open-standards/.github/workflows/wai-neural-e2e.yml

name: WAI neural decoders end-to-end

# The float neural decoders (elic / mlicpp / glc / dcvc / snac) cannot make
# WAI's cross-architecture byte-equality promise — their ONNX synthesis is
# float, so the low bits differ by hardware (this is exactly why the
# integer-exact int_synth path exists, and why only it is in the byte-equality
# matrix). What they CAN be is *correct end-to-end* on a real machine: entropy
# decode → ONNX synthesis → image assembly, reproducing a reference within ORT
# float rounding (±4). This job proves that, with no committed model blobs:
# the committed tool builds minimal synthetic CompressAI-compatible models +
# bitstreams, and the Rust decoders reproduce each reference. It runs only when
# the neural decode code or the generator changes — not on every cargo class.

on:
  push:
    branches: [main]
    paths:
      - 'wai/wai-rs/src/neural/**'
      - 'wai/wai-rs/tests/neural_native_decode.rs'
      - 'wai/tools/wai_validate_synthetic.py'
      - '.github/workflows/wai-neural-e2e.yml'
  pull_request:
    paths:
      - 'wai/wai-rs/src/neural/**'
      - 'wai/wai-rs/tests/neural_native_decode.rs'
      - 'wai/tools/wai_validate_synthetic.py'
      - '.github/workflows/wai-neural-e2e.yml'

jobs:
  neural-e2e:
    name: synthetic fixtures → Rust decode → reference (±4)
    runs-on: linux
    # The Rust decoders link `ort`, whose `download-binaries` feature pulls a
    # prebuilt static onnxruntime (1.28.0 for ort 2.0.0-rc.13). That library was
    # built against glibc >= 2.38 and GCC 13's libstdc++. The runner's default
    # job image, catthehacker/ubuntu:act-22.04, has glibc 2.35 and GCC 11, so the
    # test binary fails to LINK with five undefined symbols:
    #   __isoc23_strtol  __isoc23_strtoll  __isoc23_strtoull          (glibc 2.38+)
    #   basic_string<char>::_M_replace_cold  basic_string<wchar_t>::_M_replace_cold
    #                                                               (libstdc++, GCC 13+)
    # That link error is why this job failed at ~1 minute on every run: the
    # Python steps pass on a warm cache and the job dies at its last step. Checked
    # in both images: act-22.04 lacks all five, act-24.04 (glibc 2.39, g++ 13)
    # provides all five.
    container:
      image: catthehacker/ubuntu:act-24.04
    steps:
      - uses: actions/checkout@v5
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - name: ML stack (CPU torch + torchvision + compressai + onnxruntime)
        run: |
          python -m pip install --upgrade pip
          # torch AND torchvision from the CPU index so their compiled ops match
          # (compressai imports torchvision; a mismatched build fails at
          # `torchvision::nms does not exist`).
          pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
          # `onnx` (the exporter) is separate from `onnxruntime` (the decoder).
          pip install compressai onnx onnxruntime numpy pillow
      - name: Build the synthetic fixtures (elic / mlicpp / snac / dcvc / glc)
        working-directory: wai
        run: python tools/wai_validate_synthetic.py --size 256
      # Bootstrap rustup: the docker runner image ships no Rust.
      - name: Install Rust 1.98.0
        run: |
          export PATH="$HOME/.cargo/bin:$PATH"
          command -v rustup >/dev/null 2>&1 || \
            curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --profile minimal --default-toolchain 1.98.0
          rustup toolchain install 1.98.0 --profile minimal
          rustup default 1.98.0
          echo "$HOME/.cargo/bin" >> "$GITHUB_PATH"
      - name: Each Rust decoder reproduces its reference end-to-end
        working-directory: wai/wai-rs
        # --no-default-features: the neural decoders need only `ort`, NOT the C
        # codec chain (AV1/JXL/opus), so the job needs no system codec deps.
        #
        # The grep is the assertion, not decoration. Every `*_matches_reference`
        # test opens with `if !wai.exists() || !ref.exists() || !model.exists() {
        # eprintln!("skipping ..."); return; }` — so a missing fixture makes the
        # test SKIP, and a skip is reported as a PASS. Verified by running this
        # exact command on a machine with no /tmp fixtures: it prints
        # "6 passed; 0 failed" while comparing nothing.
        #
        # That means a green `cargo test` here does NOT mean the decoders were
        # checked against their references. If the Python fixture step ever
        # succeeds while writing nothing, this job goes green having verified
        # nothing at all — the failure mode is silent and looks like success.
        # Failing on "skipping" is what makes the job's name true.
        #
        # One test is excluded by name, not tolerated as a skip:
        # `snac_real_model_matches_reference` needs real pretrained SNAC weights,
        # which this synthetic-fixture job never builds, so it would skip on every
        # run and the guard would fail a job whose five in-scope decoders all
        # match. Excluding it keeps the guard strict for everything in scope.
        run: |
          set -o pipefail
          cargo test --no-default-features --features neural --test neural_native_decode \
            -- _matches_reference --skip snac_real_model_matches_reference --nocapture \
            2>&1 | tee /tmp/neural_e2e.log
          if grep -q "skipping" /tmp/neural_e2e.log; then
            echo "::error::a reference comparison was SKIPPED — its fixtures are missing, so this job verified nothing"
            grep "skipping" /tmp/neural_e2e.log
            exit 1
          fi