[错误] 由于缺少 ctx_other 和非自回归图执行, DFlash 2 的推测解码在 Python 中崩溃并失败

Prerequisites

• I am running the latest code. Development is very rapid, so there are no tagged versions as of now.
• I carefully followed the README.md.
• I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
• I reviewed the Discussions, and have a new bug or useful enhancement to share.

Expected Behavior

When loading the target model utautako/Qwen3.8-27B-NVFP4-MTP-Q8attn-GGUF (Qwen3.8-27B-NVFP4-MTP-Q8attn.gguf) together with the DFlash 2 draft model incoai/Qwen3.8-27B-DFlash2-GGUF (Qwen3.8-27B-DFlash2-Q4_K_M.gguf), LLaMA-cpp-Python should link the draft context to the target context via cparams.ctx_other = target_context and execute the native C++ block-diffusion speculative decoding pipeline, producing speculative speedups.

Current Behavior

1. Calling draft_llm = LLaMA(model_path="Qwen3.8-27B-DFlash2-Q4_K_M.gguf") fails in GGML with: dflash requires ctx_other to be set -> ValueError: Failed to create llama_context because DFlash 2 sidecar GGUF files do not contain their own lm_head / output.weight tensors and require ctx_other to be set during context initialization.
2. Setting draft_llm.model = draft_model_ptr fails with: AttributeError: property 'model' of 'LLaMA' object has no setter.
3. Calling LlamaDraftModel(draft_llm, num_pred_tokens=5) fails with: TypeError: LlamaDraftModel() takes no arguments because LlamaDraftModel is an abstract base class.
4. When subclassing LlamaDraftModel and returning a Python list, it crashes inside LLaMA.py with: AttributeError: 'list' object has no attribute 'astype'.
5. When returning a numpy.ndarray with dtype=np.intc, the Python loop calls draft_llm.sample(). This bypasses the C++ DFlash 2 pipeline (target hidden layer extraction llama_get_embeddings_layer_inp, encoder pass llama_encode, and candidate lattice selection build_post_sampling), resulting in a 0% draft acceptance rate and dropping generation speed to baseline autoregressive throughput (~34.27 tok/s).

Environment and Context

• Hardware: NVIDIA RTX PRO 6000 Blackwell Server Edition (sm_120), 24+ GB VRAM
• Environment: Hugging Face ZeroGPU Space
• Operating System: Debian GNU/Linux 13 (trixie) / Linux 6.12.94-123.192.amzn2023.x86_64
• glibc Version: Debian GLIBC 2.41-12
• Python Version: 3.12.12
• NVIDIA Driver: 580.159.03 (CUDA 13.0)
• LLaMA-cpp-Python: v0.3.35 compiled against submodule vendor/LLaMA.cpp using:
• Repository: https://GitHub.com/z-lab/LLaMA.cpp-fork/tree/5ecbe1ac17ec0484c5b44af0bd580cdc9c428ed4
• Branch: dflash2
• Commit Hash: 5ecbe1ac17ec0484c5b44af0bd580cdc9c428ed4
• Upstream Pull Request: https://GitHub.com/ggml-org/LLaMA.cpp/pull/27342/changes

Steps to Reproduce