merge from origin by jameswind · Pull Request #2 · jameswind/deepmd-kit

jameswind · 2020-06-27T14:49:14Z

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CodeQL found more than 20 potential problems in the proposed changes. Check the Files changed tab for more details.

Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 3.0 to 3.1. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/releases">pypa/cibuildwheel's releases</a>.</em></p> <blockquote> <h2>v3.1.0</h2> <ul> <li>🌟 CPython 3.14 wheels are now built by default - without the <code>"cpython-prerelease"</code> <code>enable</code> set. It's time to build and upload these wheels to PyPI! This release includes CPython 3.14.0rc1, which is guaranteed to be ABI compatible with the final release. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2507">#2507</a>) Free-threading is no longer experimental in 3.14, so you have to skip it explicitly with <code>'cp31?t-*'</code> if you don't support it yet. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2503">#2503</a>)</li> <li>🌟 Adds the ability to <a href="https://cibuildwheel.pypa.io/en/stable/platforms/#android">build wheels for Android</a>! Set the <a href="https://cibuildwheel.pypa.io/en/stable/options/#platform"><code>platform</code> option</a> to <code>android</code> on Linux or macOS to try it out! (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2349">#2349</a>)</li> <li>🌟 Adds Pyodide 0.28, which builds 3.13 wheels (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2487">#2487</a>)</li> <li>✨ Support for 32-bit <code>manylinux_2_28</code> (now a consistent default) and <code>manylinux_2_34</code> added (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2500">#2500</a>)</li> <li>🛠 Improved summary, will also use markdown summary output on GHA (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2469">#2469</a>)</li> <li>🛠 The riscv64 images now have a working default (as they are now part of pypy/manylinux), but are still experimental (and behind an <code>enable</code>) since you can't push them to PyPI yet (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2506">#2506</a>)</li> <li>🛠 Fixed a typo in the 3.9 MUSL riscv64 identifier (<code>cp39-musllinux_ricv64</code> -> <code>cp39-musllinux_riscv64</code>) (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2490">#2490</a>)</li> <li>🛠 Mistyping <code>--only</code> now shows the correct possibilities, and even suggests near matches on Python 3.14+ (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2499">#2499</a>)</li> <li>🛠 Only support one output from the repair step on linux like other platforms; auditwheel fixed this over four years ago! (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2478">#2478</a>)</li> <li>🛠 We now use pattern matching extensively (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2434">#2434</a>)</li> <li>📚 We now have platform maintainers for our special platforms and interpreters! (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2481">#2481</a>)</li> </ul> <h2>v3.0.1</h2> <ul> <li>🛠 Updates CPython 3.14 prerelease to 3.14.0b3 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2471">#2471</a>)</li> <li>✨ Adds a CPython 3.14 prerelease iOS build (only when prerelease builds are <a href="https://cibuildwheel.pypa.io/en/stable/options/#enable">enabled</a>) (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2475">#2475</a>)</li> </ul> </blockquote> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md">pypa/cibuildwheel's changelog</a>.</em></p> <blockquote> <h3>v2.23.1</h3> <p><em>15 March 2025</em></p> <ul> <li>⚠️ Added warnings when the shorthand values <code>manylinux1</code>, <code>manylinux2010</code>, <code>manylinux_2_24</code>, and <code>musllinux_1_1</code> are used to specify the images in linux builds. The shorthand to these (unmaintainted) images will be removed in v3.0. If you want to keep using these images, explicitly opt-in using the full image URL, which can be found in <a href="https://github.com/pypa/cibuildwheel/blob/v2.23.1/cibuildwheel/resources/pinned_docker_images.cfg">this file</a>. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2312">#2312</a>)</li> <li>🛠 Dependency updates, including a manylinux update which fixes an <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2303">issue with rustup</a>. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2315">#2315</a>)</li> </ul> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/pypa/cibuildwheel/commit/e6de07ed3921b51089aae6981989889cf1eddd0c"><code>e6de07e</code></a> Bump version: v3.1.1</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/2ca692b1e55a1f924bfb460099c9d7e015671a8d"><code>2ca692b</code></a> docs: iOS typo fix in docs (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2510">#2510</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/1ac7fa7f004958fbde774ee89523c446a5d99934"><code>1ac7fa7</code></a> fix: report defects in logs and HTML summaries (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2512">#2512</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/ffd835cef18fa11522f608fc0fa973b89f5ddc87"><code>ffd835c</code></a> Bump version: v3.1.0</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/3e2a9aa6e85824999f897fc2c060ca12a5113ef6"><code>3e2a9aa</code></a> fix: regenerate schema</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/10c727eed9fc962f75d33d472272e3ad78c3e707"><code>10c727e</code></a> feat: Python 3.14rc1 build by default (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2507">#2507</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/f628c9dd23fe6e263cb91cef755a51a0af3bcddc"><code>f628c9d</code></a> [pre-commit.ci] pre-commit autoupdate (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2505">#2505</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/0f487ee2cb00876d95290da49d04208c91237857"><code>0f487ee</code></a> feat: add support for building Android wheels (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2349">#2349</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/e2e24882d8422e974295b1b9079d4ce80a5098a4"><code>e2e2488</code></a> feat: add default riscv64 images (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2506">#2506</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/a8bff94dbb5f3a4a914e29cf8353c2f6f1b9ab8b"><code>a8bff94</code></a> [Bot] Update dependencies (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2504">#2504</a>)</li> <li>Additional commits viewable in <a href="https://github.com/pypa/cibuildwheel/compare/v3.0...v3.1">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=pypa/cibuildwheel&package-manager=github_actions&previous-version=3.0&new-version=3.1)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

updates: - [github.com/astral-sh/ruff-pre-commit: v0.12.4 → v0.12.5](astral-sh/ruff-pre-commit@v0.12.4...v0.12.5) - [github.com/njzjz/mirrors-bibtex-tidy: v1.13.0 → v1.14.0](njzjz/mirrors-bibtex-tidy@v1.13.0...v1.14.0)  Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Remove version `>=a.b.crc0` from dependencies. The stable version should be preferred. This prevents the installation of TensorFlow 2.20.0rc0, which breaks the CI. However, the incompatibility should still be fixed in the future. TensorFlow 2.20.0rc0 removes the version information from the header files.  ## Summary by CodeRabbit * **Chores** * Updated version requirements for TensorFlow and myst-nb packages to use stable releases instead of release candidate versions in build and documentation workflows. * Adjusted dependency handling for TensorFlow CPU to ensure stable versions are used during build processes.  Signed-off-by: Jinzhe Zeng <jinzhe.zeng@ustc.edu.cn>

…ed. (#4854) feat: add Mean absolute percentage error (MAPE) loss for property prediction  ## Summary by CodeRabbit * **New Features** * Added support for the Mean Absolute Percentage Error (MAPE) as a selectable loss function and metric. * **Documentation** * Updated user-facing documentation to include "mape" as an option for loss function selection.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

…sentative logging (#4850) This pull request modifies the training loop to improve the quality and readability of the reported training loss. ## Summary of Changes Previously, the training loss and associated metrics (e.g., rmse_e_trn) reported in lcurve.out and the console log at each disp_freq step represented the instantaneous value from that single training batch. This could be quite noisy and subject to high variance depending on the specific batch sampled. This PR introduces an accumulator for the training loss. The key changes are: During each training step, the loss values are accumulated. When a display step is reached, the accumulated values are averaged over the number of steps in that interval. This averaged loss is then reported in the log and lcurve.out. The accumulators are reset for the next interval. The validation logic remains unchanged, continuing to provide a periodic snapshot of model performance, which is the standard and efficient approach. ## Significance and Benefits Reporting the averaged training loss provides a much smoother and more representative training curve. The benefits include: Reduced Noise: Eliminates high-frequency fluctuations, making it easier to see the true learning trend. Improved Readability: Plotted learning curves from lcurve.out are cleaner and more interpretable. Better Comparability: Simplifies the comparison of model performance across different training runs, as the impact of single-batch anomalies is minimized. ## A Note on Formatting Please note that due to automatic code formatters (e.g., black, isort), some minor, purely stylistic changes may appear in the diff that are not directly related to the core logic.  ## Summary by CodeRabbit * **New Features** * Training loss values displayed during training are now averaged over the display interval, providing more stable and representative loss metrics for both single-task and multi-task modes. * Added an option to enable or disable averaging of training loss display via a new configuration setting. * **Improvements** * Enhanced training loss reporting for improved monitoring and analysis during model training.  --------- Signed-off-by: LI TIANCHENG <137472077+OutisLi@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Jinzhe Zeng <njzjz@qq.com>

The parameter "precision" supports bfloat16 with most backends, but it is not documented.  ## Summary by CodeRabbit * **New Features** * Added support for "bfloat16" as a selectable precision option.

updates: - [github.com/astral-sh/ruff-pre-commit: v0.12.5 → v0.12.7](astral-sh/ruff-pre-commit@v0.12.5...v0.12.7)  Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

`get_xp_precision` is more general and has more precisions (including bfloat16).  ## Summary by CodeRabbit * **Refactor** * Improved internal handling of precision settings for numerical operations, streamlining how precision is determined. No changes to user-facing functionality.  --------- Signed-off-by: Jinzhe Zeng <jinzhe.zeng@ustc.edu.cn> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

…f argument `auto_batch_size` of `DeepEval` with paddle and pytorch backend (#4865)  ## Summary by CodeRabbit * **New Features** * Automatic batch size handling is now enabled by default when creating a new DeepEval instance. * **Documentation** * Improved formatting consistency in model information and multi-task training guides by removing unnecessary blank lines.  --------- Co-authored-by: glwan <wanguolin@dp.tech> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

## Summary by CodeRabbit * **Chores** * Updated all scripts, configuration files, and documentation to use the latest LAMMPS stable release version (stable_22Jul2025) for building and installing DeePMD-kit. * Adjusted dependency specifications and environment variables to reflect the new LAMMPS version. * Revised installation instructions to reference the updated LAMMPS source archive and directory names. * Replaced external LAMMPS version extraction with a custom implementation for improved version parsing. * Removed obsolete environment variable settings related to C++ ABI configuration. * Updated build dependencies to conditionally include the MPI package based on the build environment. * Enhanced environment setup to properly handle CUDA and MPI library paths across platforms during build and runtime. * Improved library loading to explicitly load MPI libraries when building with cibuildwheel. * Clarified installation documentation to indicate automatic MPICH installation without manual MPI setup. * Added conditional interface to manage LAMMPS OP library directory retrieval based on configuration. * **Tests** * Modified tests to restrict atom ID indexing to the subset of atoms matching coordinate array lengths for consistency in validation.  --------- Signed-off-by: Jinzhe Zeng <jinzhe.zeng@ustc.edu.cn> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

This PR fixes the warning message when running on CPU: ``` torch/utils/data/dataloader.py:665: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used. warnings.warn(warn_msg) ```  ## Summary by CodeRabbit * **Bug Fixes** * Improved memory handling by enabling memory pinning only when using non-CPU devices, which may enhance performance and stability when training on GPUs.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Support DPA2/DPA3 C++ inference with lammps > [!NOTE] > The intermediate representation (IR) of PaddlePaddle's computation graph does not support the `Dict[str, Tensor]` data type. Considering the special nature of `comm_dict`, we replaced it with `List[Tensor]`, which allowed us to successfully run inference for DPA2 and DPA3. - DPA2 <img width="896" height="680" alt="4c116161d304da381ffeed968857be1f" src="https://github.com/user-attachments/assets/c4bc178d-4a14-43f4-8d08-946eb9bdf3d3" /> - DPA3 <img width="891" height="682" alt="863dfb7c8f285149cfdaf6aa4c6849fc" src="https://github.com/user-attachments/assets/b74ea212-9055-4863-85fb-18cbace3c60e" />  ## Summary by CodeRabbit ## Summary by CodeRabbit * **New Features** * Added support for distributed computation and message passing in PaddlePaddle backend, including a custom operator for efficient border data exchange. * Enabled optional retrieval of intermediate network outputs during evaluation. * Introduced input statistics computation for model fitting. * Added dynamic loading of Paddle custom operator library and integration in model descriptor and repformer layers. * **Improvements** * Enhanced model freezing and input signature flexibility with dynamic batch sizes and expanded communication tensor support. * Improved tensor operations for better device handling, explicit broadcasting, and type casting consistency. * Refined handling of atomic virial outputs and descriptor communication. * Added support for spin and non-spin modes in parallel descriptor computations using the custom operator. * Simplified device placement in tensor creation and removed redundant explicit device transfers. * Improved input validation and type annotations across models and descriptors. * Replaced ad-hoc logging in C++ API with a structured logger for better debug output. * Updated parameter documentation for improved clarity. * **Bug Fixes** * Fixed tensor padding, masking, and device placement issues for improved robustness in distributed and parallel scenarios. * Corrected handling of neighbor list masking and indexing with explicit tensor fills to avoid broadcast errors. * **Build/Chores** * Added new build scripts and CMake configurations for PaddlePaddle custom operator integration. * Improved support for PaddlePaddle model detection and operator library loading in both Python and C++ APIs. * Implemented full message passing setup in C++ API replacing previous exceptions. * Added Python setup script for Paddle custom operator extension build.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

…ue. (#4855) When enable_profiler has been set to true, it will save the profiling results into json format in the tensorboard_log_dir. Currently, when both enable_profiler and profiling are set to true, it will throw an error `RuntimeError: Trace is already saved`. As a result, just ignore the profiling option when enable_profiler is true.  ## Summary by CodeRabbit * **Bug Fixes** * Refined profiling trace export logic to prevent duplicate or unintended trace exports when the new profiler is enabled. Profiling traces are now only exported if profiling is enabled and the new profiler is not active. * **Documentation** * Updated profiling documentation to clarify that when the new profiler is enabled, profiling results are saved to TensorBoard logs instead of a Chrome JSON file.  --------- Signed-off-by: LI TIANCHENG <137472077+OutisLi@users.noreply.github.com> Co-authored-by: Jinzhe Zeng <njzjz@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

This addresses #3099.  ## Summary by CodeRabbit * **Documentation** * Added a new citation for the "DeePMD-kit v3" paper to the list of recommended references. * Clarified best practices for applying D3 dispersion corrections, advising users to train models without dispersion and apply corrections during simulation. * Introduced a new section in the documentation explaining how to combine DeePMD models with D3 dispersion in LAMMPS, including example commands. * Updated example README files with guidance on efficient model training and use of dispersion corrections.  --------- Signed-off-by: Jinzhe Zeng <njzjz@qq.com> Signed-off-by: Jinzhe Zeng <jinzhe.zeng@ustc.edu.cn> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 4 to 5. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/actions/download-artifact/releases">actions/download-artifact's releases</a>.</em></p> <blockquote> <h2>v5.0.0</h2> <h2>What's Changed</h2> <ul> <li>Update README.md by <a href="https://github.com/nebuk89"><code>@nebuk89</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/407">actions/download-artifact#407</a></li> <li>BREAKING fix: inconsistent path behavior for single artifact downloads by ID by <a href="https://github.com/GrantBirki"><code>@GrantBirki</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/416">actions/download-artifact#416</a></li> </ul> <h2>v5.0.0</h2> <h3>🚨 Breaking Change</h3> <p>This release fixes an inconsistency in path behavior for single artifact downloads by ID. <strong>If you're downloading single artifacts by ID, the output path may change.</strong></p> <h4>What Changed</h4> <p>Previously, <strong>single artifact downloads</strong> behaved differently depending on how you specified the artifact:</p> <ul> <li><strong>By name</strong>: <code>name: my-artifact</code> → extracted to <code>path/</code> (direct)</li> <li><strong>By ID</strong>: <code>artifact-ids: 12345</code> → extracted to <code>path/my-artifact/</code> (nested)</li> </ul> <p>Now both methods are consistent:</p> <ul> <li><strong>By name</strong>: <code>name: my-artifact</code> → extracted to <code>path/</code> (unchanged)</li> <li><strong>By ID</strong>: <code>artifact-ids: 12345</code> → extracted to <code>path/</code> (fixed - now direct)</li> </ul> <h4>Migration Guide</h4> <h5>✅ No Action Needed If:</h5> <ul> <li>You download artifacts by <strong>name</strong></li> <li>You download <strong>multiple</strong> artifacts by ID</li> <li>You already use <code>merge-multiple: true</code> as a workaround</li> </ul> <h5>⚠️ Action Required If:</h5> <p>You download <strong>single artifacts by ID</strong> and your workflows expect the nested directory structure.</p> <p><strong>Before v5 (nested structure):</strong></p> <pre lang="yaml"><code>- uses: actions/download-artifact@v4 with: artifact-ids: 12345 path: dist # Files were in: dist/my-artifact/ </code></pre> <blockquote> <p>Where <code>my-artifact</code> is the name of the artifact you previously uploaded</p> </blockquote> <p><strong>To maintain old behavior (if needed):</strong></p> <pre lang="yaml"><code></tr></table> </code></pre> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/actions/download-artifact/commit/634f93cb2916e3fdff6788551b99b062d0335ce0"><code>634f93c</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/416">#416</a> from actions/single-artifact-id-download-path</li> <li><a href="https://github.com/actions/download-artifact/commit/b19ff4302770b82aa4694b63703b547756dacce6"><code>b19ff43</code></a> refactor: resolve download path correctly in artifact download tests (mainly ...</li> <li><a href="https://github.com/actions/download-artifact/commit/e262cbee4ab8c473c61c59a81ad8e9dc760e90db"><code>e262cbe</code></a> bundle dist</li> <li><a href="https://github.com/actions/download-artifact/commit/bff23f9308ceb2f06d673043ea6311519be6a87b"><code>bff23f9</code></a> update docs</li> <li><a href="https://github.com/actions/download-artifact/commit/fff8c148a8fdd56aa81fcb019f0b5f6c65700c4d"><code>fff8c14</code></a> fix download path logic when downloading a single artifact by id</li> <li><a href="https://github.com/actions/download-artifact/commit/448e3f862ab3ef47aa50ff917776823c9946035b"><code>448e3f8</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/407">#407</a> from actions/nebuk89-patch-1</li> <li><a href="https://github.com/actions/download-artifact/commit/47225c44b359a5155efdbbbc352041b3e249fb1b"><code>47225c4</code></a> Update README.md</li> <li>See full diff in <a href="https://github.com/actions/download-artifact/compare/v4...v5">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/download-artifact&package-manager=github_actions&previous-version=4&new-version=5)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

updates: - [github.com/pre-commit/pre-commit-hooks: v5.0.0 → v6.0.0](pre-commit/pre-commit-hooks@v5.0.0...v6.0.0) - [github.com/astral-sh/ruff-pre-commit: v0.12.7 → v0.12.8](astral-sh/ruff-pre-commit@v0.12.7...v0.12.8) - https://github.com/pylint-dev/pylint/: v3.3.7 → v3.3.8  Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

…tem (#4876) # Fix: support "max:N" and "filter:N" batch_size rules in DeepmdDataSystem ## Problem - Using `batch_size: "max:..."` or `"filter:..."` in configs caused: - `RuntimeError: unknown batch_size rule max` during the PyTorch path (neighbor statistics). - Docs mention these rules and PyTorch `DpLoaderSet` already supports them, so behavior was inconsistent across layers. ## Cause - The common data layer `DeepmdDataSystem` only implemented `"auto"` and `"mixed"` for string `batch_size`, missing `"max"` and `"filter"`. - PT training performs neighbor statistics via `DeepmdDataSystem` before real training, so it failed early when those rules were used. ## Fix - Implement `"max:N"` and `"filter:N"` in `DeepmdDataSystem.__init__` to mirror `DpLoaderSet` semantics: - `max:N`: per-system `batch_size = max(1, N // natoms)` so `batch_size * natoms <= N`. - `filter:N`: drop systems with `natoms > N` (warn if any removed; error if none left), then set per-system `batch_size` as in `max:N`. - After filtering, update `self.data_systems`, `self.system_dirs`, and `self.nsystems` before computing other metadata. ## Impact - Aligns the common layer behavior with PyTorch `DpLoaderSet` and with the documentation. - Prevents PT neighbor-stat crashes with configs using `"max"`/`"filter"`. ## Compatibility - No change to numeric `batch_size` or existing `"auto"/"auto:N"/"mixed:N"` rules. - TF/PT/PD paths now accept the same `batch_size` rules consistently in the common layer. ## Files Changed - `deepmd/utils/data_system.py`: add parsing branches for `"max:N"` and `"filter:N"` in `DeepmdDataSystem.__init__`. ```python elif "max" == words[0]: # Determine batch size so that batch_size * natoms <= rule, at least 1 if len(words) != 2: raise RuntimeError("batch size must be specified for max systems") rule = int(words[1]) bs = [] for ii in self.data_systems: ni = ii.get_natoms() bsi = rule // ni if bsi == 0: bsi = 1 bs.append(bsi) self.batch_size = bs elif "filter" == words[0]: # Remove systems with natoms > rule, then set batch size like "max:rule" if len(words) != 2: raise RuntimeError("batch size must be specified for filter systems") rule = int(words[1]) filtered_data_systems = [] filtered_system_dirs = [] for sys_dir, data_sys in zip(self.system_dirs, self.data_systems): if data_sys.get_natoms() <= rule: filtered_data_systems.append(data_sys) filtered_system_dirs.append(sys_dir) if len(filtered_data_systems) == 0: raise RuntimeError(f"No system left after removing systems with more than {rule} atoms") if len(filtered_data_systems) != len(self.data_systems): warnings.warn(f"Remove {len(self.data_systems) - len(filtered_data_systems)} systems with more than {rule} atoms") self.data_systems = filtered_data_systems self.system_dirs = filtered_system_dirs self.nsystems = len(self.data_systems) bs = [] for ii in self.data_systems: ni = ii.get_natoms() bsi = rule // ni if bsi == 0: bsi = 1 bs.append(bsi) self.batch_size = bs ```  ## Summary by CodeRabbit * **New Features** * Added support for "max" and "filter" batch size rules, allowing more flexible control over batch sizing and filtering of data systems based on atom counts. * **Bug Fixes** * Improved error handling for incorrect batch size string formats and cases where no systems remain after filtering.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

## Summary by CodeRabbit * **New Features** * Added an option to control whether output statistics are computed or loaded across atomic models. * **Bug Fixes** * More robust parameter transfer during fine‑tuning to handle renamed branches and missing pretrained keys. * **Refactor** * Revised output-statistics workflow and refined per‑type output bias application in composite models. * **Tests** * Simplified linear-model bias checks and added a ZBL finetuning test path.  --------- Co-authored-by: anyangml <anyangpeng.ca@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

fix eta computation code  ## Summary by CodeRabbit * **Bug Fixes** * Improved ETA accuracy in training/validation progress logs by adapting calculations to recent step intervals, reducing misleading estimates early in runs. * Consistent behavior across both backends, providing more reliable remaining-time estimates without changing any public interfaces.

…ms (#4869) When using virtual atoms, the property output of virtual atom is `0`. - If predicting energy or other extensive properties, it works well, that's because the virtual atom property `0` do not contribute to the total energy or other extensive properties. - However, if predicting intensive properties, there is some error. For example, a frame has two real atoms and two virtual atoms, the atomic property contribution is [2, 2, 0, 0](the atomic property of virtual atoms are always 0), the final property should be `(2+2)/real_atoms = 2`, not be `(2+2)/total_atoms =1`. This PR is used to solve this bug mentioned above.  ## Summary by CodeRabbit * **New Features** * Models now provide accessors to retrieve property names and their fitting network; property fitting nets expose output definitions. * **Bug Fixes** * Intensive property reduction respects atom masks so padded/dummy atoms are ignored, keeping results invariant to padding. * **Tests** * Added PyTorch, JAX, and core tests validating consistent behavior with padded atoms.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Fix version finding in pip and CMake; pin TF to <2.20 on Windows; fix TENSORFLOW_ROOT in the CI.  ## Summary by CodeRabbit - New Features - Added compatibility with TensorFlow 2.20+ via runtime version detection and generated version macros. - Bug Fixes - Clearer errors when a specified TensorFlow root is invalid. - Improved version-parsing fallback for newer TensorFlow releases. - Tightened Windows CPU wheel constraint to avoid incompatible versions. - Chores - Updated devcontainer scripts and CI workflows to more reliably locate TensorFlow without importing it directly. - Linked TensorFlow during version checks to ensure accurate detection.  --------- Signed-off-by: Jinzhe Zeng <jinzhe.zeng@ustc.edu.cn> Signed-off-by: Jinzhe Zeng <njzjz@qq.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

The UT of padding atoms(pytorch backend) sometimes fails like: ``` Mismatched elements: 1 / 2 (50%) Max absolute difference among violations: 1.97471693e-08 Max relative difference among violations: 6.45619919e-07 ACTUAL: array([[-0.236542], [ 0.030586]]) DESIRED: array([[-0.236542], [ 0.030586]]) = 1 failed, 15442 passed, 4135 skipped, 97877 deselected, 224 warnings in 2825.25s (0:47:05) = ```  ## Summary by CodeRabbit - **Tests** - Adjusted numerical comparison assertions to use both absolute and relative tolerances in padding-related tests. - Aligns checks between computed results and references, improving resilience to minor floating-point variation. - Reduces intermittent test failures across environments and dependency versions. - No impact on features, performance, or user workflows.

…t in dpa3 document (#4887) update paddle installation scripts and custom border op error message  ## Summary by CodeRabbit * **Documentation** * Updated installation guides to reference PaddlePaddle 3.1.1 for CUDA 12.6, CUDA 11.8, and CPU; added nightly pre-release install examples. * Refined training docs wording and CINN note; added Paddle backend guidance and explicit OP-install instructions in DPA3 docs. * **Chores** * Improved error messages when custom Paddle operators are unavailable, adding clearer install instructions and links to documentation.  --------- Signed-off-by: HydrogenSulfate <490868991@qq.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Fix #4877.  ## Summary by CodeRabbit - Bug Fixes - Improved build compatibility with PyTorch 2.8+ on UNIX-like systems (excluding macOS) by aligning the default ABI selection with PyTorch’s behavior. This reduces potential linker/runtime issues when building against newer PyTorch versions. Behavior on other platforms and with older PyTorch remains unchanged. No runtime functionality changes for end users.

## Summary by CodeRabbit * **New Features** * Training entrypoints now accept YAML configuration files in addition to JSON, offering more flexibility when launching training. * Unified configuration loading across frameworks for consistent behavior (PyTorch, Paddle, TensorFlow). * Backward compatible: existing JSON-based workflows continue to work unchanged. * **Tests** * Added coverage to verify YAML input produces the expected training output. * Improved test cleanup to remove generated artifacts after execution.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

updates: - [github.com/astral-sh/ruff-pre-commit: v0.12.8 → v0.12.9](astral-sh/ruff-pre-commit@v0.12.8...v0.12.9)  Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

…ix bugs mentioned in issue #4906 (#4908)  ## Summary by CodeRabbit - New Features - Added optional support to pass a communication dictionary through lower-level model computations across energy, dipole, DOS, polarization, and related models. This enables advanced workflows while remaining fully backward compatible. - Refactor - Standardized internal propagation of the communication dictionary across sub-models to ensure consistent behavior.

…etup (#4911) This PR adds comprehensive development support for GitHub Copilot agents working in the DeePMD-kit codebase. ## What's included **Comprehensive Copilot Instructions (`.github/copilot-instructions.md`)** - Complete build workflow with exact timing expectations (67s Python build, 164s C++ build) - Virtual environment setup and dependency installation for all backends (TensorFlow, PyTorch, JAX, Paddle) - **Optimized testing guidance**: Emphasizes single test execution (~8-13 seconds) over full test suite (60+ minutes) for faster development feedback - Linting and formatting with ruff (1 second execution) - Multiple validation scenarios for CLI, Python interface, and training workflows - Directory structure reference and key file locations - Critical warnings with specific timeout recommendations to prevent premature cancellation - **Conventional commit specification**: Guidelines for commit messages and PR titles following `type(scope): description` format **Automated Environment Setup (`.github/workflows/copilot-setup-steps.yml`)** - Pre-configures Python environment using uv for fast dependency management - Installs TensorFlow CPU and PyTorch automatically - Builds the DeePMD-kit package with all dependencies - Sets up pre-commit hooks for code quality - Validates installation to ensure environment readiness **Development Efficiency Features** - All commands tested and validated with accurate timing measurements - Imperative tone throughout for clear action items - Copy-paste ready validation scenarios - Gitignore rules to prevent temporary test files from being committed ## Key improvements for Copilot agents - **Faster iteration**: Single test recommendations instead of 60+ minute full test suites - **Automated setup**: No manual environment configuration needed - **Precise expectations**: Exact timing guidance prevents timeout issues during builds - **Multi-backend support**: Complete coverage of TensorFlow, PyTorch, JAX, and Paddle workflows - **Consistent commit standards**: Enforces conventional commit specification for all changes The instructions enable any GitHub Copilot agent to work effectively in this codebase from a fresh clone with precise expectations for build times, test execution, and validation workflows. Fixes #4910.  --- 💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more [Copilot coding agent tips](https://gh.io/copilot-coding-agent-tips) in the docs. --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: njzjz <9496702+njzjz@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

…orboard (#5252)  ## Summary by CodeRabbit * **New Features** * Enhanced gradient diagnostics during training: when TensorBoard logging is enabled, training now records total gradient norm, per-parameter gradient norms, and a histogram of per-parameter norms. The system also highlights the top-10 largest parameter gradients as individual scalars and surfaces total-norm values in training logs to improve analysis and monitoring.

…h cross-backend consistency tests (#5260) ## Summary - Add 5 new pt_expt model types: DipoleModel, PolarModel, DOSModel, PropertyModel, and DPZBLModel, completing pt_expt's model coverage to parity with pt - Refactor dpmodel base architecture so that pt_expt models inherit directly from dpmodel via `make_model()`, removing the intermediate pt_expt atomic model layer - Consolidate scattered model-level methods (`get_out_bias`, `set_out_bias`, `get_observed_type_list`, `compute_or_load_stat`) into shared dpmodel base classes - Move `compute_fitting_input_stat` for `set-by-statistic` mode from model-level `change_out_bias` to training-level `model_change_out_bias` (pt and pd backends), keeping the `change_out_bias` logic focused on bias only (copied from #5266) - Fix array-api-compat violations in `general_fitting.change_type_map` (bare `np.zeros`/`np.ones`/`np.concatenate` → `xp` equivalents with device) - Fix dpmodel `change_type_map` not forwarding `model_with_new_type_stat` through the call chain - Add comprehensive cross-backend consistency tests for all model types (dp vs pt vs pt_expt), covering: model output, serialization round-trip, `change_out_bias`, `change_type_map`, `compute_or_load_stat`, model API methods. ## Changes ### New pt_expt models - `deepmd/pt_expt/model/dipole_model.py` - `deepmd/pt_expt/model/polar_model.py` - `deepmd/pt_expt/model/dos_model.py` - `deepmd/pt_expt/model/property_model.py` - `deepmd/pt_expt/model/dp_zbl_model.py` ### Architecture refactoring - Remove `deepmd/pt_expt/atomic_model/` layer — models now wrap dpmodel atomic models directly - Clean up `BaseModel`: remove concrete methods/data, add plugin registry - Refactor `make_model` so backends (dp, pt_expt) inherit shared model logic from dpmodel - Consolidate `get_out_bias`/`set_out_bias` into `base_atomic_model.py` - Add `get_observed_type_list` to abstract API and implement in dpmodel, pt, pd - Move fitting input stat update to `model_change_out_bias` in pt/pd training code (#5266) ### Bug fixes - `general_fitting.change_type_map`: use array-api-compat ops instead of bare numpy (breaks pt_expt) - `make_model.change_type_map`: properly forward `model_with_new_type_stat` to atomic model - `stat.py`: fix in-place mutation issue ### Tests - New cross-backend consistency tests: `test_dipole.py`, `test_polar.py`, `test_dos.py`, `test_property.py` and `test_zbl_ener.py` (~1400 lines each) - Expanded `test_ener.py` with pt_expt and full model API coverage - New pt_expt unit tests: `test_dipole_model.py`, `test_polar_model.py`, `test_dos_model.py`, `test_property_model.py`, `test_dp_zbl_model.py` - Added `test_get_model_def_script`, `test_get_min_nbor_dist`, `test_set_case_embd` across all 6 model test files - Moved atomic model output stat tests from pt_expt to dpmodel - Added `model_change_out_bias` tests in pt/pd training tests (#5266)  ## Summary by CodeRabbit * **New Features** * File-backed compute/load for descriptor and fitting statistics; new compute-or-load stat APIs, get/set output-bias, and observed-type discovery. * Exportable/traceable lower-level inference paths for dipole, dos, polar, property, zbl, and energy models. * **Refactor** * Model factory and generated models support extensible base-class composition and unified fitting backend wiring. * **Tests** * Large expansion of cross-backend (DP/PT/PT-EXPT) parity, statistics, bias, and exportability tests.  --------- Co-authored-by: Han Wang <wang_han@iapcm.ac.cn>

## Summary by CodeRabbit * **Chores** * Pinned the CPU build of PyTorch to version 2.10.0 to match existing GPU pinning; platform-specific (Darwin/Linux) handling remains unchanged, ensuring consistent runtime behavior across supported platforms.  Signed-off-by: Jinzhe Zeng <jinzhe.zeng@ustc.edu.cn>

…ng (#5278) Remove the unused learning_rate_dict configuration option that allowed per-task learning rate settings in multitask training. This simplifies the code by using a single learning_rate configuration for all tasks. Changes: - Remove learning_rate_dict branch in loss initialization - Remove per-task lr_schedule dictionary creation - Remove isinstance(dict) check in training loop - Unify single-task and multi-task code paths Both PyTorch and Paddle backends are updated consistently.  ## Summary by CodeRabbit * **Refactor** * Streamlined learning rate scheduling in multi-task training scenarios to ensure consistent initialization and computation of learning rates across all models.

…#5280)  ## Summary by CodeRabbit ## Release Notes * **Documentation** * Added comprehensive debugging guide for diagnosing gradient flow issues during model training, including step-by-step diagnostic methodology and common root causes. * Added diagnostic script with practical code examples and walkthroughs for isolating gradient behavior across different training modes.  Co-authored-by: Han Wang <wang_han@iapcm.ac.cn>

updates: - [github.com/PyCQA/isort: 8.0.0 → 8.0.1](PyCQA/isort@8.0.0...8.0.1) - [github.com/astral-sh/ruff-pre-commit: v0.15.2 → v0.15.4](astral-sh/ruff-pre-commit@v0.15.2...v0.15.4) - [github.com/pre-commit/mirrors-clang-format: v21.1.8 → v22.1.0](pre-commit/mirrors-clang-format@v21.1.8...v22.1.0)  Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

…5157)  ## Summary by CodeRabbit * **New Features** * Added a top-level, configurable optimizer section; PyTorch: Adam/AdamW/LKF/AdaMuon/HybridMuon, TensorFlow/Paddle: Adam. * Exposes Adam-style hyperparameters (beta1, beta2, weight_decay) for relevant optimizers. * **Documentation** * Training docs updated with optimizer examples and framework-specific guidance. * **Backward Compatibility** * Legacy optimizer fields are auto-migrated to the new format; tests and examples updated to use the new config.

## Summary by CodeRabbit * **New Features** * Models now record, persist, and expose observed element-type lists in model metadata; evaluation/display prefer that metadata when present. * Training and model APIs accept an optional preset observed-type override to control observed-type selection. * Utilities added to collect, save, and restore observed-type lists for caching and reuse. * **Tests** * Added unit and integration tests covering collection, file I/O, training integration, metadata propagation, fallback behavior, and user presets.  --------- Signed-off-by: Duo <50307526+iProzd@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Replaces usage of lmp_list send/recv arrays with new vectors that map indices using fwd_map and synchronize counts via MPI. Updates tensor construction to use these new vectors, improving correctness and flexibility in distributed communication.  ## Summary by CodeRabbit * **Refactor** * Message-passing now remaps send lists to actual atoms before building communication tensors, improving consistency for distributed runs. * **Bug Fixes** * Invalid or out-of-range send indices are filtered and counts updated to prevent communication mismatches and related errors. * **Tests** * Added a test covering a type-map scenario with a NULL mapping to exercise the updated handling.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Han Wang <92130845+wanghan-iapcm@users.noreply.github.com>

## Summary by CodeRabbit * **New Features** * New "PyTorch-Exportable" backend with end-to-end training CLI, model factory, loss wrapper, model wrapper, checkpointing, neighbor/stat utilities, and optional torch.compile acceleration with automatic re-tracing. * **Bug Fixes** * Added runtime validation for required parameter fields (fparam/aparam) and ensured loss/reporting arrays are device-consistent. * **Refactor** * Reworked stat input, batch normalization, and model-stat collection for clearer, backend-agnostic data flow.  --------- Co-authored-by: Han Wang <wang_han@iapcm.ac.cn>

…5284) Implement the full pt_expt inference pipeline: serialize models to .pte files via torch.export, and load them for inference via DeepPot/DeepEval. Key changes: - Add DeepEval backend for .pte files (deepmd/pt_expt/infer/deep_eval.py) - Add serialize/deserialize hooks (deepmd/pt_expt/utils/serialization.py) - Wire up backend hooks in deepmd/backend/pt_expt.py - Add forward_common_lower_exportable using make_fx + torch.export - Support dynamic nframes, nloc, and nall dimensions - Fix atomic_virial_corr to use explicit loop instead of vmap Add xp_take_first_n helper to avoid torch.export contiguity guards on [:, :nloc] slices. When torch.export traces tensor[:, :nloc] on a tensor of size nall, it records a Ne(nall, nloc) guard from the view's contiguity check, which fails when nall == nloc (no PBC). Using torch.index_select instead creates a new tensor, avoiding the guard.  ## Summary by CodeRabbit * **New Features** * PyTorch-exportable inference for exported models with flexible batching and neighbor-list support. * Model serialize/deserialize flow that preserves dynamic shapes and includes round-trip import/export. * New neighbor-statistics utility for PyTorch. * Array utility to take the first N elements with a Torch-optimized path. * **Improvements** * Enhanced export/tracing reliability and array handling to preserve dynamic shapes. * Backend hooks implemented for PyTorch evaluation paths. * **Tests** * Extensive tests for inference consistency, serialization round-trips, and multi-frame scenarios. * **Chores** * Added SPDX license headers.  --------- Co-authored-by: Han Wang <wang_han@iapcm.ac.cn>

Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 7 to 8. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/actions/download-artifact/releases">actions/download-artifact's releases</a>.</em></p> <blockquote> <h2>v8.0.0</h2> <h2>v8 - What's new</h2> <h3>Direct downloads</h3> <p>To support direct uploads in <code>actions/upload-artifact</code>, the action will no longer attempt to unzip all downloaded files. Instead, the action checks the <code>Content-Type</code> header ahead of unzipping and skips non-zipped files. Callers wishing to download a zipped file as-is can also set the new <code>skip-decompress</code> parameter to <code>false</code>.</p> <h3>Enforced checks (breaking)</h3> <p>A previous release introduced digest checks on the download. If a download hash didn't match the expected hash from the server, the action would log a warning. Callers can now configure the behavior on mismatch with the <code>digest-mismatch</code> parameter. To be secure by default, we are now defaulting the behavior to <code>error</code> which will fail the workflow run.</p> <h3>ESM</h3> <p>To support new versions of the @actions/* packages, we've upgraded the package to ESM.</p> <h2>What's Changed</h2> <ul> <li>Don't attempt to un-zip non-zipped downloads by <a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/460">actions/download-artifact#460</a></li> <li>Add a setting to specify what to do on hash mismatch and default it to <code>error</code> by <a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/461">actions/download-artifact#461</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/actions/download-artifact/compare/v7...v8.0.0">https://github.com/actions/download-artifact/compare/v7...v8.0.0</a></p> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/actions/download-artifact/commit/70fc10c6e5e1ce46ad2ea6f2b72d43f7d47b13c3"><code>70fc10c</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/461">#461</a> from actions/danwkennedy/digest-mismatch-behavior</li> <li><a href="https://github.com/actions/download-artifact/commit/f258da9a506b755b84a09a531814700b86ccfc62"><code>f258da9</code></a> Add change docs</li> <li><a href="https://github.com/actions/download-artifact/commit/ccc058e5fbb0bb2352213eaec3491e117cbc4a5c"><code>ccc058e</code></a> Fix linting issues</li> <li><a href="https://github.com/actions/download-artifact/commit/bd7976ba57ecea96e6f3df575eb922d11a12a9fd"><code>bd7976b</code></a> Add a setting to specify what to do on hash mismatch and default it to <code>error</code></li> <li><a href="https://github.com/actions/download-artifact/commit/ac21fcf45e0aaee541c0f7030558bdad38d77d6c"><code>ac21fcf</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/460">#460</a> from actions/danwkennedy/download-no-unzip</li> <li><a href="https://github.com/actions/download-artifact/commit/15999bff51058bc7c19b50ebbba518eaef7c26c0"><code>15999bf</code></a> Add note about package bumps</li> <li><a href="https://github.com/actions/download-artifact/commit/974686ed5098c7f9c9289ec946b9058e496a2561"><code>974686e</code></a> Bump the version to <code>v8</code> and add release notes</li> <li><a href="https://github.com/actions/download-artifact/commit/fbe48b1d2756394be4cd4358ed3bc1343b330e75"><code>fbe48b1</code></a> Update test names to make it clearer what they do</li> <li><a href="https://github.com/actions/download-artifact/commit/96bf374a614d4360e225874c3efd6893a3f285e7"><code>96bf374</code></a> One more test fix</li> <li><a href="https://github.com/actions/download-artifact/commit/b8c4819ef592cbe04fd93534534b38f853864332"><code>b8c4819</code></a> Fix skip decompress test</li> <li>Additional commits viewable in <a href="https://github.com/actions/download-artifact/compare/v7...v8">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/download-artifact&package-manager=github_actions&previous-version=7&new-version=8)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 6 to 7. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/actions/upload-artifact/releases">actions/upload-artifact's releases</a>.</em></p> <blockquote> <h2>v7.0.0</h2> <h2>v7 What's new</h2> <h3>Direct Uploads</h3> <p>Adds support for uploading single files directly (unzipped). Callers can set the new <code>archive</code> parameter to <code>false</code> to skip zipping the file during upload. Right now, we only support single files. The action will fail if the glob passed resolves to multiple files. The <code>name</code> parameter is also ignored with this setting. Instead, the name of the artifact will be the name of the uploaded file.</p> <h3>ESM</h3> <p>To support new versions of the <code>@actions/*</code> packages, we've upgraded the package to ESM.</p> <h2>What's Changed</h2> <ul> <li>Add proxy integration test by <a href="https://github.com/Link"><code>@Link</code></a>- in <a href="https://redirect.github.com/actions/upload-artifact/pull/754">actions/upload-artifact#754</a></li> <li>Upgrade the module to ESM and bump dependencies by <a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/762">actions/upload-artifact#762</a></li> <li>Support direct file uploads by <a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/764">actions/upload-artifact#764</a></li> </ul> <h2>New Contributors</h2> <ul> <li><a href="https://github.com/Link"><code>@Link</code></a>- made their first contribution in <a href="https://redirect.github.com/actions/upload-artifact/pull/754">actions/upload-artifact#754</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/actions/upload-artifact/compare/v6...v7.0.0">https://github.com/actions/upload-artifact/compare/v6...v7.0.0</a></p> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/actions/upload-artifact/commit/bbbca2ddaa5d8feaa63e36b76fdaad77386f024f"><code>bbbca2d</code></a> Support direct file uploads (<a href="https://redirect.github.com/actions/upload-artifact/issues/764">#764</a>)</li> <li><a href="https://github.com/actions/upload-artifact/commit/589182c5a4cec8920b8c1bce3e2fab1c97a02296"><code>589182c</code></a> Upgrade the module to ESM and bump dependencies (<a href="https://redirect.github.com/actions/upload-artifact/issues/762">#762</a>)</li> <li><a href="https://github.com/actions/upload-artifact/commit/47309c993abb98030a35d55ef7ff34b7fa1074b5"><code>47309c9</code></a> Merge pull request <a href="https://redirect.github.com/actions/upload-artifact/issues/754">#754</a> from actions/Link-/add-proxy-integration-tests</li> <li><a href="https://github.com/actions/upload-artifact/commit/02a8460834e70dab0ce194c64360c59dc1475ef0"><code>02a8460</code></a> Add proxy integration test</li> <li>See full diff in <a href="https://github.com/actions/upload-artifact/compare/v6...v7">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/upload-artifact&package-manager=github_actions&previous-version=6&new-version=7)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

### Summary Add hessian (second derivative of energy w.r.t. coordinates) support to the pt_expt backend, mirroring the JAX backend's approach. - Compute hessian on extended coordinates in forward_common_atomic (via torch.autograd.functional.hessian), then let dpmodel's communicate_extended_output naturally map from nall×nall to nloc×nloc - make_hessian_model only overrides atomic_output_def() to set r_hessian=True — no forward_common override or r_hessian toggle hack needed - Hessian is enabled at runtime via EnergyModel.enable_hessian() and returned through the user-facing forward() interface ### Changed files - deepmd/pt_expt/model/make_model.py — add _cal_hessian_ext and _WrapperForwardEnergy for hessian computation in forward_common_atomic - deepmd/pt_expt/model/make_hessian_model.py — minimal wrapper: __init__, requires_hessian, atomic_output_def - deepmd/pt_expt/model/ener_model.py — enable_hessian(), hessian output in forward() and translated_output_def() - deepmd/pt_expt/model/__init__.py — export make_hessian_model - source/tests/pt_expt/model/test_ener_hessian_model.py — unit test: autograd hessian vs finite-difference, parametrized over nv={1,2} - source/tests/consistent/model/test_ener_hessian.py — cross-backend consistency test covering PT, pt_expt, and JAX  ## Summary by CodeRabbit * **New Features** * Adds Hessian (second-order derivative) support for energy models. * Provides a mechanism to enable Hessian calculations per model instance. * When enabled, Hessian tensors are included in model prediction outputs. * **Tests** * Added comprehensive multi-backend validation of Hessian functionality. * Added unit tests comparing analytical Hessians to finite-difference references.  --------- Co-authored-by: Han Wang <wang_han@iapcm.ac.cn>

## Summary by CodeRabbit * **New Features** * Spin-aware preprocessing for training data, plus bias-adjustment and type-mapping that handle spin variants during finetuning. * **Tests** * Added comprehensive unit and end-to-end tests for spin finetuning, bias updates, transformed spin data, default-parameter propagation, and post-finetune inference validation. * **Refactor** * Centralized, cached sampler wrapper to standardize sampling and default-parameter injection. * **Chores** * Truncated bias logging for more concise diagnostics.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

## Summary - switch the `package_c` Docker build image from TensorFlow 2.18 to 2.20 - pin the installed TensorFlow wheel to `==2.20.*` via the existing `TENSORFLOW_VERSION` env ## Why The failing `Build C library` job appears to be in `package_c.yml`, not in the `cibuildwheel` workflow changed by dependabot. This change makes the Docker image and installed TensorFlow version move together, so we can test whether the failure was caused by a TensorFlow / GLIBC mismatch from pulling a newer wheel into an older build image. Authored by OpenClaw (model: gpt-5.4)  ## Summary by CodeRabbit * **Chores** * Updated C library build configuration to target TensorFlow 2.20, aligning the build matrix with the newer TensorFlow runtime.

Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 3.3 to 3.4. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/releases">pypa/cibuildwheel's releases</a>.</em></p> <blockquote> <h2>v3.4.0</h2> <ul> <li>🌟 You can now build wheels using <code>uv</code> as a build frontend. This should improve performance, especially if your project has lots of build dependencies. To use, set <a href="https://cibuildwheel.pypa.io/en/stable/options/#build-frontend"><code>build-frontend</code></a> to <code>uv</code>. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2322">#2322</a>)</li> <li>⚠️ We no longer support running on Travis CI. It may continue working but we don't run tests there anymore so we can't be sure. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2682">#2682</a>)</li> <li>✨ Improvements to building rust wheels on Android (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2650">#2650</a>)</li> <li>🐛 Fix bug with the GitHub Action on Windows, where PATH was getting unnecessarily changed, causing issues with meson builds. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2723">#2723</a>)</li> <li>✨ Add support for quiet setting on <code>build</code> and <code>uv</code> from the cibuildwheel <code>build-verbosity</code> setting. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2737">#2737</a>)</li> <li>📚 Docs updates, including guidance on using Meson on Windows (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2718">#2718</a>)</li> </ul> <h2>v3.3.1</h2> <ul> <li>🛠 Update dependencies and container pins, including updating to CPython 3.14.2. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2708">#2708</a>)</li> </ul> </blockquote> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md">pypa/cibuildwheel's changelog</a>.</em></p> <blockquote> <hr /> <h2>title: Changelog</h2> <h1>Changelog</h1> <h3>v3.4.0</h3> <p><em>5 March 2026</em></p> <ul> <li>🌟 You can now build wheels using <code>uv</code> as a build frontend. This should improve performance, especially if your project has lots of build dependencies. To use, set <a href="https://cibuildwheel.pypa.io/en/stable/options/#build-frontend"><code>build-frontend</code></a> to <code>uv</code>. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2322">#2322</a>)</li> <li>⚠️ We no longer support running on Travis CI. It may continue working but we don't run tests there anymore so we can't be sure. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2682">#2682</a>)</li> <li>✨ Improvements to building rust wheels on Android (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2650">#2650</a>)</li> <li>🐛 Fix bug with the GitHub Action on Windows, where PATH was getting unnecessarily changed, causing issues with meson builds. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2723">#2723</a>)</li> <li>✨ Add support for quiet setting on <code>build</code> and <code>uv</code> from the cibuildwheel <code>build-verbosity</code> setting. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2737">#2737</a>)</li> <li>📚 Docs updates, including guidance on using Meson on Windows (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2718">#2718</a>)</li> </ul> <h3>v3.3.1</h3> <p><em>5 January 2026</em></p> <ul> <li>🛠 Update dependencies and container pins, including updating to CPython 3.14.2. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2708">#2708</a>)</li> </ul> <h3>v3.3.0</h3> <p><em>12 November 2025</em></p> <ul> <li>🐛 Fix an incompatibility with Docker v29 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2660">#2660</a>)</li> <li>✨ Adds <code>test-runtime</code> option, to customise how tests on simulated/emulated environments are run (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2636">#2636</a>)</li> <li>✨ Adds support for new <code>manylinux_2_35</code> images on 32-bit ARM <code>armv7l</code>, offering better C++20 compatibility (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2656">#2656</a>)</li> <li>✨ <code>build[uv]</code> is now supported on Android (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2587">#2587</a>)</li> <li>✨ You can now install extras (such as <code>uv</code>) with a simple option on the GitHub Action (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2630">#2630</a>)</li> <li>✨ <code>{project}</code> and <code>{package}</code> placeholders are now supported in <code>repair-wheel-command</code> (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2589">#2589</a>)</li> <li>🛠 The versions set with <code>dependency-versions</code> no longer constrain packages specified by your <code>build-system.requires</code>. Previously, on platforms other than Linux, the constraints in this option would remain in the environment during the build. This has been tidied up make behaviour more consistent between platforms, and to prevent version conflicts. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2583">#2583</a>)</li> <li>🛠 Improve the handling of <code>test-command</code> on Android, enabling more options to be passed (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2590">#2590</a>)</li> <li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2618">#2618</a>)</li> </ul> <h3>v3.2.1</h3> <p><em>12 October 2025</em></p> <ul> <li>🛠 Update to CPython 3.14.0 final (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2614">#2614</a>)</li> <li>🐛 Fix the default MACOSX_DEPLOYMENT_TARGET on Python 3.14 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2613">#2613</a>)</li> <li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2617">#2617</a>)</li> </ul> <h3>v3.2.0</h3> <p><em>22 September 2025</em></p> <ul> <li>✨ Adds GraalPy v25 (Python 3.12) support (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2597">#2597</a>)</li> </ul>  </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/pypa/cibuildwheel/commit/ee02a1537ce3071a004a6b08c41e72f0fdc42d9a"><code>ee02a15</code></a> Bump version: v3.4.0</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/f08ce70d7891b3a6b3d41f7909a69816eeea643b"><code>f08ce70</code></a> chore: match copyright to BSD-2-clause template (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2758">#2758</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/95b4b79b96bae59effea03a5f297b0cb5aa31b6b"><code>95b4b79</code></a> Fix incorrect document regarding pyodide auditwheel (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2752">#2752</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/f046d0a94dc550b5dccb93ae748704599fa1f9ae"><code>f046d0a</code></a> Bump to Pyodide v0.29.3 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2743">#2743</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/f5a31ee79d1dfd39a9a0a87d7cd412a29afb8fe1"><code>f5a31ee</code></a> chore(deps): bump the actions group across 1 directory with 4 updates (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2755">#2755</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/35ec4565f5ca3474c283a0852fdf512d5817850f"><code>35ec456</code></a> [pre-commit.ci] pre-commit autoupdate (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2756">#2756</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/51fed90270069c1f2ae3c075bcd6b0dad1d739c2"><code>51fed90</code></a> [Bot] Update dependencies (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2734">#2734</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/b70562f37a2c65580ef235073974d067233fa82a"><code>b70562f</code></a> Debug GraalPy/uv test failures on Windows (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2741">#2741</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/0a85e29549d1fd12d1ee07727cb55c39e16173a5"><code>0a85e29</code></a> Work around CDN rate limiting on Python.org in bin/update_pythons.py (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2753">#2753</a>)</li> <li><a href="https://github.com/pypa/cibuildwheel/commit/2d33864bafd35e54234e57c036c38cd59288543c"><code>2d33864</code></a> Pin the actions/setup-python for the externally used action.yml (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2749">#2749</a>)</li> <li>Additional commits viewable in <a href="https://github.com/pypa/cibuildwheel/compare/v3.3...v3.4">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=pypa/cibuildwheel&package-manager=github_actions&previous-version=3.3&new-version=3.4)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. 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updates: - [github.com/astral-sh/ruff-pre-commit: v0.15.4 → v0.15.5](astral-sh/ruff-pre-commit@v0.15.4...v0.15.5)  Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

#5277) ## Summary This PR integrates pretrained model support directly into `deepmd-kit` under `deepmd/pretrained`, while keeping `DeepPot` usage unchanged. ### Added - New command: - `dp pretrained download <MODEL>` - New module folder: - `deepmd/pretrained/` - includes `registry.py`, `download.py`, `backend.py`, `entrypoints.py` - Built-in model registry (currently): - `DPA-3.2-5M` - `DPA-3.1-3M` - Multi-source download strategy: - parallel probe over candidate sources - rank by response latency - fastest-first with automatic fallback on timeout/failure/checksum mismatch - SHA256 verification + atomic `.part` writes - `.pretrained` backend alias support via `deepmd/backend/pretrained.py` - allows `DeepPot("DPA-3.2-5M.pretrained")` while keeping existing DeepPot API unchanged - deep-eval adapter is lazy-loaded to avoid circular import issues ### CLI wiring - Added `pretrained` parser/subparser in `deepmd/main.py` - Added dispatch in `deepmd/entrypoints/main.py` ### Tests - `source/tests/common/test_pretrained_parser.py` - `source/tests/common/test_pretrained_download.py` - `source/tests/common/test_pretrained_backend.py` ### Formatting / lint - Ran `uvx prek run --all-files` and committed auto-format updates. Authored by OpenClaw (model: custom-chat-jinzhezeng-group/gpt-5.3-codex)  ## Summary by CodeRabbit * **New Features** * New "pretrained" CLI group with `pretrained download` (optional --cache-dir) to fetch pretrained models. * Built-in pretrained model registry (includes DPA-3.2-5M and DPA-3.1-3M). * Added a pretrained backend scaffold and a lazy adapter so `.pretrained` aliases work transparently with existing evaluation flow (some backend hooks intentionally unsupported). * **Downloads & Caching** * HTTPS-only downloads with SHA256 verification, multi-source fallbacks, parallel probing, atomic writes, and centralized caching (~/.cache/deepmd/pretrained/models). * **Tests** * Added tests for backend detection, alias parsing, download/resolution behavior, URL ranking, and CLI parsing.  --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Problem - Paddle nightly wheels are fetched from https://www.paddlepaddle.org.cn/... and the site’s TLS certificate outage breaks CI installs. Change - Add `--trusted-host www.paddlepaddle.org.cn` and `--trusted-host paddlepaddle.org.cn` to the Paddle install commands in GitHub Actions workflows. Notes - This is a temporary workaround to keep CI green until Paddle fixes their cert. Authored by OpenClaw (model: gpt-5.2)  ## Summary by CodeRabbit * **Chores** * Updated build workflow configurations to include additional package host verification options during dependency installation.

Bumps [tensorflow-cpu](https://github.com/tensorflow/tensorflow) from 2.20.0 to 2.21.0. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/tensorflow/tensorflow/releases">tensorflow-cpu's releases</a>.</em></p> <blockquote> <h2>TensorFlow 2.21.0</h2> <h1>Release 2.21.0</h1> <h2>TensorFlow</h2> <h3>Breaking Changes</h3> <ul> <li>Support for Python 3.9 has been removed starting with TF 2.21.</li> <li>The TensorBoard (TB) dependency has been removed starting with TF 2.21.</li> </ul> <h3>Major Features and Improvements</h3> <ul> <li> <p><code>tf.lite</code></p> <ul> <li>Adds int8 and int16x8 support for SQRT operator.</li> <li>Adds int16x8 support for EQUAL and NOT_EQUAL operators.</li> <li>Adds support for int2 type.</li> <li>Adds support for int2/int4 in tfl.cast .</li> <li>Adds support for SRQ int2 in tfl.fully_connected.</li> <li>Adds support for int4 in tfl.slice.</li> <li>Adds support for uint4 type.</li> </ul> </li> <li> <p><code>tf.image</code></p> <ul> <li>Adds JPEG XL support in decode_image.</li> </ul> </li> </ul> <h3>Bug Fixes and Other Changes</h3> <ul> <li><code>tf.data</code> <ul> <li>Adds <code>NoneTensorSpec</code> to the public API so that <code>None</code>s in <code>element_spec</code> can be identified via <code>isinstance(..., tf.NoneTensorSpec)</code>.</li> </ul> </li> </ul> <h2>Thanks to our Contributors</h2> <p>This release contains contributions from many people at Google, as well as:</p> <p>Aaraviitkgp, Abhijeet, Abhinav Gunjal, Abhishek, Adam Paszke, Aditya Gupta, Aditya Jha, Aditya Sharma, Adrian Kuegel, Aiden Grossman, Akarsh, Akhil Goel, Alan Kelly, Aleksa Arsic, Aleksei, Aleksei Nurmukhametov, Alex, Alexander Belyaev, Alexander Grund, Alexander Lyashuk, Alexander Shaposhnikov, Alex Pivovarov, Aliia Khasanova, Alina Sbirlea, Allan Renucci, Amelia Thurdekoos, Amit Sabne, Andrei Ivanov, Andrew Dame, Andrey Portnoy, Anish Nair, Anlun Xu, Antonio Sanchez, anuj chincholikar, Anuj Chincholikar, Aravindh Balaji, aravindhbalaji1985, Arian Arfaian, Armin Felder, Artem Belevich, Ashish Rao, Ashitesh Singh, A. Unique TensorFlower, Bart Chrzaszcz, benediktjohannes, Benjamin Chetioui, Benjamin Kramer, Berkin Ilbeyi, Bhatu, Bhavani Subramanian, Bhupendra Dubey, Bill Varcho, Bixia Zheng, Blake Hechtman, Bodhi Silberling, BruceXinXin, Bryan Massoth, Buddh Prakash, Byungchul Kim, Ce Zheng, Changhui Lin, Chao, Charles Alaras, Chase Riley Roberts, Chenhao Jiang, Chris Ashton, Chris Jones, Chris Kennelly, Christian Sigg, Chuan He, Chunlei Niu, Chun-nien Chan, Chunyu Jin, Clive Verghese, Cong Liu, Corentin Kerisit, Daniel Chen, Daniel Kuts, Daniel Ng, Daniel Sosa, Daniel Suo, Danila Malyutin, David Dunleavy, David Majnemer, David Pizzuto, Deepika Rajani, deeptanshusekhri, dependabot[bot], Deqiang Chen, Derek Murray, Dillon Sharlet, Dimitar (Mitko) Asenov, Dimitris Vardoulakis, Dirk Hornung, DottsGit, Dragan Mladjenovic, Eetu Sjöblom, Elen Kalda, Emilio Cota, Emily Fertig, Eugene Zhulenev, Eusebio Durán Montaña, Evan Brown, Ezekiel Calubaquib, Faijul Amin, Felix Wang, Fengwu Yao, Fergus Henderson, Frederic Rechtenstein, Frederik Gossen, Gabriel Gerlero, Gagan Nagaraj, gaikwadrahul8, garry00107, gaurides, George Pawelczak, Georg Stefan Schmid, gns, Goran Flegar, Graham, Grant Jensen, Greg Olechwierowicz, Gregory Pataky, Grzegorz Gawryał, Gunhyun Park, guozhong.zhuang, Haibo Huang, Hana Joo, Hariprasad Ravishankar, Harsha H S, Harshit Monish, Henning Becker, Hittanshu, Hoeseong (Hayden) Kim, Hugo Mano, Hyeontaek Lim, Ibrahim Umit Akgun, ILCSFNO, Ilia Sergachev, Ilya Tikhonovskiy, Iman Hosseini, Ionel Gog, Isha Arkatkar, isharif168, Ivo Ristovski List, Jacques Pienaar, Jae H. Yoo, Jaeyoon Jung, Jake Harmon, James Hilliard, jameslovespancakes, James Spooner, Jane Liu, Jaroslav Sevcik, Jeff Parker, Jeffrey A. Dean, Jeremy Meredith, Jialei Chen, Jian Cai, Jian Li, Jie Luo, Jim Lin, Jing Pu, Jinliang Wei, Jiya Zhang, Joel Wee, Johannes Buchner, Johannes Reifferscheid, Johnny, Jorge Gorbe Moya, Joshua Lang, Joshua Wang, Joss Briody, jparkerh, Juanli Shen, Juhyun Lee, Jun Jiang, Junwhan Ahn, Kadir Barut, Kanglan Tang, Kanish Anand, Kanvi Khanna, Karlo Basioli, Ken Franko, Kevin Chen, Kevin Gleason, Kingston Mandisodza, Koki Ibukuro, Kostiantyn Liepieshov, Krishna Haridasan, Krishna Somani, Krzysztof Kosiński, Kuy Mainwaring, lambert, Larry Lansing, Lin Chai, Lord ε Rebel, Luke Baumann, Luke Hutton, madhavmadupu, Majid Dadashi, Mani Ananth, Manjunath Gaonkar, Marcello Maggioni, Marcin Radomski, Maria Lyubimtseva, Marissa Ikonomidis, Mark Daoust, Mason Chang, Matej Aleksandrov, Mateusz Sokół, Matthias Guenther, Matthias Kramm, Matt Hurd, Matt Kreileder, Maxime France-Pillois, Maxim Ermilov, Mehrdad Khani, Melissa Weber Mendonça, MERT-CKR, Michael Goldfarb, Michael Green, Michael Kuperstein, Michael Voznesensky, Michael Whittaker, Mihai Maruseac, Mikhail Goncharov, Ming-Xu Huang, Mircea Trofin, Misha Gutman, misterBart, mmakevic-amd, Mohamed AbdElmoneim, Mohamed Amine Zghal, Mohammadreza Heydary, Mohammed Anany, mraunak, Mudit Gokhale, Nayana Thorat, Nevi, nhatle, Nhat Le, Nihar0071, Nikhil, Nikita Putikhin, Niklas Vangerow, Nitin Srinivasan, Oleg Shyshkov, Olli Lupton, Om Thakkar, Pankaj Kanwar, Parker Schuh, Paul Ganssle, Pauline Sho, Pavithra Eswaramoorthy, Pedro Gonnet, pemeliya, Penporn Koanantakool, Perry Gibson, Peter Buchlovsky, Peter Gavin, Peter Hawkins, Pham Binh, Phani Paladugula, Philipp Hack, Praneeth Mandala, Praveen Batra, psinfinity, Qingwei Zhang, Quentin Khan, Quoc Truong, QZero, Rachel Han, Raffi Khatchadourian, Ram Rachum, RasheedAli-Shaik, Raviteja Gorijala, Reed Wanderman-Milne, Reilly Grant, Renjie Wu, Richard Levasseur, Robert David, Ryan M. Lefever, Sachin M, Sagun Bajra, Sai Ganesh Muthuraman, Saksham Singh Rathore, Sannidhya Chauhan, Sayan Saha, Sean Talts, Seher Ellis, Sergei Lebedev, Sergey Kozub, Sevin Fide Varoglu, Shahriar Rouf, Shanbin Ke, Shaogang Wang, Sharad Vikram, Shawn Lu, Siddhartha Menon, Siqiao Wu, skill, Smit Hinsu, snadampal, Sohaib Iftikhar, Soowon Jeong, spiao, Srijan Upadhyay, stevemcgregory, Subham Soni, Subhankar Shah, Swachhand Lokhande, Tai Ly, TensorFlower Gardener, Terry Heo, Terry Sun, Terry Tao, Theotime Combes, Thomas Joerg, Thomas Köppe, Tiago Quelhas, TJ Xu, Toli Yevtushenko, Tomás Longeri, Tom Hennigan, Tommy Chiang, Tom Natan, Tongfei Guo, Tori Baker, Uwe L. Korn, Vadym Matsishevskyi, Vamsi Manchala, Venkat6871, Victor Stone, Ville Vesilehto, Vitalii Dziuba, Vladimir Belitskiy, Vlad Sytchenko, Volodymyr Kysenko, Wai Hon Law, wan3x, Weiyi Wang, Will Froom, William S. Moses, wondertx, Xuefei Jiang, Yang Chen, Yash Katariya, Yasir Ashfaq, yasiribmcon, Yeou Chiou, Yicheng Luo, Yi Kong, Yimei Sun, Yin Zhang, Yuchen Yao, Yue Sheng, Yulia Baturina, Yunjie Xu, Yunlong Liu, Yun Peng, Yurii Topin, Zac Cranko, Zac Mustin, Zenong Zhang, Zeyu Wang, Zhanyong Wan, Zixuan Jiang, Ziyin Huang, Zviki Nozadze</p> <h2>TensorFlow 2.21.0-rc1</h2> <h1>Release 2.21.0</h1> <h2>TensorFlow</h2> <h3>Breaking Changes</h3> <ul> <li>Support for Python 3.9 has been removed starting with TF 2.21.</li> </ul> <h3>Major Features and Improvements</h3> <ul> <li><code>tf.lite</code> <ul> <li>Adds int8 and int16x8 support for SQRT operator.</li> <li>Adds int16x8 support for EQUAL and NOT_EQUAL operators.</li> </ul> </li> </ul>  </blockquote> <p>... (truncated)</p> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md">tensorflow-cpu's changelog</a>.</em></p> <blockquote> <h1>Release 2.21.0</h1> <h2>TensorFlow</h2> <h3>Breaking Changes</h3> <ul> <li>Support for Python 3.9 has been removed starting with TF 2.21.</li> <li>The TensorBoard (TB) dependency has been removed starting with TF 2.21.</li> </ul> <h3>Major Features and Improvements</h3> <ul> <li> <p><code>tf.lite</code></p> <ul> <li>Adds int8 and int16x8 support for SQRT operator.</li> <li>Adds int16x8 support for EQUAL and NOT_EQUAL operators.</li> <li>Adds support for int2 type.</li> <li>Adds support for int2/int4 in tfl.cast .</li> <li>Adds support for SRQ int2 in tfl.fully_connected.</li> <li>Adds support for int4 in tfl.slice.</li> <li>Adds support for uint4 type.</li> </ul> </li> <li> <p><code>tf.image</code></p> <ul> <li>Adds JPEG XL support in decode_image.</li> </ul> </li> </ul> <h3>Bug Fixes and Other Changes</h3> <ul> <li><code>tf.data</code> <ul> <li>Adds <code>NoneTensorSpec</code> to the public API so that <code>None</code>s in <code>element_spec</code> can be identified via <code>isinstance(..., tf.NoneTensorSpec)</code>.</li> </ul> </li> </ul> <h2>Thanks to our Contributors</h2> <p>This release contains contributions from many people at Google, as well as:</p> <p>Aaraviitkgp, Abhijeet, Abhinav Gunjal, Abhishek, Adam Paszke, Aditya Gupta, Aditya Jha, Aditya Sharma, Adrian Kuegel, Aiden Grossman, Akarsh, Akhil Goel, Alan Kelly, Aleksa Arsic, Aleksei, Aleksei Nurmukhametov, Alex, Alexander Belyaev, Alexander Grund, Alexander Lyashuk, Alexander Shaposhnikov, Alex Pivovarov, Aliia Khasanova, Alina Sbirlea, Allan Renucci, Amelia Thurdekoos, Amit Sabne, Andrei Ivanov, Andrew Dame, Andrey Portnoy, Anish Nair, Anlun Xu, Antonio Sanchez, anuj chincholikar, Anuj Chincholikar, Aravindh Balaji, aravindhbalaji1985, Arian Arfaian, Armin Felder, Artem Belevich, Ashish Rao, Ashitesh Singh, A. Unique TensorFlower, Bart Chrzaszcz, benediktjohannes, Benjamin Chetioui, Benjamin Kramer, Berkin Ilbeyi, Bhatu, Bhavani Subramanian, Bhupendra Dubey, Bill Varcho, Bixia Zheng, Blake Hechtman, Bodhi Silberling, BruceXinXin, Bryan Massoth, Buddh Prakash, Byungchul Kim, Ce Zheng, Changhui Lin, Chao, Charles Alaras, Chase Riley Roberts, Chenhao Jiang, Chris Ashton, Chris Jones, Chris Kennelly, Christian Sigg, Chuan He, Chunlei Niu, Chun-nien Chan, Chunyu Jin, Clive Verghese, Cong Liu, Corentin Kerisit, Daniel Chen, Daniel Kuts, Daniel Ng, Daniel Sosa, Daniel Suo, Danila Malyutin, David Dunleavy, David Majnemer, David Pizzuto, Deepika Rajani, deeptanshusekhri, dependabot[bot], Deqiang Chen, Derek Murray, Dillon Sharlet, Dimitar (Mitko) Asenov, Dimitris Vardoulakis, Dirk Hornung, DottsGit, Dragan Mladjenovic, Eetu Sjöblom, Elen Kalda, Emilio Cota, Emily Fertig, Eugene Zhulenev, Eusebio Durán Montaña, Evan Brown, Ezekiel Calubaquib, Faijul Amin, Felix Wang, Fengwu Yao, Fergus Henderson, Frederic Rechtenstein, Frederik Gossen, Gabriel Gerlero, Gagan Nagaraj, gaikwadrahul8, garry00107, gaurides, George Pawelczak, Georg Stefan Schmid, gns, Goran Flegar, Graham, Grant Jensen, Greg Olechwierowicz, Gregory Pataky, Grzegorz Gawryał, Gunhyun Park, guozhong.zhuang, Haibo Huang, Hana Joo, Hariprasad Ravishankar, Harsha H S, Harshit Monish, Henning Becker, Hittanshu, Hoeseong (Hayden) Kim, Hugo Mano, Hyeontaek Lim, Ibrahim Umit Akgun, ILCSFNO, Ilia Sergachev, Ilya Tikhonovskiy, Iman Hosseini, Ionel Gog, Isha Arkatkar, isharif168, Ivo Ristovski List, Jacques Pienaar, Jae H. Yoo, Jaeyoon Jung, Jake Harmon, James Hilliard, jameslovespancakes, James Spooner, Jane Liu, Jaroslav Sevcik, Jeff Parker, Jeffrey A. Dean, Jeremy Meredith, Jialei Chen, Jian Cai, Jian Li, Jie Luo, Jim Lin, Jing Pu, Jinliang Wei, Jiya Zhang, Joel Wee, Johannes Buchner, Johannes Reifferscheid, Johnny, Jorge Gorbe Moya, Joshua Lang, Joshua Wang, Joss Briody, jparkerh, Juanli Shen, Juhyun Lee, Jun Jiang, Junwhan Ahn, Kadir Barut, Kanglan Tang, Kanish Anand, Kanvi Khanna, Karlo Basioli, Ken Franko, Kevin Chen, Kevin Gleason, Kingston Mandisodza, Koki Ibukuro, Kostiantyn Liepieshov, Krishna Haridasan, Krishna Somani, Krzysztof Kosiński, Kuy Mainwaring, lambert, Larry Lansing, Lin Chai, Lord ε Rebel, Luke Baumann, Luke Hutton, madhavmadupu, Majid Dadashi, Mani Ananth, Manjunath Gaonkar, Marcello Maggioni, Marcin Radomski, Maria Lyubimtseva, Marissa Ikonomidis, Mark Daoust, Mason Chang, Matej Aleksandrov, Mateusz Sokół, Matthias Guenther, Matthias Kramm, Matt Hurd, Matt Kreileder, Maxime France-Pillois, Maxim Ermilov, Mehrdad Khani, Melissa Weber Mendonça, MERT-CKR, Michael Goldfarb, Michael Green, Michael Kuperstein, Michael Voznesensky, Michael Whittaker, Mihai Maruseac, Mikhail Goncharov, Ming-Xu Huang, Mircea Trofin, Misha Gutman, misterBart, mmakevic-amd, Mohamed AbdElmoneim, Mohamed Amine Zghal, Mohammadreza Heydary, Mohammed Anany, mraunak, Mudit Gokhale, Nayana Thorat, Nevi, nhatle, Nhat Le, Nihar0071, Nikhil, Nikita Putikhin, Niklas Vangerow, Nitin Srinivasan, Oleg Shyshkov, Olli Lupton, Om Thakkar, Pankaj Kanwar, Parker Schuh, Paul Ganssle, Pauline Sho, Pavithra Eswaramoorthy, Pedro Gonnet, pemeliya, Penporn Koanantakool, Perry Gibson, Peter Buchlovsky, Peter Gavin, Peter Hawkins, Pham Binh, Phani Paladugula, Philipp Hack, Praneeth Mandala, Praveen Batra, psinfinity, Qingwei Zhang, Quentin Khan, Quoc Truong, QZero, Rachel Han, Raffi Khatchadourian, Ram Rachum, RasheedAli-Shaik, Raviteja Gorijala, Reed Wanderman-Milne, Reilly Grant, Renjie Wu, Richard Levasseur, Robert David, Ryan M. Lefever, Sachin M, Sagun Bajra, Sai Ganesh Muthuraman, Saksham Singh Rathore, Sannidhya Chauhan, Sayan Saha, Sean Talts, Seher Ellis, Sergei Lebedev, Sergey Kozub, Sevin Fide Varoglu, Shahriar Rouf, Shanbin Ke, Shaogang Wang, Sharad Vikram, Shawn Lu, Siddhartha Menon, Siqiao Wu, skill, Smit Hinsu, snadampal, Sohaib Iftikhar, Soowon Jeong, spiao, Srijan Upadhyay, stevemcgregory, Subham Soni, Subhankar Shah, Swachhand Lokhande, Tai Ly, TensorFlower Gardener, Terry Heo, Terry Sun, Terry Tao, Theotime Combes, Thomas Joerg, Thomas Köppe, Tiago Quelhas, TJ Xu, Toli Yevtushenko, Tomás Longeri, Tom Hennigan, Tommy Chiang, Tom Natan, Tongfei Guo, Tori Baker, Uwe L. Korn, Vadym Matsishevskyi, Vamsi Manchala, Venkat6871, Victor Stone, Ville Vesilehto, Vitalii Dziuba, Vladimir Belitskiy, Vlad Sytchenko, Volodymyr Kysenko, Wai Hon Law, wan3x, Weiyi Wang, Will Froom, William S. Moses, wondertx, Xuefei Jiang, Yang Chen, Yash Katariya, Yasir Ashfaq, yasiribmcon, Yeou Chiou, Yicheng Luo, Yi Kong, Yimei Sun, Yin Zhang, Yuchen Yao, Yue Sheng, Yulia Baturina, Yunjie Xu, Yunlong Liu, Yun Peng, Yurii Topin, Zac Cranko, Zac Mustin, Zenong Zhang, Zeyu Wang, Zhanyong Wan, Zixuan Jiang, Ziyin Huang, Zviki Nozadze</p> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/tensorflow/tensorflow/commit/a481b10260dfdf833a1b16007eead49c1d7febf3"><code>a481b10</code></a> Merge pull request <a href="https://redirect.github.com/tensorflow/tensorflow/issues/111627">#111627</a> from tensorflow-jenkins/version-numbers-2.21.0-25481</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/a8f642e88e001734075133f9576ffd253b9eacc3"><code>a8f642e</code></a> Update version numbers to 2.21.0</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/3c51664da546f2e7741ad27e98f8dd22dbbf86f9"><code>3c51664</code></a> Merge pull request <a href="https://redirect.github.com/tensorflow/tensorflow/issues/111517">#111517</a> from tejaswin432/r2.21</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/460d178dda3124806d534ec0fb5e3d0830319628"><code>460d178</code></a> Update RELEASE.md with removal of TB dependency.</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/9e2628c11188f4b1f4361ff08e1de198d13cadb1"><code>9e2628c</code></a> Update RELEASE.md with removal of TB dependency.</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/00a1ba7b4fd31bf1c75482bfff620a1cf21c5815"><code>00a1ba7</code></a> Merge pull request <a href="https://redirect.github.com/tensorflow/tensorflow/issues/111234">#111234</a> from psamanoelton/remove_tb_nigthly</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/41beecf3ee272e928c59e53edfcd5d8b40bf7f2b"><code>41beecf</code></a> Remove tb-nigthly and replace it with protobuf.</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/01dec748b0d0daa3eb60a386b857519644fe15e2"><code>01dec74</code></a> Merge pull request <a href="https://redirect.github.com/tensorflow/tensorflow/issues/111216">#111216</a> from psamanoelton/remove_tb_dependency_partial_rol...</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/9657881d9689a4c06d334645932abff9d3481698"><code>9657881</code></a> Partial rollback to resolve breakage.</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/78d130aec00f8b4dbee85d3757ce32032ec7cbdd"><code>78d130a</code></a> Remove TensorBoard dependency from TensorFlow build</li> <li>Additional commits viewable in <a href="https://github.com/tensorflow/tensorflow/compare/v2.20.0...v2.21.0">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=tensorflow-cpu&package-manager=pip&previous-version=2.20.0&new-version=2.21.0)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) </details> --------- Signed-off-by: dependabot[bot] <support@github.com> Signed-off-by: Jinzhe Zeng <jinzhe.zeng@ustc.edu.cn> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Jinzhe Zeng <jinzhe.zeng@ustc.edu.cn>

fix issue introduced by this pr: #5157 This pull request updates the PaddlePaddle dependency versions across the codebase to ensure compatibility with the latest releases. It also improves the installation instructions in the documentation to reflect these updates and to recommend best practices for installing nightly builds. Dependency version updates: * Updated the `paddlepaddle-gpu` dependency in the CUDA test workflow to version `3.4.0.dev20260310` (`.github/workflows/test_cuda.yml`). * Updated the `paddlepaddle` dependency in the Python test workflow to version `3.4.0.dev20260310` (`.github/workflows/test_python.yml`). Documentation improvements: * Updated installation instructions in `doc/install/easy-install.md` and `doc/install/install-from-source.md` to reference `paddlepaddle-gpu==3.3.0` and `paddlepaddle==3.3.0` as the latest stable release versions [[1]](diffhunk://#diff-8072dac581dd568fe718ff7204aae121465797d5d2890c4bc4d3ee8d978951e7L172-R174) [[2]](diffhunk://#diff-8072dac581dd568fe718ff7204aae121465797d5d2890c4bc4d3ee8d978951e7L183-R185) [[3]](diffhunk://#diff-865b1d35cff7d06cf73ebb95b0f2d93c5cc77b19c64305753008c90dfe9370c8L100-R108). * Modified nightly build installation commands in the documentation to recommend using the `-U` (upgrade) flag for both GPU and CPU versions, ensuring users get the latest nightly build [[1]](diffhunk://#diff-8072dac581dd568fe718ff7204aae121465797d5d2890c4bc4d3ee8d978951e7L172-R174) [[2]](diffhunk://#diff-8072dac581dd568fe718ff7204aae121465797d5d2890c4bc4d3ee8d978951e7L183-R185) [[3]](diffhunk://#diff-865b1d35cff7d06cf73ebb95b0f2d93c5cc77b19c64305753008c90dfe9370c8L100-R108).  ## Summary by CodeRabbit * **New Features** * Training now supports the AdamW optimizer alongside Adam. * **Documentation** * Updated installation guides with newer PaddlePaddle stable and nightly package versions and improved nightly/pre-release pip syntax. * **Chores** * Bumped PaddlePaddle dependency versions used in CI workflows (Python and CUDA test pipelines).

## Summary by CodeRabbit * **New Features** * Optional charge/spin electronic embedding for DPA3 (toggleable). * Descriptor and model APIs accept optional per-frame parameters (fparam); when enabled, default per-frame params are forwarded into descriptor embeddings. * **Tests** * Expanded test coverage for charge/spin embedding, fparam construction/propagation, backend paths, and serialization. * **Documentation** * Added help/config entry for the charge/spin embedding option.  --------- Signed-off-by: Duo <50307526+iProzd@users.noreply.github.com>

Problem - DPA-2.4-7M is available on HuggingFace and has mirrors (hf-mirror + ModelScope), but is not listed in the built-in pretrained registry. Change - Add DPA-2.4-7M to deepmd/pretrained/registry.py with HuggingFace URL, hf-mirror URL, and ModelScope mirror URL. - Record the model file SHA256 for integrity checking. Notes - ModelScope mirror: https://modelscope.cn/models/DeepModelingCommunity/DPA-2.4-7M Authored by OpenClaw (model: custom-chat-jinzhezeng-group/gpt-5.2)  ## Summary by CodeRabbit * **New Features** * The DPA-2.4-7M pretrained model is now available for download and use. * Additional download sources/URLs added for DPA-3.2-5M and DPA-3.1-3M to improve availability.

This pull request updates the optimizer handling in the `deepmd/pd/train/training.py` file, adding support for selecting between `Adam` and `AdamW` optimizers based on configuration, and improves profiling clarity. **Optimizer selection improvements:** * Refactored optimizer initialization to dynamically select between `paddle.optimizer.Adam` and `paddle.optimizer.AdamW` depending on the value of `self.opt_type`. This allows for more flexibility in choosing the optimizer. **Profiling and logging enhancements:** * Updated the profiling context label from `"Adam update"` to `"Optimizer update"` to accurately reflect the optimizer in use, improving the clarity of profiling output.  ## Summary by CodeRabbit * **New Features** * Added support for selecting between Adam and AdamW optimizers during training.

## Summary by CodeRabbit * **New Features** * Public C API bumped to v26 and adds queries to check whether models use default frame parameters across model types and backends. * Backends and model interfaces now expose a consistent "has default fparam" status and fall back safely when backend hooks are absent. * **Tests** * Added extensive unit, integration, and LAMMPS tests plus a model fixture to validate default-frame-parameter behavior.

This PR fixes a typo pattern.  ## Summary by CodeRabbit * **Documentation** * Fixed multiple typos and wording in method docstrings and parameter descriptions (e.g., corrected "statisitcs" → "statistics", "Reveive" → "Receive", and similar inconsistencies). These edits improve clarity and consistency of technical documentation without changing behavior, interfaces, or functionality.

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