Chunk 43.0

This chunk focused on migrating the Qwen3.6-27B deployment from the decommissioned kpro6 to kpro5, then pushing speculative decoding beyond the proven MTP baseline toward DFlash and DDTree. The assistant first set up the new host—installing NVIDIA driver 580.126.09 on kpro5, unbinding two RTX A6000s from vfio-pci (leaving 3090s for existing VMs), updating the CT129 LXC config for 2 GPUs, and installing matching userspace libraries inside the container. After downloading the 52GB BF16 model, SGLang 0.5.9 initially produced degenerate output due to incompatible GDN hybrid attention handling; upgrading to 0.5.11 (as recommended by the model card) resolved this, achieving 73.5 tok/s single-request throughput with MTP speculation (NEXTN steps=3) and robust long-context performance up to 120K tokens with only 12% decode speed degradation. The core effort then shifted to DFlash speculative decoding. The assistant acquired the gated `z-lab/Qwen3.6-27B-DFlash` drafter safetensors, created a config from scratch (initially guessing `target_layer_ids`), and deployed vLLM 0.20.1 with DFlash. The acceptance rate was catastrophically low (~1.1%), prompting a deep investigation across the vLLM DFlash proposer code, the DDTree reference implementation, and the z-lab HF repositories. Three root causes emerged: (1) a layer-ID +1 offset missing in vLLM's hidden state extraction (fixed by PR #40727), (2) sliding window attention (SWA) layers in the drafter being ignored (fixed by PR #40898), and (3) possible eagle cache drop issues. The assistant installed vLLM from the unmerged PR #40898 branch, confirmed all three fixes were present, and verified the SWA layer type handling was active. The overarching theme is the gap between published research and production-ready deployment—DFlash and DDTree require unmerged PRs, custom configs, and careful alignment between the reference implementation and serving framework internals. The drafter model itself, while labeled "still under training," is likely functional with correct configuration; the near-zero acceptance was entirely a deployment integration failure, not a model quality issue. The path to DDTree remains blocked until DFlash achieves reasonable acceptance rates, requiring either the PR #40898 branch to be fully functional or manual patching of the SWA and layer-ID offset issues.</think>## Summary This chunk focused on migrating the Qwen3.6-27B deployment from the decommissioned kpro6 to kpro5, then pushing speculative decoding beyond the proven MTP baseline toward DFlash and DDTree. The assistant first set up the new host—installing NVIDIA driver 580.126.09 on kpro5, unbinding two RTX A6000s from vfio-pci (leaving 3090s for existing VMs), updating the CT129 LXC config for 2 GPUs, and installing matching userspace libraries inside the container. After downloading the 52GB BF16 model, SGLang 0.5.9 initially produced degenerate output due to incompatible GDN hybrid attention handling; upgrading to 0.5.11 (as recommended by the model card) resolved this, achieving 73.5 tok/s single-request throughput with MTP speculation (NEXTN steps=3) and robust long-context performance up to 120K tokens with only 12% decode speed degradation. The core effort then shifted to DFlash speculative decoding. The assistant acquired the gated `z-lab/Qwen3.6-27B-DFlash` drafter safetensors, created a config from scratch (initially guessing `target_layer_ids`), and deployed vLLM 0.20.1 with DFlash. The acceptance rate was catastrophically low (~1.1%), prompting a deep investigation across the vLLM DFlash proposer code, the DDTree reference implementation, and the z-lab HF repositories. Three root causes emerged: (1) a layer-ID +1 offset missing in vLLM's hidden state extraction (fixed by PR #40727), (2) sliding window attention (SWA) layers in the drafter being ignored (fixed by PR #40898), and (3) possible eagle cache drop issues. The assistant installed vLLM from the unmerged PR #40898 branch, confirmed all three fixes were present, and verified the SWA layer type handling was active. The overarching theme is the gap between published research and production-ready deployment—DFlash and DDTree require unmerged PRs, custom configs, and careful alignment between the reference implementation and serving framework internals. The drafter model itself, while labeled "still under training," is likely functional with correct configuration; the near-zero acceptance was entirely a deployment integration failure, not a model quality issue. The path to DDTree remains blocked until DFlash achieves reasonable acceptance rates, requiring either the PR #40898 branch to be fully functional or manual patching of the SWA and layer-ID offset issues.

From Migration to Training: The Long Arc of Speculative Decoding Optimization 2680 words

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