Update example Emu3 performance in mindspore 2.6.0 and 2.7.0

[chenyingshu]

What does this PR do?

Fixes # (issue)

• Modified model by using mint.nn.BatchNorm3d instead of nn.BatchNorm3d.
• Update Emu3 examples/emu3/README.md with ms2.6.0 and ms2.7.0 performance.
• Note that training only supports ms2.7.0 based on PR (bug)Zero: Only support training with ms2.7.0 when using ZeRO #1383.

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you read the contributor guideline?
• Did you make sure to update the documentation with your changes? E.g. record bug fixes or new features in What's New. Here are the
  documentation guidelines
• Did you build and run the code without any errors?
• Did you report the running environment (NPU type/MS version) and performance in the doc? (better record it for data loading, model inference, or training tasks)
• Did you write any new necessary tests?

[gemini-code-assist]

Summary of Changes

Hello @chenyingshu, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on modernizing the Emu3 example to align with recent MindSpore versions (2.6.0 and 2.7.0). It involves adapting the codebase to API changes, particularly for the BatchNorm3d module, and updating the documentation with fresh performance benchmarks. The changes aim to streamline the development process by removing obsolete conversion steps and providing up-to-date information for users.

Highlights

• MindSpore Version Compatibility: The Emu3 example has been updated to ensure compatibility with MindSpore versions 2.6.0 and 2.7.0, including necessary adjustments for the BatchNorm3d module's new location.
• Performance Benchmarks Updated: The README.md now features comprehensive performance tables for Emu3 models (Image VQA, Text-to-Image Generation, and Training) on MindSpore 2.6.0 and 2.7.0, providing current metrics for various tasks and modes.
• Code Simplification: The convert_weights.py script and its associated documentation have been removed, as the manual weight conversion for BatchNorm3d is no longer required due to MindSpore's API changes.
• Training Epoch Adjustments: The number of training epochs for Text-to-Image and VQA tasks in their respective shell scripts has been reduced.

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[gemini-code-assist]

Code Review

This pull request updates the Emu3 example with performance benchmarks for mindspore 2.6.0 and 2.7.0. A key change is the replacement of nn.BatchNorm3d with mint.nn.BatchNorm3d across the project, which is a good improvement for performance and code simplification, as it allows for the removal of the weight conversion script. The changes are applied consistently. I have one minor suggestion to improve the formatting in the README file for better consistency and readability.