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- Enabled via EXECUTORCH_ENABLE_EVENT_TRACER
- Add flag 'etdump_path' to specify the file path for the ETDump file
- Add flag 'num_executions' for number of iterations to run
- Create and pass event tracer 'ETDumpGen'
- Save ETDump to disk
- Update docs to reflect the changes
Signed-off-by: Benjamin Klimczak <[email protected]>
Change-Id: I876d5138455d1b04fba9af4016d8341e8866f9c0
Copy file name to clipboardExpand all lines: docs/source/native-delegates-executorch-xnnpack-delegate.md
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When executing the XNNPACK subgraphs, we prepare the tensor inputs and outputs and feed them to the XNNPACK runtime graph. After executing the runtime graph, the output pointers are filled with the computed tensors.
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#### **Profiling**
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We have enabled basic profiling for XNNPACK delegate that can be enabled with the following compiler flag `-DENABLE_XNNPACK_PROFILING`. With ExecuTorch's SDK integration, you can also now use the SDK tools to profile the model. You can follow the steps in [Using the ExecuTorch SDK to Profile a Model](./tutorials/sdk-integration-tutorial) on how to profile ExecuTorch models and use SDK's Inspector API to view XNNPACK's internal profiling information.
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We have enabled basic profiling for XNNPACK delegate that can be enabled with the following compiler flag `-DEXECUTORCH_ENABLE_EVENT_TRACER`. With ExecuTorch's SDK integration, you can also now use the SDK tools to profile the model. You can follow the steps in [Using the ExecuTorch SDK to Profile a Model](./tutorials/sdk-integration-tutorial) on how to profile ExecuTorch models and use SDK's Inspector API to view XNNPACK's internal profiling information. An example implementation is available in the `xnn_executor_runner` (see [tutorial here](tutorial-xnnpack-delegate-lowering.md#profiling)).
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[comment]: <>(TODO: Refactor quantizer to a more official quantization doc)
Copy file name to clipboardExpand all lines: docs/source/tutorial-xnnpack-delegate-lowering.md
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## Building and Linking with the XNNPACK Backend
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You can build the XNNPACK backend [CMake target](https://github.com/pytorch/executorch/blob/main/backends/xnnpack/CMakeLists.txt#L83), and link it with your application binary such as an Android or iOS application. For more information on this you may take a look at this [resource](demo-apps-android.md) next.
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## Profiling
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To enable profiling in the `xnn_executor_runner` pass the flags `-DEXECUTORCH_ENABLE_EVENT_TRACER=ON` and `-DEXECUTORCH_BUILD_SDK=ON` to the build command. This will enable ETDump generation when running the inference and enables command line flags for profiling (see `xnn_executor_runner --help` for details).
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