[MovieGen] update code to support MindSpore 2.7.0

[hadipash]

What does this PR do?

Fixes # (issue)

Adds # (feature)

• Add support for MindSpore 2.6 and 2.7.
• Updated benchmarks based on MS 2.7.
• Add shell script to run all benchmarks at once.

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?

Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

[gemini-code-assist]

Summary of Changes

Hello @hadipash, 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 primarily focuses on upgrading the MovieGen project's compatibility with newer MindSpore versions (2.6 and 2.7). It includes necessary code adjustments in the dataset loading pipeline to handle both image and video inputs more robustly, updates the project's performance benchmarks in the documentation to reflect the new MindSpore environment, and introduces a new shell script to simplify the execution of these benchmarks across various model configurations.

Highlights

• MindSpore Version Update: The project now officially supports MindSpore versions 2.6 and 2.7, with all related configurations and benchmarks updated accordingly.
• Enhanced Dataset Handling: The tae_dataset.py has been improved to intelligently differentiate and process both image and video files within the dataset, ensuring correct loading and transformation based on file type.
• New Benchmark Script: A comprehensive shell script (train_perf_bench.sh) has been added to automate the execution of all performance benchmarks for 1B, 5B, and 30B models across their respective training stages.
• Performance Table Refresh: The README.md now features updated performance metrics and compile times, reflecting the results obtained with MindSpore 2.7.0.
• VideoReader Integration Update: The VideoReader utility is now imported from mindone.data instead of decord, and its usage in tae_dataset.py has been adjusted to fetch_frames for improved video processing.

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[gemini-code-assist]

Code Review

This pull request updates the code to support MindSpore 2.7.0, including updating documentation, performance benchmarks, and refactoring the dataset loading logic. I've found a few critical issues in the dataset handling, particularly with video and GIF frame sampling, which could lead to incorrect data being fed into your models. I've also included some suggestions to improve the new benchmark script's robustness and maintainability. Please review the comments for details.