Step-by-step tutorial for adding a new lightweight JIT CUDA kernel to sglang.kernels JIT infrastructure and public operator groups
日本語の概要は準備中です。原文の説明を表示しています。
Audit SGLang startup logs, save evidence, and propose cleanup for user review. With no arguments, run Qwen3-8B at TP1 and TP2 plus gpt-oss-20b at TP1.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
The default outcome is saved logs and a findings report. Apply runtime changes only after the user selects them. A request to edit this skill does not itself launch servers.
A bare $clean-startup-log invocation runs these cases sequentially without
asking for commands. Explicit commands, models, or TP sizes replace this matrix.
| Case / log filename | Command |
|---|---|
qwen3-8b-tp1.log | uv run sglang serve --model-path Qwen/Qwen3-8B |
qwen3-8b-tp2.log | uv run sglang serve --model-path Qwen/Qwen3-8B --tp 2 |
gpt-oss-20b-tp1.log | uv run sglang serve --model-path openai/gpt-oss-20b |
These cover dense, tensor-parallel, and MoE/hybrid sliding-window attention startup. Reuse complete captures from the current audit when code and environment have not changed.
CUDA_VISIBLE_DEVICES and
an unused --port, recording the adjustments. Leave existing servers alone.mktemp -d /tmp/sglang-startup-audit-XXXXXX.
Save raw stdout and stderr together in a separate log for each case, for
example with set -o pipefail and COMMAND 2>&1 | tee LOG_PATH. Record commands,
GPU IDs, ports, commit, relevant overrides, and readiness status.The server is fired up and ready to roll!, then stop that server and
its workers before the next case. First runs can spend many minutes downloading
weights or compiling FlashInfer kernels; check download/compiler activity
before treating a quiet log as a hang. Preserve partial logs for failed or
stalled starts and report the last stage. Continue independent cases when possible.NCCL_DEBUG. If NCCL
verbosity needs explaining, inspect relevant shell settings, NCCL_CONF_FILE,
and /etc/nccl.conf. Do not override intentional diagnostics or recommend
NCCL_DEBUG=WARN solely because the output is long. Avoid full environment dumps.server_args, progress redraws, or NCCL
diagnostics. Normalize carriage returns for analysis only; preserve raw logs.rg searches. Inspect
its log level: SGLang's formatter may omit severity. Group shared signatures
across cases and distinguish handler duplication from separate GPU/process calls.Preserve these reviewed messages unless the user requests a different policy:
Init Unified Radix Cache. Components: ... Tree Core: ..., tree-cache summaries,
SWA allocation details, and per-rank memory/timing records.These can appear in a clean startup log. Do not repeatedly propose the declined
NUMA deduplication, backend/page-size level changes, or NCCL verbosity override.
Keep real operational warnings visible: for example, a Harmony vocabulary failure
can disable /v1/responses even when server readiness and /generate succeed.
Return a compact run table with readiness status and clickable raw-log links. For each actual cleanup candidate, give an exact representative message, affected cases/counts, source file/function, and specific proposed behavior. Distinguish confirmed findings from suspicions and operational failures from logging noise.
If there are no actionable cleanup findings, say the logs are clean and no further cleanup is needed. Otherwise, ask which numbered changes to adopt and wait for the user's selections before editing runtime code or preparing patches. Honor existing approvals and declined items without asking again.
configure_logger(), and spawned processes have
independent logger state. Keep configure_hf_hub_logger() in both
suppress_noisy_warnings() and configure_logger(); make repeated setup safe.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Step-by-step tutorial for adding a new lightweight JIT CUDA kernel to sglang.kernels JIT infrastructure and public operator groups
日本語の概要は準備中です。原文の説明を表示しています。
Step-by-step tutorial for adding a heavyweight AOT CUDA/C++ kernel to sgl-kernel (including tests & benchmarks)
日本語の概要は準備中です。原文の説明を表示しています。
Start and persistently pursue a goal to babysit an SGLang pull request until selected GitHub Actions workflows pass on the latest PR head. Use when asked to monitor, babysit, retry, or fix PR CI for lint.yml, pr-test.yml, pr-test-extra.yml, AMD, or other named workflows; classify failures as PR-related versus flaky or infrastructural, auto-fix and push only small clean fixes, rerun failed jobs only up to 10 times, and ignore unselected workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Audit the existing test tree and CI configuration for improvements, using a catalog of patterns previously applied in this repo. Use when asked to audit tests or CI, shrink CI time or cost, find redundant or misplaced tests, clean up a test group, or review whether a CI change follows established practice.
日本語の概要は準備中です。原文の説明を表示しています。
Guide to SGLang CI workflow orchestration — stage ordering, fail-fast, gating, partitioning, execution modes, and debugging CI failures. Use when modifying CI workflows, adding stages, debugging CI pipeline issues, or understanding how tests are dispatched and gated across stages.
日本語の概要は準備中です。原文の説明を表示しています。
Compute the optimal --mamba-full-memory-ratio (or --max-mamba-cache-size pin) for a hybrid attention + linear-attention (Mamba / GDN / KDA) model's two serving memory pools, from the workload and serving config. Use when a user asks what ratio to set, why concurrency is clamped, or how to size the state vs KV pools for a hybrid model.
日本語の概要は準備中です。原文の説明を表示しています。