File size: 8,251 Bytes
ca3d977 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 | """Model-independent canonical Planner Cache protocol and storage."""
from __future__ import annotations
from dataclasses import dataclass
import hashlib
import json
from pathlib import Path
import torch
from torch import Tensor
from safetensors import safe_open
from safetensors.torch import load_file, save_file
from pcm.planner.cache import PlannerCache, PlannerCacheConfig, SlotType
CANONICAL_VALUE_LABELS = tuple(
"Alice Bob Clara David Elena Frank Grace Henry Irene James Karen Louis Maria Nancy "
"Oscar Peter Queen Robert Sarah Thomas Victor Wendy Xavier London Paris Berlin Rome "
"Cairo Tokyo Sydney garden kitchen cellar library forest castle".split()
)
def tensor_state_checksum(state: dict[str, Tensor]) -> str:
"""Deterministic checksum for portable tensor-only state."""
digest = hashlib.sha256()
for name, tensor in sorted(state.items()):
value = tensor.detach().cpu().contiguous()
digest.update(name.encode())
digest.update(str(value.dtype).encode())
digest.update(json.dumps(list(value.shape)).encode())
digest.update(value.reshape(-1).view(torch.uint8).numpy().tobytes())
return digest.hexdigest()
def canonical_snapshot_checksum(
tensors: dict[str, Tensor], metadata: dict[str, str]
) -> str:
digest = hashlib.sha256(tensor_state_checksum(tensors).encode())
digest.update(json.dumps(metadata, sort_keys=True, separators=(",", ":")).encode())
return digest.hexdigest()
@dataclass(frozen=True)
class CanonicalPConfig:
slots: int = 128
width: int = 512
dtype: torch.dtype = torch.float16
device: str | torch.device = "cpu"
merge_similarity: float = 0.92
class CanonicalPStore:
"""Fixed P slots whose serialized state has no model-hidden representation."""
FORMAT = "pcm-canonical-p-v1"
def __init__(self, config: CanonicalPConfig) -> None:
self.config = config
self.cache = PlannerCache(PlannerCacheConfig(
slots=config.slots,
width=config.width,
dtype=config.dtype,
device=config.device,
merge_similarity=config.merge_similarity,
))
device = self.cache.device
self.entity_id = torch.full((config.slots,), -1, dtype=torch.int64, device=device)
self.relation_id = torch.full((config.slots,), -1, dtype=torch.int64, device=device)
self.value_id = torch.full((config.slots,), -1, dtype=torch.int64, device=device)
self.canonical_metadata_id = torch.full(
(config.slots,), -1, dtype=torch.int64, device=device
)
@property
def canonical_values(self) -> Tensor:
return self.cache.values
@property
def valid(self) -> Tensor:
return self.cache.valid
def create(
self,
canonical_value: Tensor,
*,
entity_id: int,
relation_id: int,
value_id: int,
metadata_id: int,
slot_type: SlotType = SlotType.FACT,
**cache_metadata,
) -> tuple[int, object]:
merge_mask = (
(self.entity_id == entity_id)
& (self.relation_id == relation_id)
& self.cache.valid
)
slot, operation = self.cache.create(
canonical_value, slot_type=slot_type, merge_mask=merge_mask, **cache_metadata
)
if slot >= 0:
self.entity_id[slot] = entity_id
self.relation_id[slot] = relation_id
self.value_id[slot] = value_id
self.canonical_metadata_id[slot] = metadata_id
return slot, operation
def allocation_signature(self):
fields = (self.entity_id, self.relation_id, self.value_id, self.canonical_metadata_id)
return self.cache.allocation_signature() + tuple(
(field.data_ptr(), tuple(field.shape)) for field in fields
)
def modify(
self,
slot: int,
canonical_value: Tensor,
*,
entity_id: int,
relation_id: int,
value_id: int,
metadata_id: int,
**cache_metadata,
) -> int:
result = self.cache.modify(slot, canonical_value, **cache_metadata)
self.entity_id[slot] = entity_id
self.relation_id[slot] = relation_id
self.value_id[slot] = value_id
self.canonical_metadata_id[slot] = metadata_id
return result
def invalidate(self, slot: int) -> int:
result = self.cache.invalidate(slot)
self.entity_id[slot] = -1
self.relation_id[slot] = -1
self.value_id[slot] = -1
self.canonical_metadata_id[slot] = -1
return result
def save(self, path: str | Path) -> None:
tensors = {
"canonical_values": self.cache.values.detach().cpu(),
"valid": self.cache.valid.detach().cpu(),
"slot_type": self.cache.slot_type.detach().cpu(),
"confidence": self.cache.confidence.detach().cpu(),
"importance": self.cache.importance.detach().cpu(),
"freshness": self.cache.freshness.detach().cpu(),
"persistence": self.cache.persistence.detach().cpu(),
"last_updated": self.cache.last_updated.detach().cpu(),
"source": self.cache.source.detach().cpu(),
"entity_id": self.entity_id.detach().cpu(),
"relation_id": self.relation_id.detach().cpu(),
"value_id": self.value_id.detach().cpu(),
"canonical_metadata_id": self.canonical_metadata_id.detach().cpu(),
}
metadata = {
"format": self.FORMAT,
"config": json.dumps({
"slots": self.config.slots,
"width": self.config.width,
"merge_similarity": self.config.merge_similarity,
}),
"labels": json.dumps(self.cache.labels),
}
metadata["content_sha256"] = canonical_snapshot_checksum(tensors, metadata)
save_file(tensors, str(Path(path)), metadata=metadata)
@classmethod
def load(
cls,
path: str | Path,
*,
device: str | torch.device = "cpu",
dtype: torch.dtype = torch.float16,
) -> "CanonicalPStore":
path = Path(path)
with safe_open(str(path), framework="pt", device="cpu") as handle:
metadata = handle.metadata()
if metadata.get("format") != cls.FORMAT:
raise ValueError("unsupported canonical P snapshot")
config = json.loads(metadata["config"])
tensors = load_file(str(path), device=str(device))
checksum_metadata = {
key: value for key, value in metadata.items() if key != "content_sha256"
}
if canonical_snapshot_checksum(tensors, checksum_metadata) != metadata.get(
"content_sha256"
):
raise ValueError("canonical P snapshot checksum does not match")
result = cls(CanonicalPConfig(
slots=config["slots"], width=config["width"], dtype=dtype, device=device,
merge_similarity=config.get("merge_similarity", 0.92),
))
result.cache.values.copy_(tensors["canonical_values"].to(dtype=dtype))
for name in (
"valid", "slot_type", "confidence", "importance", "freshness",
"persistence", "last_updated", "source",
):
getattr(result.cache, name).copy_(tensors[name])
for name in ("entity_id", "relation_id", "value_id", "canonical_metadata_id"):
getattr(result, name).copy_(tensors[name])
result.cache.labels = json.loads(metadata["labels"])
result.cache._clock = int(result.cache.last_updated.max())
return result
CANONICAL_P_PROTOCOL = CanonicalPStore.FORMAT
def model_config_checksum(config: object) -> str:
payload = config.to_dict() if hasattr(config, "to_dict") else config
if isinstance(payload, dict):
payload = dict(payload)
configured_path = payload.get("_name_or_path")
if configured_path and Path(str(configured_path)).is_absolute():
payload["_name_or_path"] = Path(str(configured_path)).name
encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")
return hashlib.sha256(encoded).hexdigest()
|