[trainer] refactor: code refactor for diffusion training

[zhtmike]

What does this PR do?

Major change:

• Decouple LLM and diffusion configs, losses, etc., for easier integration of diffusion models/algorithms/engines in the future. (resolve comment from [5/n][trainer] feat: flowgrpo trainer #5951)
  • Diffusion configs are now all placed in verl/trainer/config/diffusion, including diffusion_actor, dp_diffusion_actor, diffusion_fsdp, diffusion_rollout, etc.
  • Diffusion config classes are now all placed in verl/workers/config/diffusion, including actor, model, rollout.
  • Clean LLM-specific configs such as use_remove_padding, or unused configs. The content of _generated_diffusion_trainer.yaml has now been reduced by ~60%.

Other changes:

• Clean the diffusion agent loop output; remove dead code originally borrowed from the LLM agent loop, such as input_id, attention_mask, etc., which are not used in diffusion training currently.
• Refactor and remove extra_configs in diffusion configs. It is too loose and may cause confusion for users. (resolve comment from [5/n][trainer] feat: flowgrpo trainer #5951)
• Decouple LLM and diffusion losses to make diffusion losses clearer.
• Code cleanup for diffusion trainer, diffusion agent loop, etc.
• Change the vllm-omni rollout import to registration, following the diffusion trainer pattern. (resolve comment from [5/n][trainer] feat: flowgrpo trainer #5951)

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[gemini-code-assist]

Code Review

This pull request refactors the diffusion training pipeline by introducing a modular configuration structure and dedicated dataclasses for diffusion-specific components. Key changes include the implementation of registries for diffusion loss functions, advantage estimators, and vLLM-Omni pipelines, alongside the removal of the response_mask requirement and legacy reward migration logic. The review feedback highlights a runtime error in configuration dictionary conversion for dataclasses, potential dynamic attribute assignment failures in Pydantic models for metrics, and a type mismatch for the loss_scale_factor parameter.

[zhtmike]

@knlnguyen1802 please take a look of vllm-omni change thx, I think it is better to load the custom class directly in vllm-omni side?