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ENGRAM Protocol — .eng File Serializer
.eng = safetensors container with:
- __metadata__: JSON-stringified EngramMetadata (all string values per D7)
- Tensor keys: layer_{i}_keys, layer_{i}_values
- Each tensor: [n_kv_heads, ctx_len, head_dim] at compressed dtype
D7: safetensors confirmed. GGUF rejected. String-only metadata values.
Reference: arXiv:2603.04428 uses identical safetensors approach.
"""
from __future__ import annotations
import hashlib
import uuid
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
import torch
from safetensors.torch import load_file, save_file
from kvcos.core.cache_spec import infer_model_family
from kvcos.core.compression import CompressionResult, compress, decompress
from kvcos.core.types import (
ENGRAM_VERSION,
ENG_FILE_EXTENSION,
CompressionMethod,
EngramMetadata,
)
class SerializationError(Exception):
"""Raised when serialization or deserialization fails."""
class EngramSerializer:
"""Serializes/deserializes KV cache tensors to/from .eng files.
Canonical shape for KV tensors in ENGRAM:
keys: [n_layers, n_kv_heads, ctx_len, head_dim]
values: [n_layers, n_kv_heads, ctx_len, head_dim]
"""
def serialize(
self,
keys: torch.Tensor,
values: torch.Tensor,
agent_id: str,
task_description: str,
model_id: str,
output_path: Path,
compression: CompressionMethod = CompressionMethod.Q8_0,
cache_id: str | None = None,
parent_cache_id: str | None = None,
input_tokens: list[int] | None = None,
extra_metadata: dict[str, str] | None = None,
) -> dict[str, Any]:
"""Serialize KV cache tensors to a .eng file.
Args:
keys: [n_layers, n_kv_heads, ctx_len, head_dim]
values: [n_layers, n_kv_heads, ctx_len, head_dim]
agent_id: Identifier for the agent that produced this state
task_description: Human-readable description (used for EGR search)
model_id: Full model identifier
output_path: Path to write .eng file
compression: Compression method to apply
cache_id: Explicit cache ID (auto-generated if None)
parent_cache_id: ID of parent for delta chains
input_tokens: Token IDs that generated this state (for hash)
extra_metadata: Additional string key-value pairs
Returns:
Dict with cache_id, size_bytes, compression_ratio, path
"""
if keys.shape != values.shape:
raise SerializationError(
f"Keys/values shape mismatch: {keys.shape} vs {values.shape}"
)
if keys.dim() != 4:
raise SerializationError(
f"Expected 4D [n_layers, n_kv_heads, ctx_len, head_dim], "
f"got {keys.dim()}D: {keys.shape}"
)
n_layers, n_kv_heads, ctx_len, head_dim = keys.shape
tensors: dict[str, torch.Tensor] = {}
if compression == CompressionMethod.INT8:
from kvcos.core.compression import compress_int8_tensor
k_pair = compress_int8_tensor(keys)
v_pair = compress_int8_tensor(values)
for i in range(n_layers):
tensors[f"layer_{i}_keys"] = k_pair.quantized[i].contiguous()
tensors[f"layer_{i}_keys_scale"] = k_pair.scales[i].contiguous()
tensors[f"layer_{i}_values"] = v_pair.quantized[i].contiguous()
tensors[f"layer_{i}_values_scale"] = v_pair.scales[i].contiguous()
# Reuse k_compressed for metadata only — actual INT8 data is
# already written per-layer above via k_pair/v_pair.
k_compressed = compress(keys, compression)
v_compressed = k_compressed
elif compression == CompressionMethod.LAYER_DELTA:
from kvcos.core.compression import compress_layer_delta
k_ld = compress_layer_delta(keys)
v_ld = compress_layer_delta(values)
# Layer 0: fp16 baseline
tensors["layer_0_keys"] = k_ld.baseline.contiguous()
tensors["layer_0_values"] = v_ld.baseline.contiguous()
# Layers 1..N: int8 deltas + fp16 scales
for i in range(n_layers - 1):
tensors[f"layer_{i+1}_keys"] = k_ld.delta_quantized[i].contiguous()
tensors[f"layer_{i+1}_keys_scale"] = k_ld.delta_scales[i].contiguous()
tensors[f"layer_{i+1}_values"] = v_ld.delta_quantized[i].contiguous()
tensors[f"layer_{i+1}_values_scale"] = v_ld.delta_scales[i].contiguous()
# Reuse k_compressed for metadata only — actual layer-delta data
# is already written above via k_ld/v_ld.
k_compressed = compress(keys, compression)
v_compressed = k_compressed
else:
k_compressed = compress(keys, compression)
v_compressed = compress(values, compression)
for i in range(n_layers):
tensors[f"layer_{i}_keys"] = k_compressed.data[i].contiguous()
tensors[f"layer_{i}_values"] = v_compressed.data[i].contiguous()
cid = cache_id or str(uuid.uuid4())
now = datetime.now(timezone.utc).isoformat()
token_hash = ""
if input_tokens:
token_bytes = b"".join(t.to_bytes(4, "little") for t in input_tokens)
token_hash = f"sha256:{hashlib.sha256(token_bytes).hexdigest()}"
metadata: EngramMetadata = {
"engram_version": ENGRAM_VERSION,
"cache_id": cid,
"compression": compression.value,
"model_id": model_id,
"model_family": infer_model_family(model_id),
"n_layers": str(n_layers),
"n_heads": str(n_kv_heads),
"n_kv_heads": str(n_kv_heads),
"head_dim": str(head_dim),
"context_len": str(ctx_len),
"agent_id": agent_id,
"task_description": task_description,
"created_at": now,
}
if parent_cache_id:
metadata["parent_cache_id"] = parent_cache_id
if token_hash:
metadata["token_hash"] = token_hash
for key, val in k_compressed.metadata.items():
metadata[f"compression_{key}"] = val # type: ignore[literal-required]
if extra_metadata:
for key, val in extra_metadata.items():
metadata[key] = val # type: ignore[literal-required]
output_path.parent.mkdir(parents=True, exist_ok=True)
str_metadata: dict[str, str] = {k: str(v) for k, v in metadata.items()}
save_file(tensors, str(output_path), metadata=str_metadata)
original_bytes = (keys.numel() + values.numel()) * keys.element_size()
compressed_bytes = output_path.stat().st_size
return {
"cache_id": cid,
"size_bytes": compressed_bytes,
"compression_ratio": original_bytes / compressed_bytes if compressed_bytes > 0 else 1.0,
"path": str(output_path),
"n_layers": n_layers,
"context_len": ctx_len,
}
def deserialize(
self,
path: Path,
target_compression: CompressionMethod | None = None,
) -> tuple[torch.Tensor, torch.Tensor, EngramMetadata]:
"""Deserialize a .eng file into KV cache tensors.
Returns (keys, values, metadata) where tensors are
[n_layers, n_kv_heads, ctx_len, head_dim].
"""
if not path.exists():
raise SerializationError(f"Engram file not found: {path}")
tensors = load_file(str(path))
metadata = self._read_metadata(path)
n_layers = int(metadata.get("n_layers", "0"))
if n_layers == 0:
n_layers = (
max(int(k.split("_")[1]) for k in tensors if k.startswith("layer_")) + 1
)
stored_compression = metadata.get("compression", "fp16")
is_int8 = stored_compression == CompressionMethod.INT8.value
is_layer_delta = stored_compression == CompressionMethod.LAYER_DELTA.value
k_layers: list[torch.Tensor] = []
v_layers: list[torch.Tensor] = []
if is_layer_delta:
from kvcos.core.compression import decompress_int8_tensor
# Layer 0: fp16 baseline
k_layers.append(tensors["layer_0_keys"].float())
v_layers.append(tensors["layer_0_values"].float())
# Layers 1..N: accumulate int8 deltas
for i in range(1, n_layers):
k_delta = decompress_int8_tensor(
tensors[f"layer_{i}_keys"], tensors[f"layer_{i}_keys_scale"]
)
v_delta = decompress_int8_tensor(
tensors[f"layer_{i}_values"], tensors[f"layer_{i}_values_scale"]
)
k_layers.append(k_layers[-1] + k_delta.float())
v_layers.append(v_layers[-1] + v_delta.float())
keys = torch.stack([l.to(torch.float16) for l in k_layers], dim=0)
values = torch.stack([l.to(torch.float16) for l in v_layers], dim=0)
else:
for i in range(n_layers):
k_key = f"layer_{i}_keys"
v_key = f"layer_{i}_values"
if k_key not in tensors or v_key not in tensors:
raise SerializationError(f"Missing tensor for layer {i}")
if is_int8:
from kvcos.core.compression import decompress_int8_tensor
k_scale_key = f"layer_{i}_keys_scale"
v_scale_key = f"layer_{i}_values_scale"
if k_scale_key not in tensors or v_scale_key not in tensors:
raise SerializationError(f"Missing INT8 scale for layer {i}")
k_layers.append(decompress_int8_tensor(tensors[k_key], tensors[k_scale_key]))
v_layers.append(decompress_int8_tensor(tensors[v_key], tensors[v_scale_key]))
else:
k_layers.append(tensors[k_key])
v_layers.append(tensors[v_key])
keys = torch.stack(k_layers, dim=0)
values = torch.stack(v_layers, dim=0)
if target_compression is not None:
stored = CompressionMethod(metadata.get("compression", "fp16"))
keys = decompress(keys, stored)
values = decompress(values, stored)
return keys, values, metadata # type: ignore[return-value]
def _read_metadata(self, path: Path) -> dict[str, str]:
"""Read only the metadata header (no tensor data loaded)."""
from safetensors import safe_open
metadata: dict[str, str] = {}
with safe_open(str(path), framework="pt") as f:
raw_meta = f.metadata()
if raw_meta:
metadata = dict(raw_meta)
return metadata
def read_metadata_only(self, path: Path) -> EngramMetadata:
"""Read just the metadata from a .eng file. Efficient for indexing."""
raw = self._read_metadata(path)
return raw # type: ignore[return-value]
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