fix[example/hunyunvideo]:HunyuanVideo is compatible with MindSpore 2.6 and 2.7

[iugoood]

What does this PR do?

HunyuanVideo is compatible with MindSpore 2.6 and 2.7

1. example/hunyunvideo added GroupNorm class to fix the misalignment of GroupNorm precision types during model training and inference.
2. fix some bug

Fixes # (issue)

Adds # (feature)

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?

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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.

@xxx

[gemini-code-assist]

Summary of Changes

Hello @iugoood, 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 enhancing the compatibility of the HunyuanVideo project with MindSpore versions 2.6 and 2.7. It addresses several bugs, including a critical fix for Group Normalization precision during model operations, corrections in command-line argument parsing for training scripts, and adjustments to how activation functions and text encoder outputs are handled. The overall aim is to improve the stability and functionality of the HunyuanVideo framework within the specified MindSpore environments.

Highlights

• MindSpore Compatibility: The HunyuanVideo project has been updated to ensure full compatibility with MindSpore versions 2.6 and 2.7.
• GroupNorm Implementation: A new GroupNorm class has been introduced in mindone/diffusers/models/normalization.py to resolve precision type misalignment issues during model training and inference.
• Argument Parsing Correction: Command-line argument definitions in the training script (examples/hunyuanvideo/scripts/train.py) were updated to correctly use the -- prefix for validation parameters.
• Activation Function Instantiation: The unet_causal_3d_blocks.py file was modified to correctly handle the instantiation of activation functions, removing an unnecessary function call.
• Text Encoder Output Index Fix: An indexing error was corrected in the text encoder's encode method to properly retrieve the last_hidden_state.

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[gemini-code-assist]

Code Review

This pull request introduces compatibility fixes for HunyuanVideo with MindSpore 2.6 and 2.7. The changes include correcting an indexing issue for hidden states, fixing an activation function call, making command-line arguments optional, and adding a new GroupNorm class to address precision-related misalignments. The changes are generally well-implemented and address the stated goals. My review includes one suggestion to improve the maintainability of the new GroupNorm class.

[gemini-code-assist]

The construct method can be made more concise and maintainable by removing the duplicated call to group_norm. You can prepare the weight and bias tensors before a single call.

Suggested change

	if self.affine:
	x = group_norm(x, self.num_groups, self.weight.to(x.dtype), self.bias.to(x.dtype), self.eps)
	else:
	x = group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
	return x
	weight = self.weight
	bias = self.bias
	if self.affine:
	weight = weight.to(x.dtype)
	bias = bias.to(x.dtype)
	return group_norm(x, self.num_groups, weight, bias, self.eps)