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