Refactor adapter composition implementation #591
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lenglaender
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Oct 18, 2023
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Looks great to me 👍
Can be merged once the backwards compatibility tests have been run (and once the part of the TODOs we want to do in this PR has been implemented).
And maybe rename tests_adapters/methods/test_adapter_common.py to sth. like tests_adapters/methods/test_bottleneck_adapter.py
Co-authored-by: Leon Engländer <leon.englaender@gmail.com>
hSterz
reviewed
Oct 25, 2023
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@hSterz @lenglaender also updated the docs accordingly now. Feel free to have another look. |
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The documentation looks good to me |
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calpt
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Nov 18, 2023
Follow-up to #591. This PR provides initial support for adapter composition in LoRA & (IA)³ modules. Currently LoRA & (IA)³ don't support composition. With this PR, the following blocks will be supported: **Stack, BatchSplit, Average, Parallel** Additionally, the LoRA implementation is refactored a bit in an effort to make it cleaner. ### Limitations - Split & Fuse compositions are **not** supported - LoRA/ (IA)³ composition is **not** supported for models using the `LoRAMergedLinear` implementation. These currently are: **GPT-2, DeBERTa (v1)**
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Refactors the implementation of composition blocks in the model forward pass such that more of the logic is shared between all adapter methods.
Changes
methodsfolderComposableAdapterLayerBaseas subclass ofAdapterLayerbaseas shared base class of all adapter methods that support composition.Stack,Parallel,BatchSplit,Average) which can be used by all subclasses.ComposableAdapterLayerBasemust be implemented. See https://github.com/calpt/adapter-transformers/blob/55fdc0cbe2f695914108a9c0e208127b13bc617e/src/adapters/methods/adapter_layer_base.py#L132-L222.NamedTuplein the base class. Deriving classes should define concreteNamedTuple-derived state classes. E.g., see https://github.com/calpt/adapter-transformers/blob/55fdc0cbe2f695914108a9c0e208127b13bc617e/src/adapters/methods/bottleneck.py#L22Splitcomposition block to support more than two child blocks. Splits are defined as a list of split indices, ie.Split("a", "b", "c", splits=[64, 64, 64]). Breaking changeAdapterLayer->BottleneckLayer;PrefixTuningShim->PrefixTuningLayerTodo
cc @TimoImhof would be nice to run some backwards compability tests here :)