l0-test

name	l0-test
description	Reproduce all L0 PR CI checks locally for the Emerging-Optimizers repo — pre-commit lint on host, then all CPU and GPU tests inside the NGC pytorch container. Use before opening or updating a PR to catch CI failures locally.
allowed-tools	Bash

L0 Test (PR CI reproduction)

Runs the same checks that gate a pull request in .github/workflows/cicd-main.yml and code-linting.yml:

1. Lint (host) — pre-commit run --all-files (ruff check, ruff format, mypy, copyright/EOF/whitespace, no-underscore-md).
2. L0 CPU tests (container) — distributed muon utils at nproc=4,8, plus test_scalar_optimizers.py and test_procrustes_step.py.
3. L0 GPU tests (container) — every tests/test_*.py (excluding *_cpu.py) twice — once with a random seed, once with --seed=42 — then the convergence tests/convergence/*_test.py set.

Stages 2 and 3 share a single NGC container session: lint runs on the host, then a single docker run against the image declared in docker/Dockerfile.ci (the FROM line — do not hard-code a tag, so the skill picks up version bumps automatically) with --gpus all performs both CPU and GPU passes. All test execution happens inside the container; never run tests on the host. Reasons:

• tests/test_distributed_muon_utils_cpu.py imports numpy, which is preinstalled in the NGC image but not pulled in by uv sync against a host venv.
• The NGC torch build (2.11.0a0+...nv26.02) and CUDA stack are what CI uses — host torch may differ in version, build flags, or CUDA major.
• torchrun resolution differs between host and container; we use python -m torch.distributed.run everywhere so it's deterministic.

Not covered: the copyright-check external workflow (uses NVIDIA-NeMo/FW-CI-templates) and L1 long-running convergence runs.

Args

Default (no args): run all three stages in order, stopping on first failure.

• lint — only stage 1 (host).
• cpu — only stage 2 (container, CPU tests).
• gpu — only stage 3 (container, GPU tests).
• nogpu — stages 1 + 2 (skip GPU tests; container still launches for stage 2).
• keepgoing — don't stop on the first failed stage; run all and report at the end.
• affected — instead of running every test in stages 2 and 3, only run tests associated with files changed vs main (see below). Stage 1 always runs the full lint regardless. If no tests are affected, stages 2 and 3 print no affected tests and exit 0.

Args can be combined, e.g. affected gpu, cpu keepgoing.

affected — change-aware test scoping

Compute the set of changed Python files vs main (committed-since-branch + working-tree + untracked) and reduce that to a set of test files. Run this on the host before launching the container, then pass the resolved list in via env or argv:

mapfile -t changed < <(
    {
        git diff --name-only main...HEAD --
        git diff --name-only HEAD --
        git ls-files --others --exclude-standard
    } | sort -u | grep '\.py$' || true
)

tests_to_run=()

# 1. Test files that were changed directly: run them as-is.
for f in "${changed[@]}"; do
    case "$f" in
        tests/test_*.py|tests/convergence/*_test.py) tests_to_run+=("$f") ;;
    esac
done

# 2. Source files under emerging_optimizers/: find tests that import them.
for f in "${changed[@]}"; do
    case "$f" in
        emerging_optimizers/*.py)
            mod=${f%.py}; mod=${mod//\//.}     # path → dotted module
            mapfile -t hits < <(grep -lE "(^|[^.\w])(from[[:space:]]+${mod//./\\.}|import[[:space:]]+${mod//./\\.})([[:space:]]|\.|$)" tests/test_*.py tests/convergence/*_test.py 2>/dev/null || true)
            tests_to_run+=("${hits[@]}")
            ;;
    esac
done

mapfile -t tests_to_run < <(printf '%s\n' "${tests_to_run[@]}" | sort -u | grep -v  || )

Filtering rules inside the container:

• Stage 2 (CPU) runs only the affected files in {tests/test_distributed_muon_utils_cpu.py, tests/test_scalar_optimizers.py, tests/test_procrustes_step.py}. The distributed test runs at both nproc=4,8 only if it's in the affected set.
• Stage 3 (GPU) runs the intersection of tests_to_run with tests/test_*.py (excluding *_cpu.py) twice (random + --seed=42), and the intersection with tests/convergence/*_test.py once at --seed=42.
• If tests_to_run is empty after filtering, that stage prints no affected tests and exits 0.

Caveats:

• The import-grep is purely syntactic. It won't catch tests that pull in a module via __init__.py re-exports (e.g. from emerging_optimizers.orthogonalized_optimizers import muon triggered by a change to mop.py — these __init__.py files do from .mop import *). To compensate: when an __init__.py is changed, treat every test that imports the package as affected. The grep already does this since the import lines reference the package path.
• Changes to emerging_optimizers/registry.py or emerging_optimizers/mixin.py realistically affect every optimizer test. Don't try to be clever — if the grep finds many tests, run them all.
• Changes outside emerging_optimizers/ and tests/ (e.g. pyproject.toml, docker/, .github/, docs/) do not map to any test; affected will report no affected tests. Use the unscoped form before merging if those changed.

Prerequisites

• uv installed on host (https://docs.astral.sh/uv/) — only used by stage 1 (uv run pre-commit ...).
• docker with the nvidia runtime configured, plus --gpus all access. Verify with docker info | grep nvidia and nvidia-smi -L.
• The NGC pytorch image referenced by docker/Dockerfile.ci's FROM line (~22 GB). Pulled on demand if missing; an nvcr.io login may be required.

Don't hard-code versions in this skill. The image tag, the uv version, and the --no-install-package allowlist are all defined in docker/Dockerfile.ci. Resolve them at runtime so a Dockerfile bump flows through automatically:

IMAGE=$(awk 'tolower($1)=="from" {print $2; exit}' docker/Dockerfile.ci)
UV_VERSION=$(awk '$1=="ARG" && $2 ~ /^UV_VERSION=/ {sub(/^UV_VERSION=/, "", $2); print $2; exit}' docker/Dockerfile.ci)
NO_INSTALL_FLAGS=$(grep -oE -- '--no-install-package [a-zA-Z0-9._-]+' docker/Dockerfile.ci | sort -u | tr '\n' ' ')

If a value isn't in docker/Dockerfile.ci, prefer reading it from pyproject.toml/uv.lock or relying on whatever's installed in the container — never paste a literal version string into this skill.

Execution

Stage 1 — Lint (host)

uv run pre-commit run --all-files --show-diff-on-failure --color=always

This matches .github/workflows/code-linting.yml exactly. Pre-commit runs the hooks defined in .pre-commit-config.yaml: ruff (--fix then isort-only --fix then ruff-format), mypy (scoped to emerging_optimizers/), end-of-file-fixer, trailing-whitespace, and the local no-underscore-md hook. This is the only stage that runs on the host.

Stages 2 & 3 — All tests inside the NGC container

A single docker run performs the complete uv setup (per docker/Dockerfile.ci) and then executes the CPU and GPU test passes inline. Skip the tests/ci/L0_Tests_*.sh wrappers — they generate JUnit XML and coverage files that aren't useful locally. The script below tracks failures in a failed=() array and exits non-zero if any test failed.

Resolve IMAGE, UV_VERSION, and NO_INSTALL_FLAGS from docker/Dockerfile.ci (see Prerequisites) and pass them in:

docker run --rm --gpus all \
  -v "$(pwd)":/workspace -w /workspace \
  -e PIP_CONSTRAINT="" \
  -e UV_PROJECT_ENVIRONMENT=/opt/venv \
  -e PATH="/opt/venv/bin:/root/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin" \
  -e TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=0 \
  -e TORCH_COMPILE_DISABLE=1 \
  -e CUDA_VISIBLE_DEVICES=0 \
  -e UV_VERSION="$UV_VERSION" \
  -e NO_INSTALL_FLAGS="$NO_INSTALL_FLAGS" \
  "$IMAGE" \
  bash -euc '
    # ----- venv setup (mirror of docker/Dockerfile.ci) -----
    curl -LsSf "https://astral.sh/uv/${UV_VERSION}/install.sh" | sh >/dev/null 2>&1
    uv venv --system-site-packages "$UV_PROJECT_ENVIRONMENT" >/dev/null 2>&1
    # NO_INSTALL_FLAGS is intentionally word-split here.
    uv sync --link-mode copy --locked --all-groups $NO_INSTALL_FLAGS >/dev/null 2>&1

    failed=()

    # ----- Stage 2: L0 CPU tests -----
    # Use `python -m torch.distributed.run` instead of bare `torchrun`. With --system-site-packages
    # and torch in --no-install-package, `torchrun` resolves to /usr/local/bin/torchrun and spawns
    # /usr/bin/python3 (system), which lacks the editable emerging_optimizers install.
    for n in 8 4; do
        # Must use `cmd || failed+=(...)` form, not `cmd; [[ $? -eq 0 ]] || failed+=(...)`.
        # Under `bash -euc`, a bare failing command aborts the whole script before the next line
        # runs, so a distributed-test failure would be silently swallowed (no array entry, no
        # summary, remaining stages skipped). The `||` form is exempt from `set -e`.
        python -m torch.distributed.run --nproc_per_node=$n tests/test_distributed_muon_utils_cpu.py -v -2 \
            || failed+=("CPU dist n=$n: tests/test_distributed_muon_utils_cpu.py")
    done
    for t in tests/test_scalar_optimizers.py tests/test_procrustes_step.py; do
        python "$t" --device=cpu -v -2 || failed+=("CPU: $t")
    done

    # ----- Stage 3: L0 GPU tests -----
    for t in $(find tests -maxdepth 1 -type f -name "test_*.py" ! -name "*_cpu.py" | sort); do
        python "$t" --device=cuda -v -2 || failed+=("GPU random: $t")
    done
    for t in $(find tests -maxdepth 1 -type f -name "test_*.py" ! -name "*_cpu.py" | sort); do
        python "$t" --device=cuda --seed=42 -v -2 || failed+=("GPU seed=42: $t")
    done
    for t in $(find tests/convergence -type f -name "*_test.py" | sort); do
        python "$t" --device=cuda --seed=42 -v -2 || failed+=("GPU conv seed=42: $t")
    done

    if (( ${#failed[@]} )); then
        printf "FAIL: %s\n" "${failed[@]}" >&2
        exit 1
    fi
  '

When cpu or gpu is selected, drop the corresponding block from the inline script. When affected is selected, replace the bare for t in $(find ...) lists with iteration over the resolved tests_to_run array (passed in via env or by templating it into the script).

The container setup mirrors docker/Dockerfile.ci:

• PIP_CONSTRAINT="" clears the NGC image's pip constraint that breaks uv resolution.
• --system-site-packages makes the uv venv inherit the container's torch + CUDA stack.
• $NO_INSTALL_FLAGS (resolved from docker/Dockerfile.ci) prevents uv from clobbering torch, triton, absl-py, or any nvidia-*-cu12 wheel. If a new dep pulls in a CUDA wheel, extend the --no-install-package lines in docker/Dockerfile.ci — this skill picks up the change automatically.
• --link-mode copy because bind-mount layout makes hardlinks fail.
• TORCH_COMPILE_DISABLE, CUDA_VISIBLE_DEVICES, TORCH_ALLOW_TF32_CUBLAS_OVERRIDE are set on the docker invocation since we're not sourcing the tests/ci/L0_Tests_*.sh scripts.

Reporting

For each stage, surface only what failed. Don't dump full passing logs; absl already prints [OK]/[FAIL] per test.

• Stage 1: pre-commit's own diff on failure.
• Stages 2 and 3: aggregate the failed array; print one line per failed test file (and which seed it was running under) plus the absl tail with the actual error. If everything passed, a one-line L0 CPU OK / L0 GPU OK is enough.

No JUnit XML, no coverage run wrapping — those are for CI's reporting pipeline, not local iteration. If the user asks for coverage afterward, they can rerun under coverage run -p separately.

Common gotchas

• GPU not visible inside container → check docker info | grep -i runtime shows nvidia, and that --gpus all is passed. The NGC image won't fall back to CPU.
• uv sync reinstalling torch → you forgot --system-site-packages on the venv create, or dropped a --no-install-package flag.
• Tests fail only with --seed=42 → likely a tolerance issue. The L0 GPU script runs the suite twice (random + fixed) for exactly this reason.
• Parameterized test selection failing → absl synthesizes _0/_1/... suffixes for parameterized.product / parameterized.parameters; select by class name (ClassName), not bare method.
• Pre-commit modifies files → ruff and end-of-file-fixer auto-fix; rerun until clean.
• set -e swallows test failures → the inline container script runs under bash -euc. A bare failing command (e.g. python tests/foo.py) aborts the script before any post-hoc [[ $? -eq 0 ]] || failed+=(...) check can run. Always use the cmd || failed+=(...) form so the failure is part of an || list (which set -e ignores).

Do not

• Do not run any test on the host — always inside the NGC container. The host venv may not have numpy, may have a different torch build, and torchrun resolves differently. Stage 1 lint is the only host-side step.
• Do not invoke tests via pytest; this repo uses absl.testing and depends on --device / --seed flags.
• Do not skip stage 1 just because tests pass — CI gates on lint independently.
• Do not run L1 GPU tests as part of this skill; they're long-running convergence runs and gated separately.