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### Description Staged: https://faxu.github.io/onnxruntime/docs/performance/ Main changes: - Restructure performance section to break into sub-categories - Move CUDA specific perf tuning tips to [CUDA EP page](https://faxu.github.io/onnxruntime/docs/execution-providers/CUDA-ExecutionProvider.html#performance-tuning) - Update [Transformer optimizer page](https://faxu.github.io/onnxruntime/docs/performance/transformers-optimization.html) to remove version-specific content... will be supported along with https://github.com/microsoft/onnxruntime/pull/14964 - Fix links to point to new pages
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2.7 KiB
Markdown
61 lines
No EOL
2.7 KiB
Markdown
---
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title: Profiling tools
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grand_parent: Performance
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parent: Tune performance
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nav_order: 1
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---
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# Profiling Tools
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## Contents
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{: .no_toc }
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* TOC placeholder
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{:toc}
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## ONNX Go Live Tool (OLive)
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The [ONNX Go Live "OLive" tool](https://github.com/microsoft/OLive) is a Python package that automates the process of accelerating models with ONNX Runtime. It contains two parts: (1) model conversion to ONNX with correctness validation (2) auto performance tuning with ORT. Users can run these two together through a single pipeline or run them independently as needed.
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As a quickstart, please see the [notebook tutorials](https://github.com/microsoft/OLive/tree/master/notebook-tutorial) and [command line examples](https://github.com/microsoft/OLive/tree/master/cmd-example)
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## In-code performance profiling
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The onnxruntime_perf_test.exe tool (available from the build drop) can be used to test various knobs. Please find the usage instructions using `onnxruntime_perf_test.exe -h`. The [perf_view tool](https://github.com/microsoft/onnxruntime/tree/main/tools/perf_view) can also be used to render the statistics as a summarized view in the browser.
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You can enable ONNX Runtime latency profiling in code:
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```python
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import onnxruntime as rt
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sess_options = rt.SessionOptions()
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sess_options.enable_profiling = True
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```
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If you are using the onnxruntime_perf_test.exe tool, you can add `-p [profile_file]` to enable performance profiling.
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In both cases, you will get a JSON file which contains the detailed performance data (threading, latency of each operator, etc). This file is a standard performance tracing file, and to view it in a user-friendly way, you can open it by using chrome://tracing:
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* Open Chrome browser
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* Type chrome://tracing in the address bar
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* Load the generated JSON file
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To profile CUDA kernels, please add the cupti library to your PATH and use the onnxruntime binary built from source with `--enable_cuda_profiling`.
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To profile ROCm kernels, please add the roctracer library to your PATH and use the onnxruntime binary built from source with `--enable_rocm_profiling`.
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Performance numbers from the device will then be attached to those from the host. For example:
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```json
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{"cat":"Node", "name":"Add_1234", "dur":17, ...}
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{"cat":"Kernel", "name":"ort_add_cuda_kernel", dur:33, ...}
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```
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Here, the "Add" operator from the host initiated a CUDA kernel on device named "ort_add_cuda_kernel" which lasted for 33 microseconds.
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If an operator called multiple kernels during execution, the performance numbers of those kernels will all be listed following the call sequence:
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```json
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{"cat":"Node", "name":<name of the node>, ...}
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{"cat":"Kernel", "name":<name of the kernel called first>, ...}
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{"cat":"Kernel", "name":<name of the kernel called next>, ...}
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``` |