BatchCentering and BatchLipNorm classes compatible with multi-GPU - #54
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franckma31
commented
Jan 28, 2026
franckma31
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- BatchCentering support for multi-GPU training, based on a running_mean accumulator and a running_num_batches (implemented as a special case of batchLipNorm class)
- BatchLipNorm compute the variance on batch and full set by using running means of square. This layer is not Lipschitz and can be used with a SharedLipFactory to maintain the global Lipschitz constant
- Vanilla export for BatchCentering and BatchLipNorm with the layer ScaleBiasLayer
- pytests on BatchCentering and BatchLipNorm
- LipschitzModule class inherits from ScaledLipschitzModule to be able to register the coefficient_lip to a SharedLipFactory
- multigpu tests are excluded from pytest : use torchrun on a multigpu config to run tests/multigpu scripts
…ng_mean accumulator and a running_num_batches
…seful only if we want to register scaling values), + modify scaling factor to get directly the multiplied value 1.0 / sqrt(max(var))
…ovide the get_scaling_factor and support factory also in LipschitzModule class
cofri
reviewed
Jan 29, 2026
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