Guide test design for SGLang's scripted runtime by deciding when a test harness API adds real value. Favor direct reads, meaningful outcome checks, and the engine's actual execution loop over unnecessary wrappers.
Requirements for the SGLang scripted runtime, chiefly when to add (vs not add) a harness API. Use for anything related to the scripted runtime.
Review the instructions the agent will receive before you install.
Notes for anything related to the SGLang scripted runtime.
Tests read r.req.* and t._scheduler.* directly — there is no encapsulation boundary. A thin wrapper buys zero isolation; it only grows the surface.
Add an API only if it does real work:
start_req, pause_generation, abort, evict_radix, exhaust_kv). Reuse the real path; never hand-mutate state.scheduler_hook.on_run_batch or the recv proxy (chunks_done). Read-only; never monkey-patch; never add *_count to srt/.chunked_req + waiting_queue + running_batch + last_batch (is_idle, status, batch_composition).Else: don't. Read r.req.X / t._scheduler.X in the test; inline single-use accessors.
Never:
scheduler._abort_on_waiting_timeout()) from the harness/test — it runs at the wrong loop phase, bypasses the ordered recv_requests → process_input_requests injection, and can fire in states the real loop never reaches (e.g. while paused). For sweeps the engine runs itself (timeout/idle), enable the config/env and advance the loop with yield.No reviews yet. Tell others what you thought.
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Develop lightweight CUDA operations for SGLang using JIT, which compiles code on first use. A tensor-scaling example explains implementation conventions, input validation, memory access helpers, and when to choose this approach.
Add a CUDA/C++ GPU operation to sgl-kernel using AOT, meaning compilation before use. Follow the implementation through PyTorch registration, Python API exposure, SGLang integration, correctness tests, and benchmarks that account for cache reuse.
Monitor selected GitHub Actions checks on an SGLang pull request until they pass on its latest commit. Diagnose failures, push small validated fixes, and retry only failed jobs within defined limits.
Review existing tests and CI, the automated checks run during development, for redundant coverage, misplaced tests, and opportunities to reduce runtime or cost. Receive file-specific recommendations based on documented improvement patterns, with uncertain findings separated.
Understand and modify SGLang's CI pipelines, including test stage order, start conditions, early stopping after failures, and workload splitting. Use the guide to add jobs and investigate failed, skipped, or cancelled checks.
Collect and review SGLang startup logs to identify unnecessary output and trace its source. Get saved logs and specific cleanup proposals while preserving useful diagnostics, with runtime changes left for user approval.