Chunk 61.0

In this chunk, the user pivoted from training to deploying the z-lab DFlash DDTree drafter on the Pro6000 hardware. The assistant killed the active training run and investigated deployment options, discovering that SGLang’s native DFlash support is linear-only and that vLLM’s DDTree PR is blocked by removed tree attention. To get a working endpoint quickly, the assistant deployed a temporary standalone OpenAI-compatible DDTree service on CT200 using the z-lab draft model, verified with smoke tests and health checks. The assistant then researched the feasibility of integrating DDTree into SGLang or vLLM, concluding that SGLang is the better target because it already has tree-mask infrastructure for EAGLE and a working DFlash path. They created a detailed roadmap (`sglang-ddtree-roadmap.md`) outlining implementation phases—config flags, tree construction, verify input, KV management, and benchmark plans—and implemented a standalone utility module (`sglang_ddtree_utils.py`) with DDTree tree-building, visibility mask construction, tree-walk verification, and debug summary metrics. This module was staged on the eval host’s SGLang package. A key theme is the tension between rapid deployment and correctness. The assistant identified that DDTree’s tree verification must handle recurrent/linear-attention layers (not just attention masks) for hybrid models like Qwen3.6, a significant correctness blocker. The roadmap emphasizes sequential-oracle fallback and debug metrics to validate against offline z-lab baselines before enabling production. The chunk also highlights the need for proper benchmarking across tree budgets (16–1024) and the assistant’s systematic approach of creating reusable, testable primitives before modifying the live inference engine.

The Deployment Pivot: From Training to Production with DDTree Speculative Decoding 2923 words

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