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43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
from refiners.fluxion.adapters.lora import Lora, SingleLoraAdapter, LoraAdapter
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from torch import randn, allclose
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import refiners.fluxion.layers as fl
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def test_single_lora_adapter() -> None:
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chain = fl.Chain(
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fl.Chain(
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fl.Linear(in_features=1, out_features=1),
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fl.Linear(in_features=1, out_features=1),
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),
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fl.Linear(in_features=1, out_features=2),
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)
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x = randn(1, 1)
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y = chain(x)
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lora_adapter = SingleLoraAdapter(chain.Chain.Linear_1).inject(chain.Chain)
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assert isinstance(lora_adapter[1], Lora)
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assert allclose(input=chain(x), other=y)
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assert lora_adapter.parent == chain.Chain
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lora_adapter.eject()
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assert isinstance(chain.Chain[0], fl.Linear)
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assert len(chain) == 2
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lora_adapter.inject(chain.Chain)
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assert isinstance(chain.Chain[0], SingleLoraAdapter)
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def test_lora_adapter() -> None:
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chain = fl.Chain(
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fl.Chain(
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fl.Linear(in_features=1, out_features=1),
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fl.Linear(in_features=1, out_features=1),
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),
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fl.Linear(in_features=1, out_features=2),
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)
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LoraAdapter[fl.Chain](chain, sub_targets=chain.walk(fl.Linear), rank=1, scale=1.0).inject()
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assert len(list(chain.layers(Lora))) == 3
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