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"""Build a two-tier variant checkpoint with per-expert source dispatch.
Hot NVFP4 bytes come from either the v4-uniform checkpoint's compacted
arrays (experts already hot there) or the ranged downloads in
/tmp/glm52-hot-dl2 (newly-hot experts). Cold AQLM slices come from
/data/glm52-aqlm-parts. Non-expert tensors stream from v4-uniform.
Usage: build_checkpoint_v6.py <assignment.json> <dst_dir>
"""
import json
import os
import re
import shutil
import sys
import torch
from safetensors import safe_open
from safetensors.torch import save_file
SRC = "/data/glm52-v4-uniform"
DL = "/tmp/glm52-hot-dl2"
PARTS = "/data/glm52-aqlm-parts"
ASSIGN = sys.argv[1]
DST = sys.argv[2]
SHARD_BYTES = 4 << 30
N_EXP, INTER, HIDDEN = 256, 2048, 6144
assignment = {int(k): v for k, v in json.load(open(ASSIGN)).items()}
DTYPES = {"U8": torch.uint8, "F8_E4M3": torch.uint8, "BF16": torch.bfloat16,
"F32": torch.float32, "F16": torch.float16, "I16": torch.int16,
"I8": torch.int8}
class RegionReader:
def __init__(self):
self.headers = json.load(open(f"{DL}/headers.json"))
self.wm = {}
for shard, h in self.headers.items():
for name in h["header"]:
if name != "__metadata__":
self.wm[name] = shard
self.regions = {}
for shard in self.headers:
d = f"{DL}/regions/{shard}"
regs = []
if os.path.isdir(d):
for f in os.listdir(d):
p = os.path.join(d, f)
regs.append((int(f[:-4]), p, os.path.getsize(p)))
self.regions[shard] = sorted(regs)
def get(self, name):
shard = self.wm[name]
info = self.headers[shard]["header"][name]
b, e = info["data_offsets"]
for rb, path, sz in self.regions[shard]:
if rb <= b and e <= rb + sz:
with open(path, "rb") as fh:
fh.seek(b - rb)
buf = fh.read(e - b)
t = torch.frombuffer(bytearray(buf), dtype=DTYPES[info["dtype"]])
return t.reshape(info["shape"])
raise KeyError(f"{name}: not in downloaded regions")
class SrcReader:
def __init__(self):
idx = json.load(open(f"{SRC}/model.safetensors.index.json"))
self.wm = idx["weight_map"]
self._open = {}
def get(self, name):
shard = self.wm[name]
if shard not in self._open:
self._open[shard] = safe_open(f"{SRC}/{shard}", framework="pt")
return self._open[shard].get_tensor(name)
class ShardWriter:
def __init__(self, dst):
self.dst = dst
self.cur, self.cur_bytes, self.n, self.total = {}, 0, 0, 0
self.weight_map, self.files = {}, []
def add(self, name, tensor):
nb = tensor.numel() * tensor.element_size()
if self.cur_bytes + nb > SHARD_BYTES and self.cur:
self.flush()
self.cur[name] = tensor
self.cur_bytes += nb
self.total += nb
def flush(self):
if not self.cur:
return
self.n += 1
fname = f"model-{self.n:05d}.safetensors"
save_file(self.cur, os.path.join(self.dst, fname))
for k in self.cur:
self.weight_map[k] = fname
self.files.append(fname)
self.cur, self.cur_bytes = {}, 0
def finalize(self):
self.flush()
wm = {}
for i, fname in enumerate(self.files, 1):
new = f"model-{i:05d}-of-{self.n:05d}.safetensors"
os.rename(os.path.join(self.dst, fname), os.path.join(self.dst, new))
for k, v in self.weight_map.items():
if v == fname:
wm[k] = new
json.dump({"metadata": {"total_size": self.total}, "weight_map": wm},
open(f"{self.dst}/model.safetensors.index.json", "w"), indent=0)
print(f"index: {len(wm)} tensors, {self.total/1e9:.1f} GB", flush=True)
def main():
os.makedirs(DST, exist_ok=True)
sr = SrcReader()
rr = RegionReader()
writer = ShardWriter(DST)
exp_pat = re.compile(r"model\.layers\.(\d+)\.mlp\.experts\.")
for shard in sorted(set(sr.wm.values())):
with safe_open(f"{SRC}/{shard}", framework="pt") as f:
for n in f.keys():
m = exp_pat.match(n)
if m and int(m.group(1)) != 78: # keep MTP verbatim
continue
writer.add(n, f.get_tensor(n))
layer_books = {}
for li in sorted(assignment):
hot = sorted(assignment[li]["hot"])
cold = sorted(assignment[li]["cold"])
assert len(hot) + len(cold) == N_EXP
p = f"model.layers.{li}.mlp.experts"
kind_old = sr.get(f"{p}.hyb_kind")
old_hot = (kind_old == 0).nonzero().flatten().tolist()
pos = {e: j for j, e in enumerate(old_hot)}
old = {n: sr.get(f"{p}.{n}") for n in
("nvfp4_w13_packed", "nvfp4_w13_bscale", "nvfp4_w13_scale2",
"nvfp4_w2_packed", "nvfp4_w2_bscale", "nvfp4_w2_scale2")}
na = len(hot)
w13p = torch.empty(na, 2*INTER, HIDDEN//2, dtype=torch.uint8)
w13b = torch.empty(na, 2*INTER, HIDDEN//16, dtype=torch.uint8)
w13s = torch.empty(na, 2, dtype=torch.float32)
w2p = torch.empty(na, HIDDEN, INTER//2, dtype=torch.uint8)
w2b = torch.empty(na, HIDDEN, INTER//16, dtype=torch.uint8)
w2s = torch.empty(na, 1, dtype=torch.float32)
n_dl = 0
for j, e in enumerate(hot):
if e in pos:
k = pos[e]
w13p[j] = old["nvfp4_w13_packed"][k]
w13b[j] = old["nvfp4_w13_bscale"][k]
w13s[j] = old["nvfp4_w13_scale2"][k]
w2p[j] = old["nvfp4_w2_packed"][k]
w2b[j] = old["nvfp4_w2_bscale"][k]
w2s[j] = old["nvfp4_w2_scale2"][k]
else:
n_dl += 1
ep = f"model.layers.{li}.mlp.experts.{e}"
w13p[j, :INTER] = rr.get(f"{ep}.gate_proj.weight")
w13p[j, INTER:] = rr.get(f"{ep}.up_proj.weight")
w2p[j] = rr.get(f"{ep}.down_proj.weight")
w13b[j, :INTER] = rr.get(
f"{ep}.gate_proj.weight_scale").view(torch.uint8)
w13b[j, INTER:] = rr.get(
f"{ep}.up_proj.weight_scale").view(torch.uint8)
w2b[j] = rr.get(
f"{ep}.down_proj.weight_scale").view(torch.uint8)
w13s[j, 0] = rr.get(f"{ep}.gate_proj.weight_scale_2").float()
w13s[j, 1] = rr.get(f"{ep}.up_proj.weight_scale_2").float()
w2s[j, 0] = rr.get(f"{ep}.down_proj.weight_scale_2").float()
kind = torch.full((N_EXP,), 2, dtype=torch.int8)
for e in hot:
kind[e] = 0
part = torch.load(f"{PARTS}/layer_{li}.pt", map_location="cpu",
weights_only=True)
cold_idx = torch.tensor(cold, dtype=torch.long)
writer.add(f"{p}.hyb_kind", kind)
writer.add(f"{p}.w13_codes", part["w13_codes"][cold_idx].contiguous())
writer.add(f"{p}.w13_codebooks", part["w13_codebooks"].clone())
writer.add(f"{p}.w13_scales", part["w13_scales"][cold_idx].contiguous())
writer.add(f"{p}.w2m_codes",
torch.empty(0, 2, HIDDEN, INTER//8, dtype=torch.int16))
writer.add(f"{p}.w2m_codebooks", part["w2_codebooks"].clone())
writer.add(f"{p}.w2m_scales", torch.empty(0, HIDDEN, dtype=torch.float16))
writer.add(f"{p}.w2c_codes", part["w2_codes"][cold_idx, :1].clone())
writer.add(f"{p}.w2c_codebooks", part["w2_codebooks"][:1].clone())
writer.add(f"{p}.w2c_scales", part["w2_scales"][cold_idx].contiguous())
for n, t in zip(("nvfp4_w13_packed", "nvfp4_w13_bscale",
"nvfp4_w13_scale2", "nvfp4_w2_packed",
"nvfp4_w2_bscale", "nvfp4_w2_scale2"),
(w13p, w13b, w13s, w2p, w2b, w2s)):
writer.add(f"{p}.{n}", t)
layer_books[str(li)] = {"n_nvfp4": na, "n_base": 0, "n_cold": len(cold)}
print(f"layer {li}: hot={na} ({n_dl} from download) cold={len(cold)}",
flush=True)
writer.finalize()
cfg = json.load(open(f"{SRC}/config.json"))
cfg["quantization_config"]["aqlm_layer_books"] = layer_books
json.dump(cfg, open(f"{DST}/config.json", "w"), indent=2)
for f in os.listdir(SRC):
if (f.endswith(".json") and f not in
("config.json", "model.safetensors.index.json")
or f.endswith((".txt", ".jinja", ".py", ".md"))):
shutil.copy2(f"{SRC}/{f}", f"{DST}/{f}")
print("DONE:", DST)
if __name__ == "__main__":
main()
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