[DOC] Fix outdated version-pinned doc URLs#19325
[DOC] Fix outdated version-pinned doc URLs#19325XAheli wants to merge 3 commits intopytorch:mainfrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/19325
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 3 Pending, 4 Unrelated FailuresAs of commit 0bf9c33 with merge base 1debeb6 ( NEW FAILURE - The following job has failed:
FLAKY - The following jobs failed but were likely due to flakiness present on trunk:
BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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Pull request overview
This PR updates ExecuTorch documentation to remove outdated, version-pinned GitHub URLs (commit hashes and release branches) in favor of main references, and refreshes line anchors / docs-site version links so references remain accurate on the current codebase.
Changes:
- Replace pinned
github.com/pytorch/executorchblob/tree links with.../main/...across multiple docs. - Update various GitHub line-number anchors to current
mainlocations. - Update an old
docs.pytorch.org/executorch/0.4/...reference to thestabledocs site.
Reviewed changes
Copilot reviewed 10 out of 10 changed files in this pull request and generated 5 comments.
Show a summary per file
| File | Description |
|---|---|
| docs/source/using-executorch-android.md | Update workflow link for AAR generation to point at main. |
| docs/source/compiler-memory-planning.md | Update greedy memory-planning implementation link to main with a refreshed line anchor. |
| docs/source/compiler-custom-compiler-passes.md | Refresh main links/anchors for pass/partitioner references (ExecuTorch + PyTorch). |
| docs/source/bundled-io.md | Update example-runner links from release branches to main (with anchors preserved). |
| docs/source/backends/xnnpack/xnnpack-partitioner.rst | Update XNNPACK partitioner/config links from release/0.6 to main and adjust anchors. |
| docs/source/backends/nxp/nxp-partitioner.rst | Update NXP Neutron links from release/1.2 to main. |
| docs/source/backends/nxp/nxp-overview.md | Update NXP example script link from release/1.0 to main. |
| docs/source/backends/arm-ethos-u/arm-ethos-u-troubleshooting.md | Update bundled-io docs link from a versioned docs path to stable. |
| docs/source/backends-qualcomm.md | Update Qualcomm backend links from pinned commit trees to main. |
| docs/source/api-life-cycle.md | Update PyTorch/ExecuTorch example links to main with refreshed anchors. |
Comments suppressed due to low confidence (2)
docs/source/api-life-cycle.md:111
- There is an unmatched closing
</ul>tag inside this table cell (no<ul>exists anywhere in the file). This produces invalid HTML and can break rendering in some doc toolchains. Please remove the stray</ul>(or add the missing opening tag if a list is intended).
Use <code>.. warning::</code> in the docstrings of deprecated and experimental
APIs. See
<a href="https://github.com/pytorch/pytorch/blob/main/torch/nn/utils/stateless.py#L176">example
usage</a>.
</ul>
</td>
docs/source/api-life-cycle.md:124
- There is an unmatched closing
</ul>tag in this table cell (no<ul>exists in the file). Please remove this stray closing tag (or add the missing opening tag) to keep the generated HTML valid.
Use the <code>ET_DEPRECATED</code> annotation macro. See <a href="https://github.com/pytorch/executorch/blob/main/runtime/executor/program.h#L92">example usage</a>.
<p>
<p>
Use the <code>ET_EXPERIMENTAL</code> annotation macro.
</ul>
</td>
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| - ``configs``: Control which operators are delegated to XNNPACK. By default, all available operators all delegated. See `../config/__init__.py <https://github.com/pytorch/executorch/blob/release/0.6/backends/xnnpack/partition/config/__init__.py#L66>`_ for an exhaustive list of available operator configs. | ||
| - ``config_precisions``: Filter operators by data type. By default, delegate all precisions. One or more of ``ConfigPrecisionType.FP32``, ``ConfigPrecisionType.STATIC_QUANT``, or ``ConfigPrecisionType.DYNAMIC_QUANT``. See `ConfigPrecisionType <https://github.com/pytorch/executorch/blob/release/0.6/backends/xnnpack/partition/config/xnnpack_config.py#L24>`_. | ||
| - ``configs``: Control which operators are delegated to XNNPACK. By default, all available operators all delegated. See `../config/__init__.py <https://github.com/pytorch/executorch/blob/main/backends/xnnpack/partition/config/__init__.py#L76>`_ for an exhaustive list of available operator configs. |
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| This section lists the Edge operators supported by the Neutron backend. | ||
| For detailed constraints of the operators see the conditions in the ``is_supported_*`` functions in the `Node converters <https://github.com/pytorch/executorch/blob/release/1.2/backends/nxp/neutron_partitioner.py#L202>`_ | ||
| For detailed constraints of the operators see the conditions in the ``is_supported_*`` functions in the `Node converters <https://github.com/pytorch/executorch/blob/main/backends/nxp/neutron_partitioner.py#L202>`_ |
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| ### Debugging tips | ||
| - Before trying any complicated models, try out [a simple model example](https://github.com/pytorch/executorch/tree/f32cdc3de6f7176d70a80228f1a60bcd45d93437/examples/qualcomm#simple-examples-to-verify-the-backend-is-working) and see it if works one device. | ||
| - Before trying any complicated models, try out [a simple model example](https://github.com/pytorch/executorch/tree/main/examples/qualcomm#simple-examples-to-verify-the-backend-is-working) and see it if works one device. |
| ### Runtime Example | ||
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| Please checkout our [example runner](https://github.com/pytorch/executorch/blob/release/0.6/examples/devtools/README.md#bundledprogram) for a bundled program. You could run these commands to test with the BundledProgram binary (`.bpte`) file you generated in the previous step: | ||
| Please checkout our [example runner](https://github.com/pytorch/executorch/blob/main/examples/devtools/README.md#bundledprogram) for a bundled program. You could run these commands to test with the BundledProgram binary (`.bpte`) file you generated in the previous step: |
| class is used by | ||
| the partitioner to determine if a specific node in the graph belongs in the | ||
| partition. This is done by overriding the `is_node_supported` function. You can | ||
| chain multiple `OperatorSuppportBase` by using |
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@pytorchbot label "module: doc" |
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Summary
Replace version-pinned GitHub blob/tree URLs (specific commit hashes and release/0.4, release/0.6, release/1.0, release/1.2 branches) with
/main/references across 10 doc files. Updatedocs.pytorch.org/executorch/0.4/URL to use/stable/. Update all line number anchors to match the currentmainbranch.Fixes #19257
Test plan
curlmainlocallylintrunner -apasses with no lint issuescc @mergennachin @AlannaBurke @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell