android/scripts/TEST_DATA.md

Laya validation data

Large model files and captured fixtures are external to the module. Building an APK does not need them. Host parity tests require a local validation bundle specified by LAYA_TEST_DATA; they must not resolve data from another checkout or download it during a test.

validation-data/
  host_assets/
    tokenizer.json
    laya_ml_calibration.json
    token_embeddings_fp16.bin
    token_embeddings.json
  fixtures/
    ml_fixtures.json
    ml_rows_s256.json
    ml_rows_s512.json
    tokenizer_stress.json
    calibrated_reference.json
    serialization_reference.json
    fp16_conversion_reference.bin
    embedding_lookup_reference.json
    embedding_lookup_reference.bin
    gate_rows_s256.json          # optional: debug device gate

Run from the module root after supplying this bundle:

export LAYA_TEST_DATA="$PWD/validation-data"
./gradlew --no-daemon :app:testDebugUnitTest

LAYA_TEST_RESULTS optionally selects a separate report directory. A missing or incomplete bundle must not be counted as a passing parity gate. The model graphs are not used by JVM host-parity tests.

Captures and checks

The source checkpoint is convaiinnovations/laya, revision 1c5edc17a7acd8701df6fc341c0d179f1c62c982, multilingual branch. Captures came from the official laya 0.3.4 builder and decoder. Each captured row records its fixture/question identity, window, ordered question schema, unpadded sequence IDs, marker positions, question type, option count, raw marker logits, raw action logits, and official answer dictionary at T=1.

Input	Content	Required assertion
ml_fixtures.json	44 synthetic EN/JA states and schemas	State/schema source for rebuilding captured rows
ml_rows_s256.json, ml_rows_s512.json	201 question rows per window	402/402 exact sequence IDs and marker positions
Same captured rows	Raw logits and action logits	402/402 exact official four-decimal dictionaries at T=1
calibrated_reference.json	NumPy host decode of the same tensors with shipped temperatures	402/402 exact calibrated dictionaries
tokenizer_stress.json	300 synthetic strings encoded with tokenizers 0.23.2	300/300 exact token ID lists
serialization_reference.json	20 nested/string/Unicode/boolean/null states and Python json.dumps outputs	20/20 exact strings
embedding_lookup_reference.json and .bin	NumPy FP16-table lookup converted to little-endian float32 for 402 padded rows	118,554,624 values bit-exact, maximum absolute error 0
fp16_conversion_reference.bin	All 65,536 half-float bit patterns converted with NumPy	Float32 bits match, including infinities and NaN payload behavior

The stress corpus covers Japanese, English, mixed scripts, emoji, URLs, digits/units, repeated whitespace, boundary whitespace, added tokens, accents, Korean, Chinese, Arabic, and empty text. The embedding reference binary is 474,218,496 bytes; its manifest pins the source table and row IDs. Padded positions must gather token ID 0. Fixture generation uses the pinned tokenizer, Python serialization, and NumPy host math; generating these references does not execute a model.

Debug device rows

gate_rows_s256.json joins the 201 S256 captures to their source states and question schemas. It retains IDs, markers, question type, option count, raw captures, and official T=1 answers so the Android runner can tokenize and build each row independently before model inference. The current file is 1,018,117 bytes, SHA-256 037bd3c83f02f5aade4c559984a55d31cd4c231af493aee5fb880a4af35a7aa8.

The module does not bundle the gate file or raw calibration corpora. Supply the independently prepared file with scripts/install_to_device.sh --gate-rows FILE alongside the model assets. The installer places it in the app's private files/fixtures/ directory. The gate=true intent entry exists only in the debug source set; release builds launch the normal product UI.

Gate acceptance compares all 201 token sequences and marker lists exactly, all 81 choice/score argmax results under the recorded tie rule, every option/noul/action probability within 0.01 of the captured official dictionary, and finite outputs. GPU acceptance additionally requires explicit FP32 precision and the runtime's full-residency, one-partition delegate evidence. Per-row reports contain creation, tokenizer, embedding lookup, and write/run/read timings; the first graph call is separate from the remaining 200 warm rows. Matching these captures checks conversion and host parity, not classification accuracy on a held-out dataset.