Add Support for Gradient Checkpointing #759
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- oerwrite the gradient_checkpointing_enable to provide our ForwardContext during the recomputation of values during backpropagation - 2 bugs remaining: bottleneck adapter for models with the legacy implementation (BERT) & Parallel. Parallel has the problem that we manipulate the batch dimension and this currently leads to an error
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docs & style & fixes - albert: skip unsupported tests - deberta(V2): fix embedding bug with inplace operations. - deberta: fix LoRAMergedLinear Bug with device mismatch
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Hey @calpt, can you quickly review my replies to your comments and approve the PR if everything is alright so I can merge this? We need to merge this PR first, then add all new tests to test refactoring #740, and then we can merge #763 (because Julian has already merged the test refactoring into his PR). So, currently, this PR is blocking everything we want to have in the next release. |
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Add Support for Gradient Checkpointing
This PR adds support for gradient checkpointing Gradient checkpointing is a technique that trades computation for memory by recomputing intermediate activations during the backward pass instead of storing them. This is particularly useful when training large models. Because we recompute values during the backpropagation, we need to preserve the original ForwardContext in this phase. I solved this by overwriting the
gradient_checkpointing_enablefunction so that the checkpoint function receives the current ForwardContext as the backward pass context manager.