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import torch
from torch.fx._symbolic_trace import Tracer
from torch.fx.proxy import Scope
from torch.ao.nn.intrinsic import _FusedModule
from typing import List, Callable
__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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