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
| @@ -235,6 +246,13 @@ In the above example, layerwise casting is enabled on the transformer component | |||
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| However, you gain more control and flexibility by directly utilizing the [`~hooks.layerwise_casting.apply_layerwise_casting`] function instead of [`~ModelMixin.enable_layerwise_casting`]. | |||
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| <Tip> | |||
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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
| @@ -198,6 +198,17 @@ export_to_video(video, "output.mp4", fps=8) | |||
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| Group offloading (for CUDA devices with support for asynchronous data transfer streams) overlaps data transfer and computation to reduce the overall execution time compared to sequential offloading. This is enabled using layer prefetching with CUDA streams. The next layer to be executed is loaded onto the accelerator device while the current layer is being executed - this increases the memory requirements slightly. Group offloading also supports leaf-level offloading (equivalent to sequential CPU offloading) but can be made much faster when using streams. | |||
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| <Tip> | |||
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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 <dhruv.nair@gmail.com>
DN6
approved these changes
Mar 24, 2025
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Failing test looks unrelated |
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