MNN Test / CI SKILL (index)
This skill is an index. Pick the document that matches the task and follow it:
| Document | Use when |
|---|---|
test-suite.md |
Run the regression / CI suite — static checks, host (local) tests, the on-device Android arm64 matrix (./test.sh + test_stages.json); add / select / retune a test stage; audit stale CI scripts; add a new test. |
ios-llm-bench.md |
Benchmark LLM prefill/decode speed on a real iPhone/iPad (ios_llm_bench.sh); compare branches on iOS Metal/CPU; verify Metal kernel changes on device. |
The two tracks are independent: Android/host regression testing goes through
test.sh, while iOS LLM benchmarking goes through
transformers/llm/engine/ios/ios_llm_bench.sh.
| File | Role |
|---|---|
test.sh |
Bash driver. static, local (host CPU), and android <serial> modes. |
test_stages.json |
Declarative stage matrix. Edit this to add / drop / retune stages — no shell edits needed for the common cases. It is self-documenting via its _documentation block. |
docs/testing.md |
中文测试文档:阶段说明、字段表、新增算子测试流程。 |
Quick start
# Static checks only:
./test.sh static
# Host regression (CPU only): build + unit suite + smoke + LLM smoke.
./test.sh local
# Full on-device matrix on the attached arm64 device:
./test.sh android <serial> # e.g. ./test.sh android R5CY71BJJ9D
<serial> comes from adb devices (the script prefers adbk and falls back
to adb). If the device shows as unauthorized, the user must tap Allow USB
debugging on the phone first.
Running a subset (filters)
Android mode takes an optional filter as the third argument:
./test.sh android <serial> cpu # CPU unit + lowmem + llm
./test.sh android <serial> opencl # OpenCL unit (image+buffer) + opencl smoke
./test.sh android <serial> vulkan # Vulkan unit + vulkan smoke
./test.sh android <serial> gpu # opencl + vulkan
./test.sh android <serial> unit # all unit/op stages only
./test.sh android <serial> lowmem # only the low-memory matrix
./test.sh android <serial> android-ci # bench + smoke + llm only (no unit/lowmem)
Valid filters: all (default) · cpu · opencl · opencl-image ·
opencl-buffer · vulkan · gpu · unit · lowmem · android-ci.
Reading the result (agent-friendly)
- Each stage prints a delimited
═══ stage: <name> ═══block, then aPASS/FAIL/SKIPline. - A final summary prints
total / passed / failed / skippedand one line per stage.SKIPis not a failure — it means the prerequisite was absent (e.g. a GPU library, a model, or a missing build artefact). - Exit code is non-zero iff any stage failed. Gate automation on the exit code, not on log scraping.
- Combined stdout/stderr for every stage is saved under
logs/test-<UTC-timestamp>/<stage>.log— read the named log of a failing stage for the trailing output.rc=137≈ OOM-kill,rc=139≈ SIGSEGV.
Dynamic-shape device smoke tests
A zero exit code only proves that a backend context ran; it does not prove that the requested input shape selected the intended dynamic context. For a shape-sensitive device test, record and validate the runtime-observed input shape (for example, from a backend dump manifest or the runner's input tensor) before treating the test as dynamic-shape coverage.
Environment variables
| Var | Mode | Meaning |
|---|---|---|
ANDROID_NDK |
android | NDK root. Falls back to $HOME/android-ndk-r21. |
ANDROID_EXTRA_CMAKE |
android | Extra cmake flags appended to the build (e.g. -DMNN_SME2=OFF) — handy for bisecting a backend regression. |
LLM_MODEL_DIR |
both | Path to an existing on-disk MNN-format LLM model. When set, that directory is used as-is and nothing is downloaded. Defaults to models/<repo-basename>/. |
LLM_MODEL_REPO |
both | Model repo id for the LLM smoke test. Default taobao-mnn/Qwen2.5-0.5B-Instruct-MNN. |
LLM_MODEL_SOURCE |
both | Download source when LLM_MODEL_DIR is unset: huggingface (default) or modelscope. |
LLM_MODEL_URL_BASE |
both | Override the resolve URL prefix outright (wins over LLM_MODEL_SOURCE). |
MNN_TEST_SKIP |
both | Comma list of exact test names to skip (also set per-stage via the JSON skip field). |
Offline / no-network and mainland-China notes
LLM model provisioning is lazy: the download (or LLM_MODEL_DIR check) is
deferred until the llm stage actually runs, and a provisioning failure skips
only that stage. So the unit / smoke / bench stages run fine with no
network.
# Already have the model on disk → no download attempt at all:
LLM_MODEL_DIR=/path/to/Qwen2.5-0.5B-Instruct-MNN ./test.sh local
# huggingface.co unreachable (e.g. mainland China) → fetch from ModelScope:
LLM_MODEL_SOURCE=modelscope ./test.sh android <serial>
For the built-in default model the ModelScope org is remapped automatically
(taobao-mnn/* → MNN/*); an explicitly-set LLM_MODEL_REPO is used verbatim.
LLM backend/layout smoke
For backend or tensor-layout optimizations, do not stop at operator tests. Run
an end-to-end llm_demo correctness smoke with a short prompt and another
prompt long enough to cross backend prefill branch thresholds. This catches
real exported-graph layout bugs where an op test covers only the output format,
but the graph also changes an input tensor format.
macOS Metal answer A/B checks
For answer-level comparisons between two macOS Metal runtimes:
- A Metal config does not prove that Metal ran. In a restricted execution
context,
MTLCreateSystemDefaultDevice()may returnnil, after whichllm_demoprintsInit Metal Error/Can't Find type=1 backend, use 0 insteadand silently produces a plausible CPU answer. Run in a GPU-visible context and fail the case on either fallback marker. - Use the same exported model, prompt bytes, backend settings and greedy
sampler on both sides. Isolate each runtime's
tmp_path/ working directory so cache reuse cannot cross the A/B boundary. - Decide the multi-line prompt contract before running.
llm_demonormally treats every non-empty line as a separate prompt; if one file is intended to be one long prompt, flatten or use an explicitly one-line-aware runner and record the transformed-input checksum. - Fixed token limits can stop in the middle of a UTF-8 token and can leave code answers incomplete. Exact matches are strong evidence, but low character similarity after an early greedy fork needs semantic review. For code prompts, use a large enough budget and compile/test the extracted code before claiming functional correctness.
Configuring stages
Editing test_stages.json is the supported way to
add, drop, or retune unit / lowmem / smoke / bench stages. Every parameter
(forward type, precision, gpuMode, thread count, tag, memory mode,
dynamic-quant option, KleidiAI flag, per-stage skip list, smoke model list,
benchmark args) lives there, and the _documentation block at the top of the
file explains every field and every skip entry's rationale.
- Add a stage that runs an existing test in a new config → add an object to
android.stages(orlocal.stages). Seedocs/testing.md§ "增加专门阶段". - Skip a known-broken test on one stage → add its exact name to that
stage's
skiparray and document why under_documentation.skip_rationale. - Add a smoke model / bench entry → see
docs/testing.md§ "新增 smoke 模型或 bench 阶段".
Auditing stale CI/test scripts
When asked to clean up old CI or test scripts, build a usage map before recommending deletion:
- Prefer
git ls-filesplus targetedrg/git grepover broad filesystem scans, so generated build directories and local experiments do not look like maintained CI surface. - Classify scripts by role: active CI entrypoints, declarative test driver, release/package scripts, manual benchmark helpers, third-party vendored tests, and local device/debug helpers.
- Treat lack of in-repo references as a "review/deprecate" signal, not proof
of dead code; internal CI systems can invoke tracked files by convention.
Prefer a staged deprecation plan unless a script is both unreferenced and
clearly superseded by
test.sh/test_stages.json. - When renaming or consolidating test entrypoints, grep for both executable
names and generated-artifact prefixes. Update CI config,
.gitignore,test_stages.jsonself-documentation, developer docs, skill docs, and code comments in the same change so the old entrypoint disappears completely.
Adding a new test
See test-suite.md § "Adding a new test" for the full steps.
Two traps worth knowing up front: test/CMakeLists.txt globs sources at
configure time, so a new test file needs a cmake re-run before it exists in
run_test.out; and run_test.out prints an all-passed line even when the filter
matched nothing, so confirm passed:N.
For deeper work on operators themselves, see the
add-new-op skill.
Read next
docs/testing.md is the authoritative deep reference — read it for the per-stage
breakdown, the stage-object field table, and worked examples.