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Update doc to include Benchmark Dashboard (#6202)
Summary: - Linked to the dashboard - Removed static table - Updated supported backends to include MPS Pull Request resolved: #6202 Reviewed By: kirklandsign Differential Revision: D64357945 Pulled By: guangy10 fbshipit-source-id: 218c75a5420de302631dece8b8cbe3c98dd7f7f9
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‎.lintrunner.toml

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'lintrunner_adapters',
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'run',
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'grep_linter',
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'--pattern= Executorch\W+',
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# Exclude "ExecuTorch" pattern within URLs
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'--pattern= Executorch(?!\\W*(://|\\.[a-z]{2,}))\\W+',
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'--linter-name=ExecuTorchCapitalization',
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'--error-name=Incorrect capitalization for ExecuTorch',
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"""--error-description=

‎extension/benchmark/README.md

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- **Device Support**: Includes popular phones like latest Apple iPhone, Google Pixel, and Samsung Galaxy, etc.
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- **Backend Delegates**: Supports XNNPACK, Apple CoreML, Qualcomm QNN, and more in the near future.
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- **Backend Delegates**: Supports XNNPACK, Apple CoreML and MPS, Qualcomm QNN, and more in the near future.
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- **Benchmark Apps:** Generic apps that support both GenAI and non-GenAI models, capable of measuring performance offline. [Android App](android/benchmark/) | [iOS App](apple/Benchmark/). Popular Android and iOS profilers with in-depth performance analysis will be integrated with these apps in the future.
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> **Disclaimer:** The infrastructure is new and experimental. We're working on improving its accessibility and stability over time.
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## Preliminary Benchmark Results
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Below is a table summarizing some example data points obtained via the infra. These numbers represent model load time and average inference latency across different platforms and backends.
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| Model | Backend | Model Load Time (ms) | Avg Inference Latency (ms) | Device |
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|-----------------------|--------------|----------------------|----------------------------|------------------------|
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| MobileNetV3 (mv3) | XNNPACK Q8 | [34.024](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218988461) | [252.628](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218988461) | Samsung S22 |
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| MobileNetV3 (mv3) | QNN FP16 | [168.414](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218987785) | [1.182](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218987785) | Samsung S22 |
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| MobileNetV3 (mv3) | COREML FP16 | [57.372](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930691) | [0.429](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930691) | Apple iPhone 15 Pro |
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| MobileNetV2 (mv2) | XNNPACK Q8 | [14.397](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218987379) | [10.796](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218987379) | Samsung S22 |
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| MobileNetV2 (mv2) | QNN FP16 | [136.862](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218987097) | [0.673](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218987097) | Samsung S22 |
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| MobileNetV2 (mv2) | COREML FP16 | [50.892](https://github.com/pytorch/executorch/actions/runs/11171117103/job/31056078594) | [0.631](https://github.com/pytorch/executorch/actions/runs/11171117103/job/31056078594) | Apple iPhone 15 Pro |
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| InceptionV4 (ic4) | XNNPACK Q8 | [87.617](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218986716) | [117.937](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218986716) | Samsung S22 |
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| InceptionV4 (ic4) | QNN FP16 | [163.943](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218986381) | [2.734](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218986381) | Samsung S22 |
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| InceptionV4 (ic4) | COREML FP16 | [118.686](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930140) | [4.289](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930140) | Apple iPhone 15 Pro |
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| InceptionV3 (ic3) | XNNPACK Q8 | [60.708](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218986023) | [98.390](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218986023) | Samsung S22 |
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| InceptionV3 (ic3) | QNN FP16 | [134.732](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218985425) | [1.351](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218985425) | Samsung S22 |
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| InceptionV3 (ic3) | COREML FP16 | [86.728](https://github.com/pytorch/executorch/actions/runs/11171117103/job/31056078753) | [1.391](https://github.com/pytorch/executorch/actions/runs/11171117103/job/31056078753) | Apple iPhone 15 Pro |
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| DeepLabV3 (dl3) | XNNPACK Q8 | [90.616](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218985758) | [666.219](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218985758) | Samsung S22 |
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| DeepLabV3 (dl3) | QNN FP16 | [182.207](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218985141) | [9.759](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218985141) | Samsung S22 |
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| ResNet50 (resnet50) | XNNPACK Q8 | [55.462](https://github.com/pytorch/executorch/actions/runs/10911725781/job/30285857102) | [37.021](https://github.com/pytorch/executorch/actions/runs/10911725781/job/30285857102) | Apple iPhone 15 Pro |
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| ResNet50 (resnet50) | COREML FP16 | [68.943](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930818) | [1.979](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930818) | Apple iPhone 15 Pro |
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| TorchVisionVit (vit) | QNN FP16 | [174.430](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218989581) | [199.279](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218989581) | Samsung S22 |
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| Wave2Letter (w2l) | XNNPACK Q8 | [33.913](https://github.com/pytorch/executorch/actions/runs/10857890364/job/30139445319) | [135.584](https://github.com/pytorch/executorch/actions/runs/10857890364/job/30139445319) | Apple iPhone 15 Pro |
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| Wave2Letter (w2l) | COREML FP16 | [109.254](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999931566) | [28.465](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999931566) | Apple iPhone 15 |
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| MobileBERT (mobilebert) | XNNPACK FP32 | [26.499](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930558) | [33.978](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930558) | Apple iPhone 15 Pro |
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| MobileBERT (mobilebert) | COREML FP16 | [206.202](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930398) | [1.873](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999930398) | Apple iPhone 15 Pro |
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| EDSR (edsr) | XNNPACK Q8 | [3.190](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999929836) | [168.429](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999929836) | Apple iPhone 15 Pro |
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| EDSR (edsr) | COREML FP16 | [156.075](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999929690) | [77.346](https://github.com/pytorch/executorch/actions/runs/11136241814/job/30999929690) | Apple iPhone 15 Pro |
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## Dashboard
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The ExecuTorch Benchmark Dashboard tracks performance metrics for various models across different backend delegates and devices. It enables users to compare metrics, monitor trends, and identify optimizations or regressions in Executorch. The dashboard is accessible at **[ExecuTorch Benchmark Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=pytorch%2Fexecutorch)**.
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**Comprehensive Comparisons**:
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- Analyze performance differences between backend delegates (e.g., XNNPACK, CoreML, QNN, MPS) for the same model.
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- Compare performance across different models.
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- Track performance changes over time and across different commits.
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**Metrics Tracking**:
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- Monitor essential metrics such as load time and inference time. For LLMs, additional metrics like tokens/s are available.
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- Observe performance trends over time to identify improvements or regressions.
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**Visualizations**:
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- View detailed performance data through charts and graphs.
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- Color-coded highlights for improvements (green) and regressions (red) exceeding 5% compared to the baseline.
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## Supported Use Cases

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