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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_LEVEL` is introduced to control the usage, by default, it is 0, e.g. `USER_SPECIFIED`, all subgraphs definedin `ORTMODULE_MEMORY_OPT_CONFIG` will 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 in `ORTMODULE_MEMORY_OPT_CONFIG` will 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 |
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| .. | ||
| c_cxx | ||
| execution_providers/images | ||
| images | ||
| python | ||
| ABI_Dev_Notes.md | ||
| Android_testing.md | ||
| C_API_Guidelines.md | ||
| cmake_guideline.md | ||
| Coding_Conventions_and_Standards.md | ||
| ContribOperators.md | ||
| FAQ.md | ||
| How_To_Update_ONNX_Dev_Notes.md | ||
| Memory_Optimizer.md | ||
| Model_Test.md | ||
| NotesOnThreading.md | ||
| ONNX_Runtime_Server_Usage.md | ||
| onnxruntime_dependencies.dot | ||
| onnxruntime_dependencies.png | ||
| onnxruntime_extensions.md | ||
| OperatorKernels.md | ||
| ORT_Format_Update_in_1.13.md | ||
| ORT_Use_Trtion_Kernel.md | ||
| ORTMobilePackageOperatorTypeSupport.md | ||
| ORTModule_Convergence_Notes.md | ||
| ORTModule_ModuleWithLoss_Wrapper.md | ||
| ORTModule_PythonOp_Notes.md | ||
| ORTModule_Training_Guidelines.md | ||
| PR_Guidelines.md | ||
| Privacy.md | ||
| Python_Dev_Notes.md | ||
| Reduced_Operator_Kernel_build.md | ||
| ReleaseManagement.md | ||
| Roadmap.md | ||
| Server.md | ||
| TVM_EP.md | ||
| Versioning.md | ||
| WinML_principles.md | ||
