Improve compiled RT-DETR inference speed 
Author: yonigozlan
Pull request: yes

# What does this PR do?
This PR is part of an ongoing effort to optimize the inference speed of Transformers' vision models.
For more info on the specific issues these optimizations target and how they help improve the inference speed of compiled models, you can check out this [Notion page](https://www.notion.so/huggingface2/Vision-models-compilation-and-OptimVision-first-results-e2b47dabd1804cdc9cac5537ae5e9363).

The following metrics are for model inference only! Pre/post-processing time are not measured here. Currently, Transformers image processors seem to be a big bottleneck for inference speed, so end-to-end inference will sadly be much slower than this.

![benchmark_results_rt_detr_fp16](https://github.com/user-attachments/assets/c2839821-d115-4154-9e12-9b635efdb178)

![benchmark_results_rt_detr_r101](https://github.com/user-attachments/assets/be208b9d-3588-4248-8c78-45613a41f974)

It's also worth noting that while using the custom CUDA kernel for deformable attention (by setting `disable_custom_kernels=False` in the config) currently provides a slight boost to inference speed for compiled models, with these new optimizations, enabling or disabling it now leads to very similar performance.

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## Before submitting
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## Who can review?

@qubvel @amyeroberts @NielsRogge 

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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_33412). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.