Improve information about group offloading and layerwise casting #11101
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a-r-r-o-w
commented
Mar 18, 2025
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
DN6
reviewed
Mar 18, 2025
DN6
reviewed
Mar 18, 2025
DN6
reviewed
Mar 18, 2025
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| - Layerwise casting may not work with all models out-of-the-box. Sometimes, the forward implementations of the model contain weight-dependent typecasting of inputs. Such implementations are not supported due to the currently simplistic implementation of layerwise casting, which assumes that the forward pass is independent of the weight precision and that the input dtypes are always in `compute_dtype`. An example of an incompatible implementation can be found [here](https://github.com/huggingface/transformers/blob/7f5077e53682ca855afc826162b204ebf809f1f9/src/transformers/models/t5/modeling_t5.py#L294-L299). |
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Should also mention that it can be disabled on modules with the skip patterns.
DN6
reviewed
Mar 18, 2025
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| - Group offloading may not work with all models out-of-the-box. If the forward implementations of the model contain weight-dependent device-casting of inputs, it may clash with the offloading mechanism's handling of device-casting. |
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Should also mention that it can be disabled on modules with the skip patterns.
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We don't support skipping in group offloading. Will mention for layerwise casting though
Co-authored-by: Dhruv Nair <[email protected]>
DN6
approved these changes
Mar 24, 2025
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Failing test looks unrelated |
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