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pytorch/torch/_export/utils.py
Pian Pawakapan 8ff3a5be1b [export] basic auto dynamic shapes (#133620) 
Starter version of automatic dynamic shapes for export.

Creates enums `DIM.AUTO`, `DIM.STATIC`, allowing user to specify `AUTO` for dims in dynamic_shapes specs, meaning that corresponding dims are treated as dynamic, and relevant guards will do what's necessary (e.g. refine ValueRanges, set replacements based on equality, or even set static) without raising ConstraintViolationErrors. Basically allows the user to say, "a bunch of these dims can be dynamic, let export do model analysis and return the program with maximum possible dynamism, without complaining".

The usage for specifying `dynamic_shapes` is now:
```
AUTO -> dynamic by default, return whatever produce_guards() says, even if it's static
None/int/STATIC -> static
Dim/DerivedDim -> same as before - will complain if the min/max range is invalid, or if dims related to this are unspecified.
```

Caveat 1: specifying `AUTO` for a dim won't guarantee it'll be dynamic:

- specifying `AUTO` for a dim will return the maximum possible dynamism given your program and other specified constraints, but this can still mean you'll get a static program. For example, with the program below, x is specified dynamic, but it's equal to y, which is specified static, and with how we currently do things we won't promote y to dynamic, but will demote(?) x to static. So this can be surprising if you don't fully know your model, and/or missed one of your other inputs when specifying auto-dynamic shapes.
```
class Foo(torch.nn.Module):
    def forward(self, x, y):
        return x + y
inputs = (torch.randn(6), torch.randn(6))
export(Foo(), inputs, dynamic_shapes={"x": (DIM.AUTO,), "y": None})
```

Caveat 2: specifying `AUTO` and Dims in the same spec is still problematic:

- The way Dims/DerivedDims are currently handled is very strict. A Dim represents a symbol, and we require a user to specify the symbol for all dims governed by the symbol - that's why we've seen errors in the past like `The values of x must always be related to y by ...`, asking the user to specify the exact relation as in the program. We also require the specified min/max range to be a subset of the valid range from model analysis. All this doesn't compose well with specifying `AUTO` just yet - for example in the program below, ideal behavior could be to return a dynamic program, where `dx = x.size(0) = y.size(0)` has range (3,6). Unfortunately this crashes, and correct behavior is to specify `dx` for both inputs. So currently we raise a UserError and crash if both Dims + `AUTO` are present in the spec.
```
class Foo(torch.nn.Module):
    def forward(self, x, y):
        return x + y
inputs = (torch.randn(6), torch.randn(6))
export(Foo(), inputs, dynamic_shapes={"x": (DIM.AUTO,), "y": {0: Dim("dx", min=3, max=6)}})  # this doesn't work, because x & y and related
```

Implementation details:

This is done by setting `assume_static_by_default=False`, and doing a transform on the `dynamic_shapes` spec to preserve semantics. `assume_static_by_default=False` will treat unspecified dims or Nones as dynamic. This is the opposite of what `export.export()` currently does - unspecified Dims/Nones are treated as static. Historically this static-by-default behavior, where the user deals with fewer guards, has been desirable, and we would like to respect that in this implementation. So this internal spec transformation is added, `_transform_shapes_for_default_dynamic()`, does the spec conversion necessary to be compatbile with dynamic by default. Specifically, AUTOs are converted into Nones, and Nones/unspecified dims are filled in with explicitly static constraints.

For example, this would look like, for a 3-d tensor: `{0: DIM.AUTO, 1: None, 2: Dim("dx")} -> {0: None, 1: 32, 2: Dim("dx")}`

This does seem overly complicated, but it's done to preserve dynamic shapes semantics for `torch._dynamo.export()`, which already uses `assume_static_by_default=False`, and follows the same process for generating shape constraints , via `_process_dynamic_shapes`. There the semantics are:
```
None/unspecified: dynamic by default
Dim/DerivedDim: also a strict assertion
```

If we don't care about BC for `_dynamo.export(dynamic_shapes)`, then we can just modify semantics for `_process_dynamic_shapes()` and change all the relevant tests in `test/dynamo/test_export.py`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/133620
Approved by: https://github.com/avikchaudhuri
2024-08-23 22:56:39 +00:00

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# mypy: allow-untyped-defs
import ast
import dataclasses
import inspect
import math
import operator
import re
from inspect import Parameter
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
import torch
from torch._subclasses.fake_tensor import FakeTensor
from torch.export import ExportedProgram
from torch.export.exported_program import (
_name_hoo_subgraph_placeholders,
_rename_without_collisions,
)
from torch.export.graph_signature import InputKind, OutputKind
from torch.utils._pytree import (
_register_pytree_node,
Context,
FlattenFunc,
FromDumpableContextFn,
GetAttrKey,
KeyPath,
keystr,
MappingKey,
SequenceKey,
ToDumpableContextFn,
tree_flatten_with_path,
UnflattenFunc,
)
placeholder_prefixes = {
InputKind.USER_INPUT: "",
InputKind.PARAMETER: "p_",
InputKind.BUFFER: "b_",
InputKind.CONSTANT_TENSOR: "c_",
InputKind.CUSTOM_OBJ: "obj_",
InputKind.TOKEN: "token",
}
def _check_input_constraints_for_graph(
input_placeholders: List[torch.fx.Node], flat_args_with_path, range_constraints
):
def get_keystr(key_path: KeyPath) -> str:
"""For a given index into the flat_args, return a human readable string
describing how to access it, e.g. "*args["foo"][0].bar"
"""
# Prefix the keypath with "*args" or "**kwargs" to make it clearer where
# the arguments come from. Ultimately we ought to serialize the
# original arg names for the best error message here.
args_kwargs_key_path = key_path[0]
assert isinstance(args_kwargs_key_path, SequenceKey)
if args_kwargs_key_path.idx == 0:
return f"*args{keystr(key_path[1:])}"
else:
kwarg_key = key_path[1]
assert isinstance(kwarg_key, MappingKey)
name = str(kwarg_key)[1:-1] # get rid of the enclosed []
return f"{name}{keystr(key_path[2:])}"
import sympy
from torch._export.passes.add_runtime_assertions_for_constraints_pass import (
_convert_range_to_int,
)
from torch.utils._sympy.solve import try_solve
if len(flat_args_with_path) != len(input_placeholders):
raise RuntimeError(
"Unexpected number of inputs "
f"(expected {len(input_placeholders)}, got {len(flat_args_with_path)})"
)
# NOTE: export already guarantees that the same symbol is used in metadata
# for all InputDims related by equality constraints, so we can just unify
# symbols with given input dimension values to check equality constraints.
unification_map: Dict[sympy.Symbol, Any] = {}
for (key_path, arg), node in zip(flat_args_with_path, input_placeholders):
node_val = node.meta.get("val")
if isinstance(node_val, FakeTensor):
if not isinstance(arg, torch.Tensor):
raise RuntimeError(
f"Expected input at {get_keystr(key_path)} to be a tensor, but got {type(arg)}",
)
if len(node_val.shape) != len(arg.shape):
raise RuntimeError(
f"Unexpected number of dimensions in input at {get_keystr(key_path)}.shape "
f"(expected {node_val.shape}, got {arg.shape})"
)
for j, (arg_dim, node_dim) in enumerate(zip(arg.shape, node_val.shape)):
# TODO(avik): Assert the following property in the IR verifier:
# node_dim is either an int or a SymInt containing an int or a unary sympy.Expr
if (
isinstance(node_dim, torch.SymInt)
and len(node_dim.node.expr.free_symbols) == 1
):
symbol = next(iter(node_dim.node.expr.free_symbols))
if symbol in unification_map:
existing_dim = node_dim.node.expr.subs(unification_map)
if arg_dim != existing_dim:
raise RuntimeError(
f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to "
f"{existing_dim}, but got {arg_dim}",
)
else:
if (
isinstance(arg_dim, torch.SymInt)
and not arg_dim.node.expr.is_number
):
# This can happen when, say, arg is a fake tensor.
# We do not run checks on symbolic shapes of fake inputs as
# such checks can affect the shape env.
pass
else:
if isinstance(node_dim.node.expr, sympy.Symbol):
# Short cut for try_solve below. Also useful in cases where
# sympy.Eq(node_dim.node.expr, arg_dim) would evaluate to False
# purely because symbol is constrained to be size-like,
# e.g., when node_dim.node.expr = symbol and arg_dim = 0.
unification_map[symbol] = int(arg_dim)
else:
solution = try_solve(
sympy.Eq(node_dim.node.expr, arg_dim), symbol
)
if solution is None:
raise RuntimeError( # noqa: B904
f"Expected input {node.name}.shape[{j}] = {arg_dim} to be "
f"of the form {node_dim.node.expr}, where {symbol} is an integer"
)
else:
unification_map[symbol] = int(solution[1])
if node_dim.node.expr in range_constraints:
min_val, max_val = _convert_range_to_int(
range_constraints[node_dim.node.expr]
)
# NOTE: we allow dimensions to be 0/1 at runtime
if min_val > 2:
if arg_dim < min_val:
raise RuntimeError(
f"Expected input at {get_keystr(key_path)}.shape[{j}] to be >= "
f"{min_val}, but got {arg_dim}",
)
if max_val < math.inf:
if arg_dim > max_val:
raise RuntimeError(
f"Expected input at {get_keystr(key_path)}.shape[{j}] to be <= "
f"{max_val}, but got {arg_dim}",
)
else:
if arg_dim != node_dim:
if (
isinstance(node_dim, torch.SymInt)
and not node_dim.node.expr.is_number
):
# this means we deferred a guard from export analysis to runtime, let this pass
# we'll add a runtime assert checking equality to this replacement expression
continue
raise RuntimeError(
f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to "
f"{node_dim}, but got {arg_dim}",
)
elif isinstance(node_val, (int, float, str)):
if type(arg) != type(node_val) or arg != node_val:
raise RuntimeError(
f"Expected input at {get_keystr(key_path)} to be equal to {node_val}, but got {arg}",
)
def register_dataclass_as_pytree_node(
cls: Type[Any],
flatten_fn: Optional[FlattenFunc] = None,
unflatten_fn: Optional[UnflattenFunc] = None,
*,
serialized_type_name: Optional[str] = None,
to_dumpable_context: Optional[ToDumpableContextFn] = None,
from_dumpable_context: Optional[FromDumpableContextFn] = None,
return_none_fields: bool = False,
) -> None:
assert dataclasses.is_dataclass(
cls
), f"Only dataclasses can be registered with this function: {cls}"
def default_flatten_fn(obj: Any) -> Tuple[List[Any], Context]:
flattened = []
flat_names = []
none_names = []
for f in dataclasses.fields(obj):
name, val = f.name, getattr(obj, f.name)
if val is not None or return_none_fields:
flattened.append(val)
flat_names.append(name)
else:
none_names.append(name)
return flattened, [flat_names, none_names]
def default_unflatten_fn(values: Iterable[Any], context: Context) -> Any:
flat_names, none_names = context
return cls(**dict(zip(flat_names, values)), **dict.fromkeys(none_names))
def default_flatten_fn_with_keys(obj: Any) -> Tuple[List[Any], Context]:
flattened, (flat_names, none_names) = flatten_fn(obj) # type: ignore[misc]
return [(MappingKey(k), v) for k, v in zip(flat_names, flattened)], flat_names
flatten_fn = flatten_fn if flatten_fn is not None else default_flatten_fn
unflatten_fn = unflatten_fn if unflatten_fn is not None else default_unflatten_fn
if (to_dumpable_context is None) ^ (from_dumpable_context is None):
raise ValueError(
f"Both to_dumpable_context and from_dumpable_context for {cls} must "
"be None or registered."
)
_register_pytree_node(
cls,
flatten_fn,
unflatten_fn,
serialized_type_name=serialized_type_name,
flatten_with_keys_fn=default_flatten_fn_with_keys,
to_dumpable_context=to_dumpable_context,
from_dumpable_context=from_dumpable_context,
)
def is_param(program: ExportedProgram, node: torch.fx.Node) -> bool:
"""
Checks if the given node is a parameter within the exported program
"""
return node.name in program.graph_signature.inputs_to_parameters
def get_param(
program: ExportedProgram,
node: torch.fx.Node,
) -> Optional[torch.nn.Parameter]:
"""
Returns the parameter associated with the given node in the exported program.
Returns None if the node is not a parameter within the exported program
"""
if is_param(program, node):
parameter_name = program.graph_signature.inputs_to_parameters[node.name]
return program.state_dict[parameter_name]
return None
def is_buffer(program: ExportedProgram, node: torch.fx.Node) -> bool:
"""
Checks if the given node is a buffer within the exported program
"""
return node.name in program.graph_signature.inputs_to_buffers
def get_buffer(
program: ExportedProgram,
node: torch.fx.Node,
) -> Optional[torch.Tensor]:
"""
Returns the buffer associated with the given node in the exported program.
Returns None if the node is not a buffer within the exported program
"""
if is_buffer(program, node):
buffer_name = program.graph_signature.inputs_to_buffers[node.name]
if buffer_name in program.graph_signature.non_persistent_buffers:
return program.constants[buffer_name]
else:
return program.state_dict[buffer_name]
return None
def is_lifted_tensor_constant(
program: ExportedProgram,
node: torch.fx.Node,
) -> bool:
"""
Checks if the given node is a lifted tensor constant within the exported program
"""
return node.name in program.graph_signature.inputs_to_lifted_tensor_constants
def get_lifted_tensor_constant(
program: ExportedProgram,
node: torch.fx.Node,
) -> Optional[torch.Tensor]:
"""
Returns the lifted tensor constant associated with the given node in the exported program.
Returns None if the node is not a lifted tensor constant within the exported program
"""
if is_lifted_tensor_constant(program, node):
lifted_tensor_name = program.graph_signature.inputs_to_lifted_tensor_constants[
node.name
]
return program.constants[lifted_tensor_name]
return None
def sequential_split(gm: torch.fx.GraphModule, node_call_back) -> torch.fx.GraphModule:
"""
sequential_split creates a new graph module that splits the input graph module into multiple submodules
based on the node_call_back. It doesn't mutate the input graph module. The node_call_back should return
True if the node is a delimiter. Delimiter will be the first node in the next submodule.
"""
from torch.fx.passes.split_module import split_module
split_map = {}
split_id = 0
for node in gm.graph.nodes:
if node_call_back(node):
split_id += 1
split_map[node] = split_id
new_gm = split_module(
gm,
gm,
lambda node: split_map[node],
keep_original_order=True,
keep_original_node_name=True,
)
# Keep the codegen from original graph module to preserve e.g. pytree info.
new_gm.graph._codegen = gm.graph._codegen
new_gm.recompile()
return new_gm
def nodes_filter(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]:
"""Returns the nodes that match the node_call_back as a list."""
return [node for node in nodes if node_call_back(node)]
def nodes_first(
nodes: List[torch.fx.Node], node_call_back=None
) -> Optional[torch.fx.Node]:
"""
Returns the first node that matches the node_call_back. If no node matches, returns None.
When node_call_back is None, returns the first node in the node list.
"""
ret = nodes_filter(nodes, node_call_back if node_call_back else lambda node: True)
if len(ret) > 0:
return ret[0]
return None
def nodes_count(nodes: List[torch.fx.Node], node_call_back) -> int:
"""Returns the number of nodes that match the node_call_back."""
return len(nodes_filter(nodes, node_call_back))
def nodes_map(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]:
"""
Sequentially visit the nodes list and invoke node_call_back on each element.
Returns the nodes list after the node_call_back is invoked on each element.
"""
for node in nodes:
node_call_back(node)
return nodes
def node_replace_(old_node: torch.fx.Node, new_node: torch.fx.Node) -> None:
"""
Replace all uses of old_node with new_node.
"""
old_node.replace_all_uses_with(new_node)
old_node.users.clear()
old_node.graph.erase_node(old_node)
def node_inline_(call_mod_node: torch.fx.Node) -> None:
"""
Inline the submodule of the given node into the parent module.
Note: we only support the case where submodule takes tensors inputs.
"""
assert call_mod_node.op == "call_module"
gm = call_mod_node.graph.owning_module
assert isinstance(call_mod_node.target, str)
sub_gm = getattr(gm, call_mod_node.target)
phs = (node for node in sub_gm.graph.nodes if node.op == "placeholder")
body = (
node for node in sub_gm.graph.nodes if node.op not in ("placeholder", "output")
)
output = [node for node in sub_gm.graph.nodes if node.op == "output"]
for ph, arg in zip(phs, call_mod_node.args):
assert isinstance(arg, torch.fx.Node)
node_replace_(ph, arg)
with gm.graph.inserting_before(call_mod_node):
for node in body:
new_node = gm.graph.node_copy(node)
node_replace_(node, new_node)
if len(output) > 0:
assert len(output) == 1 and len(output[0].args) == 1
new_output = output[0].args[0]
if isinstance(new_output, torch.fx.Node):
# Clear the users of the output node and set
# the users to be the users of original call_module node.
new_output.users.clear()
node_replace_(call_mod_node, new_output)
elif isinstance(new_output, (list, tuple)):
# Pop subgraph output node from users.
for node in new_output:
node.users.pop(output[0])
# Inline the get_item calls for the output node.
get_item_users = nodes_filter(
list(call_mod_node.users.keys()),
lambda node: node.op == "call_function"
and node.target == operator.getitem,
)
# get_item_node.args[1] is the idx referring to new_output[idx]
nodes_map(
get_item_users,
lambda get_item_node: node_replace_(
get_item_node,
new_output[get_item_node.args[1]],
),
)
call_mod_node.graph.erase_node(call_mod_node)
else:
raise NotImplementedError(
f"Unsupported output type {type(new_output)}. Expect it to be a Node or a list/tuple of Nodes."
)
else:
call_mod_node.graph.erase_node(call_mod_node)
gm.delete_all_unused_submodules()
gm.recompile()
return gm
def _get_torch_jit_trace_forward_signature(mod: torch.nn.Module):
"""
Get source code and parse argument names using AST. The function returns
a signature of the forward() function.
# TODO: Directly provide inspect.signature compatible TS-d module.
"""
ast_mod = ast.parse(mod.code)
ast_func_def: ast.FunctionDef = ast_mod.body[0] # type: ignore[assignment]
# FIXME(jiashenc): TorchScript should only allow positional or keywords arguments.
arg_type_map = {"args": Parameter.POSITIONAL_OR_KEYWORD}
# Traverse all argument types in AST tree and create associated parameters.
param_list = []
for arg_type, param_type in arg_type_map.items():
arg_name_list = [a.arg for a in getattr(ast_func_def.args, arg_type)]
for arg_name in arg_name_list:
if arg_name == "self":
continue # Skip self argument.
param_list.append(inspect.Parameter(arg_name, param_type))
return inspect.Signature(parameters=param_list)
def _bind_signature_to_inputs(mod, fake_args, fake_kwargs):
if isinstance(mod, (torch.jit.ScriptModule, torch.jit.TracedModule)):
sig = _get_torch_jit_trace_forward_signature(mod)
# Sanity check for placeholder names coming from TorchScript.
assert len(sig.parameters) == len(fake_args) + len(fake_kwargs), (
"Arguments other than POSITIONAL_OR_KEYWORD kinds in forward() "
"are not supported in _get_torch_jit_trace_forward_signature"
)
else:
sig = inspect.signature(mod.forward)
return sig.bind(*fake_args, **fake_kwargs).arguments
def placeholder_naming_pass(
gm: torch.fx.GraphModule,
export_graph_signature: torch.export.ExportGraphSignature,
mod: torch.nn.Module,
fake_args,
fake_kwargs,
fake_params_buffers,
constants: Dict[str, Any],
) -> None:
"""
This pass is run at the end of _export_non_strict() to assign better placeholder node names:
- User inputs:
These follow the signature of mod.forward(), e.g. forward(x, y) produces nodes x, y.
For nested inputs from dictionaries, lists, tuples, or dataclasses,
the names are a concatenation of the path to the tensor.
e.g. x = {
'a': torch.randn(),
'b': [torch.randn(), torch.randn()]
}
produces nodes x_a, x_b_0, x_b_1.
- Parameters/buffers/constants/custom objects:
These follow the FQN of the object, prefixed by "p", "b", "c", "obj" respectively.
e.g. self.bar.l0.weight produces "p_bar_l0_weight".
- Effect tokens:
These are named token, token_1, ...
"""
def _strip_name(x):
if x.startswith("L__self___"):
x = x[len("L__self___") :]
x = re.sub(r"[^a-zA-Z0-9]", "_", x)
return x
def _extract_pytree_key(x):
if isinstance(x, MappingKey):
x = re.sub(r"[^a-zA-Z0-9]", "_", str(x.key))
return x
elif isinstance(x, SequenceKey):
return str(x.idx)
elif isinstance(x, GetAttrKey):
return x.name
else:
raise RuntimeError(f"Pytree key of type {type(x)} not handled for {x}")
name_map: Dict[str, str] = {}
# map user input names with mod.forward() signature
combined_args = _bind_signature_to_inputs(mod, fake_args, fake_kwargs)
flat_args_with_path, _ = tree_flatten_with_path(combined_args)
user_input_names = [
spec.arg.name
for spec in export_graph_signature.input_specs
if spec.kind == InputKind.USER_INPUT
]
# use pytree path to name nested user inputs
for (arg_path, arg), user_input_name in zip(flat_args_with_path, user_input_names):
if user_input_name:
_rename_without_collisions(
name_map,
user_input_name,
placeholder_prefixes[InputKind.USER_INPUT]
+ "_".join(_extract_pytree_key(x).lower() for x in arg_path),
is_placeholder=True,
)
# use graph signature input specs to map param/buffer/constant names
# name effect tokens as token, token_1, ... (these aren't visible to user)
for spec in export_graph_signature.input_specs:
if spec.kind == InputKind.USER_INPUT:
continue
if spec.kind == InputKind.TOKEN:
base_name = ""
else:
base_name = _strip_name(spec.target).lower()
base_name = re.sub(r"[^a-zA-Z0-9]", "_", base_name)
_rename_without_collisions(
name_map,
spec.arg.name,
placeholder_prefixes[spec.kind] + base_name,
is_placeholder=True,
)
# handle naming collisions with call_function/get_attr inputs.
# here, we want to prioritize user input names over call_function names
# e.g. not have forward(self, mul): lead to a placeholder node called mul_13,
# so we increment the suffix of call_function nodes as needed
for node in gm.graph.nodes:
if node.op == "placeholder":
continue
_rename_without_collisions(name_map, node.name, node.name)
# assign new node names
for node in gm.graph.nodes:
if node.op == "placeholder":
assert node.name in name_map
node.name = node.target = name_map[node.name]
elif node.name in name_map:
node.name = name_map[node.name]
# propagate names to higher order op subgraphs
_name_hoo_subgraph_placeholders(gm)
# re-generate graph module code
gm.recompile()
# modify graph signature (input specs, output specs, user input mutations)
for spec in export_graph_signature.input_specs:
assert spec.arg.name in name_map
spec.arg.name = name_map[spec.arg.name]
if ( # handle targets for custom objects
spec.kind == InputKind.CUSTOM_OBJ and spec.target in name_map
):
spec.target = name_map[spec.target][4:] # strip obj_ prefix
for spec in export_graph_signature.output_specs:
if spec.arg.name in name_map:
spec.arg.name = name_map[spec.arg.name]
if spec.kind == OutputKind.USER_INPUT_MUTATION and spec.target in name_map:
spec.target = name_map[spec.target]
# rename keys in constants dict for custom objects
for name in list(constants.keys()):
constant = constants[name]
if name in name_map and not isinstance(
constant, torch.Tensor
): # rename custom objects with generic names
new_name = name_map[name]
if (
new_name != name
and re.match(r"arg(\d+)_1", name)
and new_name != placeholder_prefixes[InputKind.CUSTOM_OBJ] + name
):
constants[new_name] = constant
del constants[name]
def remove_proxy_from_state_dict(state_dict: Dict, in_place: bool) -> Dict:
"""
If `in_place` is false, return a new copy of `state_dict` with "proxy" removed from `v.__dict__`.
`v` is the values in the dictionary.
If `in_place` is true, modify `state_dict` in place.
"""
if in_place:
for k, v in state_dict.items():
if hasattr(v, "proxy"):
delattr(state_dict[k], "proxy")
return state_dict
else:
new_state_dict = {}
for k, v in state_dict.items():
if hasattr(v, "proxy"):
new_state_dict[k] = v.clone().detach()
else:
new_state_dict[k] = v
return new_state_dict
def _detect_fake_mode_from_gm(
gm: torch.fx.GraphModule,
) -> torch._subclasses.fake_tensor.FakeTensorMode:
"""
For a given graph module, we look at the "val" of placeholder nodes to find the fake inputs.
Additionally, if gm doesn't have placeholders, we further look at the "example_value" or "val" of other nodes.
If no fake mode is found, we return None for fake_mode.
"""
from torch._guards import detect_fake_mode
fake_inps: List[torch.Tensor] = []
fake_vals: List[torch.Tensor] = []
for node in gm.graph.nodes:
if node.op == "placeholder" and "val" in node.meta:
fake_val = node.meta["val"]
if fake_val is not None and isinstance(fake_val, torch.Tensor):
fake_inps.append(fake_val)
elif len(fake_inps) == 0 and (
"example_value" in node.meta or "val" in node.meta
):
fake_val = None
if "example_value" in node.meta:
fake_val = node.meta["example_value"]
elif "val" in node.meta:
fake_val = node.meta["val"]
if fake_val is not None and isinstance(fake_val, torch.Tensor):
fake_vals.append(fake_val)
return detect_fake_mode(fake_inps + fake_vals)
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