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# mypy: allow-untyped-defs
import copy
from typing import Any, Dict, Set, Union
import torch
from torch.fx import GraphModule
from torch.fx.graph import Graph
__all__ = [
"FusedGraphModule",
"ObservedGraphModule",
"ObservedStandaloneGraphModule",
"QuantizedGraphModule",
]
class FusedGraphModule(GraphModule):
def __init__(
self,
root: Union[torch.nn.Module, Dict[str, Any]],
graph: Graph,
preserved_attr_names: Set[str],
):
self.preserved_attr_names = preserved_attr_names
preserved_attrs = {
attr: getattr(root, attr)
for attr in self.preserved_attr_names
if hasattr(root, attr)
}
super().__init__(root, graph)
for attr in preserved_attrs:
setattr(self, attr, preserved_attrs[attr])
# GraphModule does not copy attributes which are not in the __dict__
# of vanilla nn.Module. So, we override __deepcopy__ in order
# to copy the quantization specific attributes correctly.
def __deepcopy__(self, memo):
fake_mod = torch.nn.Module()
fake_mod.__dict__ = copy.deepcopy(self.__dict__)
return FusedGraphModule(
fake_mod,
copy.deepcopy(self.graph),
copy.deepcopy(self.preserved_attr_names),
)
class ObservedGraphModule(GraphModule):
def __init__(
self,
root: Union[torch.nn.Module, Dict[str, Any]],
graph: Graph,
preserved_attr_names: Set[str],
):
self.preserved_attr_names = {
"_activation_post_process_map",
"_activation_post_process_indexes",
"_patterns",
"_node_name_to_qconfig",
"_prepare_custom_config",
"_equalization_node_name_to_qconfig",
"_node_name_to_scope",
"_qconfig_mapping",
"_is_qat",
"_observed_node_names",
}.union(preserved_attr_names)
preserved_attrs = {
attr: getattr(root, attr)
for attr in self.preserved_attr_names
if hasattr(root, attr)
}
super().__init__(root, graph)
for attr in preserved_attrs:
setattr(self, attr, preserved_attrs[attr])
# GraphModule does not copy attributes which are not in the __dict__
# of vanilla nn.Module. So, we override __deepcopy__ in order
# to copy the quantization specific attributes correctly.
def __deepcopy__(self, memo):
fake_mod = torch.nn.Module()
fake_mod.__dict__ = copy.deepcopy(self.__dict__)
return ObservedGraphModule(
fake_mod,
copy.deepcopy(self.graph),
copy.deepcopy(self.preserved_attr_names),
)
def _is_observed_module(module: Any) -> bool:
return hasattr(module, "meta") and "_observed_graph_module_attrs" in module.meta
def _get_observed_graph_module_attr(
model: Union[torch.nn.Module, GraphModule], attr_name: str
) -> Any:
if hasattr(model, "meta") and "_observed_graph_module_attrs" in model.meta: # type: ignore[operator, index]
return getattr(model.meta["_observed_graph_module_attrs"], attr_name) # type: ignore[index]
return None
class ObservedStandaloneGraphModule(ObservedGraphModule):
def __init__(
self,
root: Union[torch.nn.Module, Dict[str, Any]],
graph: Graph,
preserved_attr_names: Set[str],
):
preserved_attr_names = preserved_attr_names.union(
{
"_standalone_module_input_quantized_idxs",
"_standalone_module_output_quantized_idxs",
}
)
super().__init__(root, graph, preserved_attr_names)
def __deepcopy__(self, memo):
fake_mod = torch.nn.Module()
fake_mod.__dict__ = copy.deepcopy(self.__dict__)
return ObservedStandaloneGraphModule(
fake_mod,
copy.deepcopy(self.graph),
copy.deepcopy(self.preserved_attr_names),
)
def _is_observed_standalone_module(module: Any) -> bool:
return (
_is_observed_module(module)
and module.meta["_observed_graph_module_attrs"].is_observed_standalone_module
)
def _save_packed_weight(self, destination, prefix, keep_vars):
for attr_name in dir(self):
if "_packed_weight" in attr_name and isinstance(
getattr(self, attr_name), torch._C.ScriptObject
): # type: ignore[attr-defined]
packed_weight = getattr(self, attr_name)
destination[prefix + attr_name] = packed_weight
class QuantizedGraphModule(GraphModule):
"""This class is created to make sure PackedParams
(e.g. LinearPackedParams, Conv2dPackedParams) to appear in state_dict
so that we can serialize and deserialize quantized graph module with
torch.save(m.state_dict()) and m.load_state_dict(state_dict)
"""
def __init__(
self,
root: Union[torch.nn.Module, Dict[str, Any]],
graph: Graph,
preserved_attr_names: Set[str],
):
self.preserved_attr_names = preserved_attr_names
preserved_attrs = {
attr: getattr(root, attr)
for attr in self.preserved_attr_names
if hasattr(root, attr)
}
super().__init__(root, graph)
for attr in preserved_attrs:
setattr(self, attr, preserved_attrs[attr])
self._register_state_dict_hook(_save_packed_weight)
def _load_from_state_dict(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
attrs_to_pop = []
for attr_name in state_dict:
if attr_name.startswith("_packed_weight") and isinstance(state_dict[attr_name], torch._C.ScriptObject): # type: ignore[attr-defined] # noqa: B950
setattr(self, attr_name, state_dict[attr_name])
attrs_to_pop.append(attr_name)
# pop the packed param attributesn
for attr_name in attrs_to_pop:
state_dict.pop(attr_name)
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
)
def __deepcopy__(self, memo):
fake_mod = torch.nn.Module()
fake_mod.__dict__ = copy.deepcopy(self.__dict__)
return QuantizedGraphModule(
fake_mod,
copy.deepcopy(self.graph),
copy.deepcopy(self.preserved_attr_names),
)
|
