feat: add sequence parallel and cache support for Flux DiT.

[z-jun03]

Description

Add sequence parallelism and DiT cache support for Flux DiT models.

This change:

• Adds Ulysses-style sequence parallel execution for Flux attention.
• Adds sequence padding, splitting, all-to-all communication, gathering, and unpadding.
• Integrates Flux with the existing DiT cache framework.
• Supports independent cache states for positive and negative CFG passes.
• Preserves existing LongCat-Image behavior through backward-compatible constructors.
• Applies the changes to the Flux, Flux Control, and Flux Fill pipelines.

Related Issues

Change Type

• Bug fix
• New feature
• Performance improvement
• Refactor
• Documentation
• Test
• Build or CI

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Self Review

• I have self-reviewed the code according to .agents/skills/code-review/references/custom-code-style.md, especially code written or assisted by AI.
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Build and Test Coverage

• Tests have been added or updated as needed.
• CUDA: python setup.py build test has passed on a CUDA machine.
• NPU: python setup.py build test has passed on an NPU machine.
• MLU: python setup.py build test has passed on an MLU machine.

Reviewer Notes

• Flux build validation passed successfully.
• Flux generation was validated on Ascend NPU with both SP=1 and SP=2.
• SP=2 output matched the single-card baseline byte-for-byte for the same prompt, seed, resolution, and inference steps.
• FBCache output matched the no-cache baseline in the tested configuration.
• Flux, Flux Control, and Flux Fill pipelines were updated.
• Existing LongCat-Image usage was preserved through backward-compatible constructors.
• The current implementation supports asynchronous communication submission but does not implement true computation-communication overlap.
• Flux-specific automated SP and DiT cache tests were not added; validation was performed through NPU build and runtime integration tests.