Native-speed vLLM transformers modeling backend

The transformers vLLM backend has achieved performance parity or superiority over custom vLLM implementations for various large language model architectures, offering ultra-fast inference for model authors using transformers implementations. This advancement allows models from the transformers library, which supports over 450 architectures, to run efficiently in vLLM without additional porting, thanks to the library's integration as a modeling backend. The integration utilizes optimized inference techniques like continuous batching and custom attention kernels, and the latest update introduces dynamic inference-specific layer fusions at runtime to enhance performance. This is achieved through static analysis using torch.fx and pattern optimization, allowing the same model code to be used for training, evaluation, and reinforcement learning rollouts, maintaining native vLLM inference speed without manual code optimization.

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