| """Prepare V2 resources and native fixtures, leaving existing V1 releases intact.""" |
| import argparse |
| import shutil |
| import sys |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parents[2] |
| sys.path.insert(0, str(ROOT / "src")) |
|
|
| import torch |
| from vimeml.deployment import v2 |
| from vimeml.deployment.bundle import fresh_directory, read_json, write_json |
| from vimeml.training.data import file_sha |
| from vimeml.training.evaluate_ime import score_candidates |
| from vimeml.tools.phrase_demo import PhraseDemo |
|
|
|
|
| def package(bundle, source, compiled, template, output): |
| if output.exists(): |
| raise ValueError("Resource output exists.") |
| source_manifest = v2.verify_package(source) |
| if source_manifest["kind"] != "linear8_fp32_compute" or source_manifest["minimum_ios"] != 18: |
| raise ValueError("Expected V2 INT8 FP32-compute iOS18 experiment.") |
| if not (compiled / "coremldata.bin").is_file(): |
| raise ValueError("Missing iOS compiled model.") |
| lm = v2.CoreMLLM(bundle, source) |
| old = read_json(template) |
| fixtures = {"format": "vime_client_fixtures_v2", "model": lm.metadata, |
| "tokenization": [], "scoring": [], "phrases": []} |
| for item in old["tokenization"]: |
| ids = lm.processor.encode(item["text"], out_type=int) |
| fixtures["tokenization"].append({"text": item["text"], "ids": ids, |
| "decoded": lm.processor.decode(ids)}) |
| for item in old["scoring"]: |
| scored = score_candidates(lm, item["context"], item["candidates"]) |
| fixtures["scoring"].append({"context": item["context"], "candidates": item["candidates"], |
| "sums": [candidate["log_probability_sum"] for candidate in scored["candidates"]]}) |
| demo = PhraseDemo(lm, []) |
| for index, item in enumerate(old["phrases"]): |
| result = demo.suggest({"prompt": item["prompt"], "mode": "beam", "count": 5, "max_tokens": 8}) |
| first = result["suggestions"][0] |
| fixtures["phrases"].append({"prompt": item["prompt"], "firstText": first["text"], |
| "firstIDs": first["new_token_ids"], "firstSum": first["log_probability_sum"]}) |
| print(f"Native phrase fixture {index+1}/{len(old['phrases'])}", flush=True) |
| fresh_directory(output) |
| resources = output / "Resources" |
| resources.mkdir() |
| shutil.copytree(compiled, resources / "TinyJapaneseV21INT8.mlmodelc") |
| shutil.copyfile(bundle / "tokenizer.model", resources / "VimeJapaneseTokenizerV2.model") |
| manifest = {"format": "vime_ios_lm_v2", "architecture": v2.ARCHITECTURE, |
| "model_version": "2.1-extend5-step40000-int8-b32-v1", "minimum_ios": 18, |
| "vocab_size": 16384, "context_length": 128, "compute_units": "CPU_ONLY", |
| "special_ids": lm.special, "tokenizer_sha256": lm.metadata["tokenizer_sha256"], |
| "compiled_files_sha256": {name: item["sha256"] for name, item in v2.inventory(compiled, hashes=True).items()}, |
| "source_manifest_sha256": lm.metadata["coreml_manifest_sha256"], |
| "bundle_manifest_sha256": lm.metadata["bundle_manifest_sha256"], |
| "checkpoint_sha256": lm.metadata["checkpoint_sha256"], |
| "kind": source_manifest["kind"], "quantization": "weight-only symmetric INT8 block32; FP32 computation", |
| "strict_fp32_logits_alignment_passed": False, |
| "quality_review": "Candidate for V2 integration; see VimeML dated quality/device reports. Device acceptance is separate."} |
| write_json(resources / "VimeLMManifestV21.json", manifest) |
| write_json(output / "VimeLMFixturesV21.json", fixtures) |
| write_json(output / "manifest.json", {"format": "vimeml_v2_ios_resources_v1", "model": lm.metadata, |
| "files": v2.inventory(output), "note": "Resources/ is installed by the Vime client resource installer. No client source snapshot included."}) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description=__doc__) |
| for name in ("bundle", "source", "compiled", "template", "output"): |
| parser.add_argument("--" + name, type=Path, required=True) |
| args = parser.parse_args() |
| torch.set_num_threads(2) |
| package(args.bundle, args.source, args.compiled, args.template, args.output) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
