Enable Bagel FlowGRPO LoRA training with vLLM-omni rollout

[princepride]

• Add DiffusionModelBase implementation for Bagel (bagel.py) with custom model loading, scheduler, and forward pass
• Add build_module hook in DiffusionModelBase and DiffusersFSDPEngine to support non-standard model loading (e.g. Bagel's BagelForTraining)
• Fix scheduler device/precision mismatch in FlowMatchSDEDiscreteScheduler (index_for_timestep nearest-neighbor, move sigmas to sample device)
• Fix pipeline_bagel to stack trajectory tensors for proper batching
• Add system prompt and negative prompt to OCR training data
• Add error handling in reward_fn for vLLM reward model failures
• Guard prompt_embeds access for models that don't produce embeddings
• Add profiler support to diffusion trainer (config + start/stop hooks)
• Fix prompt_ids -> prompt_token_ids rename in agent loop server call
• Update run_bagel_flowgrpo.sh with tuned hyperparameters

Co-authored-by: Claude

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