Allow layer-wise recompute #18566
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Allow layer-wise recompute #18566
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pengwa
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…s even smaller than the other one in forward pass (FusedMatmul which is replaced by a new node after gradient graph is built)
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Find those stashed activations that are used by backward operators. Put all those activations as candidates; For each candidate, https://github.com/microsoft/onnxruntime/blob/f3369a8bf87190552ad551a6de56df01cccf7a62/orttraining/orttraining/core/optimizer/memory_optimizer/memory_insight.cc#L272C9-L272C30 will check whether it is recomputable, and how the subgraph looks like,.
No, that's the long term goal to have all those feature ready, to help dynamically choose a good plan for users. |
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askhade
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orttraining/orttraining/python/training/ortmodule/_training_manager.py
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zhijxu-MS
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zhijxu-MS
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askhade
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Thank you @askhade, @zhijxu-MS !! |
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Allow layer-wise recompute
Early, we need users/developers to specify the subgraphs to recompute, now we introduced a more user-friendly way to enable recompute for all detected stashed activation recomputation subgraphs. This scarifies getting the best configs while makes it easier to support user requirements when they switches from PyTorch per-layer gradient checkpoint to ORTModule.
ORTMODULE_MEMORY_OPT_LEVELis introduced to control the usage, by default, it is 0, e.g.USER_SPECIFIED, all subgraphs definedinORTMODULE_MEMORY_OPT_CONFIGwill be recomputed. So this is compatible to existing recompute usage in ORTModule integrated models.Using
ORTMODULE_MEMORY_OPT_LEVEL=1, we will enable all recompute plans detected, so those configs inORTMODULE_MEMORY_OPT_CONFIGwill not be respected any more.Add Unit Tests using 3 layer blooms.
https://github.com/microsoft/onnxruntime/blob/pengwa/add_aggresive_recompute/docs/Memory_Optimizer.md