sgl-project

Add or verify model support in SGLang's Rust processor so chat requests produce the same prompt text and token IDs as Python. Use reference-generated test cases to find differences in tools, reasoning settings, conversation history, and errors.

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• Adding model support to sglang-processor
• Porting model rendering from sgl-router
• Rechecking prompt behavior after a Dynamo update

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add a test job to an SGLang CI stage
• Investigate failed or skipped CI checks
• Update test partitions and waiting job counts

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.

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• Decide whether a test harness needs a new API
• Review wrappers around directly accessible test state
• Test timeout behavior through the real engine loop

Integrate or upgrade cache-dit in SGLang diffusion, the image generation system. Follow guidance for resolving code conflicts, adding cache settings, testing feature combinations, and comparing generated images using quality measurements and logs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Upgrading cache-dit in SGLang diffusion
• Resolving conflicts when porting upstream changes
• Adding per-request cache settings

Investigate CUDA crashes in SGLang using logs and inputs saved before execution. Inspect tensor shapes, data types, and numerical anomalies, with guidance for separate process logs and additional CUDA debugging tools.

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• Investigating illegal memory access and device-side assertions
• Tracing NaN or Inf values through kernel calls
• Saving inputs before a CUDA crash

Guide changes and reviews for SGLang’s Scheduler, TokenizerManager, and ModelRunner classes. Keep initialization in small, replaceable helpers and separate computation from coordination so large classes retain clear responsibilities and explicit dependencies.

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• Reviewing changes to SGLang’s three large classes
• Splitting initialization into replaceable helpers
• Moving computation out of ModelRunner

Verify file splits, function moves, module extractions, and renames by reproducing changes from a base commit and comparing files byte for byte. Generate repeatable proof scripts and reports that expose changes beyond relocation.

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• Splitting a refactor into preparation, movement, and follow-up commits
• Generating repeatable proof scripts for code moves
• Verifying every provable commit in a branch

Add a model page to the SGLang Cookbook using shared templates. Organize verified launch settings and measured benchmarks, create configuration files and documentation, and update navigation, vendor cards, and page validation.

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• Add documentation for a newly supported model
• Organize verified launch recipes by hardware
• Publish measured speed and accuracy results

Apply consistent names to SGLang speculative decoding code, which verifies draft tokens to generate text. Distinguish tokens with or without the bonus token, counts, counters, and arrays when adding identifiers, renaming code, or reviewing changes.

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• Naming variables in speculative decoding code
• Renaming identifiers to match their meaning
• Reviewing metric and counter names

Add, rename, or review SGLang environment variables using consistent definitions, types, names, and access methods. The skill also guides migration from old names and temporary test overrides that restore the original values afterward.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new SGLang environment variable
• Migrating deprecated environment variable names
• Temporarily overriding settings in tests

Investigate recurring SGLang automated test failures by comparing logs, commit history, and GPU environments. Narrow down code changes or machine-specific problems, optionally reproduce failures remotely, and summarize the evidence in a structured report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding the change behind a recurring test failure
• Checking whether failures depend on a GPU or runner
• Reproducing a test failure on a remote GPU server

Guide changes to SGLang runtime configuration and shared process state. Identify where resolved settings, runtime flags, resources, and per-forward state belong, and use the documented access patterns and checks when developing or testing.

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• Review how server settings are read and updated
• Choose where shared and per-forward state should live
• Check configuration publication order in worker processes

Investigate SGLang serving failures by collecting server state, preserving failing requests, and replaying them on a clean target. Summarize evidence and likely causes, then choose the next debugging tool for crashes, stalls, or performance regressions.

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• Investigate failed health checks and growing queues
• Reproduce request-specific timeouts or crashes
• Investigate output changes after deployment

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.

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• Investigating a slow test suite
• Finding redundant or misplaced tests
• Reviewing CI runtime and cost

Prepare batches of merged SGLang pull requests for a release branch. It checks conflicts and previous attempts, starts the cherry-pick workflow, monitors completion, and reports created pull requests or failure reasons.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare multiple merged PRs for a release branch
• Check conflicts before starting workflows
• Detect previous cherry-pick PRs

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a CUDA/C++ operation to sgl-kernel
• Registering an operation with PyTorch and exposing a Python API
• Testing numerical results and invalid inputs

Launch an SGLang server, run a quick accuracy check, and capture an execution trace for performance inspection. Return the trace location and generated files, ready to open in Perfetto or Chrome tracing.

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• Capture an execution trace of SGLang inference
• Check basic accuracy before profiling
• Record input processing and token generation separately

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.

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• Monitor selected PR checks until they pass
• Separate code failures from infrastructure problems
• Apply small fixes and track the resulting checks

Review SGLang Cookbook pull requests for configuration errors, documentation mismatches, benchmark issues, and broken links. Get findings for each file and an overall recommendation on whether to approve, request changes, or block the contribution.

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• Reviewing a new model's Cookbook contribution
• Checking documentation against generated launch commands
• Checking benchmark definitions and measurement consistency

Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.

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• Locate where a multi-GPU inference run freezes
• Compare logs from distributed processes
• Trace the first difference in process state

Organize SGLang GPU operations when adding or moving code. It guides API placement, operation registration, test organization, and migration checks to preserve existing behavior, references, and continuous integration coverage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose where to place a new GPU operation
• Split or move existing operation files
• Review API registration and test placement

Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.

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• Identify GPU operations that dominate inference time
• Compare input processing and token generation
• Trace GPU operations back to Python code

Write and register SGLang unit and end-to-end tests, from choosing models and file locations to reusing server fixtures. Follow project rules for meaningful assertions, CI registration, dependency mocking, and safe server cleanup after tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding CPU-only unit tests for component logic
• Testing features through a running SGLang server
• Registering tests in an appropriate CI suite

Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measuring diffusion denoising latency
• Ranking slow operations from profiler traces
• Checking whether existing optimizations address a bottleneck