File size: 5,513 Bytes
1d2de8a | 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 | """Verify immutable input snapshots and create a separately checksummed BF16 model."""
import hashlib
import json
import shutil
from importlib.metadata import version
from pathlib import Path
from .artifacts import BASE_REVISION, SOLOMON_REVISION, sha256, verify_release
def verify_base(root, manifest):
root = Path(root)
if manifest["revision"] != BASE_REVISION:
raise ValueError("Wrong pinned base revision")
result = {}
for row in manifest["files"]:
path = root / row["name"]
if not path.resolve().is_relative_to(root.resolve()):
raise ValueError("Unsafe base manifest path")
if path.stat().st_size != row["size"]:
raise ValueError("Base size mismatch: " + row["name"])
actual = sha256(path)
if row["sha256"]:
if actual != row["sha256"]:
raise ValueError("Base checksum mismatch: " + row["name"])
else:
data = path.read_bytes()
git_hash = hashlib.sha1(b"blob " + str(len(data)).encode() + b"\0" + data).hexdigest()
if git_hash != row["blob_id"]:
raise ValueError("Base Git blob mismatch: " + row["name"])
result[row["name"]] = actual
return result
def prepare(base, solomon, output, manifest):
base, solomon, output = map(Path, (base, solomon, output))
source_hashes = verify_release(solomon)
base_hashes = verify_base(base, json.loads(Path(manifest).read_text()))
if output.exists():
raise FileExistsError("Use a new output directory; existing conversions are immutable")
output.mkdir(parents=True)
convert_bf16(base, output / "backbone")
shutil.copy2(solomon / "adapter/adapter.safetensors", output / "adapter.safetensors")
shutil.copy2(solomon / "adapter/heads.npz", output / "heads.npz")
for name in ("LICENSE", "NOTICE", "MODIFICATIONS.md"):
shutil.copy2(solomon / name, output / name)
files = {
str(p.relative_to(output)): sha256(p)
for p in sorted(output.rglob("*"))
if p.is_file() and ".cache" not in p.parts
}
binding = {
"schema": "solomon-mlx-binding-v1",
"profile": "quality",
"dtype": "bfloat16",
"adapter_dtype": "float32",
"head_dtype": "float32",
"recurrent_state_dtype": "float32",
"sensitive_parameters": "FP32 normalization weights, A_log and dt_bias",
"conversion": "upstream Qwen3.5 sanitization, norms promoted before unit offset",
"adapter_scale": 2.0,
"adapter_placement": "question",
"quantization": None,
"base_revision": BASE_REVISION,
"solomon_revision": SOLOMON_REVISION,
"source_hashes": source_hashes,
"base_hashes": base_hashes,
"dependencies": {p: version(p) for p in ("mlx", "mlx-vlm", "transformers", "numpy")},
"files": files,
}
(output / "binding.json").write_text(json.dumps(binding, indent=2, sort_keys=True))
return binding
def convert_bf16(base, output):
"""Convert one original shard at a time; never copy download caches.
Keep normalization offsets in FP32 before adding one. Adding the unit
offset in BF16 would irreversibly round trained normalization parameters.
Large backbone matrices remain unquantized BF16.
"""
import mlx.core as mx
from mlx.utils import tree_flatten
from mlx_vlm.models.qwen3_5.config import ModelConfig
from mlx_vlm.models.qwen3_5.qwen3_5 import Model
base, output = Path(base), Path(output)
output.mkdir(parents=True, exist_ok=False)
config = json.loads((base / "config.json").read_text())
if config.get("model_type") != "qwen3_5" or config.get("quantization"):
raise ValueError("Expected original unquantized Qwen3.5 architecture")
model = Model(ModelConfig.from_dict(config))
expected = {k: v.shape for k, v in tree_flatten(model.parameters())}
index = {"metadata": {"total_size": 0}, "weight_map": {}}
source_index = json.loads((base / "model.safetensors.index.json").read_text())
for filename in sorted(set(source_index["weight_map"].values())):
arrays = mx.load(str(base / filename))
for key, value in arrays.items():
sensitive = value.ndim == 1 and ("norm" in key or key.endswith(("A_log", "dt_bias")))
arrays[key] = value.astype(mx.float32 if sensitive else mx.bfloat16)
arrays = model.sanitize(arrays)
arrays = model.vision_tower.sanitize(arrays)
for key, value in arrays.items():
if key not in expected or value.shape != expected[key]:
raise ValueError("Converted tensor shape mismatch: " + key)
if key in index["weight_map"]:
raise ValueError("Duplicate converted tensor: " + key)
index["weight_map"][key] = filename
index["metadata"]["total_size"] += value.nbytes
mx.save_safetensors(str(output / filename), arrays, metadata={"format": "mlx"})
del arrays
mx.clear_cache()
print("Converted " + filename, flush=True)
if set(index["weight_map"]) != set(expected):
raise ValueError("Converted backbone is missing required parameters")
(output / "model.safetensors.index.json").write_text(json.dumps(index, indent=2, sort_keys=True))
for file in base.iterdir():
if file.is_file() and file.name != "model.safetensors.index.json" and file.suffix != ".safetensors":
shutil.copy2(file, output / file.name)
|