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[PT] disable grad for extracted module #3266

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Feb 11, 2025
Merged

[PT] disable grad for extracted module #3266

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2 changes: 2 additions & 0 deletions nncf/torch/extractor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -153,6 +153,8 @@ def extract_bn(node: NNCFNode, model: NNCFNetwork) -> Optional[Union[nn.BatchNor
for name, _ in chain(extracted_bn.named_parameters(), extracted_bn.named_buffers()):
setattr(extracted_bn, name, deepcopy(getattr(bn_module, name)))
extracted_bn.eval()
extracted_bn.weight.requires_grad = False
extracted_bn.bias.requires_grad = False
return extracted_bn


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17 changes: 9 additions & 8 deletions tests/torch/test_extractor.py
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Expand Up @@ -61,10 +61,10 @@ def test_extract_model(model_cls, input_node_name, output_node_name):

model = wrap_model(model_cls().eval(), example_input=example_input, trace_parameters=True)
extracted_module = extract_model(model, [input_node_name], [output_node_name])
with torch.no_grad():
ret1 = model(example_input)
ret2 = extracted_module(example_input)
assert torch.any(torch.isclose(ret1, ret2))
ret1 = model(example_input)
ret2 = extracted_module(example_input)
assert not ret2.grad_fn
assert torch.any(torch.isclose(ret1, ret2))


@pytest.mark.parametrize(
Expand Down Expand Up @@ -122,10 +122,11 @@ def test_extract_model_for_node_with_fq(model_cls, input_node_name, output_node_
q_model = transformer.transform(layout)

extracted_module = extract_model(model, [input_node_name], [output_node_name])
with torch.no_grad():
ret1 = q_model(example_input)
ret2 = extracted_module(example_input)
assert torch.all(torch.isclose(ret1, ret2))

ret1 = q_model(example_input)
ret2 = extracted_module(example_input)
assert torch.all(torch.isclose(ret1, ret2))
assert not ret2.grad_fn

extracted_fn = extracted_module
if isinstance(extracted_fn, nn.Sequential):
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