[Feature] support DFlash: Block Diffusion for Flash Speculative Decoding

### Motivation https://github.com/z-lab/dflash DFlash is a lightweight block diffusion model You can give qwen3.5 27Bvllm sglang mlx already support model modification. The following is from the publicity introduction： 🚀 Core Breakthrough DFlash is a lightweight Block Diffusion Model purpose-built for speculative decoding. It predicts an entire token block in a single forward pass, delivering unprecedented inference acceleration. Limitations of Traditional Methods Conventional speculative decoding approaches (e.g., EAGLE-3) still generate drafts in an autoregressive manner, where each token must wait for the completion of the previous one. This caps the practical speedup at only 2–3×. DFlash Innovations DFlash adopts a fundamentally different strategy: - Parallel Draft Generation: Generates a full token block in one forward pass - KV Injection Mechanism: Injects hidden layer features from the target model as contextual conditions into every layer of the draft model - Feature Fusion: Fuses multi-layer hidden states via FC + RMSNorm to provide highly consistent contextual information 📊 Remarkable Acceleration Results Qwen3-8B: 6× Lossless Acceleration On the Qwen3-8B model, DFlash achieves: - 6× lossless speedup - 2.5× faster than EAGLE-3 - Acceptance rate as high as 89%+ Qwen3.5-9B: 4.1× Acceleration On Apple Silicon platforms, the Qwen3.5-9B model delivers: - 4.1× speedup - Validates 16 tokens generated in a single batch - Optimized with custom Metal kernels 🔥 Qwen3.5-27B: 5× Inference Speed Surge Performance Comparison (Configuration) Setup 1024 tokens 2048 tokens Speedup Baseline 14 tok/s 11 tok/s 1× 8-bit Quantization 35 tok/s 26 tok/s 2.5× 4-bit Quantization 28 tok/s 20 tok/s 2.0× Key Findings 1. 8-bit quantization outperforms 4-bit: Delivers superior speedup while maintaining higher precision 2. Strong long-sequence performance: Retains 2.3× speedup when generating 2048 tokens 3. Lossless decoding: Fully preserves model output quality with zero accuracy degradation ### Related resources https://github.com/z-lab/dflash ### Additional context <img width="812" height="906" alt="Image" src="https://github.com/user-attachments/assets/71ac3e69-8371-44e7-9462-b28856c309a1" /> <img width="773" height="679" alt="Image" src="https://github.com/user-attachments/assets/cabacde1-3747-4cfa-867e-d893b6e82b82" />