Update FastPLMs files

fastplms/registry.py

@@ -22,6 +22,10 @@ from urllib.parse import urlparse
 
 _HEX_RE = re.compile(r"^[0-9a-f]+$")
 _WANDB_RUN_URL_RE = re.compile(r"^https://wandb\.ai/[^/?#]+/[^/?#]+/runs/[^/?#]+$")
+_CONFIDENCE_DATASET_URL_RE = re.compile(
+    r"https://huggingface\.co/datasets/Synthyra/[A-Za-z0-9][A-Za-z0-9_.-]*/"
+    r"(?P<view>tree|blob)/[0-9a-f]{40}(?:/(?P<path>[A-Za-z0-9_./-]+))?"
+)
 _IDENTIFIER_RE = re.compile(r"^[a-z0-9][a-z0-9_-]*$")
 _HUB_LICENSE_NAME_RE = re.compile(r"[^a-z0-9.]+")
 _WINDOWS_INVALID_PATH_CHARACTERS = frozenset('<>:"|?*')
@@ -400,6 +404,8 @@ class ConfidenceAdaptation:
     training_url: str
     evaluation_url: str
     evidence_path: str
+    release: Literal["pilot", "v1"] = "pilot"
+    frozen_base: CheckpointSource | None = None
 
 
 @dataclass(frozen=True, slots=True)
@@ -441,6 +447,17 @@ class ModelSpec:
 
         return self.fast if self.artifact_source == "fast" else self.official
 
+    @property
+    def confidence_training_base(self) -> CheckpointSource:
+        """Keep confidence training tied to its original base after release repinning."""
+
+        if (
+            self.confidence_adaptation is not None
+            and self.confidence_adaptation.frozen_base is not None
+        ):
+            return self.confidence_adaptation.frozen_base
+        return self.fast
+
     @property
     def oracle_asset_map(self) -> Mapping[str, OracleAsset]:
         """Return native oracle assets keyed by their declared role."""
@@ -1293,16 +1310,42 @@ def _parse_confidence_adaptation(
             "training_url",
             "evaluation_url",
             "evidence_path",
+            "release",
+            "frozen_base",
         }
     )
     _reject_unknown_fields(raw, fields, f"{context}.confidence_adaptation")
     adaptation_context = f"{context}.confidence_adaptation"
+    release = raw.get("release", "pilot")
+    if release not in ("pilot", "v1"):
+        raise RegistryError(f"{adaptation_context}.release must be pilot or v1.")
     digests: dict[str, str] = {}
     for key in ("head_sha256", "base_weight_sha256", "donor_weight_sha256"):
         digest = _require_str(raw, key, adaptation_context)
         if len(digest) != 64 or _HEX_RE.fullmatch(digest) is None:
             raise RegistryError(f"{adaptation_context}.{key} must be a SHA-256 digest.")
         digests[key] = digest
+    frozen_base = None
+    if "frozen_base" in raw:
+        base_context = f"{adaptation_context}.frozen_base"
+        raw_base = raw["frozen_base"]
+        if not isinstance(raw_base, dict):
+            raise RegistryError(f"{base_context} must be a table.")
+        _reject_unknown_fields(raw_base, {"repo", "revision", "files"}, base_context)
+        frozen_base = _parse_checkpoint(
+            {f"base_{key}": value for key, value in raw_base.items()},
+            "base",
+            base_context,
+        )
+        weight = frozen_base.file_map.get("model.safetensors")
+        if (
+            weight is None
+            or weight.algorithm != "sha256"
+            or weight.digest != digests["base_weight_sha256"]
+        ):
+            raise RegistryError(f"{base_context} model.safetensors must match base_weight_sha256.")
+    if release == "v1" and frozen_base is None:
+        raise RegistryError(f"{adaptation_context}.frozen_base is required for v1.")
     donor_repo = _require_str(raw, "donor_repo", adaptation_context)
     if _REPOSITORY_ID_RE.fullmatch(donor_repo) is None:
         raise RegistryError(f"{adaptation_context}.donor_repo must be a repository ID.")
@@ -1311,9 +1354,21 @@ def _parse_confidence_adaptation(
     urls: dict[str, str] = {}
     for key in ("training_url", "evaluation_url"):
         url = _require_str(raw, key, adaptation_context)
-        if _WANDB_RUN_URL_RE.fullmatch(url) is None:
+        dataset_match = (
+            _CONFIDENCE_DATASET_URL_RE.fullmatch(url) if key == "evaluation_url" else None
+        )
+        if dataset_match is not None:
+            dataset_path = dataset_match["path"]
+            if dataset_match["view"] == "blob" and dataset_path is None:
+                raise RegistryError(f"{adaptation_context}.{key} blob URL requires a file path.")
+            if dataset_path is not None:
+                _portable_relative_path(dataset_path, f"{adaptation_context}.{key}")
+        elif _WANDB_RUN_URL_RE.fullmatch(url) is None:
+            accepted_urls = "a W&B run URL"
+            if key == "evaluation_url":
+                accepted_urls += " or an immutable Synthyra Hugging Face dataset URL"
             raise RegistryError(
-                f"{adaptation_context}.{key} must be a W&B run URL without query or fragment."
+                f"{adaptation_context}.{key} must be {accepted_urls} without query or fragment."
             )
         urls[key] = url
     evidence_path = _portable_relative_path(
@@ -1331,6 +1386,8 @@ def _parse_confidence_adaptation(
         training_url=urls["training_url"],
         evaluation_url=urls["evaluation_url"],
         evidence_path=evidence_path.as_posix(),
+        release=cast(Literal["pilot", "v1"], release),
+        frozen_base=frozen_base,
     )
 
 

fastplms_bundle.py

@@ -1,6 +1,6 @@
 """Generated deterministic archive of unchanged FastPLMs runtime sources."""
 
-RUNTIME_HASH = "94b14721cefe8f43a5b08716ad38dd2b2030a6c69fd63e8e9edacdc0e5515b23"
+RUNTIME_HASH = "5c466dfbd29ac7be773a338382f0e555f6bf595d0c4231f6deef6399e42b197f"
 RUNTIME_DATA = (
     'P)h>@6aWAK2mk;8ApjK1*rl8T002P;000yK003rTb98WQZF4VQUukY>bYEXCaCwcDT~FIE6o&8fE6#kCOkKrpD{WG>fiw*S0w!&%'
     'ka3ff)~XXnw!<ih|Gr}<E=?(v+%(O3-uHYsJ!OoY2<t9R&nz_UT*(|7k*~x8LDmjUDRLYq$@LPU(nYHfT8kExcvu78H`+K0R)ehk'
@@ -3794,270 +3794,279 @@ RUNTIME_DATA = (
     'iLm}q16dNp9;V2PpF`(&7P_Xc0hNxeHq{Xo1?H+?!O(aCGB&3&`G=zp1GPwFG+9w8&(UUx_{w0P9pNNCmW(zE9a%;@C{2D>rjm4}'
     'f-+36K%6v07G<rf#Z$e<Mv)6)S^VTzoLknYT|H7h`Odah({apfNGl28cn8V)_`fNQ@$S82-~?(z1<XLnzQv($cA(eSxdF#VHX?>K'
     '#?j(|c8G6Kj>rJ=T=XS<R&^OSh_i&{8Ert@z>ViUMJ?ipG}{$}^zlqr`s9<`_-OXoZmvM2Cr1jru)disrQ-2wve;~mm07HxUQ5k2'
-    'So1~w9cshfRV)5w7k2r7P)h>@6aWAK2mk;8Apo>GQ9}kf002}C0RR*L003rTb98WQZF4VjWoKz~baHtvaCzl@X?Nqs5#V?I3K-tE'
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+    'UuR`+ZDn+Fc`j;mZ*EXa0Rj{N6aWAK2mk;8Apl9V20lRv005mI001cf0000000000005+cTyq2fW?^%5aBOXJFKusRWo&aVWpiz2'
+    'Z){{TFK}{ibY*F7Uv+e8Y;!Jfc~DCM0u%rg000080000X0IJxO?6v{`0M7;h04o3h00000000000HlHBd;|byVRLkFY;AKdZEs{{'
+    'Y;!MVb8TjCY-BPoa%FIGZ)A02X<}(?X>@rmaCuNm0Rj{N6aWAK2mk;8ApmztRhju9004fI000&M0000000000005+c<9`GIW?^%5'
+    'aBOXJFKusRWo&aVbaZqsaCuNm0Rj{N6aWAK2mk;8ApnCd+|Hgm005d70RR*L0000000000005+c5TOJBW?^%5aBOXJFLGsPX>)XP'
+    'c`k5yP)h*<6aW+e000O8001EXi^2h}Py+w}?Fj$?6951J0000000000q=Dev1OR4Xb98WQZF4Vjb#8QNZDlTSc~DCQ1^@s606_pj'
+    '05lc=0Ab(+0000'
 )

modeling_fastplms.py

@@ -13,7 +13,7 @@ from zipfile import ZIP_DEFLATED, ZipFile
 
 from .fastplms_bundle import RUNTIME_DATA, RUNTIME_HASH
 
-if RUNTIME_HASH != "94b14721cefe8f43a5b08716ad38dd2b2030a6c69fd63e8e9edacdc0e5515b23":
+if RUNTIME_HASH != "5c466dfbd29ac7be773a338382f0e555f6bf595d0c4231f6deef6399e42b197f":
     raise RuntimeError("FastPLMs runtime identity differs from the bridge.")
 
 _RUNTIME_TEMPORARIES = []