[docker] feat: add Ascend A3 Dockerfile and docs

[phdddd]

What does this PR do?

This PR has added a Dockerfile for Ascend A3 and provided documentation for image construction. Additionally, some configuration code errors related to mixed precision training have been corrected.

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  • Allowed modules: agent, ci, ckpt, config, data, dist, docker, docs, logging, misc, model, omni, optim, ops, parallel, perf, release, task, trainer
  • Allowed types: feat, fix, refactor, chore, test
  • Breaking changes: prepend [BREAKING] — e.g. [BREAKING][parallel, model] feat: dynamic batching

Test

Validation results (training curves, eval metrics) for changes not covered by CI.

API and Usage Example

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Design & Code Changes

High-level design description and specific change list.

Checklist Before Submitting

• Read the Contribute Guide
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• Added/updated documentation
• If tasks/ training scripts were moved or renamed: updated docs/ examples and verified python3 scripts/ci/check_doc_task_paths.py passes (also enforced by the Check doc task paths CI workflow)
• Added tests to CI workflow (or explained why not feasible)

[gemini-code-assist]

Code Review

This pull request introduces support for Ascend A3 hardware by adding a specialized Dockerfile and a comprehensive build and usage guide. It also refactors several training scripts to utilize a centralized FSDP mixed precision configuration. However, the review identified several critical issues in the implementation: the build_parallelize_model function is being called with an incorrect keyword argument, and several other functions are receiving a configuration object where a boolean value is expected, which will lead to logic errors or runtime failures. Additionally, the new Dockerfile contains redundant layers and references an undefined build variable.