File size: 12,962 Bytes
4606829 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 | """Bounded CPU Coder390 BF16 -> MLX affine q4 conversion; never loads a model.
Sanitization is delegated to a SHA-pinned upstream mlx-vlm implementation.
Each source is fully hashed before use; source weights are never modified.
MTP remains in the source for a separate native sidecar extraction.
"""
from __future__ import annotations
import argparse
import gc
import hashlib
import importlib.util
import json
import math
from pathlib import Path
import shutil
import struct
import time
from types import SimpleNamespace
import mlx.core as mx
import numpy as np
ROOT = Path(__file__).resolve().parent
SANITIZER = ROOT / "raw/upstream/qwen3_5.py"
SANITIZER_SHA = "7c0e360a35e1f5d3819677cd6f7e4d7349f4f3dd2fad5fc8f1e374bd514e8f1b"
SANITIZER_REV = "952d4f6bc65bd5095e74abe71c78038764ee08aa"
DTYPES = {"BF16": (np.dtype("<u2"), mx.bfloat16),
"F32": (np.dtype("<f4"), mx.float32),
"F16": (np.dtype("<f2"), mx.float16)}
LINEAR_SUFFIXES = (".mlp.up_proj.weight", ".mlp.gate_proj.weight", ".mlp.down_proj.weight",
".self_attn.q_proj.weight", ".self_attn.k_proj.weight", ".self_attn.v_proj.weight",
".self_attn.o_proj.weight", ".linear_attn.in_proj_qkv.weight",
".linear_attn.in_proj_z.weight", ".linear_attn.in_proj_a.weight",
".linear_attn.in_proj_b.weight", ".linear_attn.out_proj.weight",
".embed_tokens.weight", "lm_head.weight")
def sha256(path):
digest = hashlib.sha256()
with Path(path).open("rb") as handle:
for block in iter(lambda: handle.read(8 * 1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def sanitizer():
if sha256(SANITIZER) != SANITIZER_SHA:
raise ValueError("Pinned sanitizer hash mismatch")
spec = importlib.util.spec_from_file_location("mlx_vlm.models.qwen3_5._coder390_sanitize", SANITIZER)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module.Model.sanitize
def read_header(path):
with path.open("rb") as handle:
prefix = handle.read(8)
if len(prefix) != 8:
raise ValueError("Truncated safetensors header")
length = struct.unpack("<Q", prefix)[0]
if length > 16 * 1024 * 1024:
raise ValueError("Safetensors header bound exceeded")
raw = handle.read(length)
if len(raw) != length:
raise ValueError("Truncated safetensors header")
header = json.loads(raw)
entries = {key: value for key, value in header.items() if key != "__metadata__"}
payload = path.stat().st_size - length - 8
spans = []
for key, meta in entries.items():
if meta["dtype"] not in DTYPES:
raise ValueError(f"Unsupported source dtype: {key}: {meta['dtype']}")
start, end = meta["data_offsets"]
shape = meta["shape"]
if any(type(n) is not int or n < 0 for n in shape):
raise ValueError(f"Invalid tensor shape: {key}")
size = math.prod(shape) * DTYPES[meta["dtype"]][0].itemsize
if start < 0 or end - start != size or end > payload:
raise ValueError(f"Invalid source tensor offsets: {key}")
spans.append((start, end))
spans.sort()
cursor = 0
for start, end in spans:
if start != cursor:
raise ValueError("Safetensors payload gaps or overlaps")
cursor = end
if cursor != payload:
raise ValueError("Safetensors payload size mismatch")
return entries, 8 + length
def quantizable(name, shape):
if name.startswith("model.visual."):
return False
if name.startswith("mtp."):
return False
if len(shape) == 2 and name.endswith(LINEAR_SUFFIXES):
if shape[-1] % 64:
raise ValueError(f"Unsupported group alignment: {name}")
return True
known = (".input_layernorm.weight", ".post_attention_layernorm.weight", ".norm.weight",
".q_norm.weight", ".k_norm.weight", ".conv1d.weight", ".A_log", ".dt_bias")
if name.endswith(known):
return False
raise ValueError(f"Unsupported/unknown source tensor: {name}")
def tensor_bytes(array):
mx.eval(array)
dtype = array.dtype
if dtype == mx.bfloat16:
return np.asarray(array.view(mx.uint16), dtype="<u2").tobytes(), "BF16"
if dtype == mx.float32:
return np.asarray(array, dtype="<f4").tobytes(), "F32"
if dtype == mx.float16:
return np.asarray(array, dtype="<f2").tobytes(), "F16"
if dtype == mx.uint32:
return np.asarray(array, dtype="<u4").tobytes(), "U32"
raise ValueError(f"Unsupported output dtype: {dtype}")
def pack_shard(output, pending, sequence):
filename = f"model-{sequence:05d}.safetensors"
header = {"__metadata__": {"format": "mlx"}}
offset = 0
for record in pending:
end = offset + record["bytes"]
header[record["name"]] = {"dtype": record["dtype"], "shape": record["shape"],
"data_offsets": [offset, end]}
offset = end
raw = json.dumps(header, separators=(",", ":")).encode()
raw += b" " * ((-len(raw)) % 8)
path = output / filename
with path.open("xb") as handle:
handle.write(struct.pack("<Q", len(raw)))
handle.write(raw)
for record in pending:
with record["path"].open("rb") as source:
shutil.copyfileobj(source, handle, 8 * 1024 * 1024)
record["path"].unlink()
return filename
def convert(source, output, expected, *, row_chunk=128, shard_bytes=512 * 1024 * 1024):
mx.set_default_device(mx.cpu)
source, output = Path(source).resolve(), Path(output).resolve()
if output.exists() or output == source or source in output.parents:
raise ValueError("Destination exists or is inside source; refusing overwrite")
if not 1 <= row_chunk <= 512:
raise ValueError("row_chunk must be in 1..512")
index = json.loads((source / "model.safetensors.index.json").read_text())["weight_map"]
config = json.loads((source / "config.json").read_text())
source_records = {}
headers = {}
for filename in sorted(set(index.values())):
if Path(filename).name != filename:
raise ValueError("Unsafe source shard path")
path = source / filename
want = expected.get(filename)
if not want or path.stat().st_size != want["size"]:
raise ValueError(f"Source size/manifest mismatch: {filename}")
print(f"Verifying full SHA-256: {filename}", flush=True)
digest = sha256(path)
if digest != want["sha256"]:
raise ValueError(f"Source SHA-256 mismatch: {filename}")
source_records[filename] = {"size": path.stat().st_size, "sha256": digest}
headers[filename] = read_header(path)
if set(headers[filename][0]) != {key for key, value in index.items() if value == filename}:
raise ValueError(f"Index/header mismatch: {filename}")
output.mkdir(parents=True)
spool = output / ".tensor-spool"
spool.mkdir()
sanitize = sanitizer()
owner = SimpleNamespace(config=SimpleNamespace(
text_config=SimpleNamespace(tie_word_embeddings=config["text_config"].get("tie_word_embeddings", False)),
vision_config=SimpleNamespace(in_channels=config.get("vision_config", {}).get("in_channels", 3))))
pending, weight_map, transforms = [], {}, []
total_size, sequence, spool_id = 0, 1, 0
started = time.monotonic()
for number, (name, filename) in enumerate(index.items(), 1):
if name.startswith("mtp."):
continue
meta, base = headers[filename]
meta = meta[name]
shape = meta["shape"]
do_quant = quantizable(name, shape)
dtype_np, dtype_mx = DTYPES[meta["dtype"]]
mapped = np.memmap(source / filename, dtype=dtype_np, mode="r",
offset=base + meta["data_offsets"][0], shape=tuple(shape))
rows = shape[0] if shape else 1
records = {}
for start in range(0, rows, row_chunk):
part = np.array(mapped[start:start + row_chunk] if shape else mapped, copy=True)
array = mx.array(part)
if dtype_mx == mx.bfloat16:
array = array.view(mx.bfloat16)
if not bool(mx.all(mx.isfinite(array))):
raise ValueError(f"Nonfinite source tensor: {name}")
clean = sanitize(owner, {name: array})
if len(clean) != 1:
raise ValueError(f"Unexpected sanitizer output: {name}")
new_name, array = next(iter(clean.items()))
if do_quant:
q, scale, bias = mx.quantize(array, group_size=64, bits=4, mode="affine")
values = {new_name: q, new_name.removesuffix(".weight") + ".scales": scale,
new_name.removesuffix(".weight") + ".biases": bias}
else:
values = {new_name: array}
for key, value in values.items():
if not bool(mx.all(mx.isfinite(value))):
raise ValueError(f"Nonfinite converted tensor: {key}")
data, kind = tensor_bytes(value)
if key not in records:
spool_id += 1
path = spool / f"{spool_id:06d}.payload"
records[key] = {"name": key, "dtype": kind, "shape": list(value.shape), "bytes": 0, "path": path}
if shape:
records[key]["shape"][0] = 0
record = records[key]
if shape:
record["shape"][0] += value.shape[0]
with record["path"].open("ab") as handle:
handle.write(data)
record["bytes"] += len(data)
del array, clean, values, part
if do_quant:
del q, scale, bias
del value, data
del mapped
new_records = list(records.values())
size = sum(record["bytes"] for record in new_records)
if pending and sum(record["bytes"] for record in pending) + size > shard_bytes:
shard = pack_shard(output, pending, sequence)
for record in pending:
weight_map[record["name"]] = shard
pending = []
sequence += 1
pending.extend(new_records)
total_size += size
transforms.append({"source": name, "output": list(records), "quantized": do_quant})
gc.collect()
print(f"Converted {number}/{len(index)} {name} -> {size} bytes (CPU)", flush=True)
if pending:
shard = pack_shard(output, pending, sequence)
for record in pending:
if record["name"] in weight_map:
raise ValueError("Duplicate converted tensor name")
weight_map[record["name"]] = shard
spool.rmdir()
index_out = {"metadata": {"total_size": total_size}, "weight_map": dict(sorted(weight_map.items()))}
(output / "model.safetensors.index.json").write_text(json.dumps(index_out, indent=2) + "\n")
quantization = {"group_size": 64, "bits": 4, "mode": "affine"}
config["quantization"] = quantization
config["quantization_config"] = dict(quantization)
(output / "config.json").write_text(json.dumps(config, indent=2) + "\n")
for filename in ("tokenizer.json", "tokenizer_config.json", "chat_template.jinja", "generation_config.json",
"preprocessor_config.json", "video_preprocessor_config.json"):
if (source / filename).is_file():
shutil.copy2(source / filename, output / filename)
files = {path.name: {"size": path.stat().st_size, "sha256": sha256(path)}
for path in sorted(output.iterdir()) if path.is_file()}
manifest = {"device": "cpu", "quantization": quantization, "vision": "source BF16, unquantized",
"mtp": "excluded from target; original sidecar retained in source", "source_files": source_records,
"sanitizer_revision": SANITIZER_REV, "sanitizer_sha256": SANITIZER_SHA,
"row_chunk": row_chunk, "output_files": files, "transforms": transforms,
"elapsed_s": time.monotonic() - started, "inference_qualified": False}
(output / "conversion-manifest.json").write_text(json.dumps(manifest, indent=2) + "\n")
return manifest
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--source", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--expected", type=Path, required=True)
parser.add_argument("--row-chunk", type=int, default=128)
args = parser.parse_args()
result = convert(args.source, args.output, json.loads(args.expected.read_text()), row_chunk=args.row_chunk)
print(json.dumps({key: result[key] for key in ["device", "elapsed_s", "inference_qualified"]}, indent=2))
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