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- from collections.abc 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)
- )
|
