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from typing import Callable
import torch
from torch.ao.nn.intrinsic import _FusedModule
from torch.fx._symbolic_trace import Tracer
from torch.fx.proxy import Scope
__all__ = [
"QuantizationTracer",
]
class ScopeContextManager(torch.fx.proxy.ScopeContextManager):
def __init__(
self, scope: Scope, current_module: torch.nn.Module, current_module_path: str
):
super().__init__(scope, Scope(current_module_path, type(current_module)))
class QuantizationTracer(Tracer):
def __init__(
self, skipped_module_names: list[str], skipped_module_classes: list[Callable]
):
super().__init__()
self.skipped_module_names = skipped_module_names
self.skipped_module_classes = skipped_module_classes
# NB: initialized the module_type of top level module to None
# we are assuming people won't configure the model with the type of top level
# module here, since people can use "" for global config
# We can change this if there is a use case that configures
# qconfig using top level module type
self.scope = Scope("", None)
self.record_stack_traces = True
def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
return (
(
(
m.__module__.startswith("torch.nn")
or m.__module__.startswith("torch.ao.nn")
)
and not isinstance(m, torch.nn.Sequential)
)
or module_qualified_name in self.skipped_module_names
or type(m) in self.skipped_module_classes
or isinstance(m, _FusedModule)
)
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