File size: 8,400 Bytes
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"""Pack pre-quantized Ling-3 assets into the mmap-friendly .l3r format."""
from __future__ import annotations
import argparse
import hashlib
import json
import os
import struct
import zlib
from pathlib import Path
ALIGNMENT = 4096
HEADER = struct.Struct("<8s6I6Q20sI28I4f24s")
ENTRY = struct.Struct("<12I4Q2f4I32s24s")
DTYPES = {
"unknown": 0,
"bf16": 1,
"fp16": 2,
"fp32": 3,
"f32": 3,
"int8": 4,
"int4": 5,
"int16": 6,
"int32": 7,
"rknn": 8,
}
ROLES = {
"unknown": 0,
"embedding": 1,
"norm": 2,
"linear_weight": 3,
"linear_scale": 4,
"bias": 5,
"rknn_island": 6,
"tokenizer": 7,
}
QUANTS = {"none": 0, "per_tensor": 1, "per_output_channel": 2, "fixed_activation": 3}
LAYOUTS = {"row_major": 0, "rknn_native_b": 1, "packed_int4": 2, "opaque": 3}
MODEL_FIELDS = (
"vocab_size",
"hidden_size",
"layer_count",
"attention_heads",
"head_dim",
"kv_lora_rank",
"q_lora_rank",
"qk_nope_dim",
"qk_rope_dim",
"value_head_dim",
"dense_ffn_dim",
"expert_ffn_dim",
"shared_ffn_dim",
"expert_count",
"experts_per_token",
"expert_group_count",
"selected_group_count",
"layer_group_size",
"leading_dense_layers",
"convolution_kernel",
"max_context",
"eos_token",
"pad_token",
"bos_token",
"mla_layer_count",
"kda_layer_count",
"default_weight_bits",
"default_activation_bits",
)
def align(value: int) -> int:
return (value + ALIGNMENT - 1) & ~(ALIGNMENT - 1)
def enum_value(mapping: dict[str, int], value: str, label: str) -> int:
try:
return mapping[value]
except KeyError as error:
raise ValueError(f"unknown {label} {value!r}") from error
def resolve(base: Path, value: str) -> Path:
path = Path(value)
return path if path.is_absolute() else base / path
def copy_file(source: Path, output, hasher: hashlib._Hash) -> int:
total = 0
with source.open("rb") as stream:
while chunk := stream.read(8 * 1024 * 1024):
output.write(chunk)
hasher.update(chunk)
total += len(chunk)
return total
def pad_to(output, offset: int) -> None:
current = output.tell()
if current > offset:
raise ValueError("package layout overlapped while writing")
output.write(b"\0" * (offset - current))
def make_header(manifest: dict, tensor_count: int, string_offset: int, string_bytes: int,
payload_offset: int, payload_bytes: int, file_bytes: int, crc: int) -> bytes:
model = manifest["model"]
source_revision = bytes.fromhex(manifest["source_revision"])
if len(source_revision) != 20:
raise ValueError("source_revision must be a 40-character Git SHA-1")
integers = [int(model[name]) for name in MODEL_FIELDS]
floats = [
float(model["rms_epsilon"]),
float(model["rope_theta"]),
float(model["routed_scale"]),
float(model["kda_lower_bound"]),
]
return HEADER.pack(
b"L3RKNN1\0",
1,
HEADER.size,
0x01020304,
tensor_count,
ENTRY.size,
int(manifest.get("flags", 0)),
HEADER.size,
string_offset,
string_bytes,
payload_offset,
payload_bytes,
file_bytes,
source_revision,
crc,
*integers,
*floats,
bytes(24),
)
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("manifest", type=Path)
parser.add_argument("output", type=Path)
parser.add_argument(
"--include-prefix",
action="append",
default=[],
help="pack only tensors whose names start with this prefix (repeatable)",
)
args = parser.parse_args()
manifest_path = args.manifest.resolve()
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
base = manifest_path.parent
tensors = manifest.get("tensors", [])
if args.include_prefix:
tensors = [
item for item in tensors
if any(item["name"].startswith(prefix) for prefix in args.include_prefix)
]
if not tensors:
raise ValueError("--include-prefix did not match any tensors")
names = bytearray()
prepared = []
for item in tensors:
name = item["name"].encode("utf-8")
if not name or b"\0" in name:
raise ValueError("tensor names must be non-empty UTF-8 without NUL")
dims = [int(value) for value in item.get("dims", [])]
if len(dims) > 4:
raise ValueError(f"tensor {item['name']!r} has more than four dimensions")
source = resolve(base, item["path"])
if not source.is_file():
raise FileNotFoundError(source)
aux = resolve(base, item["aux_path"]) if item.get("aux_path") else None
if aux is not None and not aux.is_file():
raise FileNotFoundError(aux)
prepared.append((item, len(names), name, dims, source, aux))
names.extend(name)
table_bytes = len(prepared) * ENTRY.size
string_offset = HEADER.size + table_bytes
payload_offset = align(string_offset + len(names))
cursor = payload_offset
layouts = []
for item, name_offset, name, dims, source, aux in prepared:
data_offset = align(cursor)
data_bytes = source.stat().st_size
cursor = data_offset + data_bytes
aux_offset = align(cursor) if aux is not None else 0
aux_bytes = aux.stat().st_size if aux is not None else 0
if aux is not None:
cursor = aux_offset + aux_bytes
layouts.append((item, name_offset, name, dims, source, aux, data_offset, data_bytes, aux_offset, aux_bytes))
file_bytes = align(cursor)
payload_bytes = file_bytes - payload_offset
args.output.parent.mkdir(parents=True, exist_ok=True)
entries = []
with args.output.open("w+b") as output:
output.write(bytes(HEADER.size + table_bytes))
output.write(names)
pad_to(output, payload_offset)
for item, name_offset, name, dims, source, aux, data_offset, data_bytes, aux_offset, aux_bytes in layouts:
pad_to(output, data_offset)
digest = hashlib.sha256()
if copy_file(source, output, digest) != data_bytes:
raise IOError(f"short read while packing {source}")
if aux is not None:
pad_to(output, aux_offset)
aux_digest = hashlib.sha256()
if copy_file(aux, output, aux_digest) != aux_bytes:
raise IOError(f"short read while packing {aux}")
padded_dims = dims + [0] * (4 - len(dims))
entries.append(ENTRY.pack(
name_offset,
len(name),
enum_value(DTYPES, item["dtype"], "dtype"),
len(dims),
enum_value(ROLES, item.get("role", "unknown"), "role"),
enum_value(QUANTS, item.get("quant", "none"), "quantization"),
enum_value(LAYOUTS, item.get("layout", "row_major"), "layout"),
int(item.get("flags", 0)),
*padded_dims,
data_offset,
data_bytes,
aux_offset,
aux_bytes,
float(item.get("scale", 1.0)),
float(item.get("zero_point", 0.0)),
int(item.get("layer", 0xFFFFFFFF)),
int(item.get("op", 0)),
int(item.get("expert", 0xFFFFFFFF)),
int(item.get("core", 0xFFFFFFFF)),
digest.digest(),
bytes(24),
))
pad_to(output, file_bytes)
header = make_header(manifest, len(entries), string_offset, len(names), payload_offset,
payload_bytes, file_bytes, 0)
crc = zlib.crc32(header) & 0xFFFFFFFF
header = make_header(manifest, len(entries), string_offset, len(names), payload_offset,
payload_bytes, file_bytes, crc)
output.seek(0)
output.write(header)
for entry in entries:
output.write(entry)
output.flush()
os.fsync(output.fileno())
print(f"packed {len(entries)} tensors, {file_bytes} bytes -> {args.output}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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