Spec-v2 draft extend can receive token ids from producers whose dtype is not already int64, while DP collective paths require a stable integer dtype across ranks. EAGLE draft CUDA graph replay also pads raw batches to a captured batch size, so the metadata/replay path must see seq_lens_sum consistent with the padded seq_lens and then restore the caller-visible raw value. Constraint: Keep this as a narrow correctness port from upstream rather than pulling the larger spec-v2 refactor chain. Rejected: Cherry-pick broader attention-backend and decode-result refactors | current branch lacks the same upstream forward-context scaffolding and would require a separate port. Confidence: high Scope-risk: narrow Directive: Do not remove the seq_lens_sum restore without rechecking padded EAGLE draft CUDA graph metadata construction. Tested: python -m pytest test/registered/spec/eagle/test_eagle_v2_draft_extend_contract.py -q Tested: remote g0034/cjy-glm5-new PYTHONPATH=python python3 -m pytest test/registered/spec/eagle/test_eagle_v2_draft_extend_contract.py -q Not-tested: full multi-node GLM5 spec-v2 decode startup smoke Co-authored-by: OmX <omx@oh-my-codex.dev>
Registered Tests
Tests under this directory are auto-discovered by run_suite.py via CI registration decorators.
Where Should I Put My New Test?
No server / engine launch required
| What you're testing | Directory | Requires |
|---|---|---|
| Component logic in isolation (cache, scheduler, config, parser, etc.) | unit/<module>/ |
CPU or GPU |
| CUDA kernel correctness | kernels/ |
GPU |
Server / engine launch required (E2E)
| What you're testing | Directory | Requires |
|---|---|---|
| Model inference correctness | models/, 4-gpu-models/, 8-gpu-models/ |
GPU |
| Feature-specific (OpenAI API, LoRA, speculative, distributed, VLM, etc.) | openai_server/, lora/, spec/, distributed/, ... |
GPU |
| Benchmarks (performance, accuracy, stress) | benchmark/ |
GPU |
| Platform-specific | amd/, ascend/ |
Vendor GPU |
See unit/README.md for unit test conventions.