True
self.tracer.fake_tensor_mode = fake_tensor_mode
dispatcher_mode = enable_python_dispatcher() # type: ignore[assignment]
self.fake_tensor_mode = self.tracer.fake_tensor_mode
with fake_tensor_mode, dispatcher_mode: # type: ignore[assignment, union-attr]
result = self.call_submodule(graph_module, tuple(inputs))
return result
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